This bibliography is extracted from various primary sources using automatic language understanding tools.  A good faith effort has been made to eliminate errors and minimize omissions.  Please bring any remaining errors or omissions to the attention of CLSP by writing to [email protected].

1. H. Hashemi, C. Rosset, B. V. Durme, J. Eisner, and C. Kedzie, “\textscLLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   [BibTeX] [Link]

   @InProceedings{hashemi-et-al-2024,
   author = "Helia Hashemi and Corby Rosset and Benjamin Van Durme
   and Jason Eisner and Chris Kedzie",
   title = "\textsc{LLM-Rubric}: {A} Multidimensional, Calibrated
   Approach to Automated Evaluation of Natural Language
   Texts",
   booktitle = "Proceedings of the 62nd Annual Meeting of the
   Association for Computational Linguistics (ACL)",
   year = "2024",
   month = aug,
   URL = "http://cs.jhu.edu/~jason/papers/#hashemi-et-al-2024",
   }

2. B. Wang, H. Fang, J. Eisner, B. Durme, and Y. Su, “LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   [BibTeX] [Link]

   @InProceedings{wang-et-al-2024-tools,
   author = "Boshi Wang and Hao Fang and Jason Eisner and Benjamin
   Van Durme and Yu Su",
   title = "{LLMs} in the Imaginarium: Tool Learning through
   Simulated Trial and Error",
   booktitle = "Proceedings of the 62nd Annual Meeting of the
   Association for Computational Linguistics (ACL)",
   year = "2024",
   month = aug,
   URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-tools",
   }

3. S. CH-Wang, B. V. Durme, J. Eisner, and C. Kedzie, “Do Androids Know They’re Only Dreaming of Electric Sheep?,” in Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   [BibTeX] [Link]

   @InProceedings{wang-et-al-2024-hallucination,
   author = "Sky CH-Wang and Benjamin Van Durme and Jason Eisner
   and Chris Kedzie",
   title = "Do Androids Know They’re Only Dreaming of Electric
   Sheep?",
   booktitle = "Findings of the 62nd Annual Meeting of the Association
   for Computational Linguistics (ACL)",
   year = "2024",
   month = aug,
   URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-hallucination",
   }

4. G. Monea, M. Peyrard, M. Josifoski, V. Chaudhary, J. Eisner, K{\i}c. i, H. Palangi, B. Patra, and R. West, “A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   [BibTeX] [Link]

   @InProceedings{monea-et-al-2024,
   author = "Giovanni Monea and Maxime Peyrard and Martin Josifoski
   and Vishrav Chaudhary and Jason Eisner and Emre
   K{\i}c{\i}man and Hamid Palangi and Barun Patra and
   Robert West",
   title = "A Glitch in the {M}atrix? {L}ocating and Detecting
   Language Model Grounding with {F}akepedia",
   booktitle = "Proceedings of the 62nd Annual Meeting of the
   Association for Computational Linguistics (ACL)",
   year = "2024",
   month = aug,
   URL = "http://cs.jhu.edu/~jason/papers/#monea-et-al-2024",
   }

5. L. Du, J. Eisner, H. Lee, and Ryan Cotterell, “When is a Language Process a Language Model?,” in Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
   [BibTeX] [Link]

   @InProceedings{du-et-al-2024-tight,
   author = "Li Du and Jason Eisner and Holden Lee and Ryan
   Cotterell",
   title = "When is a Language Process a Language Model?",
   booktitle = "Findings of the 62nd Annual Meeting of the Association
   for Computational Linguistics (ACL)",
   year = "2024",
   month = aug,
   URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-tight",
   }

6. L. Du, A. Amini, L. T. Hennigen, X. V. Yu, H. Lee, J. Eisner, and R. Cotterell, “Principled Gradient-Based MCMC for Conditional Sampling of Text,” in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
   [BibTeX] [Link]

   @InProceedings{du-et-al-2024-mcmc,
   author = "Li Du and Afra Amini and Lucas Torroba Hennigen and
   Xinyan Velocity Yu and Holden Lee and Jason Eisner and
   Ryan Cotterell",
   title = "Principled Gradient-Based {MCMC} for Conditional
   Sampling of Text",
   booktitle = "Proceedings of the 41st International Conference on
   Machine Learning (ICML)",
   year = "2024",
   month = jul,
   URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-mcmc",
   }

7. N. Bafna, P. Koehn, and D. Yarowsky, “Pointer-Generator Networks for Low-Resource Machine Translation: Don’t Copy That!,” in Proceedings of the Fifth Workshop on Insights from Negative Results in NLP, Mexico City, Mexico, 2024, p. 60–72.
   [BibTeX] [Abstract] [Link]
   

   While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural {“}shortcuts{”}, such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.

   @inproceedings{bafna-etal-2024-pointer,
   title = "Pointer-Generator Networks for Low-Resource Machine Translation: Don{'}t Copy That!",
   author = "Bafna, Niyati and
   Koehn, Philipp and
   Yarowsky, David",
   editor = "Tafreshi, Shabnam and
   Akula, Arjun and
   Sedoc, Jo{\~a}o and
   Drozd, Aleksandr and
   Rogers, Anna and
   Rumshisky, Anna",
   booktitle = "Proceedings of the Fifth Workshop on Insights from Negative Results in NLP",
   month = jun,
   year = "2024",
   address = "Mexico City, Mexico",
   publisher = "Association for Computational Linguistics",
   url = "https://aclanthology.org/2024.insights-1.9",
   pages = "60--72",
   abstract = "While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural {``}shortcuts{''}, such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.",
   }

8. J. Su, M. Ahmed, B. Wen, L. Ao, M. Zhu, and Y. Liu, “Naive Bayes-based Context Extension for Large Language Models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 7791–7807.
   [BibTeX] [Abstract] [Link]
   

   Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM{‘}s maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes{‘} theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master

   @inproceedings{su-etal-2024-naive,
   title = "Naive {B}ayes-based Context Extension for Large Language Models",
   author = "Su, Jianlin and
   Ahmed, Murtadha and
   Wen, Bo and
   Ao, Luo and
   Zhu, Mingren and
   Liu, Yunfeng",
   editor = "Duh, Kevin and
   Gomez, Helena and
   Bethard, Steven",
   booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
   month = jun,
   year = "2024",
   address = "Mexico City, Mexico",
   publisher = "Association for Computational Linguistics",
   url = "https://aclanthology.org/2024.naacl-long.431",
   pages = "7791--7807",
   abstract = "Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM{'}s maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes{'} theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master",
   }

9. V. Raunak, T. Kocmi, and M. Post, “SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), Mexico City, Mexico, 2024, p. 205–211.
   [BibTeX] [Abstract] [Link]
   

   Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations.

   @inproceedings{raunak-etal-2024-slide,
   title = "{SLIDE}: Reference-free Evaluation for Machine Translation using a Sliding Document Window",
   author = "Raunak, Vikas and
   Kocmi, Tom and
   Post, Matt",
   editor = "Duh, Kevin and
   Gomez, Helena and
   Bethard, Steven",
   booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
   month = jun,
   year = "2024",
   address = "Mexico City, Mexico",
   publisher = "Association for Computational Linguistics",
   url = "https://aclanthology.org/2024.naacl-short.18",
   pages = "205--211",
   abstract = "Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations.",
   }

10. S. Vashishtha, A. Martin, W. Gantt, B. Van Durme, and A. White, “FAMuS: Frames Across Multiple Sources,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 8250–8273.
    [BibTeX] [Abstract] [Link]
    

    Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{–-}determining whether a document is a valid source for a target report event{–-}and cross-document argument extraction{–-}full-document argument extraction for a target event from both its report and the correct source article.

    @inproceedings{vashishtha-etal-2024-famus,
    title = "{FAM}u{S}: Frames Across Multiple Sources",
    author = "Vashishtha, Siddharth and
    Martin, Alexander and
    Gantt, William and
    Van Durme, Benjamin and
    White, Aaron",
    editor = "Duh, Kevin and
    Gomez, Helena and
    Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.457",
    pages = "8250--8273",
    abstract = "Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{---}determining whether a document is a valid source for a target report event{---}and cross-document argument extraction{---}full-document argument extraction for a target event from both its report and the correct source article.",
    }

11. A. Hou, J. Zhang, T. He, Y. Wang, Y. Chuang, H. Wang, L. Shen, B. Van Durme, D. Khashabi, and Y. Tsvetkov, “SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 4067–4082.
    [BibTeX] [Abstract] [Link]
    

    Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a {“}bigram paraphrase{”} attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation.

    @inproceedings{hou-etal-2024-semstamp,
    title = "{S}em{S}tamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation",
    author = "Hou, Abe and
    Zhang, Jingyu and
    He, Tianxing and
    Wang, Yichen and
    Chuang, Yung-Sung and
    Wang, Hongwei and
    Shen, Lingfeng and
    Van Durme, Benjamin and
    Khashabi, Daniel and
    Tsvetkov, Yulia",
    editor = "Duh, Kevin and
    Gomez, Helena and
    Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.226",
    pages = "4067--4082",
    abstract = "Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a {``}bigram paraphrase{''} attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation.",
    }

12. N. Moghe, P. Xia, J. Andreas, J. Eisner, B. V. Durme, and Harsh Jhamtani, “Interpreting User Requests in the Context of Natural Language Standing Instructions,” in Proceedings of the North American Conference on Cmputational Linguistics (NAACL), 2024.
    [BibTeX] [Link]

    @InProceedings{moghe-et-al-2024,
    author = "Nikita Moghe and Patrick Xia and Jacob Andreas and
    Jason Eisner and Benjamin Van Durme and Harsh
    Jhamtani",
    title = "Interpreting User Requests in the Context of Natural
    Language Standing Instructions",
    booktitle = "Proceedings of the North American Conference on
    Cmputational Linguistics (NAACL)",
    volume = "arXiv:2311.09796",
    year = "2024",
    month = jun,
    URL = "http://cs.jhu.edu/~jason/papers/#moghe-et-al-2024",
    }

13. R. Huang, M. Yarmohammadi, J. Trmal, J. Liu, D. Raj, L. P. Garcia, A. V. Ivanov, P. Ehlen, M. Yu, D. Povey, and S. Khudanpur, “ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 3700–3706.
    [BibTeX] [Abstract] [Link]
    

    Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {–-} such as the speaker{‘}s contacts or recent song playlists {–-} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({“}ConEC{”}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{‘} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology

    @inproceedings{huang-etal-2024-conec,
    title = "{C}on{EC}: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition",
    author = "Huang, Ruizhe and
    Yarmohammadi, Mahsa and
    Trmal, Jan and
    Liu, Jing and
    Raj, Desh and
    Garcia, Leibny Paola and
    Ivanov, Alexei V. and
    Ehlen, Patrick and
    Yu, Mingzhi and
    Povey, Dan and
    Khudanpur, Sanjeev",
    editor = "Calzolari, Nicoletta and
    Kan, Min-Yen and
    Hoste, Veronique and
    Lenci, Alessandro and
    Sakti, Sakriani and
    Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.328",
    pages = "3700--3706",
    abstract = "Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology",
    }

14. N. Verma, K. Murray, and K. Duh, “Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 6909–6921.
    [BibTeX] [Abstract] [Link]
    

    Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity.

    @inproceedings{verma-etal-2024-exploring,
    title = "Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models",
    author = "Verma, Neha and
    Murray, Kenton and
    Duh, Kevin",
    editor = "Calzolari, Nicoletta and
    Kan, Min-Yen and
    Hoste, Veronique and
    Lenci, Alessandro and
    Sakti, Sakriani and
    Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.604",
    pages = "6909--6921",
    abstract = "Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity.",
    }

15. W. Gantt, S. Behzad, H. An, Y. Chen, A. White, B. Van Durme, and M. Yarmohammadi, “MultiMUC: Multilingual Template Filling on MUC-4,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 349–368.
    [BibTeX] [Abstract] [Link]
    

    We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.

    @inproceedings{gantt-etal-2024-multimuc,
    title = "{M}ulti{MUC}: Multilingual Template Filling on {MUC}-4",
    author = "Gantt, William and
    Behzad, Shabnam and
    An, Hannah and
    Chen, Yunmo and
    White, Aaron and
    Van Durme, Benjamin and
    Yarmohammadi, Mahsa",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.21",
    pages = "349--368",
    abstract = "We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.",
    }

16. J. Chim, A. Tsakalidis, D. Gkoumas, D. Atzil-Slonim, Y. Ophir, A. Zirikly, P. Resnik, and M. Liakata, “Overview of the CLPsych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts,” in Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), St. Julians, Malta, 2024, p. 177–190.
    [BibTeX] [Abstract] [Link]
    

    We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level ({`}Low{‘}, {`}Mod- erate{‘} or {`}High{‘}) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.

    @inproceedings{chim-etal-2024-overview,
    title = "Overview of the {CLP}sych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts",
    author = "Chim, Jenny and
    Tsakalidis, Adam and
    Gkoumas, Dimitris and
    Atzil-Slonim, Dana and
    Ophir, Yaakov and
    Zirikly, Ayah and
    Resnik, Philip and
    Liakata, Maria",
    editor = "Yates, Andrew and
    Desmet, Bart and
    Prud{'}hommeaux, Emily and
    Zirikly, Ayah and
    Bedrick, Steven and
    MacAvaney, Sean and
    Bar, Kfir and
    Ireland, Molly and
    Ophir, Yaakov",
    booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.clpsych-1.15",
    pages = "177--190",
    abstract = "We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level ({`}Low{'}, {`}Mod- erate{'} or {`}High{'}) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.",
    }

17. H. Sirin and T. Lippincott, “Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses,” in Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), St. Julians, Malta, 2024, p. 231–236.
    [BibTeX] [Abstract] [Link]
    

    We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.

    @inproceedings{sirin-lippincott-2024-dynamic,
    title = "Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses",
    author = "Sirin, Hale and
    Lippincott, Thomas",
    editor = "Bizzoni, Yuri and
    Degaetano-Ortlieb, Stefania and
    Kazantseva, Anna and
    Szpakowicz, Stan",
    booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.latechclfl-1.22",
    pages = "231--236",
    abstract = "We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.",
    }

18. O. Weller, M. Marone, N. Weir, D. Lawrie, D. Khashabi, and B. Van Durme, ““According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2288–2301.
    [BibTeX] [Abstract] [Link]
    

    Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of {“}according to sources{”}, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.

    @inproceedings{weller-etal-2024-according,
    title = "{``}According to . . . {''}: Prompting Language Models Improves Quoting from Pre-Training Data",
    author = "Weller, Orion and
    Marone, Marc and
    Weir, Nathaniel and
    Lawrie, Dawn and
    Khashabi, Daniel and
    Van Durme, Benjamin",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.140",
    pages = "2288--2301",
    abstract = "Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of {``}according to sources{''}, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.",
    }

19. O. Weller, K. Lo, D. Wadden, D. Lawrie, B. Van Durme, A. Cohan, and L. Soldaini, “When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets,” in Findings of the Association for Computational Linguistics: EACL 2024, St. Julian{‘}s, Malta, 2024, p. 1987–2003.
    [BibTeX] [Abstract] [Link]
    

    Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.

    @inproceedings{weller-etal-2024-generative,
    title = "When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets",
    author = "Weller, Orion and
    Lo, Kyle and
    Wadden, David and
    Lawrie, Dawn and
    Van Durme, Benjamin and
    Cohan, Arman and
    Soldaini, Luca",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-eacl.134",
    pages = "1987--2003",
    abstract = "Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.",
    }

20. O. Weller, D. Lawrie, and B. Van Durme, “NevIR: Negation in Neural Information Retrieval,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2274–2287.
    [BibTeX] [Abstract] [Link]
    

    Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.

    @inproceedings{weller-etal-2024-nevir,
    title = "{N}ev{IR}: Negation in Neural Information Retrieval",
    author = "Weller, Orion and
    Lawrie, Dawn and
    Van Durme, Benjamin",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.139",
    pages = "2274--2287",
    abstract = "Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.",
    }

21. Y. Lu, H. Yu, and D. Khashabi, “GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 112–138.
    [BibTeX] [Abstract] [Link]
    

    Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use.

    @inproceedings{lu-etal-2024-gear,
    title = "{GEAR}: Augmenting Language Models with Generalizable and Efficient Tool Resolution",
    author = "Lu, Yining and
    Yu, Haoping and
    Khashabi, Daniel",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.7",
    pages = "112--138",
    abstract = "Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use.",
    }

22. O. Weller, A. Khan, N. Weir, D. Lawrie, and B. Van Durme, “Defending Against Disinformation Attacks in Open-Domain Question Answering,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), St. Julian{‘}s, Malta, 2024, p. 402–417.
    [BibTeX] [Abstract] [Link]
    

    Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20{\%} exact match across varying levels of data poisoning/knowledge conflicts.

    @inproceedings{weller-etal-2024-defending,
    title = "Defending Against Disinformation Attacks in Open-Domain Question Answering",
    author = "Weller, Orion and
    Khan, Aleem and
    Weir, Nathaniel and
    Lawrie, Dawn and
    Van Durme, Benjamin",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-short.35",
    pages = "402--417",
    abstract = "Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20{\%} exact match across varying levels of data poisoning/knowledge conflicts.",
    }

23. Z. Li, C. Xie, B. Van Durme, and A. Yuille, “Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2378–2390.
    [BibTeX] [Abstract] [Link]
    

    Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.

    @inproceedings{li-etal-2024-localization,
    title = "Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models",
    author = "Li, Zhuowan and
    Xie, Cihang and
    Van Durme, Benjamin and
    Yuille, Alan",
    editor = "Graham, Yvette and
    Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.146",
    pages = "2378--2390",
    abstract = "Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.",
    }

24. Dongwei Jiang, Jingyu Zhang, Orion Weller, Nathaniel Weir, Benjamin Van Durme, and Daniel Khashabi, “SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{269005582,
    title = {SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses},
    author = {{Dongwei Jiang} and {Jingyu Zhang} and {Orion Weller} and {Nathaniel Weir} and {Benjamin Van Durme} and {Daniel Khashabi}},
    year = 2024,
    month = {4},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9e3d14e4697a325fcabc6d952232a3ad7e9fa809},
    }

25. Ruizhe Huang, Xiaohui Zhang, Zhaoheng Ni, Li Sun, Moto Hira, Jeff Hwang, Vimal Manohar, Vineel Pratap, Matthew Wiesner, Shinji Watanabe, Daniel Povey, and S. Khudanpur, “Less Peaky and More Accurate CTC Forced Alignment by Label Priors,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2024.
    [BibTeX] [Link]

    @inproceedings{268577202,
    title = {Less Peaky and More Accurate CTC Forced Alignment by Label Priors},
    author = {{Ruizhe Huang} and {Xiaohui Zhang} and {Zhaoheng Ni} and {Li Sun} and {Moto Hira} and {Jeff Hwang} and {Vimal Manohar} and {Vineel Pratap} and {Matthew Wiesner} and {Shinji Watanabe} and {Daniel Povey} and {S. Khudanpur}},
    year = 2024,
    month = {4},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/ca6c9789f4dd10e1908aaeaa6a37bd682af4f86a},
    }

26. H. E. Echo Wang, Jonathan P. Weiner, S. Saria, Harold P Lehmann, and Hadi Kharrazi, “Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models.,” in Journal of Biomedical Informatics, 2024.
    [BibTeX] [Link]

    @inproceedings{270746357,
    title = {Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models.},
    author = {{H. E. Echo Wang} and {Jonathan P. Weiner} and {S. Saria} and {Harold P Lehmann} and {Hadi Kharrazi}},
    year = 2024,
    month = {6},
    booktitle = {Journal of Biomedical Informatics},
    url = {https://www.semanticscholar.org/paper/205acb6a954600dacbcb877a625605a16ca261f2},
    }

27. Jingyu Zhang, Marc Marone, Tianjian Li, Benjamin Van Durme, and Daniel Khashabi, “Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{268987627,
    title = {Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data},
    author = {{Jingyu Zhang} and {Marc Marone} and {Tianjian Li} and {Benjamin Van Durme} and {Daniel Khashabi}},
    year = 2024,
    month = {4},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9689b5fdb0d3a1bad802d03d348bd32aa5a4c2df},
    }

28. Thomas Thebaud, Gabriel Hernández, Sarah Flora Samson Juan, and Marie Tahon, “A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients,” in The Speaker and Language Recognition Workshop, 2024.
    [BibTeX] [Link]

    @inproceedings{271211023,
    title = {A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients},
    author = {{Thomas Thebaud} and {Gabriel Hernández} and {Sarah Flora Samson Juan} and {Marie Tahon}},
    year = 2024,
    month = {6},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/098e0d7c2f273bc052a74f1bd91db8cf2b57cd1f},
    }

29. Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, and S. Saria, “FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267413013,
    title = {FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion},
    author = {{Xing Han} and {Huy Nguyen} and {Carl Harris} and {Nhat Ho} and {S. Saria}},
    year = 2024,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/f71ea9673be9c7a76d0fa6695a5713f150b64304},
    }

30. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Slowness Regularized Contrastive Predictive Coding for Acoustic Unit Discovery,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2024.
    [BibTeX] [Link]

    @inproceedings{267144330,
    title = {Slowness Regularized Contrastive Predictive Coding for Acoustic Unit Discovery},
    author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
    year = 2024,
    booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
    url = {https://www.semanticscholar.org/paper/1748de2018438a1015f557ed72424602b144f5ba},
    }

31. Sijia Zhao, Benjamin Skirritt-Davis, Mounya Elhilali, Fred Dick, and M. Chait, “Sustained EEG responses to rapidly unfolding stochastic sounds reflect precision tracking,” in bioRxiv, 2024.
    [BibTeX] [Link]

    @inproceedings{266873790,
    title = {Sustained EEG responses to rapidly unfolding stochastic sounds reflect precision tracking},
    author = {{Sijia Zhao} and {Benjamin Skirritt-Davis} and {Mounya Elhilali} and {Fred Dick} and {M. Chait}},
    year = 2024,
    month = {1},
    booktitle = {bioRxiv},
    url = {https://www.semanticscholar.org/paper/e69427d53f37698a57706e91a275d57e582baba4},
    }

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    @inproceedings{270619762,
    title = {Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization},
    author = {{Niyati Bafna} and {Kenton Murray} and {David Yarowsky}},
    year = 2024,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/cf2f09a0b81f7b2b1b92c5bddeaa6544617d53a9},
    }

47. Ruizhe Huang, M. Yarmohammadi, S. Khudanpur, and Dan Povey, “Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation.” 2024.
    [BibTeX] [Link]

    @inproceedings{271212774,
    title = {Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation},
    author = {{Ruizhe Huang} and {M. Yarmohammadi} and {S. Khudanpur} and {Dan Povey}},
    year = 2024,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/6ebfef1daea743456536d620e894eee8992aa124},
    }

48. Deming Li, A. Butala, L. Moro-Velázquez, Trevor Meyer, Esther S. Oh, Chelsey Motley, J. Villalba, and N. Dehak, “Automating the analysis of eye movement for different neurodegenerative disorders,” in Comput. Biol. Medicine, 2024.
    [BibTeX] [Link]

    @inproceedings{266798865,
    title = {Automating the analysis of eye movement for different neurodegenerative disorders},
    author = {{Deming Li} and {A. Butala} and {L. Moro-Velázquez} and {Trevor Meyer} and {Esther S. Oh} and {Chelsey Motley} and {J. Villalba} and {N. Dehak}},
    year = 2024,
    month = {1},
    booktitle = {Comput. Biol. Medicine},
    url = {https://www.semanticscholar.org/paper/f375c9d0a595152ff21f96a0a5606c7d033548f3},
    }

49. Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Tiyyala, Nicholas Andrews, and Daniel Khashabi, “AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267750458,
    title = {AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies},
    author = {{Xiao Ye} and {Andrew Wang} and {Jacob Choi} and {Yining Lu} and {Shreya Sharma} and {Lingfeng Shen} and {Vijay Tiyyala} and {Nicholas Andrews} and {Daniel Khashabi}},
    year = 2024,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/5c24dd41f46fd7107997b0a46e1207e0fed63b34},
    }

50. Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, and Daniel Khashabi, “The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267200158,
    title = {The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts},
    author = {{Lingfeng Shen} and {Weiting Tan} and {Sihao Chen} and {Yunmo Chen} and {Jingyu Zhang} and {Haoran Xu} and {Boyuan Zheng} and {Philipp Koehn} and {Daniel Khashabi}},
    year = 2024,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/3cd81b0123b5f8477f6b5777681030ef6b05dd46},
    }

51. N. Robinson, R. Dabre, A. Shurtz, R. Dent, O. Onesi, C. Monroc, L. Grobol, H. Muhammad, A. Garg, N. Etori, V. M. Tiyyala, O. Samuel, M. Stutzman, B. Odoom, S. Khudanpur, S. Richardson, and K. Murray, “Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 3083–3110.
    [BibTeX] [Abstract] [Link]
    

    A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations{–-}11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages{–-}the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.

    @inproceedings{robinson-etal-2024-kreyol,
    title = "Krey{\`o}l-{MT}: Building {MT} for {L}atin {A}merican, {C}aribbean and Colonial {A}frican Creole Languages",
    author = {Robinson, Nathaniel and
    Dabre, Raj and
    Shurtz, Ammon and
    Dent, Rasul and
    Onesi, Onenamiyi and
    Monroc, Claire and
    Grobol, Lo{\"\i}c and
    Muhammad, Hasan and
    Garg, Ashi and
    Etori, Naome and
    Tiyyala, Vijay Murari and
    Samuel, Olanrewaju and
    Stutzman, Matthew and
    Odoom, Bismarck and
    Khudanpur, Sanjeev and
    Richardson, Stephen and
    Murray, Kenton},
    editor = "Duh, Kevin and
    Gomez, Helena and
    Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.170",
    pages = "3083--3110",
    abstract = "A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations{---}11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages{---}the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.",
    }

52. H. Sirin, S. Li, and T. Lippincott, “Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with Fourier Analysis,” in Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), St. Julians, Malta, 2024, p. 46–50.
    [BibTeX] [Abstract] [Link]
    

    In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.

    @inproceedings{sirin-etal-2024-detecting,
    title = "Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with {F}ourier Analysis",
    author = "Sirin, Hale and
    Li, Sabrina and
    Lippincott, Thomas",
    editor = "Bizzoni, Yuri and
    Degaetano-Ortlieb, Stefania and
    Kazantseva, Anna and
    Szpakowicz, Stan",
    booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.latechclfl-1.6",
    pages = "46--50",
    abstract = "In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.",
    }

53. Weiting Tan, Yunmo Chen, Tongfei Chen, Guanghui Qin, Haoran Xu, Heidi C. Zhang, Benjamin Van Durme, and Philipp Koehn, “Streaming Sequence Transduction through Dynamic Compression,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267406764,
    title = {Streaming Sequence Transduction through Dynamic Compression},
    author = {{Weiting Tan} and {Yunmo Chen} and {Tongfei Chen} and {Guanghui Qin} and {Haoran Xu} and {Heidi C. Zhang} and {Benjamin Van Durme} and {Philipp Koehn}},
    year = 2024,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/50b2a8c4c8a0d4b26621e84e3e0b5b300775e030},
    }

54. Boshi Wang, Hao Fang, Jason Eisner, Benjamin Van Durme, and Yu Su, “LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{268264353,
    title = {LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error},
    author = {{Boshi Wang} and {Hao Fang} and {Jason Eisner} and {Benjamin Van Durme} and {Yu Su}},
    year = 2024,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/ae4635297ad87fcb3ec4105a51b5cbcb4075e5e2},
    }

55. M. Jahan, H. Wang, T. Thebaud, Y. Sun, G. H. Le, Z. Fagyal, O. Scharenborg, M. Hasegawa-Johnson, L. Moro Velazquez, and N. Dehak, “Finding Spoken Identifications: Using GPT-4 Annotation for an Efficient and Fast Dataset Creation Pipeline,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 7296–7306.
    [BibTeX] [Abstract] [Link]
    

    The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI{‘}s GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4{‘}s performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93{\%}. GPT-4{‘}s tagging performance showed a trade-off between precision and recall, where the latter got as high as 97{\%}, but precision never exceeded 45{\%}. Our approach reduces the time required for the filtering and tagging tasks by 95{\%} and 80{\%}, respectively. We also present an in-depth error analysis of GPT-4{‘}s performance.

    @inproceedings{jahan-etal-2024-finding,
    title = "Finding Spoken Identifications: Using {GPT}-4 Annotation for an Efficient and Fast Dataset Creation Pipeline",
    author = "Jahan, Maliha and
    Wang, Helin and
    Thebaud, Thomas and
    Sun, Yinglun and
    Le, Giang Ha and
    Fagyal, Zsuzsanna and
    Scharenborg, Odette and
    Hasegawa-Johnson, Mark and
    Moro Velazquez, Laureano and
    Dehak, Najim",
    editor = "Calzolari, Nicoletta and
    Kan, Min-Yen and
    Hoste, Veronique and
    Lenci, Alessandro and
    Sakti, Sakriani and
    Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.641",
    pages = "7296--7306",
    abstract = "The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI{'}s GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4{'}s performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93{\%}. GPT-4{'}s tagging performance showed a trade-off between precision and recall, where the latter got as high as 97{\%}, but precision never exceeded 45{\%}. Our approach reduces the time required for the filtering and tagging tasks by 95{\%} and 80{\%}, respectively. We also present an in-depth error analysis of GPT-4{'}s performance.",
    }

56. Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim, “Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267028540,
    title = {Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
    author = {{Haoran Xu} and {Amr Sharaf} and {Yunmo Chen} and {Weiting Tan} and {Lingfeng Shen} and {Benjamin Van Durme} and {Kenton Murray} and {Young Jin Kim}},
    year = 2024,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/ebd1c04c61f73f46def3305ca11d038c46665b65},
    }

57. Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan L. Yuille, “LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context Compression.” 2024.
    [BibTeX] [Link]

    @inproceedings{270845380,
    title = {LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context Compression},
    author = {{Jieneng Chen} and {Luoxin Ye} and {Ju He} and {Zhao-Yang Wang} and {Daniel Khashabi} and {Alan L. Yuille}},
    year = 2024,
    month = {6},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/80461ace9ab0565aeef74ad0fbbe3ed8e0be5438},
    }

58. Yining Lu, Dixuan Wang, Tianjian Li, Dongwei Jiang, and Daniel Khashabi, “Benchmarking Language Model Creativity: A Case Study on Code Generation.” 2024.
    [BibTeX] [Link]

    @inproceedings{271162279,
    title = {Benchmarking Language Model Creativity: A Case Study on Code Generation},
    author = {{Yining Lu} and {Dixuan Wang} and {Tianjian Li} and {Dongwei Jiang} and {Daniel Khashabi}},
    year = 2024,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/20be3ba3f361d9630cc0c17442a0d0132873e63d},
    }

59. Lucas Goncalves, Ali N. Salman, Abinay Reddy Naini, Laureano Moro Velázquez, Thomas Thebaud, Leibny Paola, Najim Garcia, Berrak Dehak, Carlos Sisman, and Busso, “Odyssey 2024 – Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results,” in The Speaker and Language Recognition Workshop, 2024.
    [BibTeX] [Link]

    @inproceedings{269459473,
    title = {Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results},
    author = {{Lucas Goncalves} and {Ali N. Salman} and {Abinay Reddy Naini} and {Laureano Moro Velázquez} and {Thomas Thebaud} and {Leibny Paola} and {Najim Garcia} and {Berrak Dehak} and {Carlos Sisman} and {Busso}},
    year = 2024,
    month = {6},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/44a30157f437065fd0672b1327edaa32a9239ce5},
    }

60. Yiwen Shao, Shizhong Zhang, Yong Xu, Meng Yu, Dong Yu, Dan Povey, and S. Khudanpur, “Multi-Channel Multi-Speaker ASR Using Target Speaker’s Solo Segment.” 2024.
    [BibTeX] [Link]

    @inproceedings{270521572,
    title = {Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment},
    author = {{Yiwen Shao} and {Shizhong Zhang} and {Yong Xu} and {Meng Yu} and {Dong Yu} and {Dan Povey} and {S. Khudanpur}},
    year = 2024,
    month = {6},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/18eadf3b7cf2ceb7b3c4e034b948af90ceec7219},
    }

61. Sangwook Park, Angeles Salles, Kathryne Allen, Cynthia Moss, and Mounya Elhilali, “Biomimetic Mappings for Active Sonar Object Recognition in Clutter,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2024.
    [BibTeX] [Link]

    @inproceedings{268527006,
    title = {Biomimetic Mappings for Active Sonar Object Recognition in Clutter},
    author = {{Sangwook Park} and {Angeles Salles} and {Kathryne Allen} and {Cynthia Moss} and {Mounya Elhilali}},
    year = 2024,
    month = {4},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/aba6e97d493b8b593eac929b49ee8d1c7bf9c953},
    }

62. A. Favaro, N. Dehak, Thomas Thebaud, J. Villalba, Esther S Oh, and L. Moro-Velázquez, “Discovering Invariant Patterns of Cognitive Decline Via an Automated Analysis of the Cookie Thief Picture Description Task,” in The Speaker and Language Recognition Workshop, 2024.
    [BibTeX] [Link]

    @inproceedings{271207138,
    title = {Discovering Invariant Patterns of Cognitive Decline Via an Automated Analysis of the Cookie Thief Picture Description Task},
    author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {J. Villalba} and {Esther S Oh} and {L. Moro-Velázquez}},
    year = 2024,
    month = {6},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/99dec8ab1d7aa47117062e1daf36dcbcce4aece2},
    }

63. Taiming Lu, Muhan Gao, Kuai Yu, Adam Byerly, and Daniel Khashabi, “Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{270688653,
    title = {Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell},
    author = {{Taiming Lu} and {Muhan Gao} and {Kuai Yu} and {Adam Byerly} and {Daniel Khashabi}},
    year = 2024,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/2138841e6e5ecf59d881a0108e0d9551484cfe32},
    }

64. Kate Sanders, Nathaniel Weir, and Benjamin Van Durme, “TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{268091324,
    title = {TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning},
    author = {{Kate Sanders} and {Nathaniel Weir} and {Benjamin Van Durme}},
    year = 2024,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/0042b9380f7da8335be040a3516e4f6765320834},
    }

65. Sandeep Reddy Kothinti and Mounya Elhilali, “Multi-rate modulation encoding via unsupervised learning for audio event detection,” in EURASIP Journal on Audio, Speech, and Music Processing, 2024.
    [BibTeX] [Link]

    @inproceedings{268863052,
    title = {Multi-rate modulation encoding via unsupervised learning for audio event detection},
    author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}},
    year = 2024,
    month = {4},
    booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
    url = {https://www.semanticscholar.org/paper/3cc427a861147fc147b316bfd73da03761e41a4d},
    }

66. MR Pinsky, Armando Bedoya, A. Bihorac, L. Celi, Matthew Churpek, Nicoleta J. Economou-Zavlanos, Paul Elbers, S. Saria, Vincent Liu, Patrick G. Lyons, B. Shickel, Patrick Toral, David Tscholl, and Gilles Clermont, “Use of artificial intelligence in critical care: opportunities and obstacles,” in Critical Care, 2024.
    [BibTeX] [Link]

    @inproceedings{269006160,
    title = {Use of artificial intelligence in critical care: opportunities and obstacles},
    author = {{MR Pinsky} and {Armando Bedoya} and {A. Bihorac} and {L. Celi} and {Matthew Churpek} and {Nicoleta J. Economou-Zavlanos} and {Paul Elbers} and {S. Saria} and {Vincent Liu} and {Patrick G. Lyons} and {B. Shickel} and {Patrick Toral} and {David Tscholl} and {Gilles Clermont}},
    year = 2024,
    month = {4},
    booktitle = {Critical Care},
    url = {https://www.semanticscholar.org/paper/a386424f2f61647ebec3dd27e33c6db92c1c07ac},
    }

67. Kevin Xu, Yeganeh Kordi, Kate Sanders, Yizhong Wang, Adam Byerly, Jingyu Zhang, Benjamin Van Durme, and Daniel Khashabi, “Tur[k]ingBench: A Challenge Benchmark for Web Agents,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{268531391,
    title = {Tur[k]ingBench: A Challenge Benchmark for Web Agents},
    author = {{Kevin Xu} and {Yeganeh Kordi} and {Kate Sanders} and {Yizhong Wang} and {Adam Byerly} and {Jingyu Zhang} and {Benjamin Van Durme} and {Daniel Khashabi}},
    year = 2024,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/341da3f8af6edd31edd8f5a3d9452957aeaaa744},
    }

68. Jiarui Hai, Karan Thakkar, Helin Wang, Zengyi Qin, and Mounya Elhilali, “DreamVoice: Text-Guided Voice Conversion.” 2024.
    [BibTeX] [Link]

    @inproceedings{270702574,
    title = {DreamVoice: Text-Guided Voice Conversion},
    author = {{Jiarui Hai} and {Karan Thakkar} and {Helin Wang} and {Zengyi Qin} and {Mounya Elhilali}},
    year = 2024,
    month = {6},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/fe5cccd11f8cdcfa6584b25b3762d06253bdd813},
    }

69. Zhengping Jiang, Jingyu Zhang, Nathaniel Weir, Seth Ebner, Miriam Wanner, Kate Sanders, Daniel Khashabi, Anqi Liu, and Benjamin Van Durme, “Core: Robust Factual Precision Scoring with Informative Sub-Claim Identification.” 2024.
    [BibTeX] [Link]

    @inproceedings{271039322,
    title = {Core: Robust Factual Precision Scoring with Informative Sub-Claim Identification},
    author = {{Zhengping Jiang} and {Jingyu Zhang} and {Nathaniel Weir} and {Seth Ebner} and {Miriam Wanner} and {Kate Sanders} and {Daniel Khashabi} and {Anqi Liu} and {Benjamin Van Durme}},
    year = 2024,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/2edfe299bbed8c8769fa0716769e6510fe083223},
    }

70. Jiefu Ou, Arda Uzunouglu, Benjamin Van Durme, and Daniel Khashabi, “WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment.” 2024.
    [BibTeX] [Link]

    @inproceedings{271088562,
    title = {WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment},
    author = {{Jiefu Ou} and {Arda Uzunouglu} and {Benjamin Van Durme} and {Daniel Khashabi}},
    year = 2024,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/143ff789f500307bab6725c466da97492cb5c771},
    }

71. Drew Prinster, Samuel Stanton, Anqi Liu, and S. Saria, “Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them),” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{269741223,
    title = {Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)},
    author = {{Drew Prinster} and {Samuel Stanton} and {Anqi Liu} and {S. Saria}},
    year = 2024,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/bb86b50363e9d100606a534c6a877dacbf8b0e25},
    }

72. Sonal Joshi, Thomas Thebaud, J. Villalba, and N. Dehak, “Unraveling Adversarial Examples against Speaker Identification – Techniques for Attack Detection and Victim Model Classification,” in The Speaker and Language Recognition Workshop, 2024.
    [BibTeX] [Link]

    @inproceedings{268091248,
    title = {Unraveling Adversarial Examples against Speaker Identification - Techniques for Attack Detection and Victim Model Classification},
    author = {{Sonal Joshi} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak}},
    year = 2024,
    month = {2},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/af87c6786c1e7f8345f3c5768668617df6cc2771},
    }

73. Helin Wang, J. Villalba, L. Moro-Velázquez, Jiarui Hai, Thomas Thebaud, and N. Dehak, “Noise-robust Speech Separation with Fast Generative Correction.” 2024.
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    @inproceedings{270380183,
    title = {Noise-robust Speech Separation with Fast Generative Correction},
    author = {{Helin Wang} and {J. Villalba} and {L. Moro-Velázquez} and {Jiarui Hai} and {Thomas Thebaud} and {N. Dehak}},
    year = 2024,
    month = {6},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/6983f5ef3a136df52cbaf0b623c16a7983e99d59},
    }

74. Desh Raj, Matthew Wiesner, Matthew Maciejewski, Leibny Paola García-Perera, Daniel Povey, and S. Khudanpur, “On Speaker Attribution with SURT,” in The Speaker and Language Recognition Workshop, 2024.
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    @inproceedings{267311651,
    title = {On Speaker Attribution with SURT},
    author = {{Desh Raj} and {Matthew Wiesner} and {Matthew Maciejewski} and {Leibny Paola García-Perera} and {Daniel Povey} and {S. Khudanpur}},
    year = 2024,
    month = {1},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/924d189fbe7aa43ab9de9989ce45d4e4936f533d},
    }

75. Miguel Angrick, Shiyu Luo, Qinwan Rabbani, Daniel Candrea, Samyak Shah, Griffin W. Milsap, William S Anderson, Chad R Gordon, Kathryn R Rosenblatt, Lora Clawson, Donna C. Tippett, Nicholas J Maragakis, Francesco V Tenore, M. Fifer, H. Hermansky, Nick F Ramsey, and Nathan Crone, “Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS,” in Scientific Reports, 2024.
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    @inproceedings{269408252,
    title = {Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS},
    author = {{Miguel Angrick} and {Shiyu Luo} and {Qinwan Rabbani} and {Daniel Candrea} and {Samyak Shah} and {Griffin W. Milsap} and {William S Anderson} and {Chad R Gordon} and {Kathryn R Rosenblatt} and {Lora Clawson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {Francesco V Tenore} and {M. Fifer} and {H. Hermansky} and {Nick F Ramsey} and {Nathan Crone}},
    year = 2024,
    month = {4},
    booktitle = {Scientific Reports},
    url = {https://www.semanticscholar.org/paper/fa58f21ecda2053d2c1c9360e682b1140b6ff4c1},
    }

76. A. Favaro, N. Dehak, Thomas Thebaud, Esther S Oh, and L. Moro-Velázquez, “Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression,” in Alzheimer’s & Dementia, 2023.
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    @inproceedings{266523165,
    title = {Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression},
    author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {Esther S Oh} and {L. Moro-Velázquez}},
    year = 2023,
    month = {12},
    booktitle = {Alzheimer's & Dementia},
    url = {https://www.semanticscholar.org/paper/3434b5755b9c8bdb4250cabaabad655aee402440},
    }

77. Sky CH-Wang, Benjamin Van Durme, Jason Eisner, and Chris Kedzie, “Do Androids Know They’re Only Dreaming of Electric Sheep?,” in arXiv.org, 2023.
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    @inproceedings{266573169,
    title = {Do Androids Know They're Only Dreaming of Electric Sheep?},
    author = {{Sky CH-Wang} and {Benjamin Van Durme} and {Jason Eisner} and {Chris Kedzie}},
    year = 2023,
    month = {12},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/6789e0a19f70e283b120fd6a3792162d01a021d5},
    }

78. Thomas Thebaud, Casey Chen, L. Moro-Velázquez, N. Dehak, and Esther S Oh, “Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment,” in Alzheimer’s & Dementia, 2023.
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    @inproceedings{266522435,
    title = {Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment},
    author = {{Thomas Thebaud} and {Casey Chen} and {L. Moro-Velázquez} and {N. Dehak} and {Esther S Oh}},
    year = 2023,
    month = {12},
    booktitle = {Alzheimer's & Dementia},
    url = {https://www.semanticscholar.org/paper/65b786c68cef24ed41374bd9f279617d694e2dd4},
    }

79. Deming Li, Trevor Meyer, Esther S Oh, A. Butala, N. Dehak, and L. Moro-Velázquez, “Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2023.
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    @inproceedings{266522904,
    title = {Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease},
    author = {{Deming Li} and {Trevor Meyer} and {Esther S Oh} and {A. Butala} and {N. Dehak} and {L. Moro-Velázquez}},
    year = 2023,
    month = {12},
    booktitle = {Alzheimer's & Dementia},
    url = {https://www.semanticscholar.org/paper/d958ba9662d442878a1d1d11d4e0968e6df42e4d},
    }

80. Deming Li, Trevor Meyer, Esther S Oh, A. Butala, N. Dehak, and L. Moro-Velázquez, “Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2023.
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    @inproceedings{266523855,
    title = {Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease},
    author = {{Deming Li} and {Trevor Meyer} and {Esther S Oh} and {A. Butala} and {N. Dehak} and {L. Moro-Velázquez}},
    year = 2023,
    month = {12},
    booktitle = {Alzheimer's & Dementia},
    url = {https://www.semanticscholar.org/paper/c2f99d03369b3583618c774b58c871c9707724bb},
    }

81. A. Favaro, N. Dehak, Thomas Thebaud, Esther S Oh, and L. Moro-Velázquez, “Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression,” in Alzheimer’s & Dementia, 2023.
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    @inproceedings{266523081,
    title = {Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression},
    author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {Esther S Oh} and {L. Moro-Velázquez}},
    year = 2023,
    month = {12},
    booktitle = {Alzheimer's & Dementia},
    url = {https://www.semanticscholar.org/paper/2f88f04aeb6eb8cac8c5706c294bcd3045faa966},
    }

82. Thomas Thebaud, Sonal Joshi, Henry Li, Martin Sustek, J. Villalba, S. Khudanpur, and N. Dehak, “Clustering Unsupervised Representations as Defense Against Poisoning Attacks on Speech Commands Classification System,” in Automatic Speech Recognition & Understanding, 2023.
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    @inproceedings{267043595,
    title = {Clustering Unsupervised Representations as Defense Against Poisoning Attacks on Speech Commands Classification System},
    author = {{Thomas Thebaud} and {Sonal Joshi} and {Henry Li} and {Martin Sustek} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}},
    year = 2023,
    month = {12},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/d59282b7adbdd1aef7754309aa72e98598059c1a},
    }

83. Martin Sustek, Sonal Joshi, Henry Li, Thomas Thebaud, J. Villalba, S. Khudanpur, and N. Dehak, “Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks,” in Automatic Speech Recognition & Understanding, 2023.
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    @inproceedings{267044159,
    title = {Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks},
    author = {{Martin Sustek} and {Sonal Joshi} and {Henry Li} and {Thomas Thebaud} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}},
    year = 2023,
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    }

84. Jiarui Hai, Yu-Jeh Liu, and Mounya Elhilali, “Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis,” in Automatic Speech Recognition & Understanding, 2023.
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    author = {{Jiarui Hai} and {Yu-Jeh Liu} and {Mounya Elhilali}},
    year = 2023,
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    }

85. Maliha Jahan, L. Moro-Velázquez, Thomas Thebaud, N. Dehak, and J. Villalba, “Model-Based Fairness Metric for Speaker Verification,” in Automatic Speech Recognition & Understanding, 2023.
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    }

86. Thomas Thebaud, Casey Chen, L. Moro-Velázquez, N. Dehak, and Esther S Oh, “Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment,” in Alzheimer’s & Dementia, 2023.
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    author = {{Thomas Thebaud} and {Casey Chen} and {L. Moro-Velázquez} and {N. Dehak} and {Esther S Oh}},
    year = 2023,
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    }

87. B. Englitz, S. Akram, Mounya Elhilali, and S. Shamma, “Decoding contextual influences on auditory perception from primary auditory cortex,” in bioRxiv, 2023.
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    author = {{B. Englitz} and {S. Akram} and {Mounya Elhilali} and {S. Shamma}},
    year = 2023,
    month = {12},
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    }

88. Nigam H Shah, John D Halamka, S. Saria, Michael Pencina, Troy Tazbaz, Micky Tripathi, Alison Callahan, Hailey Hildahl, and Brian Anderson, “A Nationwide Network of Health AI Assurance Laboratories.,” in Journal of the American Medical Association (JAMA), 2023.
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    title = {A Nationwide Network of Health AI Assurance Laboratories.},
    author = {{Nigam H Shah} and {John D Halamka} and {S. Saria} and {Michael Pencina} and {Troy Tazbaz} and {Micky Tripathi} and {Alison Callahan} and {Hailey Hildahl} and {Brian Anderson}},
    year = 2023,
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    }

89. Y. Wang, L. Moro-Velázquez, A. Favaro, D. Li, E. Oh, A. Butala, J. Villalba, and N. Dehak, “Binocular Discoordination Kinetic Features: A Novel Approach to Evaluate Neurodegenerative Diseases,” in IEEE Signal Processing in Medicine and Biology Symposium, 2023.
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    }

90. W. Tan, C. Lin, and J. Eisner, “Structure-Aware Path Inference for Neural Finite State Transducers,” in Proceedings of the NeurIPS 2023 Workshop “I Can’t Believe It’s Not Better: Failure Modes in the Age of Foundation Models”, 2023.
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    @InProceedings{tan-et-al-2023,
    author = "Weiting Tan and Chu-Cheng Lin and Jason Eisner",
    title = "Structure-Aware Path Inference for Neural Finite State
    Transducers",
    booktitle = "Proceedings of the {NeurIPS} 2023 Workshop ``{I}
    Can’t Believe It’s Not Better: Failure Modes in the
    Age of Foundation Models''",
    year = "2023",
    month = dec,
    URL = "http://cs.jhu.edu/~jason/papers/#tan-et-al-2023",
    }

91. S. Roy, S. Thomson, T. Chen, R. Shin, A. Pauls, J. Eisner, and B. V. Durme, “BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing,” in Proceedings of the Thirty-Seventh Conference on Neural Information Processing Systems, 2023.
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    @InProceedings{roy-et-al-2023,
    author = "Subhro Roy and Sam Thomson and Tongfei Chen and
    Richard Shin and Adam Pauls and Jason Eisner and
    Benjamin Van Durme",
    title = "{BenchCLAMP}: {A} Benchmark for Evaluating Language
    Models on Syntactic and Semantic Parsing",
    booktitle = "Proceedings of the Thirty-Seventh Conference on Neural
    Information Processing Systems",
    note = "Datasets and Benchmarks Track",
    year = "2023",
    month = dec,
    URL = "http://cs.jhu.edu/~jason/papers/#roy-et-al-2023",
    }

92. R. Zhong, C. Snell, D. Klein, and Jason Eisner, “Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, p. 5126–5152.
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    @InProceedings{zhong-et-al-2023,
    aclid = "2023.emnlp-main.312",
    author = "Ruiqi Zhong and Charlie Snell and Dan Klein and Jason
    Eisner",
    title = "Non-Programmers Can Label Programs Indirectly via
    Active Examples: {A} Case Study with Text-to-{SQL}",
    booktitle = "Proceedings of the 2023 Conference on Empirical
    Methods in Natural Language Processing",
    pages = "5126--5152",
    year = "2023",
    month = dec,
    URL = "http://cs.jhu.edu/~jason/papers/#zhong-et-al-2023",
    }

93. Trevor Meyer, Camden Shultz, N. Dehak, L. Moro-Velázquez, and Pedro P. Irazoqui, “Time Scale Network: A Shallow Neural Network For Time Series Data,” in arXiv.org, 2023.
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    author = {{Trevor Meyer} and {Camden Shultz} and {N. Dehak} and {L. Moro-Velázquez} and {Pedro P. Irazoqui}},
    year = 2023,
    month = {11},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/deacbb4906e1d2e597602a65b434a8132953ad8d},
    }

94. Andrew Blair-Stanek, Nils Holzenberger, and Benjamin Van Durme, “BLT: Can Large Language Models Handle Basic Legal Text?,” in arXiv.org, 2023.
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    @inproceedings{265220677,
    title = {BLT: Can Large Language Models Handle Basic Legal Text?},
    author = {{Andrew Blair-Stanek} and {Nils Holzenberger} and {Benjamin Van Durme}},
    year = 2023,
    month = {11},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/793a1116ba447091e829a39513c3b644ace29890},
    }

95. Nikita Moghe, Patrick Xia, Jacob Andreas, J. Eisner, Benjamin Van Durme, and Harsh Jhamtani, “Interpreting User Requests in the Context of Natural Language Standing Instructions,” in arXiv.org, 2023.
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    @inproceedings{265220995,
    title = {Interpreting User Requests in the Context of Natural Language Standing Instructions},
    author = {{Nikita Moghe} and {Patrick Xia} and {Jacob Andreas} and {J. Eisner} and {Benjamin Van Durme} and {Harsh Jhamtani}},
    year = 2023,
    month = {11},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/f2abeec1256f80970827d60f0151c7a19f2dbe7a},
    }

96. Karan Thakkar, Jiarui Hai, and Mounya Elhilali, “Investigating Self-Supervised Deep Representations for EEG-based Auditory Attention Decoding,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
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    @inproceedings{264935647,
    title = {Investigating Self-Supervised Deep Representations for EEG-based Auditory Attention Decoding},
    author = {{Karan Thakkar} and {Jiarui Hai} and {Mounya Elhilali}},
    year = 2023,
    month = {11},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/57b903a709dc9ab64b3bd81378f33547eabb01bd},
    }

97. William Fleshman and Benjamin Van Durme, “Toucan: Token-Aware Character Level Language Modeling,” in arXiv.org, 2023.
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    @inproceedings{265213263,
    title = {Toucan: Token-Aware Character Level Language Modeling},
    author = {{William Fleshman} and {Benjamin Van Durme}},
    year = 2023,
    month = {11},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/28c75ab7f6951f1c2bb349b34abc3204ba8b9498},
    }

98. Sandeep Reddy Kothinti and Mounya Elhilali, “Are acoustics enough? Semantic effects on auditory salience in natural scenes,” in Frontiers in Psychology, 2023.
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    @inproceedings{265539658,
    title = {Are acoustics enough? Semantic effects on auditory salience in natural scenes},
    author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}},
    year = 2023,
    month = {11},
    booktitle = {Frontiers in Psychology},
    url = {https://www.semanticscholar.org/paper/cdb491d1d121a7461fac00c4c71dfc45b9c8ae7a},
    }

99. Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, and Yunmo Chen, “Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles,” in arXiv.org, 2023.
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    @inproceedings{265033117,
    title = {Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles},
    author = {{Weiting Tan} and {Haoran Xu} and {Lingfeng Shen} and {Shuyue Stella Li} and {Kenton Murray} and {Philipp Koehn} and {Benjamin Van Durme} and {Yunmo Chen}},
    year = 2023,
    month = {11},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/bac133a9cb14eadea94c55ec15ea3bb866bf6c03},
    }

100. Helin Wang, Venkatesh Ravichandran, Milind Rao, Becky Lammers, Myra Sydnor, Nicholas J Maragakis, A. Butala, Jayne Zhang, Lora Clawson, Victoria Chovaz, and L. Moro-Velázquez, “Improving fairness for spoken language understanding in atypical speech with Text-to-Speech.” 2023.
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     @inproceedings{265281100,
     title = {Improving fairness for spoken language understanding in atypical speech with Text-to-Speech},
     author = {{Helin Wang} and {Venkatesh Ravichandran} and {Milind Rao} and {Becky Lammers} and {Myra Sydnor} and {Nicholas J Maragakis} and {A. Butala} and {Jayne Zhang} and {Lora Clawson} and {Victoria Chovaz} and {L. Moro-Velázquez}},
     year = 2023,
     month = {11},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/45e3115df40de802f9c4095f329ea374aac56825},
     }

101. Geoffrey M. Gray, Ayah Zirikly, Luis M. Ahumada, Masoud Rouhizadeh, Thomas M Richards, C. Kitchen, Iman Foroughmand, and E. Hatef, “Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system,” in JAMIA Open, 2023.
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     @inproceedings{263669151,
     title = {Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system},
     author = {{Geoffrey M. Gray} and {Ayah Zirikly} and {Luis M. Ahumada} and {Masoud Rouhizadeh} and {Thomas M Richards} and {C. Kitchen} and {Iman Foroughmand} and {E. Hatef}},
     year = 2023,
     month = {10},
     booktitle = {JAMIA Open},
     url = {https://www.semanticscholar.org/paper/1be931a9ebfeaa018e47abc582b1a9760ced4710},
     }

102. Tianjian Li, Haoran Xu, Philipp Koehn, Daniel Khashabi, and Kenton Murray, “Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models,” in arXiv.org, 2023.
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     @inproceedings{263605981,
     title = {Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models},
     author = {{Tianjian Li} and {Haoran Xu} and {Philipp Koehn} and {Daniel Khashabi} and {Kenton Murray}},
     year = 2023,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/d15021d750dbe9cee120b562acea857ca02d9104},
     }

103. Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Do pretrained Transformers Learn In-Context by Gradient Descent?.” 2023.
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     @inproceedings{263909464,
     title = {Do pretrained Transformers Learn In-Context by Gradient Descent?},
     author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}},
     year = 2023,
     month = {10},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/f30ddf0c7455f89f016c540564e235b191c503db},
     }

104. Guanghui Qin and Benjamin Van Durme, “Nugget: Neural Agglomerative Embeddings of Text,” in International Conference on Machine Learning, 2023.
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     @inproceedings{260957214,
     title = {Nugget: Neural Agglomerative Embeddings of Text},
     author = {{Guanghui Qin} and {Benjamin Van Durme}},
     year = 2023,
     month = {10},
     booktitle = {International Conference on Machine Learning},
     url = {https://www.semanticscholar.org/paper/531b37c44c7e39539f617fb1a4149ef8cce8f4ec},
     }

105. Shiyu Luo, Miguel Angrick, Christopher Coogan, Daniel Candrea, Kimberley Wyse-Sookoo, Samyak Shah, Qinwan Rabbani, Griffin W. Milsap, Alexander R Weiss, William S Anderson, Donna C. Tippett, Nicholas J Maragakis, Lora Clawson, M. Vansteensel, Brock Andrew Wester, Francesco V Tenore, H. Hermansky, M. Fifer, Nick F Ramsey, and Nathan Crone, “Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months,” in Advancement of science, 2023.
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     @inproceedings{264448311,
     title = {Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months},
     author = {{Shiyu Luo} and {Miguel Angrick} and {Christopher Coogan} and {Daniel Candrea} and {Kimberley Wyse-Sookoo} and {Samyak Shah} and {Qinwan Rabbani} and {Griffin W. Milsap} and {Alexander R Weiss} and {William S Anderson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {Lora Clawson} and {M. Vansteensel} and {Brock Andrew Wester} and {Francesco V Tenore} and {H. Hermansky} and {M. Fifer} and {Nick F Ramsey} and {Nathan Crone}},
     year = 2023,
     month = {10},
     booktitle = {Advancement of science},
     url = {https://www.semanticscholar.org/paper/dee851d6c5652ee423118132e1483bc0af9f30fc},
     }

106. Yunmo Chen, William Gantt, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme, “A Unified View of Evaluation Metrics for Structured Prediction,” in Conference on Empirical Methods in Natural Language Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{264426523,
     title = {A Unified View of Evaluation Metrics for Structured Prediction},
     author = {{Yunmo Chen} and {William Gantt} and {Tongfei Chen} and {Aaron Steven White} and {Benjamin Van Durme}},
     year = 2023,
     month = {10},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/a0df8169889043dae6ac111136a61162a5185a77},
     }

107. Amir Feder, Yoav Wald, Claudia Shi, S. Saria, and David M. Blei, “Data Augmentations for Improved (Large) Language Model Generalization.” 2023.
     [BibTeX] [Link]

     @inproceedings{264305897,
     title = {Data Augmentations for Improved (Large) Language Model Generalization},
     author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}},
     year = 2023,
     month = {10},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/23f96db82ae02c5c3c0a861571e7aa8d27c91bc9},
     }

108. Guanghui Qin, Corby Rosset, Ethan C. Chau, Nikhil Rao, and Benjamin Van Durme, “Dodo: Dynamic Contextual Compression for Decoder-only LMs.” 2023.
     [BibTeX] [Link]

     @inproceedings{263620438,
     title = {Dodo: Dynamic Contextual Compression for Decoder-only LMs},
     author = {{Guanghui Qin} and {Corby Rosset} and {Ethan C. Chau} and {Nikhil Rao} and {Benjamin Van Durme}},
     year = 2023,
     month = {10},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/883187d0bacf57238ac95e2749bcc601baf2c212},
     }

109. Alex Walker, Ayah Zirikly, Melissa D. Stockbridge, and H. C. Wilcox, “A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.,” in Crisis, 2023.
     [BibTeX] [Link]

     @inproceedings{263827534,
     title = {A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.},
     author = {{Alex Walker} and {Ayah Zirikly} and {Melissa D. Stockbridge} and {H. C. Wilcox}},
     year = 2023,
     month = {10},
     booktitle = {Crisis},
     url = {https://www.semanticscholar.org/paper/a0ee01acead1ccb6064f603f75186f8aa25d2562},
     }

110. Jiarui Hai and Mounya Elhilali, “Diff-Pitcher: Diffusion-Based Singing Voice Pitch Correction,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2023.
     [BibTeX] [Link]

     @inproceedings{261899586,
     title = {Diff-Pitcher: Diffusion-Based Singing Voice Pitch Correction},
     author = {{Jiarui Hai} and {Mounya Elhilali}},
     year = 2023,
     month = {10},
     booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
     url = {https://www.semanticscholar.org/paper/377ffdc7cf16822e8aa12ea28ab16d0f5bc8f0c2},
     }

111. Jiarui Hai, Helin Wang, Dongchao Yang, Karan Thakkar, N. Dehak, and Mounya Elhilali, “DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{263830793,
     title = {DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction},
     author = {{Jiarui Hai} and {Helin Wang} and {Dongchao Yang} and {Karan Thakkar} and {N. Dehak} and {Mounya Elhilali}},
     year = 2023,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/70aec6486668cc5ca25d45240c68de223a8deda7},
     }

112. Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, and E. Nouri, “InstructExcel: A Benchmark for Natural Language Instruction in Excel,” in Conference on Empirical Methods in Natural Language Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{264426857,
     title = {InstructExcel: A Benchmark for Natural Language Instruction in Excel},
     author = {{Justin Payan} and {Swaroop Mishra} and {Mukul Singh} and {Carina Negreanu} and {Christian Poelitz} and {Chitta Baral} and {Subhro Roy} and {Rasika Chakravarthy} and {Benjamin Van Durme} and {E. Nouri}},
     year = 2023,
     month = {10},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/706fc333fc951f48ea26169d88478b5e4c36e82c},
     }

113. S. Sia and K. Duh, “In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models,” in Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, Macau SAR, China, 2023, p. 173–185.
     [BibTeX] [Abstract] [Link]
     

     The phenomena of in-context learning has typically been thought of as {“}learning from examples{”}. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation.

     @inproceedings{sia-duh-2023-context,
     title = "In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models",
     author = "Sia, Suzanna and
     Duh, Kevin",
     editor = "Utiyama, Masao and
     Wang, Rui",
     booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track",
     month = sep,
     year = "2023",
     address = "Macau SAR, China",
     publisher = "Asia-Pacific Association for Machine Translation",
     url = "https://aclanthology.org/2023.mtsummit-research.15",
     pages = "173--185",
     abstract = "The phenomena of in-context learning has typically been thought of as {``}learning from examples{''}. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation.",
     }

114. J. Chi, B. Lu, J. Eisner, P. Bell, P. Jyothi, and A. M. Ali, “Unsupervised Code-Switched Text Generation from Parallel Text,” in Proceedings of INTERSPEECH, Dublin, 2023.
     [BibTeX] [Link]

     @InProceedings{chi-et-al-2023,
     author = "Jie Chi and Brian Lu and Jason Eisner and Peter Bell
     and Preethi Jyothi and Ahmed M. Ali",
     title = "Unsupervised Code-Switched Text Generation from
     Parallel Text",
     booktitle = "Proceedings of INTERSPEECH",
     year = "2023",
     month = aug,
     address = "Dublin",
     URL = "http://cs.jhu.edu/~jason/papers/#chi-et-al-2023",
     }

115. D. Verma, Y. K. Lal, S. Sinha, B. Van Durme, and A. Poliak, “Evaluating Paraphrastic Robustness in Textual Entailment Models,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 880–892. doi:10.18653/v1/2023.acl-short.76
     [BibTeX] [Abstract] [Link]
     

     We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models{‘} predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16{\%} of paraphrased examples, indicating that there is still room for improvement.

     @inproceedings{verma-etal-2023-evaluating,
     title = "Evaluating Paraphrastic Robustness in Textual Entailment Models",
     author = "Verma, Dhruv and
     Lal, Yash Kumar and
     Sinha, Shreyashee and
     Van Durme, Benjamin and
     Poliak, Adam",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-short.76",
     doi = "10.18653/v1/2023.acl-short.76",
     pages = "880--892",
     abstract = "We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models{'} predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16{\%} of paraphrased examples, indicating that there is still room for improvement.",
     }

116. E. Spaulding, G. Kazantsev, and M. Dredze, “Joint End-to-end Semantic Proto-role Labeling,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 723–736. doi:10.18653/v1/2023.acl-short.63
     [BibTeX] [Abstract] [Link]
     

     Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.

     @inproceedings{spaulding-etal-2023-joint,
     title = "Joint End-to-end Semantic Proto-role Labeling",
     author = "Spaulding, Elizabeth and
     Kazantsev, Gary and
     Dredze, Mark",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-short.63",
     doi = "10.18653/v1/2023.acl-short.63",
     pages = "723--736",
     abstract = "Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.",
     }

117. M. Antoniak, A. Field, J. Mun, M. Walsh, L. Klein, and M. Sap, “Riveter: Measuring Power and Social Dynamics Between Entities,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Toronto, Canada, 2023, p. 377–388. doi:10.18653/v1/2023.acl-demo.36
     [BibTeX] [Abstract] [Link]
     

     Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.

     @inproceedings{antoniak-etal-2023-riveter,
     title = "Riveter: Measuring Power and Social Dynamics Between Entities",
     author = "Antoniak, Maria and
     Field, Anjalie and
     Mun, Jimin and
     Walsh, Melanie and
     Klein, Lauren and
     Sap, Maarten",
     editor = "Bollegala, Danushka and
     Huang, Ruihong and
     Ritter, Alan",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-demo.36",
     doi = "10.18653/v1/2023.acl-demo.36",
     pages = "377--388",
     abstract = "Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.",
     }

118. J. Maillard, C. Gao, E. Kalbassi, K. R. Sadagopan, V. Goswami, P. Koehn, A. Fan, and F. Guzman, “Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 2740–2756. doi:10.18653/v1/2023.acl-long.154
     [BibTeX] [Abstract] [Link]
     

     For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.

     @inproceedings{maillard-etal-2023-small,
     title = "Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation",
     author = "Maillard, Jean and
     Gao, Cynthia and
     Kalbassi, Elahe and
     Sadagopan, Kaushik Ram and
     Goswami, Vedanuj and
     Koehn, Philipp and
     Fan, Angela and
     Guzman, Francisco",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.154",
     doi = "10.18653/v1/2023.acl-long.154",
     pages = "2740--2756",
     abstract = "For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.",
     }

119. E. Schumacher, J. Mayfield, and M. Dredze, “On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking,” in Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), Toronto, ON, Canada, 2023, p. 58–69. doi:10.18653/v1/2023.matching-1.6
     [BibTeX] [Abstract] [Link]
     

     Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.

     @inproceedings{schumacher-etal-2023-surprising,
     title = "On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking",
     author = "Schumacher, Elliot and
     Mayfield, James and
     Dredze, Mark",
     editor = "Hruschka, Estevam and
     Mitchell, Tom and
     Rahman, Sajjadur and
     Mladeni{\'c}, Dunja and
     Grobelnik, Marko",
     booktitle = "Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, ON, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.matching-1.6",
     doi = "10.18653/v1/2023.matching-1.6",
     pages = "58--69",
     abstract = "Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.",
     }

120. V. Raunak, A. Menezes, M. Post, and H. Hassan, “Do GPTs Produce Less Literal Translations?,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 1041–1050. doi:10.18653/v1/2023.acl-short.90
     [BibTeX] [Abstract] [Link]
     

     Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.

     @inproceedings{raunak-etal-2023-gpts,
     title = "Do {GPT}s Produce Less Literal Translations?",
     author = "Raunak, Vikas and
     Menezes, Arul and
     Post, Matt and
     Hassan, Hany",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-short.90",
     doi = "10.18653/v1/2023.acl-short.90",
     pages = "1041--1050",
     abstract = "Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.",
     }

121. J. Gwinnup, T. Anderson, B. Ore, E. Hansen, and K. Duh, “Enhancing Video Translation Context with Object Labels,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 130–137. doi:10.18653/v1/2023.iwslt-1.8
     [BibTeX] [Abstract] [Link]
     

     We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset.

     @inproceedings{gwinnup-etal-2023-enhancing,
     title = "Enhancing Video Translation Context with Object Labels",
     author = "Gwinnup, Jeremy and
     Anderson, Tim and
     Ore, Brian and
     Hansen, Eric and
     Duh, Kevin",
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Carpuat, Marine",
     booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.iwslt-1.8",
     doi = "10.18653/v1/2023.iwslt-1.8",
     pages = "130--137",
     abstract = "We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset.",
     }

122. S. Behzad, S. Ebner, M. Marone, B. Van Durme, and M. Yarmohammadi, “The Effect of Alignment Correction on Cross-Lingual Annotation Projection,” in Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII), Toronto, Canada, 2023, p. 244–251. doi:10.18653/v1/2023.law-1.24
     [BibTeX] [Abstract] [Link]
     

     Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{–-}automatic, manual, and mixed{–-}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.

     @inproceedings{behzad-etal-2023-effect,
     title = "The Effect of Alignment Correction on Cross-Lingual Annotation Projection",
     author = "Behzad, Shabnam and
     Ebner, Seth and
     Marone, Marc and
     Van Durme, Benjamin and
     Yarmohammadi, Mahsa",
     editor = "Prange, Jakob and
     Friedrich, Annemarie",
     booktitle = "Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.law-1.24",
     doi = "10.18653/v1/2023.law-1.24",
     pages = "244--251",
     abstract = "Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{---}automatic, manual, and mixed{---}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.",
     }

123. S. Zhang, S. Wu, O. Irsoy, S. Lu, M. Bansal, M. Dredze, and D. Rosenberg, “MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 9027–9050. doi:10.18653/v1/2023.acl-long.502
     [BibTeX] [Abstract] [Link]
     

     Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P {–} that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may {“}over-generalize{”}, in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies.

     @inproceedings{zhang-etal-2023-mixce,
     title = "{M}ix{CE}: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies",
     author = "Zhang, Shiyue and
     Wu, Shijie and
     Irsoy, Ozan and
     Lu, Steven and
     Bansal, Mohit and
     Dredze, Mark and
     Rosenberg, David",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.502",
     doi = "10.18653/v1/2023.acl-long.502",
     pages = "9027--9050",
     abstract = "Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P {--} that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may {``}over-generalize{''}, in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies.",
     }

124. M. Agarwal, S. Agrawal, A. Anastasopoulos, L. Bentivogli, O. Bojar, C. Borg, M. Carpuat, R. Cattoni, M. Cettolo, M. Chen, W. Chen, K. Choukri, A. Chronopoulou, A. Currey, T. Declerck, Q. Dong, K. Duh, Y. Estève, M. Federico, S. Gahbiche, B. Haddow, B. Hsu, P. Mon Htut, H. Inaguma, D. Javorský, J. Judge, Y. Kano, T. Ko, R. Kumar, P. Li, X. Ma, P. Mathur, E. Matusov, P. McNamee, J. P. McCrae, K. Murray, M. Nadejde, S. Nakamura, M. Negri, H. Nguyen, J. Niehues, X. Niu, A. Kr. Ojha, J. E. Ortega, P. Pal, J. Pino, L. van der Plas, P. Polák, E. Rippeth, E. Salesky, J. Shi, M. Sperber, S. Stüker, K. Sudoh, Y. Tang, B. Thompson, K. Tran, M. Turchi, A. Waibel, M. Wang, S. Watanabe, and R. Zevallos, “FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 1–61. doi:10.18653/v1/2023.iwslt-1.1
     [BibTeX] [Abstract] [Link]
     

     This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.

     @inproceedings{agrawal-etal-2023-findings,
     title = "{FINDINGS} {OF} {THE} {IWSLT} 2023 {EVALUATION} {CAMPAIGN}",
     author = {Agarwal, Milind and
     Agrawal, Sweta and
     Anastasopoulos, Antonios and
     Bentivogli, Luisa and
     Bojar, Ond{\v{r}}ej and
     Borg, Claudia and
     Carpuat, Marine and
     Cattoni, Roldano and
     Cettolo, Mauro and
     Chen, Mingda and
     Chen, William and
     Choukri, Khalid and
     Chronopoulou, Alexandra and
     Currey, Anna and
     Declerck, Thierry and
     Dong, Qianqian and
     Duh, Kevin and
     Est{\`e}ve, Yannick and
     Federico, Marcello and
     Gahbiche, Souhir and
     Haddow, Barry and
     Hsu, Benjamin and
     Mon Htut, Phu and
     Inaguma, Hirofumi and
     Javorsk{\'y}, D{\'a}vid and
     Judge, John and
     Kano, Yasumasa and
     Ko, Tom and
     Kumar, Rishu and
     Li, Pengwei and
     Ma, Xutai and
     Mathur, Prashant and
     Matusov, Evgeny and
     McNamee, Paul and
     P. McCrae, John and
     Murray, Kenton and
     Nadejde, Maria and
     Nakamura, Satoshi and
     Negri, Matteo and
     Nguyen, Ha and
     Niehues, Jan and
     Niu, Xing and
     Kr. Ojha, Atul and
     E. Ortega, John and
     Pal, Proyag and
     Pino, Juan and
     van der Plas, Lonneke and
     Pol{\'a}k, Peter and
     Rippeth, Elijah and
     Salesky, Elizabeth and
     Shi, Jiatong and
     Sperber, Matthias and
     St{\"u}ker, Sebastian and
     Sudoh, Katsuhito and
     Tang, Yun and
     Thompson, Brian and
     Tran, Kevin and
     Turchi, Marco and
     Waibel, Alex and
     Wang, Mingxuan and
     Watanabe, Shinji and
     Zevallos, Rodolfo},
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Carpuat, Marine",
     booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.iwslt-1.1",
     doi = "10.18653/v1/2023.iwslt-1.1",
     pages = "1--61",
     abstract = "This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.",
     }

125. X. Zhang, K. Duh, and P. McNamee, “A Hyperparameter Optimization Toolkit for Neural Machine Translation Research,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Toronto, Canada, 2023, p. 161–168. doi:10.18653/v1/2023.acl-demo.15
     [BibTeX] [Abstract] [Link]
     

     Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}

     @inproceedings{zhang-etal-2023-hyperparameter,
     title = "A Hyperparameter Optimization Toolkit for Neural Machine Translation Research",
     author = "Zhang, Xuan and
     Duh, Kevin and
     McNamee, Paul",
     editor = "Bollegala, Danushka and
     Huang, Ruihong and
     Ritter, Alan",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-demo.15",
     doi = "10.18653/v1/2023.acl-demo.15",
     pages = "161--168",
     abstract = "Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}",
     }

126. K. Harrigian, A. Zirikly, B. Chee, A. Ahmad, A. Links, S. Saha, M. C. Beach, and M. Dredze, “Characterization of Stigmatizing Language in Medical Records,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 312–329. doi:10.18653/v1/2023.acl-short.28
     [BibTeX] [Abstract] [Link]
     

     Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.

     @inproceedings{harrigian-etal-2023-characterization,
     title = "Characterization of Stigmatizing Language in Medical Records",
     author = "Harrigian, Keith and
     Zirikly, Ayah and
     Chee, Brant and
     Ahmad, Alya and
     Links, Anne and
     Saha, Somnath and
     Beach, Mary Catherine and
     Dredze, Mark",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-short.28",
     doi = "10.18653/v1/2023.acl-short.28",
     pages = "312--329",
     abstract = "Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.",
     }

127. A. Hussein, C. Xiao, N. Verma, T. Thebaud, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2023 Dialect Speech Translation System Description,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 283–290. doi:10.18653/v1/2023.iwslt-1.26
     [BibTeX] [Abstract] [Link]
     

     This paper presents JHU{‘}s submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively.

     @inproceedings{hussein-etal-2023-jhu,
     title = "{JHU} {IWSLT} 2023 Dialect Speech Translation System Description",
     author = "Hussein, Amir and
     Xiao, Cihan and
     Verma, Neha and
     Thebaud, Thomas and
     Wiesner, Matthew and
     Khudanpur, Sanjeev",
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Carpuat, Marine",
     booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.iwslt-1.26",
     doi = "10.18653/v1/2023.iwslt-1.26",
     pages = "283--290",
     abstract = "This paper presents JHU{'}s submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively.",
     }

128. G. Portillo Wightman, A. Delucia, and M. Dredze, “Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement,” in Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023), Toronto, Canada, 2023, p. 326–362. doi:10.18653/v1/2023.trustnlp-1.28
     [BibTeX] [Abstract] [Link]
     

     Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes.

     @inproceedings{portillo-wightman-etal-2023-strength,
     title = "Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement",
     author = "Portillo Wightman, Gwenyth and
     Delucia, Alexandra and
     Dredze, Mark",
     editor = "Ovalle, Anaelia and
     Chang, Kai-Wei and
     Mehrabi, Ninareh and
     Pruksachatkun, Yada and
     Galystan, Aram and
     Dhamala, Jwala and
     Verma, Apurv and
     Cao, Trista and
     Kumar, Anoop and
     Gupta, Rahul",
     booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.trustnlp-1.28",
     doi = "10.18653/v1/2023.trustnlp-1.28",
     pages = "326--362",
     abstract = "Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes.",
     }

129. N. Selvam, S. Dev, D. Khashabi, T. Khot, and K. Chang, “The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 1373–1386. doi:10.18653/v1/2023.acl-short.118
     [BibTeX] [Abstract] [Link]
     

     How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases.

     @inproceedings{selvam-etal-2023-tail,
     title = "The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks",
     author = "Selvam, Nikil and
     Dev, Sunipa and
     Khashabi, Daniel and
     Khot, Tushar and
     Chang, Kai-Wei",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-short.118",
     doi = "10.18653/v1/2023.acl-short.118",
     pages = "1373--1386",
     abstract = "How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases.",
     }

130. N. Gandhi, A. Field, and E. Strubell, “Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 10543–10558. doi:10.18653/v1/2023.acl-long.588
     [BibTeX] [Abstract] [Link]
     

     Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14{\%} improvement in average F1 without increasing annotator time.

     @inproceedings{gandhi-etal-2023-annotating,
     title = "Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution",
     author = "Gandhi, Nupoor and
     Field, Anjalie and
     Strubell, Emma",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.588",
     doi = "10.18653/v1/2023.acl-long.588",
     pages = "10543--10558",
     abstract = "Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14{\%} improvement in average F1 without increasing annotator time.",
     }

131. Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-Instruct: Aligning Language Models with Self-Generated Instructions,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 13484–13508. doi:10.18653/v1/2023.acl-long.754
     [BibTeX] [Abstract] [Link]
     

     Large {“}instruction-tuned{”} language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33{\%} absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5{\%} absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.

     @inproceedings{wang-etal-2023-self-instruct,
     title = "Self-Instruct: Aligning Language Models with Self-Generated Instructions",
     author = "Wang, Yizhong and
     Kordi, Yeganeh and
     Mishra, Swaroop and
     Liu, Alisa and
     Smith, Noah A. and
     Khashabi, Daniel and
     Hajishirzi, Hannaneh",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.754",
     doi = "10.18653/v1/2023.acl-long.754",
     pages = "13484--13508",
     abstract = "Large {``}instruction-tuned{''} language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33{\%} absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5{\%} absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.",
     }

132. A. Mallen, A. Asai, V. Zhong, R. Das, D. Khashabi, and H. Hajishirzi, “When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 9802–9822. doi:10.18653/v1/2023.acl-long.546
     [BibTeX] [Abstract] [Link]
     

     Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs{‘} strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary.

     @inproceedings{mallen-etal-2023-trust,
     title = "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories",
     author = "Mallen, Alex and
     Asai, Akari and
     Zhong, Victor and
     Das, Rajarshi and
     Khashabi, Daniel and
     Hajishirzi, Hannaneh",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.546",
     doi = "10.18653/v1/2023.acl-long.546",
     pages = "9802--9822",
     abstract = "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs{'} strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary.",
     }

133. E. Stengel-Eskin, J. Guallar-Blasco, Y. Zhou, and B. Van Durme, “Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 10220–10237. doi:10.18653/v1/2023.acl-long.569
     [BibTeX] [Abstract] [Link]
     

     Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision.

     @inproceedings{stengel-eskin-etal-2023-chicken,
     title = "Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in {VQA}",
     author = "Stengel-Eskin, Elias and
     Guallar-Blasco, Jimena and
     Zhou, Yi and
     Van Durme, Benjamin",
     editor = "Rogers, Anna and
     Boyd-Graber, Jordan and
     Okazaki, Naoaki",
     booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.acl-long.569",
     doi = "10.18653/v1/2023.acl-long.569",
     pages = "10220--10237",
     abstract = "Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision.",
     }

134. H. Fang, A. Balakrishnan, H. Jhamtani, J. Bufe, J. Crawford, Jayant Krishnamurthy, A. Pauls, J. Eisner, Jacob Andreas, and D. Klein, “The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding,” in Findings of the Association for Computational Linguistics: ACL 2023, 2023, p. 5682–5700.
     [BibTeX] [Link]

     @InProceedings{fang-et-al-2023,
     author = "Hao Fang and Anusha Balakrishnan and Harsh Jhamtani
     and John Bufe and Jean Crawford and Jayant
     Krishnamurthy and Adam Pauls and Jason Eisner and Jacob
     Andreas and Dan Klein",
     title = "The Whole Truth and Nothing But the Truth: Faithful
     and Controllable Dialogue Response Generation with
     Dataflow Transduction and Constrained Decoding",
     booktitle = "Findings of the Association for Computational
     Linguistics: ACL 2023",
     year = "2023",
     month = jul,
     pages = "5682--5700",
     URL = "http://cs.jhu.edu/~jason/papers/#fang-et-al-2023",
     }

135. B. Z. Li, J. Eisner, A. Pauls, and Sam Thomson, “Toward Interactive Dictation,” in Proceedings of the Association for Computational Linguistics (ACL), 2023, p. 15319–15338.
     [BibTeX] [Link]

     @InProceedings{li-et-al-2023-dictation,
     author = "Belinda Z. Li and Jason Eisner and Adam Pauls and Sam
     Thomson",
     title = "Toward Interactive Dictation",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2023",
     month = jul,
     pages = "15319--15338",
     URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2023-dictation",
     }

136. F. Mireshghallah, Y. Su, Tatsunori Hashimoto, J. Eisner, and R. Shin, “Privacy-Preserving Domain Adaptation of Semantic Parsers,” in Proceedings of the Association for Computational Linguistics (ACL), 2023, p. 4950–4970.
     [BibTeX] [Link]

     @InProceedings{mireshghallah-et-al-2023,
     author = "Fatemehsadat Mireshghallah and Yu Su and Tatsunori
     Hashimoto and Jason Eisner and Richard Shin",
     title = "Privacy-Preserving Domain Adaptation of Semantic
     Parsers",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2023",
     month = jul,
     pages = "4950--4970",
     URL = "http://cs.jhu.edu/~jason/papers/#mireshghallah-et-al-2023",
     }

137. X. L. Li, A. Holtzman, D. Fried, P. Liang, J. Eisner, T. Hashimoto, L. Zettlemoyer, and M. Lewis, “Contrastive Decoding: Open-ended Text Generation as Optimization,” in Proceedings of the Association for Computational Linguistics (ACL), 2023, p. 12286–12312.
     [BibTeX] [Link]

     @InProceedings{li-et-al-2023-cd,
     author = "Xiang Lisa Li and Ari Holtzman and Daniel Fried and
     Percy Liang and Jason Eisner and Tatsunori Hashimoto
     and Luke Zettlemoyer and Mike Lewis",
     title = "Contrastive Decoding: Open-ended Text Generation as
     Optimization",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2023",
     month = jul,
     pages = "12286--12312",
     URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2023-cd",
     }

138. L. Du, L. T. Hennigen, T. Pimentel, C. Meister, J. Eisner, and R. Cotterell, “A Measure-Theoretic Characterization of Tight Language Models,” in Proceedings of the Association for Computational Linguistics (ACL), 2023, p. 9744–9770.
     [BibTeX] [Link]

     @InProceedings{du-et-al-2023,
     author = "Li Du and Lucas Torroba Hennigen and Tiago Pimentel
     and Clara Meister and Jason Eisner and Ryan Cotterell",
     title = "A Measure-Theoretic Characterization of Tight Language
     Models",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2023",
     month = jul,
     pages = "9744--9770",
     URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2023",
     }

139. A. Opedal, R. Zmigrod, T. Vieira, Ryan Cotterell, and J. Eisner, “Efficient Semiring-Weighted Earley Parsing,” in Proceedings of the Association for Computational Linguistics (ACL), 2023, p. 3687–3713.
     [BibTeX] [Link]

     @InProceedings{opedal-et-al-2023,
     author = "Andreas Opedal and Ran Zmigrod and Tim Vieira and Ryan
     Cotterell and Jason Eisner",
     title = "Efficient Semiring-Weighted {E}arley Parsing",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2023",
     month = jul,
     pages = "3687--3713",
     URL = "http://cs.jhu.edu/~jason/papers/#opedal-et-al-2023",
     }

140. K. Duh and X. Zhang, “AutoML for NLP,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Dubrovnik, Croatia, 2023, p. 25–26. doi:10.18653/v1/2023.eacl-tutorials.5
     [BibTeX] [Abstract] [Link]
     

     Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.

     @inproceedings{duh-zhang-2023-automl,
     title = "{A}uto{ML} for {NLP}",
     author = "Duh, Kevin and
     Zhang, Xuan",
     editor = "Zanzotto, Fabio Massimo and
     Pradhan, Sameer",
     booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
     month = may,
     year = "2023",
     address = "Dubrovnik, Croatia",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.eacl-tutorials.5",
     doi = "10.18653/v1/2023.eacl-tutorials.5",
     pages = "25--26",
     abstract = "Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.",
     }

141. G. Qin, Y. Feng, and B. Van Durme, “The NLP Task Effectiveness of Long-Range Transformers,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 3774–3790. doi:10.18653/v1/2023.eacl-main.273
     [BibTeX] [Abstract] [Link]
     

     Transformer models cannot easily scale to long sequences due to their O(N{\^{}}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.

     @inproceedings{qin-etal-2023-nlp,
     title = "The {NLP} Task Effectiveness of Long-Range Transformers",
     author = "Qin, Guanghui and
     Feng, Yukun and
     Van Durme, Benjamin",
     editor = "Vlachos, Andreas and
     Augenstein, Isabelle",
     booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
     month = may,
     year = "2023",
     address = "Dubrovnik, Croatia",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.eacl-main.273",
     doi = "10.18653/v1/2023.eacl-main.273",
     pages = "3774--3790",
     abstract = "Transformer models cannot easily scale to long sequences due to their O(N{\^{}}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.",
     }

142. W. Tan, K. Heffernan, H. Schwenk, and P. Koehn, “Multilingual Representation Distillation with Contrastive Learning,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 1477–1490. doi:10.18653/v1/2023.eacl-main.108
     [BibTeX] [Abstract] [Link]
     

     Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.

     @inproceedings{tan-etal-2023-multilingual,
     title = "Multilingual Representation Distillation with Contrastive Learning",
     author = "Tan, Weiting and
     Heffernan, Kevin and
     Schwenk, Holger and
     Koehn, Philipp",
     editor = "Vlachos, Andreas and
     Augenstein, Isabelle",
     booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
     month = may,
     year = "2023",
     address = "Dubrovnik, Croatia",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.eacl-main.108",
     doi = "10.18653/v1/2023.eacl-main.108",
     pages = "1477--1490",
     abstract = "Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.",
     }

143. Y. Chen, W. Gantt, W. Gu, T. Chen, A. White, and B. Van Durme, “Iterative Document-level Information Extraction via Imitation Learning,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 1858–1874. doi:10.18653/v1/2023.eacl-main.136
     [BibTeX] [Abstract] [Link]
     

     We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{‘}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {–} 4-ary relation extraction on SciREX and template extraction on MUC-4 {–} as well as a strong baseline on the new BETTER Granular task.

     @inproceedings{chen-etal-2023-iterative,
     title = "Iterative Document-level Information Extraction via Imitation Learning",
     author = "Chen, Yunmo and
     Gantt, William and
     Gu, Weiwei and
     Chen, Tongfei and
     White, Aaron and
     Van Durme, Benjamin",
     editor = "Vlachos, Andreas and
     Augenstein, Isabelle",
     booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
     month = may,
     year = "2023",
     address = "Dubrovnik, Croatia",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.eacl-main.136",
     doi = "10.18653/v1/2023.eacl-main.136",
     pages = "1858--1874",
     abstract = "We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{'}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {--} 4-ary relation extraction on SciREX and template extraction on MUC-4 {--} as well as a strong baseline on the new BETTER Granular task.",
     }

144. Desh Raj, Daniel Povey, and S. Khudanpur, “SURT 2.0: Advances in Transducer-Based Multi-Talker Speech Recognition,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{259202526,
     title = {SURT 2.0: Advances in Transducer-Based Multi-Talker Speech Recognition},
     author = {{Desh Raj} and {Daniel Povey} and {S. Khudanpur}},
     year = 2023,
     month = {6},
     booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
     url = {https://www.semanticscholar.org/paper/a6fffd418fabef307cba5e70324a3ba89c7ffc39},
     }

145. Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen, Si-Jia Yang, Shaozuo Yu, O. Zendel, C. Theobalt, A. Yuille, and Adam Kortylewski, “OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258236142,
     title = {OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images},
     author = {{Bingchen Zhao} and {Jiahao Wang} and {Wufei Ma} and {Artur Jesslen} and {Si-Jia Yang} and {Shaozuo Yu} and {O. Zendel} and {C. Theobalt} and {A. Yuille} and {Adam Kortylewski}},
     year = 2023,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/f5cd9b3f48e81e1a91923ef423765edeb9bdd50e},
     }

146. Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Do pretrained Transformers Really Learn In-context by Gradient Descent?,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{268499126,
     title = {Do pretrained Transformers Really Learn In-context by Gradient Descent?},
     author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}},
     year = 2023,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/a9d460f8eb9001b1bed11b7fb2af555185c70fcf},
     }

147. A. Kala, Eric D. McCollum, and Mounya Elhilali, “Reference free auscultation quality metric and its trends,” in Biomedical Signal Processing and Control, 2023.
     [BibTeX] [Link]

     @inproceedings{254853589,
     title = {Reference free auscultation quality metric and its trends},
     author = {{A. Kala} and {Eric D. McCollum} and {Mounya Elhilali}},
     year = 2023,
     month = {8},
     booktitle = {Biomedical Signal Processing and Control},
     url = {https://www.semanticscholar.org/paper/4276e26be8c196ba4b496b4a0acc4102d32c0bd8},
     }

148. H. L. Xinyuan, N. Verma, B. Bamfo Odoom, U. Pradeep, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2023 Multilingual Speech Translation System Description,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 302–310. doi:10.18653/v1/2023.iwslt-1.28
     [BibTeX] [Abstract] [Link]
     

     We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.

     @inproceedings{xinyuan-etal-2023-jhu,
     title = "{JHU} {IWSLT} 2023 Multilingual Speech Translation System Description",
     author = "Xinyuan, Henry Li and
     Verma, Neha and
     Bamfo Odoom, Bismarck and
     Pradeep, Ujvala and
     Wiesner, Matthew and
     Khudanpur, Sanjeev",
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Carpuat, Marine",
     booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
     month = jul,
     year = "2023",
     address = "Toronto, Canada (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.iwslt-1.28",
     doi = "10.18653/v1/2023.iwslt-1.28",
     pages = "302--310",
     abstract = "We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.",
     }

149. Z. Smith, N. Hoekstra, T. Mvalo, I. McLane, A. Kala, M. Chiume, C. Verwey, D. Olson, C. Buck, J. Mulindwa, E. Fitzgerald, M. Chagomerana, Mounya Elhilali, M. Hosseinipour, and E. McCollum, “Evaluation of a Novel Digital Stethoscope Prototype in a Low-resource Setting: Expert Listening Panel Agreement With Conventional Auscultation in Hospitalized Malawian Children With Severe Pneumonia,” in C25. OPPORTUNITIES AND ADVANCES IN PEDIATRIC GLOBAL HEALTH, 2023.
     [BibTeX] [Link]

     @inproceedings{258444556,
     title = {Evaluation of a Novel Digital Stethoscope Prototype in a Low-resource Setting: Expert Listening Panel Agreement With Conventional Auscultation in Hospitalized Malawian Children With Severe Pneumonia},
     author = {{Z. Smith} and {N. Hoekstra} and {T. Mvalo} and {I. McLane} and {A. Kala} and {M. Chiume} and {C. Verwey} and {D. Olson} and {C. Buck} and {J. Mulindwa} and {E. Fitzgerald} and {M. Chagomerana} and {Mounya Elhilali} and {M. Hosseinipour} and {E. McCollum}},
     year = 2023,
     month = {5},
     booktitle = {C25. OPPORTUNITIES AND ADVANCES IN PEDIATRIC GLOBAL HEALTH},
     url = {https://www.semanticscholar.org/paper/00ff74d263d80498ea78cca8850c565b66057476},
     }

150. W. G. C. Bandara and Vishal M. Patel, “Deep Metric Learning for Unsupervised Remote Sensing Change Detection,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{257557771,
     title = {Deep Metric Learning for Unsupervised Remote Sensing Change Detection},
     author = {{W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/30d02457a38374398deca536682c193f0f0b1a24},
     }

151. Nathan C Higgins, Alexandra N Scurry, Fang Jiang, David F. Little, Claude Alain, Mounya Elhilali, and J. Snyder, “Adaptation in the sensory cortex drives bistable switching during auditory stream segregation,” in Neuroscience of Consciousness, 2023.
     [BibTeX] [Link]

     @inproceedings{256604402,
     title = {Adaptation in the sensory cortex drives bistable switching during auditory stream segregation},
     author = {{Nathan C Higgins} and {Alexandra N Scurry} and {Fang Jiang} and {David F. Little} and {Claude Alain} and {Mounya Elhilali} and {J. Snyder}},
     year = 2023,
     month = {1},
     booktitle = {Neuroscience of Consciousness},
     url = {https://www.semanticscholar.org/paper/c1c4a48270174de06f609bb2dc98c8e896ce78a3},
     }

152. Haoran Xu, Weiting Tan, Shuyue Stella Li, Yunmo Chen, Benjamin Van Durme, Philipp Koehn, and Kenton Murray, “Condensing Multilingual Knowledge with Lightweight Language-Specific Modules,” in Conference on Empirical Methods in Natural Language Processing, 2023.
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153. Saurabhchand Bhati, J. Villalba, L. Moro-Velázquez, Thomas Thebaud, and N. Dehak, “Segmental SpeechCLIP: Utilizing Pretrained Image-text Models for Audio-Visual Learning,” in Interspeech, 2023.
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154. A. Favaro, L. Moro-Velázquez, A. Butala, C. Motley, Tianyu Cao, R. Stevens, J. Villalba, and N. Dehak, “Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson’s disease,” in Frontiers in Neurology, 2023.
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155. Yixiong Chen, A. Yuille, and Zongwei Zhou, “Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks,” in International Conference on Learning Representations, 2023.
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156. M. Marín-Jiménez, Shiqi Yu, Yasushi Makihara, Vishal M. Patel, Maneet Singh, and M. De Marsico, “Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era,” in IEEE Journal on Selected Topics in Signal Processing, 2023.
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157. Jonah P. Sengupta and A. Andreou, “Retinomorphic Channel Design and Considerations,” in Annual Conference on Information Sciences and Systems, 2023.
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158. Martin Sustek, Samik Sadhu, L. Burget, H. Hermansky, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Stabilized training of joint energy-based models and their practical applications,” in arXiv.org, 2023.
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159. Jeya Maria Jose Valanarasu, Rahul Garg, Andeep S. Toor, Xin Tong, Weijuan Xi, Andreas Lugmayr, Vishal M. Patel, and A. Menini, “ReBotNet: Fast Real-time Video Enhancement,” in arXiv.org, 2023.
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160. Chen Wang, Angtian Wang, Junbo Li, A. Yuille, and Cihang Xie, “Benchmarking Robustness in Neural Radiance Fields,” in arXiv.org, 2023.
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161. Kate Sanders, David Etter, Reno Kriz, and Benjamin Van Durme, “MultiVENT: Multilingual Videos of Events and Aligned Natural Text,” in Neural Information Processing Systems, 2023.
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162. Elias Stengel-Eskin, Kyle Rawlins, and Benjamin Van Durme, “Zero and Few-shot Semantic Parsing with Ambiguous Inputs,” in arXiv.org, 2023.
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163. Miguel Angrick, Shiyu Luo, Qinwan Rabbani, Daniel Candrea, Samyak Shah, Griffin W. Milsap, W. Anderson, C. Gordon, Kathryn R Rosenblatt, L. Clawson, N. Maragakis, F. Tenore, M. Fifer, H. Hermansky, N. Ramsey, and N. Crone, “Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS,” in medRxiv, 2023.
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164. Hexin Liu, Leibny Paola García, Xiangyu Zhang, Andy W. H. Khong, and S. Khudanpur, “Enhancing Code-switching Speech Recognition with Interactive Language Biases,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
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165. Daniel R. Mendat, Jonah P. Sengupta, Gaspar Tognetti, M. Villemur, P. Pouliquen, Sergio Montano, Kayode A. Sanni, J. Molin, Nishant Zachariah, I. Doxas, and A. Andreou, “A RISC-V Neuromorphic Micro-Controller Unit (vMCU) with Event-Based Physical Interface and Computational Memory for Low-Latency Machine Perception and Intelligence at the Edge,” in International Symposium on Circuits and Systems, 2023.
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166. Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Benjamin Van Durme, “Natural Language Decomposition and Interpretation of Complex Utterances,” in arXiv.org, 2023.
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167. Anjalie Field, Prateek Verma, Nay San, J. Eberhardt, and Dan Jurafsky, “Developing Speech Processing Pipelines for Police Accountability,” in Interspeech, 2023.
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168. Qixing Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, A. Yuille, and Zongwei Zhou, “Label-Free Liver Tumor Segmentation,” in Computer Vision and Pattern Recognition, 2023.
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169. J. Brugts, S. Radhoe, P. Clephas, Dilan Aydin, M. Gent, M. Szymanski, M. Rienstra, M. V. D. Heuvel, Carlos A da Fonseca, G. Linssen, C. Borleffs, E. Boersma, F. Asselbergs, A. Mosterd, H. Rocca, R. A. Boer, M. Emans, S. Beeres, L. Heerebeek, C. Kirchhof, J. Ramshorst, R. Spee, T. Smilde, M. V. Eck, E. Kaplan, R. Hazeleger, R. Tukkie, M. Feenema, W. Kok, V. V. Halm, M. L. Handoko, R. Kimmenade, Matt Post, N. V. Mieghem, and O. Manintveld, “Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial,” in The Lancet, 2023.
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170. Samik Sadhu and H. Hermansky, “Self-supervised Learning with Speech Modulation Dropout,” in arXiv.org, 2023.
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171. A. Hussein, Dorsa Zeinali, Ondrej Klejch, Matthew Wiesner, Brian Yan, Shammur A. Chowdhury, Ahmed Ali, Shinji Watanabe, and S. Khudanpur, “Speech collage: code-switched audio generation by collaging monolingual corpora,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
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172. Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, P. Kambadur, D. Rosenberg, and Gideon Mann, “BloombergGPT: A Large Language Model for Finance,” in arXiv.org, 2023.
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173. Gerardo Flores, George H. Chen, T. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce Ho, and Tristan Naumann, “Conference on Health, Inference, and Learning, CHIL 2023, Broad Institute of MIT and Harvard (Merkin Building), 415 Main Street, Cambridge, MA, USA,” in ACM Conference on Health, Inference, and Learning, 2023.
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174. V. Rennoll, Ian McLane, Adebayo A. Eisape, D. Grant, Helena Hahn, Mounya Elhilali, and James E. West, “Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.,” in ACS Applied Bio Materials, 2023.
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175. Ryan T. Scott, Lauren M. Sanders, E. Antonsen, Jaden J. A. Hastings, Seung-min Park, Graham Mackintosh, R. Reynolds, A. Hoarfrost, A. Sawyer, Casey S. Greene, Benjamin S. Glicksberg, C. Theriot, D. Berrios, Jack Miller, Joel Babdor, Richard Barker, S. Baranzini, A. Beheshti, Stuart Chalk, Guillermo M. Delgado-Aparicio, Melissa Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Héctor García Martín, Christopher E. Mason, Mona Matar, G. Mias, J. Myers, Charlotte A. Nelson, Jonathan Oribello, P. Parsons-Wingerter, R. K. Prabhu, A. Qutub, J. Rask, Amanda M. Saravia-Butler, S. Saria, N. Singh, M. Snyder, Frank Soboczenski, Karthik Soman, David Van Valen, K. Venkateswaran, L. Warren, Liz Worthey, Jason H. Yang, M. Zitnik, and S. Costes, “Biomonitoring and precision health in deep space supported by artificial intelligence,” in Nature Machine Intelligence, 2023.
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176. Yoav Wald and S. Saria, “Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection,” in Conference on Uncertainty in Artificial Intelligence, 2023.
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177. Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, A. Yuille, and Zongwei Zhou, “AbdomenAtlas-8K: Annotating 8, 000 CT Volumes for Multi-Organ Segmentation in Three Weeks,” in Neural Information Processing Systems, 2023.
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178. Anjalie Field, Amanda Coston, Nupoor Gandhi, A. Chouldechova, Emily Putnam-Hornstein, David Steier, and Yulia Tsvetkov, “Examining risks of racial biases in NLP tools for child protective services,” in Conference on Fairness, Accountability and Transparency, 2023.
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179. M. Post, T. Gowda, R. Grundkiewicz, H. Khayrallah, R. Jain, and M. Junczys-Dowmunt, “SOTASTREAM: A Streaming Approach to Machine Translation Training,” in Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), Singapore, 2023, p. 110–119. doi:10.18653/v1/2023.nlposs-1.13
     [BibTeX] [Abstract] [Link]
     

     Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.

     @inproceedings{post-etal-2023-sotastream,
     title = "{SOTASTREAM}: A Streaming Approach to Machine Translation Training",
     author = "Post, Matt and
     Gowda, Thamme and
     Grundkiewicz, Roman and
     Khayrallah, Huda and
     Jain, Rohit and
     Junczys-Dowmunt, Marcin",
     editor = "Tan, Liling and
     Milajevs, Dmitrijs and
     Chauhan, Geeticka and
     Gwinnup, Jeremy and
     Rippeth, Elijah",
     booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
     month = dec,
     year = "2023",
     address = "Singapore",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.nlposs-1.13",
     doi = "10.18653/v1/2023.nlposs-1.13",
     pages = "110--119",
     abstract = "Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.",
     }

180. Yuhui Xu, Lingxi Xie, Cihang Xie, Wenrui Dai, Jieru Mei, Siyuan Qiao, Wei Shen, H. Xiong, and A. Yuille, “BNET: Batch Normalization With Enhanced Linear Transformation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
     [BibTeX] [Link]

     @inproceedings{255669634,
     title = {BNET: Batch Normalization With Enhanced Linear Transformation},
     author = {{Yuhui Xu} and {Lingxi Xie} and {Cihang Xie} and {Wenrui Dai} and {Jieru Mei} and {Siyuan Qiao} and {Wei Shen} and {H. Xiong} and {A. Yuille}},
     year = 2023,
     month = {1},
     booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/edcf374466f791118acf3bbd8430d4fd73e4ea79},
     }

181. Drew Prinster, S. Saria, and Anqi Liu, “Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions,” in International Symposium on Conformal and Probabilistic Prediction with Applications, 2023.
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     @inproceedings{262070652,
     title = {Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions},
     author = {{Drew Prinster} and {S. Saria} and {Anqi Liu}},
     year = 2023,
     booktitle = {International Symposium on Conformal and Probabilistic Prediction with Applications},
     url = {https://www.semanticscholar.org/paper/ea62b78ec46e7ca0ad4dd5337cd87bb27ab0ec06},
     }

182. Leibny Paola García Perera, Y. H. V. Chua, Hexin Liu, Fei Ting Woon, Andy W. H. Khong, J. Dauwels, S. Khudanpur, and S. Styles, “MERLIon CCS Challenge Evaluation Plan.” 2023.
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     @inproceedings{258987816,
     title = {MERLIon CCS Challenge Evaluation Plan},
     author = {{Leibny Paola García Perera} and {Y. H. V. Chua} and {Hexin Liu} and {Fei Ting Woon} and {Andy W. H. Khong} and {J. Dauwels} and {S. Khudanpur} and {S. Styles}},
     year = 2023,
     month = {5},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/6616c330539e1f38b8d80d5aec6eaf0be98f9314},
     }

183. Andrew Blair-Stanek, Nils Holzenberger, and Benjamin Van Durme, “OpenAI Cribbed Our Tax Example, But Can GPT-4 Really Do Tax?,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{262056669,
     title = {OpenAI Cribbed Our Tax Example, But Can GPT-4 Really Do Tax?},
     author = {{Andrew Blair-Stanek} and {Nils Holzenberger} and {Benjamin Van Durme}},
     year = 2023,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/a77de1f2e2dc253798e5ca9bee71e4d651dc30cd},
     }

184. T. Meyer, A. Favaro, Tianyu Cao, A. Butala, E. Oh, C. Motley, P. Irazoqui, N. Dehak, and L. Moro-Velázquez, “Deep Stroop: Using eye tracking and speech processing to characterize people with neurodegenerative disorders while performing the Stroop Test,” in medRxiv, 2023.
     [BibTeX] [Link]

     @inproceedings{258997982,
     title = {Deep Stroop: Using eye tracking and speech processing to characterize people with neurodegenerative disorders while performing the Stroop Test},
     author = {{T. Meyer} and {A. Favaro} and {Tianyu Cao} and {A. Butala} and {E. Oh} and {C. Motley} and {P. Irazoqui} and {N. Dehak} and {L. Moro-Velázquez}},
     year = 2023,
     month = {6},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/172e04d89d89109626cba6a5b2d4d8a736bd145d},
     }

185. Artur Jesslen, Guofeng Zhang, Angtian Wang, Wufei Ma, A. Yuille, and Adam Kortylewski, “NOVUM: Neural Object Volumes for Robust Object Classification.” 2023.
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     @inproceedings{258865176,
     title = {NOVUM: Neural Object Volumes for Robust Object Classification},
     author = {{Artur Jesslen} and {Guofeng Zhang} and {Angtian Wang} and {Wufei Ma} and {A. Yuille} and {Adam Kortylewski}},
     year = 2023,
     month = {5},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/f4e289af252ea6f0f3b109933c9d0ea01f9125b4},
     }

186. Helin Wang, Thomas Thebaud, J. Villalba, Myra Sydnor, Becky Lammers, N. Dehak, and L. Moro-Velázquez, “DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model,” in Interspeech, 2023.
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     @inproceedings{259202766,
     title = {DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model},
     author = {{Helin Wang} and {Thomas Thebaud} and {J. Villalba} and {Myra Sydnor} and {Becky Lammers} and {N. Dehak} and {L. Moro-Velázquez}},
     year = 2023,
     month = {6},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/bf6339e920466f2dc7dc0da5edde5b3187cf9d0d},
     }

187. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Mitesh M. Khapra, Pratyush Kumar, V. Rudra, Murthy Anoop, Kunchukuttan. 2022, Naama-677, Adam Roberts, Stella Biderman, Teven Le Scao, Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hai-682 ley Schoelkopf, Xiangru Tang, Dragomir R. Radev, Al-683 ham, Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, Iryna Gurevych, Clifton Poth, Aishwarya, Ivan Kamath, Sebastian Vuli´c, Kyunghyun Ruder, Gregor Geigle, Max Glockner, Jonas Beck, Nils Pfeiffer, Reimers Iryna, Victor Sanh, Colin Raffel, Lintang Bach, Zaid Sutawika, Antoine Alyafeai, Arnaud Chaffin, Arun Stiegler, Manan Raja, Dey Saiful, Canwen Bari, Urmish Xu, Thakker, Shanya Sharma, Eliza Szczechla, Taewoon, Gunjan Kim, Nihal Chhablani, Nayak, Debajyoti, Jonathan Datta, Mike Tian-Jian Chang, Han Jiang, Matteo Wang, S. Mânica, Zheng Xin Shen, Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tripathi Neeraj, Jos Rozen, Abheesht Sharma, A. Santilli, Thibault Févry, Jason Alan Fries, Maarten Sap, Hannah Rashkin, Derek Chen, Ronan, Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Adam R. Brown, Adam Santoro, Adrià Gupta, Agnieszka Garriga-Alonso, Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Par-765, Allen Nie, Aman Hussain, Amanda Askell, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, A. Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, A. Lampinen, Angela Zou, Angela Jiang, Anh Chen, Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabas-773, Arul Menezes, Arun Kirubarajan, Asher Mul-774, Ashish lokandov, Austin Sabharwal, Herrick, Avia, A. Efrat, Ayla Erdem, B. Karaka¸s, Ryan Roberts, B. S. Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam, Benjamin Neyshabur, Benno Inden, Berk Stein, Ek-779 mekci, Bill Yuchen, B. Lin, Cameron Howald, Cameron Diao, Catherine Dour, Cedrick Stinson, Ar-781 César, Chandan Ferri Ramírez, Charles Singh, Christopher D. Manning, Christopher Potts, Cindy 785 Ramirez, Clara Rivera, Clemencia Siro, Colin Raf-786, Courtney Ashcraft, Cristina Garbacea, Dan Sileo, Daniel H Garrette, Dan Hendrycks, Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. Drakard, David Ju-792, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Tam, mán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-807 López, Gregor Betz, Guy Gur-Ari, Hana Galijase-808 vic, Hannah Kim, Harsh Mehta, H. Bogar, Henry Shevlin, Hinrich Schütze, H. Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jae-813 hoon Lee, J. Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Ko-815 co´n, Jana Thompson, Jared Kaplan, Jarema Radom, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gim-827 pel, Kevin Omondi, K. Mathewson, Kristen Chi-828 afullo, Ksenia Shkaruta, Kumar Shridhar, Kyle Mc-829 Donell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Maarten ¸Senel, Maarten Bosma, Manaal Farooqi, Mantas Faruqui, Marco Mazeika, Marco Baturan, Marco Marelli, Maria Jose Maru, Marie Ramírez Quintana, Tolkiehn Mario, Martha Giulianelli, Martin Lewis, L. PotthastMatthew, Matthew L. Leavitt, Mátyás Schu-840 bert Hagen, Medina Orduna, Melody Baitemirova, Arnaud Melvin, Michael A McElrath, Michael Yee, Michael Co-842 hen, Michael Gu, Michael Ivanitskiy, Michael Star-843 ritt, M. Strube, Michele Sw˛edrowski, Michihiro Bevilacqua, Mihir Yasunaga, Mike Kale, Mimee Cain, Mirac Xu, Mo Suzgun, Monica Tiwari, Moin Bansal, Mor Aminnaseri, Mozhdeh Geva, Mukund Gheini, T. Varma, Nanyun Peng, tish Shirish Keskar, Niveditha Iyer, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio, Moreno Casares, Parth Doshi, Jason Wei, Maarten Bosma, Vincent Y. Zhao, Adams Wei Guu, Brian Yu, Nan Lester, An-921 Du, M. Dai, Quoc V. Le, Finetuned, Adina Williams, Nikita Nangia, Samuel R. Bowman, Thomas Wolf, Lysandre Debut, Clement Chaumond, Anthony Delangue, Pier-339 Moi, Tim ric Cistac, Rémi Rault, Morgan Louf, Funtow-900 Joe, Sam Davison, Patrick Shleifer, V. Platen, Clara Ma, Yacine Jernite, J. Plu, Canwen Xu, Sylvain Gugger, Mariama Drame, Yinfei Yang, Yuan Zhang, Chris Tar, Hailey Schoelkopf, Niklas Muen-954, Alham Fikri, D. Adelani, M Saiful Bari, Lintang Sutawika, Zhihan Zhang, Wenhao Yu, Mengxia Yu, Zhichun Guo, and Jonathan May, “What are Adapters Really Efficient At?.” 2023.
     [BibTeX] [Link]

     @inproceedings{259267429,
     title = {What are Adapters Really Efficient At?},
     author = {{Alexis Conneau} and {Kartikay Khandelwal} and {Naman Goyal} and {Mitesh M. Khapra} and {Pratyush Kumar} and {V. Rudra} and {Murthy Anoop} and {Kunchukuttan. 2022} and {Naama-677} and {Adam Roberts} and {Stella Biderman} and {Teven Le Scao} and {Saiful Bari} and {Sheng Shen} and {Zheng-Xin Yong} and {Hai-682 ley Schoelkopf} and {Xiangru Tang} and {Dragomir R. Radev} and {Al-683 ham} and {Fikri Aji} and {Khalid Almubarak} and {Samuel Albanie} and {Zaid Alyafeai} and {Albert Webson} and {Edward Raff} and {Jonas Pfeiffer} and {Aishwarya Kamath} and {Andreas Rücklé} and {Kyunghyun Cho} and {Iryna Gurevych} and {Clifton Poth} and {Aishwarya} and {Ivan Kamath} and {Sebastian Vuli´c} and {Kyunghyun Ruder} and {Gregor Geigle} and {Max Glockner} and {Jonas Beck} and {Nils Pfeiffer} and {Reimers Iryna} and {Victor Sanh} and {Colin Raffel} and {Lintang Bach} and {Zaid Sutawika} and {Antoine Alyafeai} and {Arnaud Chaffin} and {Arun Stiegler} and {Manan Raja} and {Dey Saiful} and {Canwen Bari} and {Urmish Xu} and {Thakker} and {Shanya Sharma} and {Eliza Szczechla} and {Taewoon} and {Gunjan Kim} and {Nihal Chhablani} and {Nayak} and {Debajyoti} and {Jonathan Datta} and {Mike Tian-Jian Chang} and {Han Jiang} and {Matteo Wang} and {S. Mânica} and {Zheng Xin Shen} and {Yong} and {Harshit Pandey} and {Rachel Bawden} and {Thomas Wang} and {Tripathi Neeraj} and {Jos Rozen} and {Abheesht Sharma} and {A. Santilli} and {Thibault Févry} and {Jason Alan Fries} and {Maarten Sap} and {Hannah Rashkin} and {Derek Chen} and {Ronan} and {Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Adam R. Brown} and {Adam Santoro} and {Adrià Gupta} and {Agnieszka Garriga-Alonso} and {Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Par-765} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmüller} and {Andrew M. Dai} and {Andrew La} and {A. Lampinen} and {Angela Zou} and {Angela Jiang} and {Anh Chen} and {Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {Arfa Tabas-773} and {Arul Menezes} and {Arun Kirubarajan} and {Asher Mul-774} and {Ashish lokandov} and {Austin Sabharwal} and {Herrick} and {Avia} and {A. Efrat} and {Ayla Erdem} and {B. Karaka¸s} and {Ryan Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Özyurt} and {Behnam Hedayatnia} and {Behnam} and {Benjamin Neyshabur} and {Benno Inden} and {Berk Stein} and {Ek-779 mekci} and {Bill Yuchen} and {B. Lin} and {Cameron Howald} and {Cameron Diao} and {Catherine Dour} and {Cedrick Stinson} and {Ar-781 César} and {Chandan Ferri Ramírez} and {Charles Singh} and {Christopher D. Manning} and {Christopher Potts} and {Cindy 785 Ramirez} and {Clara Rivera} and {Clemencia Siro} and {Colin Raf-786} and {Courtney Ashcraft} and {Cristina Garbacea} and {Dan Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {Kilman} and {Dan Roth} and {Daniel Freeman} and {Daniel Khashabi} and {Daniel Levy} and {Daniel Moseguí González} and {Perszyk} and {Danny Hernandez} and {Danqi Chen} and {Daphne Ippolito} and {D. Gilboa} and {David Dohan} and {D. Drakard} and {David Ju-792} and {Debajyoti Datta} and {Deep Ganguli} and {Denis Emelin} and {Denis Kleyko} and {Deniz Yuret} and {Derek Tam} and {mán Kruszewski} and {Giambattista Parascandolo} and {Giorgio Mariani} and {Gloria Wang} and {Gonzalo Jaimovitch-807 López} and {Gregor Betz} and {Guy Gur-Ari} and {Hana Galijase-808 vic} and {Hannah Kim} and {Harsh Mehta} and {H. Bogar} and {Henry Shevlin} and {Hinrich Schütze} and {H. Yakura} and {Hongming Zhang} and {Hugh Mee Wong} and {Ian Ng} and {Isaac Noble} and {Jaap Jumelet} and {Jack Geissinger} and {John Kernion} and {Jacob Hilton} and {Jae-813 hoon Lee} and {J. Fisac} and {James B. Simon} and {James Koppel} and {James Zheng} and {James Zou} and {Jan Ko-815 co´n} and {Jana Thompson} and {Jared Kaplan} and {Jarema Radom} and {Joyce Chua} and {Kamil Kanclerz} and {Karen Livescu} and {Karl Krauth} and {Karthik Gopalakrishnan} and {Katerina Ignatyeva} and {K. Markert} and {Kaustubh D. Dhole} and {Kevin Gim-827 pel} and {Kevin Omondi} and {K. Mathewson} and {Kristen Chi-828 afullo} and {Ksenia Shkaruta} and {Kumar Shridhar} and {Kyle Mc-829 Donell} and {Kyle Richardson} and {Laria Reynolds} and {Leo Gao} and {Li Zhang} and {Liam Dugan} and {Lianhui Qin} and {Lidia Contreras Ochando} and {Louis-Philippe Morency} and {Luca Moschella} and {Maarten ¸Senel} and {Maarten Bosma} and {Manaal Farooqi} and {Mantas Faruqui} and {Marco Mazeika} and {Marco Baturan} and {Marco Marelli} and {Maria Jose Maru} and {Marie Ramírez Quintana} and {Tolkiehn Mario} and {Martha Giulianelli} and {Martin Lewis} and {L. PotthastMatthew} and {Matthew L. Leavitt} and {Mátyás Schu-840 bert Hagen} and {Medina Orduna} and {Melody Baitemirova} and {Arnaud Melvin} and {Michael A McElrath} and {Michael Yee} and {Michael Co-842 hen} and {Michael Gu} and {Michael Ivanitskiy} and {Michael Star-843 ritt} and {M. Strube} and {Michele Sw˛edrowski} and {Michihiro Bevilacqua} and {Mihir Yasunaga} and {Mike Kale} and {Mimee Cain} and {Mirac Xu} and {Mo Suzgun} and {Monica Tiwari} and {Moin Bansal} and {Mor Aminnaseri} and {Mozhdeh Geva} and {Mukund Gheini} and {T. Varma} and {Nanyun Peng} and {tish Shirish Keskar} and {Niveditha Iyer} and {Noah Fiedel} and {Nuan Wen} and {Oliver Zhang} and {Omar Agha} and {Omar Elbaghdadi} and {Omer Levy} and {Owain Evans} and {Pablo Antonio} and {Moreno Casares} and {Parth Doshi} and {Jason Wei} and {Maarten Bosma} and {Vincent Y. Zhao} and {Adams Wei Guu} and {Brian Yu} and {Nan Lester} and {An-921 Du} and {M. Dai} and {Quoc V. Le} and {Finetuned} and {Adina Williams} and {Nikita Nangia} and {Samuel R. Bowman} and {Thomas Wolf} and {Lysandre Debut} and {Clement Chaumond} and {Anthony Delangue} and {Pier-339 Moi} and {Tim ric Cistac} and {Rémi Rault} and {Morgan Louf} and {Funtow-900 Joe} and {Sam Davison} and {Patrick Shleifer} and {V. Platen} and {Clara Ma} and {Yacine Jernite} and {J. Plu} and {Canwen Xu} and {Sylvain Gugger} and {Mariama Drame} and {Yinfei Yang} and {Yuan Zhang} and {Chris Tar} and {Hailey Schoelkopf} and {Niklas Muen-954} and {Alham Fikri} and {D. Adelani} and {M Saiful Bari} and {Lintang Sutawika} and {Zhihan Zhang} and {Wenhao Yu} and {Mengxia Yu} and {Zhichun Guo} and {Jonathan May}},
     year = 2023,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/6ce31ab55c3cad599b91e1a36c5e2928d31e3986},
     }

188. Jie Liu, Yixiao Zhang, Jieneng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, A. Yuille, Yucheng Tang, and Zongwei Zhou, “CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection,” in IEEE International Conference on Computer Vision, 2023.
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     @inproceedings{255372928,
     title = {CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection},
     author = {{Jie Liu} and {Yixiao Zhang} and {Jieneng Chen} and {Junfei Xiao} and {Yongyi Lu} and {Bennett A. Landman} and {Yixuan Yuan} and {A. Yuille} and {Yucheng Tang} and {Zongwei Zhou}},
     year = 2023,
     month = {1},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/125632627bfad80c2c688bcbed7f3ee915de7359},
     }

189. Pengfei Yu, Heng Ji, Shih-Fu Chang, and Kevin Duh, “MULTIMEDIA CURRICULUM LEARNING FOR LANGUAGE ACQUISITION.” 2023.
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     @inproceedings{259923580,
     title = {MULTIMEDIA CURRICULUM LEARNING FOR LANGUAGE ACQUISITION},
     author = {{Pengfei Yu} and {Heng Ji} and {Shih-Fu Chang} and {Kevin Duh}},
     year = 2023,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/7c7d8f106f8cd1bdadfd3b46f6ebb1509cb1be42},
     }

190. Nisarg A. Shah, S. Sikder, S. Vedula, and Vishal M. Patel, “GLSFormer: Gated – Long, Short Sequence Transformer for Step Recognition in Surgical Videos,” in arXiv.org, 2023.
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     @inproceedings{259991385,
     title = {GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos},
     author = {{Nisarg A. Shah} and {S. Sikder} and {S. Vedula} and {Vishal M. Patel}},
     year = 2023,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/330bf5c3162606581ebfba1f744e1f7da90c5de4},
     }

191. Jonah P. Sengupta, M. A. Tomlinson, Daniel R. Mendat, M. Villemur, and A. Andreou, “Asynchronous, Spatiotemporal Filtering using an Analog Cellular Neural Network Processor,” in International Symposium on Circuits and Systems, 2023.
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     @inproceedings{260003954,
     title = {Asynchronous, Spatiotemporal Filtering using an Analog Cellular Neural Network Processor},
     author = {{Jonah P. Sengupta} and {M. A. Tomlinson} and {Daniel R. Mendat} and {M. Villemur} and {A. Andreou}},
     year = 2023,
     month = {5},
     booktitle = {International Symposium on Circuits and Systems},
     url = {https://www.semanticscholar.org/paper/30446a1b3ca0fc61c3b672d5a284e0dcb761fe6d},
     }

192. Amir Feder, Yoav Wald, Claudia Shi, S. Saria, and David M. Blei, “Causal-structure Driven Augmentations for Text OOD Generalization,” in Neural Information Processing Systems, 2023.
     [BibTeX] [Link]

     @inproceedings{268064142,
     title = {Causal-structure Driven Augmentations for Text OOD Generalization},
     author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}},
     year = 2023,
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/dce7db550d6edda246d7848a37f777ba3b9bbf2f},
     }

193. Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal M. Patel, and Li Liu, “Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{257532548,
     title = {Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey},
     author = {{Huali Xu} and {Shuaifeng Zhi} and {Shuzhou Sun} and {Vishal M. Patel} and {Li Liu}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/095138d9207da38bce4914c569e2f312927213b5},
     }

194. Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Yaoyao Liu, Adam Kortylewski, and A. Yuille, “Generating Images with 3D Annotations Using Diffusion Models.” 2023.
     [BibTeX] [Link]

     @inproceedings{259164709,
     title = {Generating Images with 3D Annotations Using Diffusion Models},
     author = {{Wufei Ma} and {Qihao Liu} and {Jiahao Wang} and {Angtian Wang} and {Yaoyao Liu} and {Adam Kortylewski} and {A. Yuille}},
     year = 2023,
     month = {6},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/1036f39069a1fe3d5f818f1d7bc07286ad3f1363},
     }

195. Y. Ranasinghe, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models.” 2023.
     [BibTeX] [Link]

     @inproceedings{257663507,
     title = {$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models},
     author = {{Y. Ranasinghe} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2023,
     month = {3},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/315d4007e3cc0384c9e340160c44fd698a9ec052},
     }

196. Dongji Gao, Hainan Xu, Desh Raj, Leibny Paola García Perera, Daniel Povey, and S. Khudanpur, “Learning From Flawed Data: Weakly Supervised Automatic Speech Recognition,” in Automatic Speech Recognition & Understanding, 2023.
     [BibTeX] [Link]

     @inproceedings{263152210,
     title = {Learning From Flawed Data: Weakly Supervised Automatic Speech Recognition},
     author = {{Dongji Gao} and {Hainan Xu} and {Desh Raj} and {Leibny Paola García Perera} and {Daniel Povey} and {S. Khudanpur}},
     year = 2023,
     month = {9},
     booktitle = {Automatic Speech Recognition & Understanding},
     url = {https://www.semanticscholar.org/paper/4df2d56e2c81d315e8ead7c3eaf840064ea3665e},
     }

197. Tianjian Li and Kenton Murray, “Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution,” in Annual Meeting of the Association for Computational Linguistics, 2023.
     [BibTeX] [Link]

     @inproceedings{258959429,
     title = {Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution},
     author = {{Tianjian Li} and {Kenton Murray}},
     year = 2023,
     month = {5},
     booktitle = {Annual Meeting of the Association for Computational Linguistics},
     url = {https://www.semanticscholar.org/paper/aa933e27c470eeecbe7bbec5debdd8c5d2faa4be},
     }

198. Cihan Xiao, Henry Li Xinyuan, Jinyi Yang, Dongji Gao, Matthew Wiesner, Kevin Duh, and S. Khudanpur, “HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation,” in Interspeech, 2023.
     [BibTeX] [Link]

     @inproceedings{259203410,
     title = {HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation},
     author = {{Cihan Xiao} and {Henry Li Xinyuan} and {Jinyi Yang} and {Dongji Gao} and {Matthew Wiesner} and {Kevin Duh} and {S. Khudanpur}},
     year = 2023,
     month = {6},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/74173dec94055d7f4051aa2e80be31ccd2bde596},
     }

199. Saurabhchand Bhati, J. Villalba, L. Moro-Velázquez, Thomas Thebaud, and N. Dehak, “Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{261682358,
     title = {Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning},
     author = {{Saurabhchand Bhati} and {J. Villalba} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak}},
     year = 2023,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ada7b279876196a283a8379729212338386c7eba},
     }

200. K. Shridhar, Harsh Jhamtani, Hao Fang, Benjamin Van Durme, Jason Eisner, and Patrick Xia, “SCREWS: A Modular Framework for Reasoning with Revisions,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{262466051,
     title = {SCREWS: A Modular Framework for Reasoning with Revisions},
     author = {{K. Shridhar} and {Harsh Jhamtani} and {Hao Fang} and {Benjamin Van Durme} and {Jason Eisner} and {Patrick Xia}},
     year = 2023,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/5d5dbba58aaf5b3fa4044cc3ffc71a3fe2b8c654},
     }

201. Manoj Jain, Salil Bhargava, R. Arora, R. Joshi, Ravinder Kumar, Deepak Saxena, Kiran Rade, and Rebecca Martin, “Using a Quality Improvement Tool, Plan-Do-Study-Act Cycle, to Boost TB Notification in India post-Covid-19 Pandemic,” in Indian Journal of Tuberculosis, 2023.
     [BibTeX] [Link]

     @inproceedings{262185166,
     title = {Using a Quality Improvement Tool, Plan-Do-Study-Act Cycle, to Boost TB Notification in India post-Covid-19 Pandemic},
     author = {{Manoj Jain} and {Salil Bhargava} and {R. Arora} and {R. Joshi} and {Ravinder Kumar} and {Deepak Saxena} and {Kiran Rade} and {Rebecca Martin}},
     year = 2023,
     month = {9},
     booktitle = {Indian Journal of Tuberculosis},
     url = {https://www.semanticscholar.org/paper/acbaffb72d4c3bd7c9a12d6c756a4a207dea3703},
     }

202. A. DeLucia, M. Dredze, and A. L. Buczak, “A Multi-instance Learning Approach to Civil Unrest Event Detection on Twitter,” in Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, Varna, Bulgaria, 2023, p. 18–33.
     [BibTeX] [Abstract] [Link]
     

     Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.

     @inproceedings{delucia-etal-2023-multi,
     title = "A Multi-instance Learning Approach to Civil Unrest Event Detection on {T}witter",
     author = "DeLucia, Alexandra and
     Dredze, Mark and
     Buczak, Anna L.",
     editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
     Tanev, Hristo and
     Zavarella, Vanni and
     Yeniterzi, Reyyan and
     Y{\"o}r{\"u}k, Erdem and
     Slavcheva, Milena},
     booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text",
     month = sep,
     year = "2023",
     address = "Varna, Bulgaria",
     publisher = "INCOMA Ltd., Shoumen, Bulgaria",
     url = "https://aclanthology.org/2023.case-1.3",
     pages = "18--33",
     abstract = "Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.",
     }

203. Malsha V. Perera and Vishal M. Patel, “Analyzing Bias in Diffusion-based Face Generation Models,” in 2023 IEEE International Joint Conference on Biometrics (IJCB), 2023.
     [BibTeX] [Link]

     @inproceedings{258615767,
     title = {Analyzing Bias in Diffusion-based Face Generation Models},
     author = {{Malsha V. Perera} and {Vishal M. Patel}},
     year = 2023,
     month = {5},
     booktitle = {2023 IEEE International Joint Conference on Biometrics (IJCB)},
     url = {https://www.semanticscholar.org/paper/94831cbd104369092b08f3711e6ac95c5f5f2c7b},
     }

204. A. Favaro, Tianyu Cao, Thomas Thebaud, J. Villalba, A. Butala, N. Dehak, and L. Moro-Velázquez, “Do Phonatory Features Display Robustness to Characterize Parkinsonian Speech Across Corpora?,” in Interspeech, 2023.
     [BibTeX] [Link]

     @inproceedings{260914548,
     title = {Do Phonatory Features Display Robustness to Characterize Parkinsonian Speech Across Corpora?},
     author = {{A. Favaro} and {Tianyu Cao} and {Thomas Thebaud} and {J. Villalba} and {A. Butala} and {N. Dehak} and {L. Moro-Velázquez}},
     year = 2023,
     month = {8},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/562d06b0cddb553a76e6b68f6f2ba470a17bb5d4},
     }

205. Ju He, Jieneng Chen, Ming-Xian Lin, Qihang Yu, and A. Yuille, “Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation,” in Computer Vision and Pattern Recognition, 2023.
     [BibTeX] [Link]

     @inproceedings{259145290,
     title = {Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation},
     author = {{Ju He} and {Jieneng Chen} and {Ming-Xian Lin} and {Qihang Yu} and {A. Yuille}},
     year = 2023,
     month = {6},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/c49666de550031cd63514dacc74b5c4a632da6a6},
     }

206. Yuxiang Guo, Cheng-Fang Peng, R. Prabhakar, Chun Pong Lau, and R. Chellappa, “GADER: GAit DEtection and Recognition in the Wild,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{260202959,
     title = {GADER: GAit DEtection and Recognition in the Wild},
     author = {{Yuxiang Guo} and {Cheng-Fang Peng} and {R. Prabhakar} and {Chun Pong Lau} and {R. Chellappa}},
     year = 2023,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/883f84e5fd894cec4b0364999a0461534f048cee},
     }

207. Ruizhe Huang, Matthew Wiesner, Leibny Paola García-Perera, Daniel Povey, J. Trmal, and S. Khudanpur, “Building Keyword Search System from End-To-End Asr Systems,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{258535938,
     title = {Building Keyword Search System from End-To-End Asr Systems},
     author = {{Ruizhe Huang} and {Matthew Wiesner} and {Leibny Paola García-Perera} and {Daniel Povey} and {J. Trmal} and {S. Khudanpur}},
     year = 2023,
     month = {6},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/1b610ce986449cbef77d0f6bdd28421fd8495268},
     }

208. Elias Stengel-Eskin and Benjamin Van Durme, “Did You Mean…? Confidence-based Trade-offs in Semantic Parsing,” in Conference on Empirical Methods in Natural Language Processing, 2023.
     [BibTeX] [Link]

     @inproceedings{257805227,
     title = {Did You Mean...? Confidence-based Trade-offs in Semantic Parsing},
     author = {{Elias Stengel-Eskin} and {Benjamin Van Durme}},
     year = 2023,
     month = {3},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/695a24f4bd79293d7c4dc41ce3f86c66d601f930},
     }

209. Lauren M. Sanders, Ryan T. Scott, Jason H. Yang, Amina Ann Qutub, Héctor García Martín, D. Berrios, Jaden J. A. Hastings, J. Rask, Graham Mackintosh, A. Hoarfrost, Stuart Chalk, John Kalantari, Kia Khezeli, E. Antonsen, Joel Babdor, Richard Barker, S. Baranzini, A. Beheshti, Guillermo M. Delgado-Aparicio, B. Glicksberg, Casey S. Greene, Melissa Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, Svetlana V. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Christopher E. Mason, Mona Matar, G. Mias, Jack M. Miller, J. Myers, Charlotte A. Nelson, Jonathan Oribello, Seung-min Park, P. Parsons-Wingerter, R. K. Prabhu, R. Reynolds, Amanda M. Saravia-Butler, S. Saria, A. Sawyer, N. Singh, M. Snyder, Frank Soboczenski, Karthik Soman, C. Theriot, David Van Valen, K. Venkateswaran, L. Warren, Liz Worthey, M. Zitnik, and S. Costes, “Biological research and self-driving labs in deep space supported by artificial intelligence,” in Nature Machine Intelligence, 2023.
     [BibTeX] [Link]

     @inproceedings{257697867,
     title = {Biological research and self-driving labs in deep space supported by artificial intelligence},
     author = {{Lauren M. Sanders} and {Ryan T. Scott} and {Jason H. Yang} and {Amina Ann Qutub} and {Héctor García Martín} and {D. Berrios} and {Jaden J. A. Hastings} and {J. Rask} and {Graham Mackintosh} and {A. Hoarfrost} and {Stuart Chalk} and {John Kalantari} and {Kia Khezeli} and {E. Antonsen} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {Guillermo M. Delgado-Aparicio} and {B. Glicksberg} and {Casey S. Greene} and {Melissa Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {Svetlana V. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {Jack M. Miller} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {Seung-min Park} and {P. Parsons-Wingerter} and {R. K. Prabhu} and {R. Reynolds} and {Amanda M. Saravia-Butler} and {S. Saria} and {A. Sawyer} and {N. Singh} and {M. Snyder} and {Frank Soboczenski} and {Karthik Soman} and {C. Theriot} and {David Van Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {S. Costes}},
     year = 2023,
     month = {3},
     booktitle = {Nature Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/880e7f45c1952189e350545dd98a73ef47465cba},
     }

210. Andrew Blair-Stanek, Nils Holzenberger, and Benjamin Van Durme, “Can GPT-3 Perform Statutory Reasoning?,” in International Conference on Artificial Intelligence and Law, 2023.
     [BibTeX] [Link]

     @inproceedings{256826996,
     title = {Can GPT-3 Perform Statutory Reasoning?},
     author = {{Andrew Blair-Stanek} and {Nils Holzenberger} and {Benjamin Van Durme}},
     year = 2023,
     month = {2},
     booktitle = {International Conference on Artificial Intelligence and Law},
     url = {https://www.semanticscholar.org/paper/5f5253fb15ac382e96ade0335baf1cfaa240fb1d},
     }

211. Lingfeng Shen, Sihao Chen, Linfeng Song, Lifeng Jin, Baolin Peng, Haitao Mi, Daniel Khashabi, and Dong Yu, “The Trickle-down Impact of Reward (In-)consistency on RLHF,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{263134276,
     title = {The Trickle-down Impact of Reward (In-)consistency on RLHF},
     author = {{Lingfeng Shen} and {Sihao Chen} and {Linfeng Song} and {Lifeng Jin} and {Baolin Peng} and {Haitao Mi} and {Daniel Khashabi} and {Dong Yu}},
     year = 2023,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/541b66bad4a9bf9b7fd97f13f94ab9061c7c0c47},
     }

212. Yu Zeng, Mo Zhou, Yuan Xue, and Vishal M. Patel, “Securing Deep Generative Models with Universal Adversarial Signature,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258888240,
     title = {Securing Deep Generative Models with Universal Adversarial Signature},
     author = {{Yu Zeng} and {Mo Zhou} and {Yuan Xue} and {Vishal M. Patel}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/98e87cd4c19dad7018270b4561dc64b0109ee360},
     }

213. Jeremy Gwinnup and Kevin Duh, “A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{259137602,
     title = {A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation},
     author = {{Jeremy Gwinnup} and {Kevin Duh}},
     year = 2023,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/c581d2ad3b092a2cc152d0c6f55fd6320f78eb3a},
     }

214. Ishani Mondal, Michelle Yuan, N. Anandhavelu, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, and Jordan L. Boyd-Graber, “InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258865888,
     title = {InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction},
     author = {{Ishani Mondal} and {Michelle Yuan} and {N. Anandhavelu} and {Aparna Garimella} and {Francis Ferraro} and {Andrew Blair-Stanek} and {Benjamin Van Durme} and {Jordan L. Boyd-Graber}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/21e0a1324522b39e5cec94885501e906942c43d0},
     }

215. Deepti Hegde, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition,” in 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023.
     [BibTeX] [Link]

     @inproceedings{257632366,
     title = {CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition},
     author = {{Deepti Hegde} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2023,
     month = {3},
     booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
     url = {https://www.semanticscholar.org/paper/b460a263abec8b1aaa039963be9b371a581e7b21},
     }

216. Liangyu Chen, A. Yuille, and Zongwei Zhou, “Making Your First Choice: To Address Cold Start Problem in Medical Active Learning,” in International Conference on Medical Imaging with Deep Learning, 2023.
     [BibTeX] [Link]

     @inproceedings{259373518,
     title = {Making Your First Choice: To Address Cold Start Problem in Medical Active Learning},
     author = {{Liangyu Chen} and {A. Yuille} and {Zongwei Zhou}},
     year = 2023,
     booktitle = {International Conference on Medical Imaging with Deep Learning},
     url = {https://www.semanticscholar.org/paper/251516c1549fc4566b801788c932ef1f18f343b3},
     }

217. Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo, and Vishal M. Patel, “Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation,” in Computer Vision and Pattern Recognition, 2023.
     [BibTeX] [Link]

     @inproceedings{257766699,
     title = {Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation},
     author = {{Shao-Yuan Lo} and {Poojan Oza} and {Sumanth Chennupati} and {Alejandro Galindo} and {Vishal M. Patel}},
     year = 2023,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/47cd9158e970329355a575ed992d4452ac498784},
     }

218. Jiahao Yang, Wufei Ma, Angtian Wang, Xiaoding Yuan, A. Yuille, and Adam Kortylewski, “Robust Category-Level 3D Pose Estimation from Synthetic Data,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258887517,
     title = {Robust Category-Level 3D Pose Estimation from Synthetic Data},
     author = {{Jiahao Yang} and {Wufei Ma} and {Angtian Wang} and {Xiaoding Yuan} and {A. Yuille} and {Adam Kortylewski}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/55aa226650e6eeed51e181195b7b7a9b87102bc5},
     }

219. VS Vibashan, Ning Yu, Chen Xing, Can Qin, M. Gao, Juan Carlos Niebles, Vishal M. Patel, and Ran Xu, “Mask-Free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations,” in Computer Vision and Pattern Recognition, 2023.
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     @inproceedings{257804958,
     title = {Mask-Free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations},
     author = {{VS Vibashan} and {Ning Yu} and {Chen Xing} and {Can Qin} and {M. Gao} and {Juan Carlos Niebles} and {Vishal M. Patel} and {Ran Xu}},
     year = 2023,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/7aa528b8732033cfb7a6d130bb321723a4e49700},
     }

220. Qihao Liu, Adam Kortylewski, Yutong Bai, Song Bai, and A. Yuille, “Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search.” 2023.
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     @inproceedings{258999203,
     title = {Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search},
     author = {{Qihao Liu} and {Adam Kortylewski} and {Yutong Bai} and {Song Bai} and {A. Yuille}},
     year = 2023,
     month = {6},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/b469d5e906eee4f682726fb8b7f899fd96bcd8a3},
     }

221. A. Kala and Mounya Elhilali, “Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023.
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     @inproceedings{266199727,
     title = {Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds},
     author = {{A. Kala} and {Mounya Elhilali}},
     year = 2023,
     month = {7},
     booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
     url = {https://www.semanticscholar.org/paper/d463716d4860ccc8a42190b9d90bc94af45db1ac},
     }

222. Dongji Gao, Matthew Wiesner, Hainan Xu, Leibny Paola García, Daniel Povey, and S. Khudanpur, “Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts,” in Interspeech, 2023.
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     title = {Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts},
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223. Yixiao Zhang, Xinyi Li, Huimiao Chen, A. Yuille, Yaoyao Liu, and Zongwei Zhou, “Continual Learning for Abdominal Multi-Organ and Tumor Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
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224. Thanh Nguyen-Tang and R. Arora, “VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation,” in International Conference on Learning Representations, 2023.
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226. Enayat Ullah, Harry Lang, R. Arora, and V. Braverman, “Clustering using Approximate Nearest Neighbour Oracles,” in Trans. Mach. Learn. Res., 2023.
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229. Jiacong Xu, Yi Zhang, Jia-Xiong Peng, Wufei Ma, Artur Jesslen, Pengliang Ji, Qixing Hu, Jiehua Zhang, Qihao Liu, Jiahao Wang, Wei Ji, Chen Wang, Xiaoding Yuan, Prakhar Kaushik, Guofeng Zhang, Jie Liu, Yushan Xie, Yawen Cui, A. Yuille, and Adam Kortylewski, “Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape,” in IEEE International Conference on Computer Vision, 2023.
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232. D. Li, A. Butala, T. Meyer, E. Oh, C. Motley, L. Moro-Velázquez, and N. Dehak, “Automating analysis of eye movement and feature extraction for different neurodegenerative disorders,” in medRxiv, 2023.
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233. Drew Prinster, S. Saria, and Anqi Liu, “JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift,” in International Conference on Machine Learning, 2023.
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234. Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang, Ziyue Xu, Can Zhao, Wenqi Li, Vishal M. Patel, Bennett A. Landman, Daguang Xu, Yufan He, and V. Nath, “Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training,” in arXiv.org, 2023.
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239. Qihao Liu, Junfeng Wu, Yi Jiang, Xiang Bai, A. Yuille, and S. Bai, “InstMove: Instance Motion for Object-centric Video Segmentation,” in Computer Vision and Pattern Recognition, 2023.
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240. Elizabeth Salesky, Neha Verma, Philipp Koehn, and Matt Post, “Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer,” in Conference on Empirical Methods in Natural Language Processing, 2023.
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243. A. Favaro, C. Motley, Tianyu Cao, Miguel Iglesias, A. Butala, E. Oh, R. Stevens, J. Villalba, N. Dehak, and L. Moro-Velázquez, “A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders,” in Spoken Language Technology Workshop, 2023.
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245. Paul McNamee and Kevin Duh, “An Extensive Exploration of Back-Translation in 60 Languages,” in Annual Meeting of the Association for Computational Linguistics, 2023.
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246. Wei Ji, Jingjing Li, Cheng Bian, Zongwei Zhou, Jiaying Zhao, A. Yuille, and Li Cheng, “Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline,” in Computer Vision and Pattern Recognition, 2023.
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     booktitle = {7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023)},
     url = {https://www.semanticscholar.org/paper/d4d5fe4a35e9de845877015075f727415e83d18f},
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255. Matt Post and Marcin Junczys-Dowmunt, “Escaping the sentence-level paradigm in machine translation,” in arXiv.org, 2023.
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     title = {Escaping the sentence-level paradigm in machine translation},
     author = {{Matt Post} and {Marcin Junczys-Dowmunt}},
     year = 2023,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/59e2cfbb1395a4a02e9efecadd4f4005af462c1b},
     }

256. Kapil D. Katyal, R. Chellappa, Ketul Shah, Arun V. Reddy, Judy Hoffman, William Paul, Rohita Mocharla, D. Handelman, and Celso de Melo, “Leveraging synthetic data for robust gesture recognition,” in Defense + Commercial Sensing, 2023.
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     year = 2023,
     month = {6},
     booktitle = {Defense + Commercial Sensing},
     url = {https://www.semanticscholar.org/paper/922198774621861436721bd923dc0f0028872a84},
     }

257. S. Styles, Victoria Y. H. Chua, Fei Ting Woon, Hexin Liu, Leibny Paola García Perera, S. Khudanpur, Andy W. H. Khong, and J. Dauwels, “Investigating model performance in language identification: beyond simple error statistics,” in Interspeech, 2023.
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     month = {5},
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     url = {https://www.semanticscholar.org/paper/5550d0db7030aa77480bfb10c4ec00862bb233eb},
     }

258. Sai Saketh Rambhatla, Ishan Misra, R. Chellappa, and Abhinav Shrivastava, “MOST: Multiple Object localization with Self-supervised Transformers for object discovery,” in IEEE International Conference on Computer Vision, 2023.
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     year = 2023,
     month = {4},
     booktitle = {IEEE International Conference on Computer Vision},
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259. Angtian Wang, Wufei Ma, A. Yuille, and Adam Kortylewski, “Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023.
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260. Zihao Xiao, A. Yuille, and Yi-Ting Chen, “Learning Road Scene-level Representations via Semantic Region Prediction,” in Conference on Robot Learning, 2023.
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261. Lingfeng Shen, Weiting Tan, Boyuan Zheng, and Daniel Khashabi, “Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,” in Conference on Empirical Methods in Natural Language Processing, 2023.
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262. Yi Zhang, Pengliang Ji, Angtian Wang, Jieru Mei, Adam Kortylewski, and A. Yuille, “3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation,” in IEEE International Conference on Computer Vision, 2023.
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263. J. Villalba, Jonas Borgstrom, Maliha Jahan, Saurabh Kataria, Leibny Paola García, P. Torres-Carrasquillo, and N. Dehak, “Advances in Language Recognition in Low Resource African Languages: The JHU-MIT Submission for NIST LRE22,” in Interspeech, 2023.
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264. Jieneng Chen, Yingda Xia, Jiawen Yao, K. Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Ming Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, X. Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, A. Yuille, Zai-De Liu, and Ling Zhang, “CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans,” in IEEE International Conference on Computer Vision, 2023.
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265. J. Nolazco-Flores, Ana Verónica Guerrero-Galván, Carolina del-Valle-Soto, and Leibny Paola García-Perera, “Genre Classification of Books on Spanish,” in IEEE Access, 2023.
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266. R. Chellappa, “The unsung hero: how synthetic data has helped computer vision, machine learning, and AI,” in Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 2023.
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     title = {The unsung hero: how synthetic data has helped computer vision, machine learning, and AI},
     author = {{R. Chellappa}},
     year = 2023,
     month = {6},
     booktitle = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications},
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     }

267. Jay N. Paranjape, S. Sikder, Vishal M. Patel, and S. Vedula, “Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
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     year = 2023,
     month = {7},
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268. H. E. Wang, Jonathan P. Weiner, S. Saria, and Hadi Kharrazi, “Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis,” in Journal of Medical Internet Research, 2023.
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     }

269. Qihao Liu, Adam Kortylewski, and A. Yuille, “PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation,” in Computer Vision and Pattern Recognition, 2023.
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     @inproceedings{257495961,
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     year = 2023,
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     }

270. Kate Sanders, David Etter, Reno Kriz, and Benjamin Van Durme, “MultiVENT: Multilingual Videos of Events with Aligned Natural Text,” in arXiv.org, 2023.
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     @inproceedings{259361037,
     title = {MultiVENT: Multilingual Videos of Events with Aligned Natural Text},
     author = {{Kate Sanders} and {David Etter} and {Reno Kriz} and {Benjamin Van Durme}},
     year = 2023,
     month = {7},
     booktitle = {arXiv.org},
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     }

271. Thanh Nguyen-Tang and R. Arora, “Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards,” in arXiv.org, 2023.
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     @inproceedings{257206027,
     title = {Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards},
     author = {{Thanh Nguyen-Tang} and {R. Arora}},
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     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/a8dac0d0837ac4800f4462a121c59a98a05531ee},
     }

272. Denis Newman-Griffis, Bart Desmet, Ayah Zirikly, Suzanne Tamang, and Chih-Hung Chang, “Editorial: Artificial intelligence for human function and disability,” in Frontiers Digit. Health, 2023.
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     @inproceedings{261637808,
     title = {Editorial: Artificial intelligence for human function and disability},
     author = {{Denis Newman-Griffis} and {Bart Desmet} and {Ayah Zirikly} and {Suzanne Tamang} and {Chih-Hung Chang}},
     year = 2023,
     month = {9},
     booktitle = {Frontiers Digit. Health},
     url = {https://www.semanticscholar.org/paper/251b6ab5f8aa64447bcab84b2078a7198afc4ac3},
     }

273. Chen Wei, K. Mangalam, Po-Yao (Bernie) Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, A. Yuille, and Christoph Feichtenhofer, “Diffusion Models as Masked Autoencoders,” in IEEE International Conference on Computer Vision, 2023.
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     title = {Diffusion Models as Masked Autoencoders},
     author = {{Chen Wei} and {K. Mangalam} and {Po-Yao (Bernie) Huang} and {Yanghao Li} and {Haoqi Fan} and {Hu Xu} and {Huiyu Wang} and {Cihang Xie} and {A. Yuille} and {Christoph Feichtenhofer}},
     year = 2023,
     month = {4},
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     url = {https://www.semanticscholar.org/paper/b032f324a0d4a24fd917551345bd100dc368e41a},
     }

274. A. Hussein, Brian Yan, Antonios Anastasopoulos, Shinji Watanabe, and S. Khudanpur, “Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
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     @inproceedings{263152687,
     title = {Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization},
     author = {{A. Hussein} and {Brian Yan} and {Antonios Anastasopoulos} and {Shinji Watanabe} and {S. Khudanpur}},
     year = 2023,
     month = {9},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/c2745e86ecc9bec372690cced53ccfdf44f407f8},
     }

275. Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan Yuille Johns Hopkins University, U. California, Max Planck Institute for Informatics, and U. Freiburg, “Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning,” in Computer Vision and Pattern Recognition, 2022.
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     @inproceedings{254125164,
     title = {Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning},
     author = {{Zhuowan Li} and {Xingrui Wang} and {Elias Stengel-Eskin} and {Adam Kortylewski} and {Wufei Ma} and {Benjamin Van Durme} and {Alan Yuille Johns Hopkins University} and {U. California} and {Max Planck Institute for Informatics} and {U. Freiburg}},
     year = 2022,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/cb6365a1aa3133318ce7fa2461b6d1d48cd8152e},
     }

276. Kangfu Mei, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors,” in arXiv.org, 2022.
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     title = {Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors},
     author = {{Kangfu Mei} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
     year = 2022,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/e5fec7e9103c9edbc4c6b4bb1a47e53593c667bb},
     }

277. Kangda Wei, Dawn J Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller, “When Do Decompositions Help for Machine Reading?,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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     @inproceedings{254877586,
     title = {When Do Decompositions Help for Machine Reading?},
     author = {{Kangda Wei} and {Dawn J Lawrie} and {Benjamin Van Durme} and {Yunmo Chen} and {Orion Weller}},
     year = 2022,
     month = {12},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/624ea7bdaf7e8e3f7bd76f72aa665b562f0dd70a},
     }

278. C. Chen, L. Moro-Velázquez, A. Ožbolt, A. Butala, A. Pantelyat, and N. Dehak, “Phonatory Analysis on Parkinson’s Disease Patients Attending Singing and Discussion Therapy (Parkinsonics) using Signal Processing Techniques,” in IEEE Signal Processing in Medicine and Biology Symposium, 2022.
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     @inproceedings{256034037,
     title = {Phonatory Analysis on Parkinson's Disease Patients Attending Singing and Discussion Therapy (Parkinsonics) using Signal Processing Techniques},
     author = {{C. Chen} and {L. Moro-Velázquez} and {A. Ožbolt} and {A. Butala} and {A. Pantelyat} and {N. Dehak}},
     year = 2022,
     month = {12},
     booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
     url = {https://www.semanticscholar.org/paper/513937e2300445136193356fb6fdae3753d09770},
     }

279. Wenpin Hou, Mingyu Zhang, Yuelong Ji, X. Hong, Guoying Wang, Richard Xu, L. Liang, S. Saria, and Hongkai Ji, “A prospective birth cohort study of maternal prenatal cigarette smoking assessed by self-report and biomarkers on childhood risk of overweight or obesity,” in Precision Nutrition, 2022.
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     title = {A prospective birth cohort study of maternal prenatal cigarette smoking assessed by self-report and biomarkers on childhood risk of overweight or obesity},
     author = {{Wenpin Hou} and {Mingyu Zhang} and {Yuelong Ji} and {X. Hong} and {Guoying Wang} and {Richard Xu} and {L. Liang} and {S. Saria} and {Hongkai Ji}},
     year = 2022,
     month = {12},
     booktitle = {Precision Nutrition},
     url = {https://www.semanticscholar.org/paper/2304edd25b8f08b99c6992c3de6434459742ccad},
     }

280. Nathaniel Weir, Ryan Thomas, Randolph D’Amore, Kellie Hill, Benjamin Van Durme, and Harsh Jhamtani, “Ontologically Faithful Generation of Non-Player Character Dialogues,” in arXiv.org, 2022.
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     title = {Ontologically Faithful Generation of Non-Player Character Dialogues},
     author = {{Nathaniel Weir} and {Ryan Thomas} and {Randolph D'Amore} and {Kellie Hill} and {Benjamin Van Durme} and {Harsh Jhamtani}},
     year = 2022,
     month = {12},
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     url = {https://www.semanticscholar.org/paper/68f1b94bbc900d2b5c60192a7e9eea4b046dd18a},
     }

281. Desh Raj, Daniel Povey, and S. Khudanpur, “GPU-accelerated Guided Source Separation for Meeting Transcription,” in Interspeech, 2022.
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     title = {GPU-accelerated Guided Source Separation for Meeting Transcription},
     author = {{Desh Raj} and {Daniel Povey} and {S. Khudanpur}},
     year = 2022,
     month = {12},
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     url = {https://www.semanticscholar.org/paper/ad9199a35c5ce4738c39bb4af8abf7caee418365},
     }

282. A. Favaro, Seneca Motley, Quincy M. Samus, A. Butala, N. Dehak, Esther S. Oh, and L. Moro-Velázquez, “Artificial Intelligence Tools to Evaluate Language and Speech Patterns in Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2022.
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     @inproceedings{254880773,
     title = {Artificial Intelligence Tools to Evaluate Language and Speech Patterns in Alzheimer's Disease},
     author = {{A. Favaro} and {Seneca Motley} and {Quincy M. Samus} and {A. Butala} and {N. Dehak} and {Esther S. Oh} and {L. Moro-Velázquez}},
     year = 2022,
     month = {12},
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     url = {https://www.semanticscholar.org/paper/e8f74514d4b195230ddd7dd6b60cabbc7ed240b1},
     }

283. H. Kim, Y. Yu, L. Jiang, X. Lu, D. Khashabi, G. Kim, Y. Choi, and M. Sap, “ProsocialDialog: A Prosocial Backbone for Conversational Agents,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 4005–4029. doi:10.18653/v1/2022.emnlp-main.267
     [BibTeX] [Abstract] [Link]
     

     Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.

     @inproceedings{kim-etal-2022-prosocialdialog,
     title = "{P}rosocial{D}ialog: A Prosocial Backbone for Conversational Agents",
     author = "Kim, Hyunwoo and
     Yu, Youngjae and
     Jiang, Liwei and
     Lu, Ximing and
     Khashabi, Daniel and
     Kim, Gunhee and
     Choi, Yejin and
     Sap, Maarten",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.267",
     doi = "10.18653/v1/2022.emnlp-main.267",
     pages = "4005--4029",
     abstract = "Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.",
     }

284. K. Marchisio, A. Saad-Eldin, K. Duh, C. Priebe, and P. Koehn, “Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2545–2561. doi:10.18653/v1/2022.emnlp-main.164
     [BibTeX] [Abstract] [Link]
     

     Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.

     @inproceedings{marchisio-etal-2022-bilingual,
     title = "Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport",
     author = "Marchisio, Kelly and
     Saad-Eldin, Ali and
     Duh, Kevin and
     Priebe, Carey and
     Koehn, Philipp",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.164",
     doi = "10.18653/v1/2022.emnlp-main.164",
     pages = "2545--2561",
     abstract = "Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.",
     }

285. Y. Feng, F. Li, and P. Koehn, “Toward the Limitation of Code-Switching in Cross-Lingual Transfer,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 5966–5971. doi:10.18653/v1/2022.emnlp-main.400
     [BibTeX] [Abstract] [Link]
     

     Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores.

     @inproceedings{feng-etal-2022-toward,
     title = "Toward the Limitation of Code-Switching in Cross-Lingual Transfer",
     author = "Feng, Yukun and
     Li, Feng and
     Koehn, Philipp",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.400",
     doi = "10.18653/v1/2022.emnlp-main.400",
     pages = "5966--5971",
     abstract = "Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores.",
     }

286. O. Ogundepo, X. Zhang, S. Sun, K. Duh, and J. Lin, “AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 8721–8728. doi:10.18653/v1/2022.emnlp-main.597
     [BibTeX] [Abstract] [Link]
     

     Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers{‘} ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse{–}dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.

     @inproceedings{ogundepo-etal-2022-africlirmatrix,
     title = "{A}fri{CLIRM}atrix: Enabling Cross-Lingual Information Retrieval for {A}frican Languages",
     author = "Ogundepo, Odunayo and
     Zhang, Xinyu and
     Sun, Shuo and
     Duh, Kevin and
     Lin, Jimmy",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.597",
     doi = "10.18653/v1/2022.emnlp-main.597",
     pages = "8721--8728",
     abstract = "Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers{'} ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse{--}dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.",
     }

287. E. Rippeth and M. Post, “Additive Interventions Yield Robust Multi-Domain Machine Translation Models,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 220–232.
     [BibTeX] [Abstract] [Link]
     

     Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder{‘}s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.

     @inproceedings{rippeth-post-2022-additive,
     title = "Additive Interventions Yield Robust Multi-Domain Machine Translation Models",
     author = "Rippeth, Elijah and
     Post, Matt",
     editor = {Koehn, Philipp and
     Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Jimeno Yepes, Antonio and
     Kocmi, Tom and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo and
     N{\'e}v{\'e}ol, Aur{\'e}lie and
     Neves, Mariana and
     Popel, Martin and
     Turchi, Marco and
     Zampieri, Marcos},
     booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wmt-1.14",
     pages = "220--232",
     abstract = "Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder{'}s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.",
     }

288. W. Gu, B. Zheng, Y. Chen, T. Chen, and B. Van Durme, “An Empirical Study on Finding Spans,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 3976–3983. doi:10.18653/v1/2022.emnlp-main.264
     [BibTeX] [Abstract] [Link]
     

     We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.

     @inproceedings{gu-etal-2022-empirical,
     title = "An Empirical Study on Finding Spans",
     author = "Gu, Weiwei and
     Zheng, Boyuan and
     Chen, Yunmo and
     Chen, Tongfei and
     Van Durme, Benjamin",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.264",
     doi = "10.18653/v1/2022.emnlp-main.264",
     pages = "3976--3983",
     abstract = "We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.",
     }

289. K. Marchisio, N. Verma, K. Duh, and P. Koehn, “IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 6019–6033. doi:10.18653/v1/2022.emnlp-main.404
     [BibTeX] [Abstract] [Link]
     

     The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces{–-}their degree of {“}isomorphism.{”} We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec.

     @inproceedings{marchisio-etal-2022-isovec,
     title = "{I}so{V}ec: Controlling the Relative Isomorphism of Word Embedding Spaces",
     author = "Marchisio, Kelly and
     Verma, Neha and
     Duh, Kevin and
     Koehn, Philipp",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.404",
     doi = "10.18653/v1/2022.emnlp-main.404",
     pages = "6019--6033",
     abstract = "The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces{---}their degree of {``}isomorphism.{''} We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec.",
     }

290. S. Sia, K. Jaidka, H. Ahuja, N. Chhaya, and K. Duh, “Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11939–11950. doi:10.18653/v1/2022.emnlp-main.818
     [BibTeX] [Abstract] [Link]
     

     In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.

     @inproceedings{sia-etal-2022-offer,
     title = "Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates",
     author = "Sia, Suzanna and
     Jaidka, Kokil and
     Ahuja, Hansin and
     Chhaya, Niyati and
     Duh, Kevin",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.818",
     doi = "10.18653/v1/2022.emnlp-main.818",
     pages = "11939--11950",
     abstract = "In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.",
     }

291. I. Lin, L. Njoo, A. Field, A. Sharma, K. Reinecke, T. Althoff, and Y. Tsvetkov, “Gendered Mental Health Stigma in Masked Language Models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2152–2170. doi:10.18653/v1/2022.emnlp-main.139
     [BibTeX] [Abstract] [Link]
     

     Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models{‘} propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32{\%} vs. 19{\%}), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models{‘} gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models{‘} social biases.

     @inproceedings{lin-etal-2022-gendered,
     title = "Gendered Mental Health Stigma in Masked Language Models",
     author = "Lin, Inna and
     Njoo, Lucille and
     Field, Anjalie and
     Sharma, Ashish and
     Reinecke, Katharina and
     Althoff, Tim and
     Tsvetkov, Yulia",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.139",
     doi = "10.18653/v1/2022.emnlp-main.139",
     pages = "2152--2170",
     abstract = "Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models{'} propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32{\%} vs. 19{\%}), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models{'} gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models{'} social biases.",
     }

292. E. Stengel-Eskin, E. A. Platanios, A. Pauls, S. Thomson, H. Fang, B. Van Durme, J. Eisner, and Y. Su, “When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11473–11487. doi:10.18653/v1/2022.emnlp-main.789
     [BibTeX] [Abstract] [Link]
     

     In natural language understanding (NLU) production systems, users{‘} evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.

     @inproceedings{stengel-eskin-etal-2022-data,
     title = "When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems",
     author = "Stengel-Eskin, Elias and
     Platanios, Emmanouil Antonios and
     Pauls, Adam and
     Thomson, Sam and
     Fang, Hao and
     Van Durme, Benjamin and
     Eisner, Jason and
     Su, Yu",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.789",
     doi = "10.18653/v1/2022.emnlp-main.789",
     pages = "11473--11487",
     abstract = "In natural language understanding (NLU) production systems, users{'} evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.",
     }

293. F. Casacuberta, G. Foster, G. Huang, P. Koehn, G. Kovacs, L. Liu, S. Shi, T. Watanabe, and C. Zong, “Findings of the Word-Level AutoCompletion Shared Task in WMT 2022,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 812–820.
     [BibTeX] [Abstract] [Link]
     

     Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.

     @inproceedings{casacuberta-etal-2022-findings,
     title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2022",
     author = "Casacuberta, Francisco and
     Foster, George and
     Huang, Guoping and
     Koehn, Philipp and
     Kovacs, Geza and
     Liu, Lemao and
     Shi, Shuming and
     Watanabe, Taro and
     Zong, Chengqing",
     editor = {Koehn, Philipp and
     Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Jimeno Yepes, Antonio and
     Kocmi, Tom and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo and
     N{\'e}v{\'e}ol, Aur{\'e}lie and
     Neves, Mariana and
     Popel, Martin and
     Turchi, Marco and
     Zampieri, Marcos},
     booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wmt-1.75",
     pages = "812--820",
     abstract = "Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.",
     }

294. M. Keymanesh, A. Benton, and M. Dredze, “What Makes Data-to-Text Generation Hard for Pretrained Language Models?,” in Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 539–554. doi:10.18653/v1/2022.gem-1.50
     [BibTeX] [Abstract] [Link]
     

     Expressing natural language descriptions of structured facts or relations {–} data-to-text generation (D2T) {–} increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.

     @inproceedings{keymanesh-etal-2022-makes,
     title = "What Makes Data-to-Text Generation Hard for Pretrained Language Models?",
     author = "Keymanesh, Moniba and
     Benton, Adrian and
     Dredze, Mark",
     editor = "Bosselut, Antoine and
     Chandu, Khyathi and
     Dhole, Kaustubh and
     Gangal, Varun and
     Gehrmann, Sebastian and
     Jernite, Yacine and
     Novikova, Jekaterina and
     Perez-Beltrachini, Laura",
     booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.gem-1.50",
     doi = "10.18653/v1/2022.gem-1.50",
     pages = "539--554",
     abstract = "Expressing natural language descriptions of structured facts or relations {--} data-to-text generation (D2T) {--} increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.",
     }

295. David Mueller, Nicholas Andrews, and Mark Dredze, “Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?,” in Conference on Empirical Methods in Natural Language Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{254591386,
     title = {Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?},
     author = {{David Mueller} and {Nicholas Andrews} and {Mark Dredze}},
     year = 2022,
     month = {12},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/2843661ee0d5fa159165beba50c345566cc44c57},
     }

296. T. Kocmi, R. Bawden, O. Bojar, A. Dvorkovich, C. Federmann, M. Fishel, T. Gowda, Y. Graham, R. Grundkiewicz, B. Haddow, R. Knowles, P. Koehn, C. Monz, M. Morishita, M. Nagata, T. Nakazawa, M. Novák, M. Popel, and M. Popović, “Findings of the 2022 Conference on Machine Translation (WMT22),” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 1–45.
     [BibTeX] [Abstract] [Link]
     

     This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).

     @inproceedings{kocmi-etal-2022-findings,
     title = "Findings of the 2022 Conference on Machine Translation ({WMT}22)",
     author = "Kocmi, Tom and
     Bawden, Rachel and
     Bojar, Ond{\v{r}}ej and
     Dvorkovich, Anton and
     Federmann, Christian and
     Fishel, Mark and
     Gowda, Thamme and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Haddow, Barry and
     Knowles, Rebecca and
     Koehn, Philipp and
     Monz, Christof and
     Morishita, Makoto and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Nov{\'a}k, Michal and
     Popel, Martin and
     Popovi{\'c}, Maja",
     editor = {Koehn, Philipp and
     Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Jimeno Yepes, Antonio and
     Kocmi, Tom and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo and
     N{\'e}v{\'e}ol, Aur{\'e}lie and
     Neves, Mariana and
     Popel, Martin and
     Turchi, Marco and
     Zampieri, Marcos},
     booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wmt-1.1",
     pages = "1--45",
     abstract = "This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).",
     }

297. Si-Jia Yang, Longlong Jing, Junfei Xiao, Hang Zhao, A. Yuille, and Yingwei Li, “AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254366617,
     title = {AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation},
     author = {{Si-Jia Yang} and {Longlong Jing} and {Junfei Xiao} and {Hang Zhao} and {A. Yuille} and {Yingwei Li}},
     year = 2022,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/1358ad196c4e300612fb3b65a2f3578836941384},
     }

298. Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254125357,
     title = {Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models},
     author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2022,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/24e6c62fd28da4ecf748620e1f25eae7337bad40},
     }

299. K. Deb, X. Zhang, and K. Duh, “Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines,” in Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 51–61. doi:10.18653/v1/2022.blackboxnlp-1.5
     [BibTeX] [Abstract] [Link]
     

     Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions.

     @inproceedings{deb-etal-2022-post,
     title = "Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines",
     author = "Deb, Kiron and
     Zhang, Xuan and
     Duh, Kevin",
     editor = "Bastings, Jasmijn and
     Belinkov, Yonatan and
     Elazar, Yanai and
     Hupkes, Dieuwke and
     Saphra, Naomi and
     Wiegreffe, Sarah",
     booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.blackboxnlp-1.5",
     doi = "10.18653/v1/2022.blackboxnlp-1.5",
     pages = "51--61",
     abstract = "Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions.",
     }

300. Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Naik, A. Ashok, A. S. Dhanasekaran, A. Arunkumar, D. Stap, E. Pathak, G. Karamanolakis, H. Lai, I. Purohit, I. Mondal, J. Anderson, K. Kuznia, K. Doshi, K. K. Pal, M. Patel, M. Moradshahi, M. Parmar, M. Purohit, N. Varshney, P. R. Kaza, P. Verma, R. S. Puri, R. Karia, S. Doshi, S. K. Sampat, S. Mishra, S. Reddy A, S. Patro, T. Dixit, and X. Shen, “Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 5085–5109. doi:10.18653/v1/2022.emnlp-main.340
     [BibTeX] [Abstract] [Link]
     

     How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{–-}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.

     @inproceedings{wang-etal-2022-super,
     title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks",
     author = "Wang, Yizhong and
     Mishra, Swaroop and
     Alipoormolabashi, Pegah and
     Kordi, Yeganeh and
     Mirzaei, Amirreza and
     Naik, Atharva and
     Ashok, Arjun and
     Dhanasekaran, Arut Selvan and
     Arunkumar, Anjana and
     Stap, David and
     Pathak, Eshaan and
     Karamanolakis, Giannis and
     Lai, Haizhi and
     Purohit, Ishan and
     Mondal, Ishani and
     Anderson, Jacob and
     Kuznia, Kirby and
     Doshi, Krima and
     Pal, Kuntal Kumar and
     Patel, Maitreya and
     Moradshahi, Mehrad and
     Parmar, Mihir and
     Purohit, Mirali and
     Varshney, Neeraj and
     Kaza, Phani Rohitha and
     Verma, Pulkit and
     Puri, Ravsehaj Singh and
     Karia, Rushang and
     Doshi, Savan and
     Sampat, Shailaja Keyur and
     Mishra, Siddhartha and
     Reddy A, Sujan and
     Patro, Sumanta and
     Dixit, Tanay and
     Shen, Xudong",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.340",
     doi = "10.18653/v1/2022.emnlp-main.340",
     pages = "5085--5109",
     abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.",
     }

301. Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, A. Yuille, Yuyin Zhou, and Cihang Xie, “Unleashing the Power of Visual Prompting At the Pixel Level,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254877370,
     title = {Unleashing the Power of Visual Prompting At the Pixel Level},
     author = {{Junyang Wu} and {Xianhang Li} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
     year = 2022,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7786825fd653b398c3975c3ff876459307d871f4},
     }

302. D. D. Kairamkonda, P. S. Mandaleeka, A. Favaro, C. Motley, A. Butala, E. Oh, R. Stevens, N. Dehak, and L. Moro-Velázquez, “Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases,” in IEEE Signal Processing in Medicine and Biology Symposium, 2022.
     [BibTeX] [Link]

     @inproceedings{256034700,
     title = {Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases},
     author = {{D. D. Kairamkonda} and {P. S. Mandaleeka} and {A. Favaro} and {C. Motley} and {A. Butala} and {E. Oh} and {R. Stevens} and {N. Dehak} and {L. Moro-Velázquez}},
     year = 2022,
     month = {12},
     booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
     url = {https://www.semanticscholar.org/paper/d10f7b6ab049a92c19e1d9c7792063e85ce60d22},
     }

303. Trevor Meyer, L. Moro-Velázquez, Seneca Motley, A. Butala, Ashley M Paul, Quincy M. Samus, Pedro P. Irazoqui, N. Dehak, and Esther S. Oh, “Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2022.
     [BibTeX] [Link]

     @inproceedings{254879636,
     title = {Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer's Disease},
     author = {{Trevor Meyer} and {L. Moro-Velázquez} and {Seneca Motley} and {A. Butala} and {Ashley M Paul} and {Quincy M. Samus} and {Pedro P. Irazoqui} and {N. Dehak} and {Esther S. Oh}},
     year = 2022,
     month = {12},
     booktitle = {Alzheimer's & Dementia},
     url = {https://www.semanticscholar.org/paper/e5a0988cdd73b981611be9fe06e0b7328ff1c0d0},
     }

304. Hongru Zhu, Yijun Ge, Alexander Bratch, A. Yuille, Kendrick Norris Kay, and D. Kersten, “Distributed representations of natural body pose in visual cortex,” in Journal of Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{254767215,
     title = {Distributed representations of natural body pose in visual cortex},
     author = {{Hongru Zhu} and {Yijun Ge} and {Alexander Bratch} and {A. Yuille} and {Kendrick Norris Kay} and {D. Kersten}},
     year = 2022,
     month = {12},
     booktitle = {Journal of Vision},
     url = {https://www.semanticscholar.org/paper/0f737f04ade2ef8f4a360dc42296476a55fa49d3},
     }

305. R. Wicks and M. Post, “Does Sentence Segmentation Matter for Machine Translation?,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 843–854.
     [BibTeX] [Abstract] [Link]
     

     For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, {“}does segmentation matter?{”} and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.

     @inproceedings{wicks-post-2022-sentence,
     title = "Does Sentence Segmentation Matter for Machine Translation?",
     author = "Wicks, Rachel and
     Post, Matt",
     editor = {Koehn, Philipp and
     Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Jimeno Yepes, Antonio and
     Kocmi, Tom and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo and
     N{\'e}v{\'e}ol, Aur{\'e}lie and
     Neves, Mariana and
     Popel, Martin and
     Turchi, Marco and
     Zampieri, Marcos},
     booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wmt-1.78",
     pages = "843--854",
     abstract = "For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, {``}does segmentation matter?{''} and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.",
     }

306. Wenpin Hou, Mingyu Zhang, Yuelong Ji, X. Hong, Guoying Wang, Richard Xu, Liming Liang, S. Saria, and Hongkai Ji, “A prospective birth cohort study of maternal prenatal cigarette smoking assessed by self-report and biomarkers on childhood risk of overweight or obesity.,” in Precision Nutrition, 2022.
     [BibTeX] [Link]

     @inproceedings{262383173,
     title = {A prospective birth cohort study of maternal prenatal cigarette smoking assessed by self-report and biomarkers on childhood risk of overweight or obesity.},
     author = {{Wenpin Hou} and {Mingyu Zhang} and {Yuelong Ji} and {X. Hong} and {Guoying Wang} and {Richard Xu} and {Liming Liang} and {S. Saria} and {Hongkai Ji}},
     year = 2022,
     month = {12},
     booktitle = {Precision Nutrition},
     url = {https://www.semanticscholar.org/paper/e898ce790bbb170c93ff44e139e83c3448b590ab},
     }

307. E. Stengel-Eskin and B. Van Durme, “The Curious Case of Control,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11065–11076. doi:10.18653/v1/2022.emnlp-main.760
     [BibTeX] [Abstract] [Link]
     

     Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.

     @inproceedings{stengel-eskin-van-durme-2022-curious,
     title = "The Curious Case of Control",
     author = "Stengel-Eskin, Elias and
     Van Durme, Benjamin",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.760",
     doi = "10.18653/v1/2022.emnlp-main.760",
     pages = "11065--11076",
     abstract = "Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.",
     }

308. H. Xu, P. Koehn, and K. Murray, “The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 170–183. doi:10.18653/v1/2022.emnlp-main.13
     [BibTeX] [Abstract] [Link]
     

     Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT{‘}14 dataset.

     @inproceedings{xu-etal-2022-importance,
     title = "The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains",
     author = "Xu, Haoran and
     Koehn, Philipp and
     Murray, Kenton",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.13",
     doi = "10.18653/v1/2022.emnlp-main.13",
     pages = "170--183",
     abstract = "Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT{'}14 dataset.",
     }

309. Y. Feng, P. Xia, B. Van Durme, and J. Sedoc, “Automatic Document Selection for Efficient Encoder Pretraining,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 9522–9530. doi:10.18653/v1/2022.emnlp-main.647
     [BibTeX] [Abstract] [Link]
     

     Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.

     @inproceedings{feng-etal-2022-automatic,
     title = "Automatic Document Selection for Efficient Encoder Pretraining",
     author = "Feng, Yukun and
     Xia, Patrick and
     Van Durme, Benjamin and
     Sedoc, Jo{\~a}o",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.647",
     doi = "10.18653/v1/2022.emnlp-main.647",
     pages = "9522--9530",
     abstract = "Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.",
     }

310. Kangfu Mei and Vishal M. Patel, “VIDM: Video Implicit Diffusion Models,” in AAAI Conference on Artificial Intelligence, 2022.
     [BibTeX] [Link]

     @inproceedings{254125713,
     title = {VIDM: Video Implicit Diffusion Models},
     author = {{Kangfu Mei} and {Vishal M. Patel}},
     year = 2022,
     month = {12},
     booktitle = {AAAI Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/13c7b29a100f67d285eb3625c160d06882d4c092},
     }

311. Z. Jiang, A. Liu, and B. Van Durme, “Calibrating Zero-shot Cross-lingual (Un-)structured Predictions,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2648–2674. doi:10.18653/v1/2022.emnlp-main.170
     [BibTeX] [Abstract] [Link]
     

     We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration.

     @inproceedings{jiang-etal-2022-calibrating,
     title = "Calibrating Zero-shot Cross-lingual (Un-)structured Predictions",
     author = "Jiang, Zhengping and
     Liu, Anqi and
     Van Durme, Benjamin",
     editor = "Goldberg, Yoav and
     Kozareva, Zornitsa and
     Zhang, Yue",
     booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.emnlp-main.170",
     doi = "10.18653/v1/2022.emnlp-main.170",
     pages = "2648--2674",
     abstract = "We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration.",
     }

312. E. Schumacher, J. Mayfield, and M. Dredze, “Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models,” in Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 38–51. doi:10.18653/v1/2022.mrl-1.4
     [BibTeX] [Abstract] [Link]
     

     Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.

     @inproceedings{schumacher-etal-2022-zero,
     title = "Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models",
     author = "Schumacher, Elliot and
     Mayfield, James and
     Dredze, Mark",
     editor = {Ataman, Duygu and
     Gonen, Hila and
     Ruder, Sebastian and
     Firat, Orhan and
     G{\"u}l Sahin, G{\"o}zde and
     Mirzakhalov, Jamshidbek},
     booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
     month = dec,
     year = "2022",
     address = "Abu Dhabi, United Arab Emirates (Hybrid)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.mrl-1.4",
     doi = "10.18653/v1/2022.mrl-1.4",
     pages = "38--51",
     abstract = "Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.",
     }

313. M. Iglesias, A. Favaro, C. Motley, E. Oh, R. Stevens, A. Butala, L. Moro-Velázquez, and N. Dehak, “Cognitive and Acoustic Speech and Language Patterns Occurring in Different Neurodegenerative Disorders while Performing Neuropsychological Tests,” in IEEE Signal Processing in Medicine and Biology Symposium, 2022.
     [BibTeX] [Link]

     @inproceedings{256033943,
     title = {Cognitive and Acoustic Speech and Language Patterns Occurring in Different Neurodegenerative Disorders while Performing Neuropsychological Tests},
     author = {{M. Iglesias} and {A. Favaro} and {C. Motley} and {E. Oh} and {R. Stevens} and {A. Butala} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2022,
     month = {12},
     booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
     url = {https://www.semanticscholar.org/paper/ee067fbced756c332d18a34d6d4f59ab512f9013},
     }

314. A. Svete, B. Dayan, R. Cotterell, T. Vieira, and J. Eisner, “Acyclic Weighted Finite-State Automata with Failure Transitions,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, 2022, p. 8289–8305.
     [BibTeX] [Link]

     @InProceedings{svete-et-al-2022,
     aclid = "2022.emnlp-main.567",
     author = "Anej Svete and Benjamin Dayan and Ryan Cotterell and
     Tim Vieira and Jason Eisner",
     title = "Acyclic Weighted Finite-State Automata with Failure
     Transitions",
     booktitle = "Proceedings of the 2022 Conference on Empirical
     Methods in Natural Language Processing",
     pages = "8289--8305",
     year = "2022",
     month = dec,
     address = "Abu Dhabi",
     URL = "http://cs.jhu.edu/~jason/papers/#svete-et-al-2022",
     }

315. E. Stengel-Eskin, E. A. Platanios, A. Pauls, S. Thomson, H. Fang, B. V. Durme, J. Eisner, and Y. Su, “When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, 2022, p. 11473–11487.
     [BibTeX] [Link]

     @InProceedings{stengeleskin-et-al-2022,
     aclid = "2022.emnlp-main.789",
     author = "Elias Stengel-Eskin and Emmanouil Antonios Platanios
     and Adam Pauls and Sam Thomson and Hao Fang and
     Benjamin Van Durme and Jason Eisner and Yu Su",
     title = "When More Data Hurts: {A} Troubling Quirk in
     Developing Broad-Coverage Natural Language
     Understanding Systems",
     booktitle = "Proceedings of the 2022 Conference on Empirical
     Methods in Natural Language Processing",
     pages = "11473--11487",
     year = "2022",
     month = dec,
     address = "Abu Dhabi",
     URL = "http://cs.jhu.edu/~jason/papers/#stengeleskin-et-al-2022",
     }

316. Bardia Safaei, VS Vibashan, Celso M. de Melo, Shuowen Hu, and Vishal M. Patel, “Open-Set Automatic Target Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{253499210,
     title = {Open-Set Automatic Target Recognition},
     author = {{Bardia Safaei} and {VS Vibashan} and {Celso M. de Melo} and {Shuowen Hu} and {Vishal M. Patel}},
     year = 2022,
     month = {11},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/878d61661e35c80c0b981fe4512fbad6c55ab037},
     }

317. V. Rennoll, Ian McLane, Mounya Elhilali, and James E. West, “Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors,” in Italian National Conference on Sensors, 2022.
     [BibTeX] [Link]

     @inproceedings{253903852,
     title = {Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors},
     author = {{V. Rennoll} and {Ian McLane} and {Mounya Elhilali} and {James E. West}},
     year = 2022,
     month = {11},
     booktitle = {Italian National Conference on Sensors},
     url = {https://www.semanticscholar.org/paper/0d7b6b5a15b47c1cd1d688f043fd06ff6822d5a1},
     }

318. Zili Huang, Desh Raj, Leibny Paola García-Perera, and S. Khudanpur, “Adapting Self-Supervised Models to Multi-Talker Speech Recognition Using Speaker Embeddings,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{253244355,
     title = {Adapting Self-Supervised Models to Multi-Talker Speech Recognition Using Speaker Embeddings},
     author = {{Zili Huang} and {Desh Raj} and {Leibny Paola García-Perera} and {S. Khudanpur}},
     year = 2022,
     month = {11},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/2226b25c6656e1d7c99667b4e685cd01348e8577},
     }

319. Elias Stengel-Eskin and Benjamin Van Durme, “Calibrated Interpretation: Confidence Estimation in Semantic Parsing,” in Transactions of the Association for Computational Linguistics, 2022.
     [BibTeX] [Link]

     @inproceedings{253510101,
     title = {Calibrated Interpretation: Confidence Estimation in Semantic Parsing},
     author = {{Elias Stengel-Eskin} and {Benjamin Van Durme}},
     year = 2022,
     month = {11},
     booktitle = {Transactions of the Association for Computational Linguistics},
     url = {https://www.semanticscholar.org/paper/c428f1621f79925311082d8d7425dd4d50cd64ed},
     }

320. Vikas Raunak, Matt Post, and Arul Menezes, “Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254125113,
     title = {Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models},
     author = {{Vikas Raunak} and {Matt Post} and {Arul Menezes}},
     year = 2022,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ad2149957cd288a5626adcce48f9981a2ab59184},
     }

321. Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, S. Venkatesh, and R. Arora, “On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation,” in AAAI Conference on Artificial Intelligence, 2022.
     [BibTeX] [Link]

     @inproceedings{253801674,
     title = {On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation},
     author = {{Thanh Nguyen-Tang} and {Ming Yin} and {Sunil Gupta} and {S. Venkatesh} and {R. Arora}},
     year = 2022,
     month = {11},
     booktitle = {AAAI Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/b61a3d718a192e39a437d32a6ed4037b8c29cc41},
     }

322. W. G. C. Bandara, Naman Patel, A. Gholami, Mehdi Nikkhah, M. Agrawal, and Vishal M. Patel, “AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders,” in Computer Vision and Pattern Recognition, 2022.
     [BibTeX] [Link]

     @inproceedings{253553494,
     title = {AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders},
     author = {{W. G. C. Bandara} and {Naman Patel} and {A. Gholami} and {Mehdi Nikkhah} and {M. Agrawal} and {Vishal M. Patel}},
     year = 2022,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/a135632a05cc1f3311859fdebcd1350b4e9e1ee7},
     }

323. Shuyang Sun, Jieneng Chen, Ruifei He, A. Yuille, Philip H. S. Torr, and Song Bai, “LUMix: Improving Mixup by Better Modelling Label Uncertainty,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254069733,
     title = {LUMix: Improving Mixup by Better Modelling Label Uncertainty},
     author = {{Shuyang Sun} and {Jieneng Chen} and {Ruifei He} and {A. Yuille} and {Philip H. S. Torr} and {Song Bai}},
     year = 2022,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/62ce349a6dbc58f64ae02d7203c2f9a06cf6f6d4},
     }

324. Shuyue Stella Li and Kenton Murray, “Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{253510862,
     title = {Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns},
     author = {{Shuyue Stella Li} and {Kenton Murray}},
     year = 2022,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/96fdfc1ba9588d1fab990d504aa590233216326a},
     }

325. Dongji Gao, Jiatong Shi, Shun-Po Chuang, Leibny Paola García-Perera, Hung-yi Lee, Shinji Watanabe, and S. Khudanpur, “Euro: Espnet Unsupervised ASR Open-Source Toolkit,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{254095971,
     title = {Euro: Espnet Unsupervised ASR Open-Source Toolkit},
     author = {{Dongji Gao} and {Jiatong Shi} and {Shun-Po Chuang} and {Leibny Paola García-Perera} and {Hung-yi Lee} and {Shinji Watanabe} and {S. Khudanpur}},
     year = 2022,
     month = {11},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/012771aa3a8d59401d22fade9416dbaad22f42b1},
     }

326. Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, J. Collomosse, Jason Kuen, and Vishal M. Patel, “SceneComposer: Any-Level Semantic Image Synthesis,” in Computer Vision and Pattern Recognition, 2022.
     [BibTeX] [Link]

     @inproceedings{253734941,
     title = {SceneComposer: Any-Level Semantic Image Synthesis},
     author = {{Yu Zeng} and {Zhe Lin} and {Jianming Zhang} and {Qing Liu} and {J. Collomosse} and {Jason Kuen} and {Vishal M. Patel}},
     year = 2022,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/4cc5266166478592ec8539a2b940740b8d380cdd},
     }

327. Yuanze Lin, Chen Wei, Huiyu Wang, A. Yuille, and Cihang Xie, “SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training,” in IEEE International Conference on Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{253735003,
     title = {SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training},
     author = {{Yuanze Lin} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Cihang Xie}},
     year = 2022,
     month = {11},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/210f6ffbed4bf3a0f020cfcb48dab9d6a9939cdb},
     }

328. Jiatong Shi, Chan-Jan Hsu, Ho-Lam Chung, Dongji Gao, Leibny Paola García-Perera, Shinji Watanabe, Ann Lee, and Hung-yi Lee, “Bridging Speech and Textual Pre-Trained Models With Unsupervised ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{253383773,
     title = {Bridging Speech and Textual Pre-Trained Models With Unsupervised ASR},
     author = {{Jiatong Shi} and {Chan-Jan Hsu} and {Ho-Lam Chung} and {Dongji Gao} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Ann Lee} and {Hung-yi Lee}},
     year = 2022,
     month = {11},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/92302ab168429c7c3a8f699b35ba8302916c6e7c},
     }

329. Chenglin Yang, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, A. Yuille, Hartwig Adam, and Liang-Chieh Chen, “MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models,” in International Conference on Learning Representations, 2022.
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     @inproceedings{252715598,
     title = {MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models},
     author = {{Chenglin Yang} and {Siyuan Qiao} and {Qihang Yu} and {Xiaoding Yuan} and {Yukun Zhu} and {A. Yuille} and {Hartwig Adam} and {Liang-Chieh Chen}},
     year = 2022,
     month = {10},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/a8a2a8229f99c291bf71ec92b801a073854c52e2},
     }

330. Yuxiang Guo, Cheng Peng, Chun Pong Lau, and R. Chellappa, “Multi-Modal Human Authentication Using Silhouettes, Gait and RGB,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2022.
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     @inproceedings{252780362,
     title = {Multi-Modal Human Authentication Using Silhouettes, Gait and RGB},
     author = {{Yuxiang Guo} and {Cheng Peng} and {Chun Pong Lau} and {R. Chellappa}},
     year = 2022,
     month = {10},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/e89d9b5c7b5d9c4b490ba1d5fdbbca423920c3e1},
     }

331. Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, T. Shen, Daniel Khashabi, and Yejin Choi, “Generating Sequences by Learning to Self-Correct,” in International Conference on Learning Representations, 2022.
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     @inproceedings{253244506,
     title = {Generating Sequences by Learning to Self-Correct},
     author = {{Sean Welleck} and {Ximing Lu} and {Peter West} and {Faeze Brahman} and {T. Shen} and {Daniel Khashabi} and {Yejin Choi}},
     year = 2022,
     month = {10},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/538288d24bdad73d831dfed44b706958287ed318},
     }

332. Shota Horiguchi, Yuki Takashima, Shinji Watanabe, and Leibny Paola García-Perera, “Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization,” in Spoken Language Technology Workshop, 2022.
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     @inproceedings{252762304,
     title = {Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization},
     author = {{Shota Horiguchi} and {Yuki Takashima} and {Shinji Watanabe} and {Leibny Paola García-Perera}},
     year = 2022,
     month = {10},
     booktitle = {Spoken Language Technology Workshop},
     url = {https://www.semanticscholar.org/paper/30472f3386177fb929a8454cbbb70462e30d9c61},
     }

333. Junfei Xiao, Zhichao Xu, Shiyi Lan, Zhiding Yu, A. Yuille, and Anima Anandkumar, “1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{253098673,
     title = {1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track},
     author = {{Junfei Xiao} and {Zhichao Xu} and {Shiyi Lan} and {Zhiding Yu} and {A. Yuille} and {Anima Anandkumar}},
     year = 2022,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/17a6bee0ef616822d8a883f6bc373dd676242793},
     }

334. Chris Nalty, Neehar Peri, Joshua Gleason, C. Castillo, Shuowen Hu, T. Bourlai, and R. Chellappa, “A Brief Survey on Person Recognition at a Distance,” in Asilomar Conference on Signals, Systems and Computers, 2022.
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     @inproceedings{254853697,
     title = {A Brief Survey on Person Recognition at a Distance},
     author = {{Chris Nalty} and {Neehar Peri} and {Joshua Gleason} and {C. Castillo} and {Shuowen Hu} and {T. Bourlai} and {R. Chellappa}},
     year = 2022,
     month = {10},
     booktitle = {Asilomar Conference on Signals, Systems and Computers},
     url = {https://www.semanticscholar.org/paper/6934bd40d21e3bddce5328d29a7e1083e21d0aad},
     }

335. Hexin Liu, Leibny Paola García-Perera, Andy W. H. Khong, E. Chng, S. Styles, and S. Khudanpur, “Efficient Self-Supervised Learning Representations for Spoken Language Identification,” in IEEE Journal on Selected Topics in Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{251829168,
     title = {Efficient Self-Supervised Learning Representations for Spoken Language Identification},
     author = {{Hexin Liu} and {Leibny Paola García-Perera} and {Andy W. H. Khong} and {E. Chng} and {S. Styles} and {S. Khudanpur}},
     year = 2022,
     month = {10},
     booktitle = {IEEE Journal on Selected Topics in Signal Processing},
     url = {https://www.semanticscholar.org/paper/130693386f2f7b7c1a98c4298c4ed27b9a56f79e},
     }

336. Xiang Xiang, Feng Wang, Yuwen Tan, and A. Yuille, “Imbalanced regression for intensity series of pain expression from videos by regularizing spatio-temporal face nets,” in Pattern Recognition Letters, 2022.
     [BibTeX] [Link]

     @inproceedings{252726978,
     title = {Imbalanced regression for intensity series of pain expression from videos by regularizing spatio-temporal face nets},
     author = {{Xiang Xiang} and {Feng Wang} and {Yuwen Tan} and {A. Yuille}},
     year = 2022,
     month = {10},
     booktitle = {Pattern Recognition Letters},
     url = {https://www.semanticscholar.org/paper/e9eab79d381d7799e74afd9917e91d47953aa69d},
     }

337. Weiyu Guo, Zhaoshuo Li, Yongkui Yang, Z. Wang, Russell H. Taylor, M. Unberath, A. Yuille, and Yingwei Li, “Context-Enhanced Stereo Transformer,” in European Conference on Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{253080413,
     title = {Context-Enhanced Stereo Transformer},
     author = {{Weiyu Guo} and {Zhaoshuo Li} and {Yongkui Yang} and {Z. Wang} and {Russell H. Taylor} and {M. Unberath} and {A. Yuille} and {Yingwei Li}},
     year = 2022,
     month = {10},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/3fe123f4777bcb86d796de230b3767c15f28ed7d},
     }

338. VS Vibashan, Poojan Oza, Vishwanath A. Sindagi, and Vishal M. Patel, “Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection,” in International Conference on Information Photonics, 2022.
     [BibTeX] [Link]

     @inproceedings{253347117,
     title = {Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection},
     author = {{VS Vibashan} and {Poojan Oza} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
     year = 2022,
     month = {10},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/96a609d83a2aaf739fedc4cbfa3f96b14ae234cb},
     }

339. Hexin Liu, Haihua Xu, Leibny Paola García, Andy W. H. Khong, Yi He, and S. Khudanpur, “Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{253116576,
     title = {Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization},
     author = {{Hexin Liu} and {Haihua Xu} and {Leibny Paola García} and {Andy W. H. Khong} and {Yi He} and {S. Khudanpur}},
     year = 2022,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/1fab5a425ad712bb8245741c5abec00aa80fc123},
     }

340. Kate Sanders, Reno Kriz, Anqi Liu, and Benjamin Van Durme, “Ambiguous Images With Human Judgments for Robust Visual Event Classification,” in Neural Information Processing Systems, 2022.
     [BibTeX] [Link]

     @inproceedings{252735237,
     title = {Ambiguous Images With Human Judgments for Robust Visual Event Classification},
     author = {{Kate Sanders} and {Reno Kriz} and {Anqi Liu} and {Benjamin Van Durme}},
     year = 2022,
     month = {10},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/2a55f57716576fdd5840252d673aabe9a676fced},
     }

341. P. Xia and B. Van Durme, “Online Neural Coreference Resolution with Rollback,” in Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference, Gyeongju, Republic of Korea, 2022, p. 13–21.
     [BibTeX] [Abstract] [Link]
     

     Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.

     @inproceedings{xia-van-durme-2022-online,
     title = "Online Neural Coreference Resolution with Rollback",
     author = "Xia, Patrick and
     Van Durme, Benjamin",
     editor = "Ogrodniczuk, Maciej and
     Pradhan, Sameer and
     Nedoluzhko, Anna and
     Ng, Vincent and
     Poesio, Massimo",
     booktitle = "Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference",
     month = oct,
     year = "2022",
     address = "Gyeongju, Republic of Korea",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.crac-1.2",
     pages = "13--21",
     abstract = "Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.",
     }

342. Junfei Xiao, Yutong Bai, A. Yuille, and Zongwei Zhou, “Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{253098023,
     title = {Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification},
     author = {{Junfei Xiao} and {Yutong Bai} and {A. Yuille} and {Zongwei Zhou}},
     year = 2022,
     month = {10},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/249e00445585586214e27d1f4ade032533132d0a},
     }

343. Qixing Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jieneng Chen, A. Yuille, and Zongwei Zhou, “Synthetic Tumors Make AI Segment Tumors Better,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{253117124,
     title = {Synthetic Tumors Make AI Segment Tumors Better},
     author = {{Qixing Hu} and {Junfei Xiao} and {Yixiong Chen} and {Shuwen Sun} and {Jieneng Chen} and {A. Yuille} and {Zongwei Zhou}},
     year = 2022,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/0077f46c9cf3de56319aad65e419131e2466b848},
     }

344. Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, B. Wen, A. Yuille, and Zongwei Zhou, “Making Your First Choice: To Address Cold Start Problem in Vision Active Learning,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{252715847,
     title = {Making Your First Choice: To Address Cold Start Problem in Vision Active Learning},
     author = {{Liangyu Chen} and {Yutong Bai} and {Siyu Huang} and {Yongyi Lu} and {B. Wen} and {A. Yuille} and {Zongwei Zhou}},
     year = 2022,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/a4af00f50f0b397b14ae5dc22e0e766c31adaaa8},
     }

345. W. Wu and D. Yarowsky, “Known Words Will Do: Unknown Concept Translation via Lexical Relations,” in Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), Gyeongju, Republic of Korea, 2022, p. 15–22.
     [BibTeX] [Abstract] [Link]
     

     Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.

     @inproceedings{wu-yarowsky-2022-known,
     title = "Known Words Will Do: Unknown Concept Translation via Lexical Relations",
     author = "Wu, Winston and
     Yarowsky, David",
     editor = "Ojha, Atul Kr. and
     Liu, Chao-Hong and
     Vylomova, Ekaterina and
     Abbott, Jade and
     Washington, Jonathan and
     Oco, Nathaniel and
     Pirinen, Tommi A and
     Malykh, Valentin and
     Logacheva, Varvara and
     Zhao, Xiaobing",
     booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
     month = oct,
     year = "2022",
     address = "Gyeongju, Republic of Korea",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.loresmt-1.3",
     pages = "15--22",
     abstract = "Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.",
     }

346. J. Zhang, A. DeLucia, and M. Dredze, “Changes in Tweet Geolocation over Time: A Study with Carmen 2.0,” in Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), Gyeongju, Republic of Korea, 2022, p. 1–14.
     [BibTeX] [Abstract] [Link]
     

     Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.

     @inproceedings{zhang-etal-2022-changes,
     title = "Changes in Tweet Geolocation over Time: A Study with Carmen 2.0",
     author = "Zhang, Jingyu and
     DeLucia, Alexandra and
     Dredze, Mark",
     booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
     month = oct,
     year = "2022",
     address = "Gyeongju, Republic of Korea",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wnut-1.1",
     pages = "1--14",
     abstract = "Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.",
     }

347. N. Verma, K. Murray, and K. Duh, “Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 31–44.
     [BibTeX] [Abstract] [Link]
     

     Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART{‘}s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.

     @inproceedings{verma-etal-2022-strategies,
     title = "Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation",
     author = "Verma, Neha and
     Murray, Kenton and
     Duh, Kevin",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
     month = sep,
     year = "2022",
     address = "Orlando, USA",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2022.amta-research.3",
     pages = "31--44",
     abstract = "Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART{'}s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.",
     }

348. S. Sia and K. Duh, “Prefix Embeddings for In-context Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 45–57.
     [BibTeX] [Abstract] [Link]
     

     Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001{\%} of the model parameters, the simple method improves {\textasciitilde}0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings{‘} training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.

     @inproceedings{sia-duh-2022-prefix,
     title = "Prefix Embeddings for In-context Machine Translation",
     author = "Sia, Suzanna and
     Duh, Kevin",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
     month = sep,
     year = "2022",
     address = "Orlando, USA",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2022.amta-research.4",
     pages = "45--57",
     abstract = "Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001{\%} of the model parameters, the simple method improves {\textasciitilde}0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings{'} training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.",
     }

349. D. Licht, C. Gao, J. Lam, F. Guzman, M. Diab, and P. Koehn, “Consistent Human Evaluation of Machine Translation across Language Pairs,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 309–321.
     [BibTeX] [Abstract] [Link]
     

     Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.

     @inproceedings{licht-etal-2022-consistent,
     title = "Consistent Human Evaluation of Machine Translation across Language Pairs",
     author = "Licht, Daniel and
     Gao, Cynthia and
     Lam, Janice and
     Guzman, Francisco and
     Diab, Mona and
     Koehn, Philipp",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
     month = sep,
     year = "2022",
     address = "Orlando, USA",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2022.amta-research.24",
     pages = "309--321",
     abstract = "Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.",
     }

350. W. Tan, S. Ding, H. Khayrallah, and P. Koehn, “Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 157–174.
     [BibTeX] [Abstract] [Link]
     

     Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.

     @inproceedings{tan-etal-2022-doubly,
     title = "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation",
     author = "Tan, Weiting and
     Ding, Shuoyang and
     Khayrallah, Huda and
     Koehn, Philipp",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
     month = sep,
     year = "2022",
     address = "Orlando, USA",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2022.amta-research.12",
     pages = "157--174",
     abstract = "Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.",
     }

351. R. Volum, S. Rao, M. Xu, G. DesGarennes, C. Brockett, B. Van Durme, O. Deng, A. Malhotra, and B. Dolan, “Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code,” in Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022), Seattle, United States, 2022, p. 25–43. doi:10.18653/v1/2022.wordplay-1.3
     [BibTeX] [Abstract] [Link]
     

     Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players{‘} interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.

     @inproceedings{volum-etal-2022-craft,
     title = "Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code",
     author = "Volum, Ryan and
     Rao, Sudha and
     Xu, Michael and
     DesGarennes, Gabriel and
     Brockett, Chris and
     Van Durme, Benjamin and
     Deng, Olivia and
     Malhotra, Akanksha and
     Dolan, Bill",
     editor = "C{\^o}t{\'e}, Marc-Alexandre and
     Yuan, Xingdi and
     Ammanabrolu, Prithviraj",
     booktitle = "Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022)",
     month = jul,
     year = "2022",
     address = "Seattle, United States",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.wordplay-1.3",
     doi = "10.18653/v1/2022.wordplay-1.3",
     pages = "25--43",
     abstract = "Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players{'} interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.",
     }

352. A. Tsakalidis, J. Chim, I. M. Bilal, A. Zirikly, D. Atzil-Slonim, F. Nanni, P. Resnik, M. Gaur, K. Roy, B. Inkster, J. Leintz, and M. Liakata, “Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts,” in Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, Seattle, USA, 2022, p. 184–198. doi:10.18653/v1/2022.clpsych-1.16
     [BibTeX] [Abstract] [Link]
     

     We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of {`}Moments of Change{‘} in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year{‘}s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual{‘}s mood (dras- tic changes-{`}Switches{‘}- and gradual changes -{`}Escalations{‘}- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual {–} a continuation of the CLPsych 2019 Shared Task{–} where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).

     @inproceedings{tsakalidis-etal-2022-overview,
     title = "Overview of the {CLP}sych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts",
     author = "Tsakalidis, Adam and
     Chim, Jenny and
     Bilal, Iman Munire and
     Zirikly, Ayah and
     Atzil-Slonim, Dana and
     Nanni, Federico and
     Resnik, Philip and
     Gaur, Manas and
     Roy, Kaushik and
     Inkster, Becky and
     Leintz, Jeff and
     Liakata, Maria",
     editor = "Zirikly, Ayah and
     Atzil-Slonim, Dana and
     Liakata, Maria and
     Bedrick, Steven and
     Desmet, Bart and
     Ireland, Molly and
     Lee, Andrew and
     MacAvaney, Sean and
     Purver, Matthew and
     Resnik, Rebecca and
     Yates, Andrew",
     booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
     month = jul,
     year = "2022",
     address = "Seattle, USA",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.clpsych-1.16",
     doi = "10.18653/v1/2022.clpsych-1.16",
     pages = "184--198",
     abstract = "We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of {`}Moments of Change{'} in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year{'}s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual{'}s mood (dras- tic changes-{`}Switches{'}- and gradual changes -{`}Escalations{'}- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual {--} a continuation of the CLPsych 2019 Shared Task{--} where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).",
     }

353. C. Zhang, B. Van Durme, Z. Li, and E. Stengel-Eskin, “Visual Commonsense in Pretrained Unimodal and Multimodal Models,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 5321–5335. doi:10.18653/v1/2022.naacl-main.390
     [BibTeX] [Abstract] [Link]
     

     Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.

     @inproceedings{zhang-etal-2022-visual,
     title = "Visual Commonsense in Pretrained Unimodal and Multimodal Models",
     author = "Zhang, Chenyu and
     Van Durme, Benjamin and
     Li, Zhuowan and
     Stengel-Eskin, Elias",
     editor = "Carpuat, Marine and
     de Marneffe, Marie-Catherine and
     Meza Ruiz, Ivan Vladimir",
     booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
     month = jul,
     year = "2022",
     address = "Seattle, United States",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.naacl-main.390",
     doi = "10.18653/v1/2022.naacl-main.390",
     pages = "5321--5335",
     abstract = "Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.",
     }

354. O. Weller, M. Marone, V. Braverman, D. Lawrie, and B. Van Durme, “Pretrained Models for Multilingual Federated Learning,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 1413–1421. doi:10.18653/v1/2022.naacl-main.101
     [BibTeX] [Abstract] [Link]
     

     Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.

     @inproceedings{weller-etal-2022-pretrained,
     title = "Pretrained Models for Multilingual Federated Learning",
     author = "Weller, Orion and
     Marone, Marc and
     Braverman, Vladimir and
     Lawrie, Dawn and
     Van Durme, Benjamin",
     editor = "Carpuat, Marine and
     de Marneffe, Marie-Catherine and
     Meza Ruiz, Ivan Vladimir",
     booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
     month = jul,
     year = "2022",
     address = "Seattle, United States",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.naacl-main.101",
     doi = "10.18653/v1/2022.naacl-main.101",
     pages = "1413--1421",
     abstract = "Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.",
     }

355. A. Zirikly and M. Dredze, “Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks,” in Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, Seattle, USA, 2022, p. 30–39. doi:10.18653/v1/2022.clpsych-1.3
     [BibTeX] [Abstract] [Link]
     

     Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.

     @inproceedings{zirikly-dredze-2022-explaining,
     title = "Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks",
     author = "Zirikly, Ayah and
     Dredze, Mark",
     editor = "Zirikly, Ayah and
     Atzil-Slonim, Dana and
     Liakata, Maria and
     Bedrick, Steven and
     Desmet, Bart and
     Ireland, Molly and
     Lee, Andrew and
     MacAvaney, Sean and
     Purver, Matthew and
     Resnik, Rebecca and
     Yates, Andrew",
     booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
     month = jul,
     year = "2022",
     address = "Seattle, USA",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.clpsych-1.3",
     doi = "10.18653/v1/2022.clpsych-1.3",
     pages = "30--39",
     abstract = "Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.",
     }

356. A. Blair-stanek and B. Van Durme, “Improved Induction of Narrative Chains via Cross-Document Relations,” in Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, Seattle, Washington, 2022, p. 208–212. doi:10.18653/v1/2022.starsem-1.18
     [BibTeX] [Abstract] [Link]
     

     The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.

     @inproceedings{blair-stanek-van-durme-2022-improved,
     title = "Improved Induction of Narrative Chains via Cross-Document Relations",
     author = "Blair-stanek, Andrew and
     Van Durme, Benjamin",
     editor = "Nastase, Vivi and
     Pavlick, Ellie and
     Pilehvar, Mohammad Taher and
     Camacho-Collados, Jose and
     Raganato, Alessandro",
     booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
     month = jul,
     year = "2022",
     address = "Seattle, Washington",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.starsem-1.18",
     doi = "10.18653/v1/2022.starsem-1.18",
     pages = "208--212",
     abstract = "The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.",
     }

357. R. Shin and B. Van Durme, “Few-Shot Semantic Parsing with Language Models Trained on Code,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 5417–5425. doi:10.18653/v1/2022.naacl-main.396
     [BibTeX] [Abstract] [Link]
     

     Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.

     @inproceedings{shin-van-durme-2022-shot,
     title = "Few-Shot Semantic Parsing with Language Models Trained on Code",
     author = "Shin, Richard and
     Van Durme, Benjamin",
     editor = "Carpuat, Marine and
     de Marneffe, Marie-Catherine and
     Meza Ruiz, Ivan Vladimir",
     booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
     month = jul,
     year = "2022",
     address = "Seattle, United States",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.naacl-main.396",
     doi = "10.18653/v1/2022.naacl-main.396",
     pages = "5417--5425",
     abstract = "Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.",
     }

358. K. Batsuren, O. Goldman, S. Khalifa, N. Habash, W. Kiera{‘s}, G. Bella, B. Leonard, G. Nicolai, K. Gorman, Y. G. Ate, M. Ryskina, S. Mielke, E. Budianskaya, C. El-Khaissi, T. Pimentel, M. Gasser, W. A. Lane, M. Raj, M. Coler, J. R. M. Samame, D. S. Camaiteri, E. Z. Rojas, D. López Francis, A. Oncevay, J. López Bautista, G. C. S. Villegas, L. T. Hennigen, A. Ek, D. Guriel, P. Dirix, J. Bernardy, A. Scherbakov, A. Bayyr-ool, A. Anastasopoulos, R. Zariquiey, K. Sheifer, S. Ganieva, H. Cruz, R. Karahó{v{g}}a, S. Markantonatou, G. Pavlidis, M. Plugaryov, E. Klyachko, A. Salehi, C. Angulo, J. Baxi, A. Krizhanovsky, N. Krizhanovskaya, E. Salesky, C. Vania, S. Ivanova, J. White, R. H. Maudslay, J. Valvoda, R. Zmigrod, P. Czarnowska, I. Nikkarinen, A. Salchak, B. Bhatt, C. Straughn, Z. Liu, J. N. Washington, Y. Pinter, D. Ataman, M. Wolinski, T. Suhardijanto, A. Yablonskaya, N. Stoehr, H. Dolatian, Z. Nuriah, S. Ratan, F. M. Tyers, E. M. Ponti, G. Aiton, A. Arora, R. J. Hatcher, R. Kumar, J. Young, D. Rodionova, A. Yemelina, T. Andrushko, I. Marchenko, P. Mashkovtseva, A. Serova, E. Prud{‘}hommeaux, M. Nepomniashchaya, F. Giunchiglia, E. Chodroff, M. Hulden, M. Silfverberg, A. D. McCarthy, D. Yarowsky, R. Cotterell, R. Tsarfaty, and E. Vylomova, “UniMorph 4.0: Universal Morphology,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 840–855.
     [BibTeX] [Abstract] [Link]
     

     The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

     @inproceedings{batsuren-etal-2022-unimorph,
     title = "{U}ni{M}orph 4.0: {U}niversal {M}orphology",
     author = "Batsuren, Khuyagbaatar and
     Goldman, Omer and
     Khalifa, Salam and
     Habash, Nizar and
     Kiera{\'s}, Witold and
     Bella, G{\'a}bor and
     Leonard, Brian and
     Nicolai, Garrett and
     Gorman, Kyle and
     Ate, Yustinus Ghanggo and
     Ryskina, Maria and
     Mielke, Sabrina and
     Budianskaya, Elena and
     El-Khaissi, Charbel and
     Pimentel, Tiago and
     Gasser, Michael and
     Lane, William Abbott and
     Raj, Mohit and
     Coler, Matt and
     Samame, Jaime Rafael Montoya and
     Camaiteri, Delio Siticonatzi and
     Rojas, Esa{\'u} Zumaeta and
     L{\'o}pez Francis, Didier and
     Oncevay, Arturo and
     L{\'o}pez Bautista, Juan and
     Villegas, Gema Celeste Silva and
     Hennigen, Lucas Torroba and
     Ek, Adam and
     Guriel, David and
     Dirix, Peter and
     Bernardy, Jean-Philippe and
     Scherbakov, Andrey and
     Bayyr-ool, Aziyana and
     Anastasopoulos, Antonios and
     Zariquiey, Roberto and
     Sheifer, Karina and
     Ganieva, Sofya and
     Cruz, Hilaria and
     Karah{\'o}{\v{g}}a, Ritv{\'a}n and
     Markantonatou, Stella and
     Pavlidis, George and
     Plugaryov, Matvey and
     Klyachko, Elena and
     Salehi, Ali and
     Angulo, Candy and
     Baxi, Jatayu and
     Krizhanovsky, Andrew and
     Krizhanovskaya, Natalia and
     Salesky, Elizabeth and
     Vania, Clara and
     Ivanova, Sardana and
     White, Jennifer and
     Maudslay, Rowan Hall and
     Valvoda, Josef and
     Zmigrod, Ran and
     Czarnowska, Paula and
     Nikkarinen, Irene and
     Salchak, Aelita and
     Bhatt, Brijesh and
     Straughn, Christopher and
     Liu, Zoey and
     Washington, Jonathan North and
     Pinter, Yuval and
     Ataman, Duygu and
     Wolinski, Marcin and
     Suhardijanto, Totok and
     Yablonskaya, Anna and
     Stoehr, Niklas and
     Dolatian, Hossep and
     Nuriah, Zahroh and
     Ratan, Shyam and
     Tyers, Francis M. and
     Ponti, Edoardo M. and
     Aiton, Grant and
     Arora, Aryaman and
     Hatcher, Richard J. and
     Kumar, Ritesh and
     Young, Jeremiah and
     Rodionova, Daria and
     Yemelina, Anastasia and
     Andrushko, Taras and
     Marchenko, Igor and
     Mashkovtseva, Polina and
     Serova, Alexandra and
     Prud{'}hommeaux, Emily and
     Nepomniashchaya, Maria and
     Giunchiglia, Fausto and
     Chodroff, Eleanor and
     Hulden, Mans and
     Silfverberg, Miikka and
     McCarthy, Arya D. and
     Yarowsky, David and
     Cotterell, Ryan and
     Tsarfaty, Reut and
     Vylomova, Ekaterina",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.lrec-1.89",
     pages = "840--855",
     abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.",
     }

359. L. Kanashiro Pereira, “Attention-Focused Adversarial Training for Robust Temporal Reasoning,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 7352–7359.
     [BibTeX] [Abstract] [Link]
     

     We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.

     @inproceedings{kanashiro-pereira-2022-attention,
     title = "Attention-Focused Adversarial Training for Robust Temporal Reasoning",
     author = "Kanashiro Pereira, Lis",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.lrec-1.800",
     pages = "7352--7359",
     abstract = "We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.",
     }

360. P. McNamee and K. Duh, “The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 910–918.
     [BibTeX] [Abstract] [Link]
     

     Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21{\%} was observed, demonstrating the utility of the corpus.

     @inproceedings{mcnamee-duh-2022-multilingual,
     title = "The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text",
     author = "McNamee, Paul and
     Duh, Kevin",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.lrec-1.96",
     pages = "910--918",
     abstract = "Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21{\%} was observed, demonstrating the utility of the corpus.",
     }

361. W. Wu and D. Yarowsky, “On the Robustness of Cognate Generation Models,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 4299–4305.
     [BibTeX] [Abstract] [Link]
     

     We evaluate two popular neural cognate generation models{‘} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{‘} results with respect to each error type to explain how and why these models perform as they do.

     @inproceedings{wu-yarowsky-2022-robustness,
     title = "On the Robustness of Cognate Generation Models",
     author = "Wu, Winston and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.lrec-1.458",
     pages = "4299--4305",
     abstract = "We evaluate two popular neural cognate generation models{'} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{'} results with respect to each error type to explain how and why these models perform as they do.",
     }

362. A. Zirikly, B. Desmet, J. Porcino, J. Camacho Maldonado, P. Ho, R. Jimenez Silva, and M. Sacco, “A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 2850–2855.
     [BibTeX] [Abstract] [Link]
     

     Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.

     @inproceedings{zirikly-etal-2022-whole,
     title = "A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach",
     author = "Zirikly, Ayah and
     Desmet, Bart and
     Porcino, Julia and
     Camacho Maldonado, Jonathan and
     Ho, Pei-Shu and
     Jimenez Silva, Rafael and
     Sacco, Maryanne",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.lrec-1.305",
     pages = "2850--2855",
     abstract = "Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.",
     }

363. N. Weber, A. Belyy, N. Holzenberger, R. Rudinger, and B. Van Durme, “Human Schema Curation via Causal Association Rule Mining,” in Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, Marseille, France, 2022, p. 139–150.
     [BibTeX] [Abstract] [Link]
     

     Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to {“}program{”} complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.

     @inproceedings{weber-etal-2022-human,
     title = "Human Schema Curation via Causal Association Rule Mining",
     author = "Weber, Noah and
     Belyy, Anton and
     Holzenberger, Nils and
     Rudinger, Rachel and
     Van Durme, Benjamin",
     editor = "Pradhan, Sameer and
     Kuebler, Sandra",
     booktitle = "Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022",
     month = jun,
     year = "2022",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2022.law-1.17",
     pages = "139--150",
     abstract = "Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to {``}program{''} complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.",
     }

364. T. Nguyen, A. Yates, A. Zirikly, B. Desmet, and A. Cohan, “Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 8446–8459. doi:10.18653/v1/2022.acl-long.578
     [BibTeX] [Abstract] [Link]
     

     Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9{‘}s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.

     @inproceedings{nguyen-etal-2022-improving,
     title = "Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires",
     author = "Nguyen, Thong and
     Yates, Andrew and
     Zirikly, Ayah and
     Desmet, Bart and
     Cohan, Arman",
     editor = "Muresan, Smaranda and
     Nakov, Preslav and
     Villavicencio, Aline",
     booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.acl-long.578",
     doi = "10.18653/v1/2022.acl-long.578",
     pages = "8446--8459",
     abstract = "Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9{'}s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.",
     }

365. S. Panthaplackel, A. Benton, and M. Dredze, “Updated Headline Generation: Creating Updated Summaries for Evolving News Stories,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 6438–6461. doi:10.18653/v1/2022.acl-long.446
     [BibTeX] [Abstract] [Link]
     

     We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.

     @inproceedings{panthaplackel-etal-2022-updated,
     title = "Updated Headline Generation: Creating Updated Summaries for Evolving News Stories",
     author = "Panthaplackel, Sheena and
     Benton, Adrian and
     Dredze, Mark",
     editor = "Muresan, Smaranda and
     Nakov, Preslav and
     Villavicencio, Aline",
     booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.acl-long.446",
     doi = "10.18653/v1/2022.acl-long.446",
     pages = "6438--6461",
     abstract = "We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.",
     }

366. S. Wu, B. Van Durme, and M. Dredze, “Zero-shot Cross-lingual Transfer is Under-specified Optimization,” in Proceedings of the 7th Workshop on Representation Learning for NLP, Dublin, Ireland, 2022, p. 236–248. doi:10.18653/v1/2022.repl4nlp-1.25
     [BibTeX] [Abstract] [Link]
     

     Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.

     @inproceedings{wu-etal-2022-zero,
     title = "Zero-shot Cross-lingual Transfer is Under-specified Optimization",
     author = "Wu, Shijie and
     Van Durme, Benjamin and
     Dredze, Mark",
     editor = "Gella, Spandana and
     He, He and
     Majumder, Bodhisattwa Prasad and
     Can, Burcu and
     Giunchiglia, Eleonora and
     Cahyawijaya, Samuel and
     Min, Sewon and
     Mozes, Maximilian and
     Li, Xiang Lorraine and
     Augenstein, Isabelle and
     Rogers, Anna and
     Cho, Kyunghyun and
     Grefenstette, Edward and
     Rimell, Laura and
     Dyer, Chris",
     booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.repl4nlp-1.25",
     doi = "10.18653/v1/2022.repl4nlp-1.25",
     pages = "236--248",
     abstract = "Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.",
     }

367. A. Anastasopoulos, L. Barrault, L. Bentivogli, M. Zanon Boito, O. Bojar, R. Cattoni, A. Currey, G. Dinu, K. Duh, M. Elbayad, C. Emmanuel, Y. Estève, M. Federico, C. Federmann, S. Gahbiche, H. Gong, R. Grundkiewicz, B. Haddow, B. Hsu, D. Javorský, V. Kloudová, S. Lakew, X. Ma, P. Mathur, P. McNamee, K. Murray, M. N{v{a}}dejde, S. Nakamura, M. Negri, J. Niehues, X. Niu, J. Ortega, J. Pino, E. Salesky, J. Shi, M. Sperber, S. Stüker, K. Sudoh, M. Turchi, Y. Virkar, A. Waibel, C. Wang, and S. Watanabe, “Findings of the IWSLT 2022 Evaluation Campaign,” in Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), Dublin, Ireland (in-person and online), 2022, p. 98–157. doi:10.18653/v1/2022.iwslt-1.10
     [BibTeX] [Abstract] [Link]
     

     The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

     @inproceedings{anastasopoulos-etal-2022-findings,
     title = "Findings of the {IWSLT} 2022 Evaluation Campaign",
     author = {Anastasopoulos, Antonios and
     Barrault, Lo{\"\i}c and
     Bentivogli, Luisa and
     Zanon Boito, Marcely and
     Bojar, Ond{\v{r}}ej and
     Cattoni, Roldano and
     Currey, Anna and
     Dinu, Georgiana and
     Duh, Kevin and
     Elbayad, Maha and
     Emmanuel, Clara and
     Est{\`e}ve, Yannick and
     Federico, Marcello and
     Federmann, Christian and
     Gahbiche, Souhir and
     Gong, Hongyu and
     Grundkiewicz, Roman and
     Haddow, Barry and
     Hsu, Benjamin and
     Javorsk{\'y}, D{\'a}vid and
     Kloudov{\'a}, V{\u{e}}ra and
     Lakew, Surafel and
     Ma, Xutai and
     Mathur, Prashant and
     McNamee, Paul and
     Murray, Kenton and
     N{\v{a}}dejde, Maria and
     Nakamura, Satoshi and
     Negri, Matteo and
     Niehues, Jan and
     Niu, Xing and
     Ortega, John and
     Pino, Juan and
     Salesky, Elizabeth and
     Shi, Jiatong and
     Sperber, Matthias and
     St{\"u}ker, Sebastian and
     Sudoh, Katsuhito and
     Turchi, Marco and
     Virkar, Yogesh and
     Waibel, Alexander and
     Wang, Changhan and
     Watanabe, Shinji},
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Costa-juss{\`a}, Marta",
     booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.iwslt-1.10",
     doi = "10.18653/v1/2022.iwslt-1.10",
     pages = "98--157",
     abstract = "The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.",
     }

368. J. Yang, A. Hussein, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2022 Dialect Speech Translation System Description,” in Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), Dublin, Ireland (in-person and online), 2022, p. 319–326. doi:10.18653/v1/2022.iwslt-1.29
     [BibTeX] [Abstract] [Link]
     

     This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.

     @inproceedings{yang-etal-2022-jhu,
     title = "{JHU} {IWSLT} 2022 Dialect Speech Translation System Description",
     author = "Yang, Jinyi and
     Hussein, Amir and
     Wiesner, Matthew and
     Khudanpur, Sanjeev",
     editor = "Salesky, Elizabeth and
     Federico, Marcello and
     Costa-juss{\`a}, Marta",
     booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland (in-person and online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.iwslt-1.29",
     doi = "10.18653/v1/2022.iwslt-1.29",
     pages = "319--326",
     abstract = "This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.",
     }

369. A. Belyy, C. Huang, J. Andreas, E. A. Platanios, S. Thomson, R. Shin, S. Roy, A. Nisnevich, C. Chen, and B. Van Durme, “Guided K-best Selection for Semantic Parsing Annotation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Dublin, Ireland, 2022, p. 114–126. doi:10.18653/v1/2022.acl-demo.11
     [BibTeX] [Abstract] [Link]
     

     Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.

     @inproceedings{belyy-etal-2022-guided,
     title = "Guided K-best Selection for Semantic Parsing Annotation",
     author = "Belyy, Anton and
     Huang, Chieh-yang and
     Andreas, Jacob and
     Platanios, Emmanouil Antonios and
     Thomson, Sam and
     Shin, Richard and
     Roy, Subhro and
     Nisnevich, Aleksandr and
     Chen, Charles and
     Van Durme, Benjamin",
     editor = "Basile, Valerio and
     Kozareva, Zornitsa and
     Stajner, Sanja",
     booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.acl-demo.11",
     doi = "10.18653/v1/2022.acl-demo.11",
     pages = "114--126",
     abstract = "Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.",
     }

370. K. Yang, O. Deng, C. Chen, R. Shin, S. Roy, and B. Van Durme, “Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation,” in Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, 2022, p. 3685–3695. doi:10.18653/v1/2022.findings-acl.291
     [BibTeX] [Abstract] [Link]
     

     We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33{\%} relative improvement over a non-data-augmented baseline in top-1 match.

     @inproceedings{yang-etal-2022-addressing,
     title = "Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation",
     author = "Yang, Kevin and
     Deng, Olivia and
     Chen, Charles and
     Shin, Richard and
     Roy, Subhro and
     Van Durme, Benjamin",
     editor = "Muresan, Smaranda and
     Nakov, Preslav and
     Villavicencio, Aline",
     booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.findings-acl.291",
     doi = "10.18653/v1/2022.findings-acl.291",
     pages = "3685--3695",
     abstract = "We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33{\%} relative improvement over a non-data-augmented baseline in top-1 match.",
     }

371. S. Sun, A. Fan, J. Cross, V. Chaudhary, C. Tran, P. Koehn, and F. Guzmán, “Alternative Input Signals Ease Transfer in Multilingual Machine Translation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 5291–5305. doi:10.18653/v1/2022.acl-long.363
     [BibTeX] [Abstract] [Link]
     

     Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble {–} training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference, outperforms strong ensemble baselines by 1.3 BLEU points on both language families. Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5{\%} of the total training data is accessible. Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems.

     @inproceedings{sun-etal-2022-alternative,
     title = "Alternative Input Signals Ease Transfer in Multilingual Machine Translation",
     author = "Sun, Simeng and
     Fan, Angela and
     Cross, James and
     Chaudhary, Vishrav and
     Tran, Chau and
     Koehn, Philipp and
     Guzm{\'a}n, Francisco",
     editor = "Muresan, Smaranda and
     Nakov, Preslav and
     Villavicencio, Aline",
     booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.acl-long.363",
     doi = "10.18653/v1/2022.acl-long.363",
     pages = "5291--5305",
     abstract = "Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble {--} training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference, outperforms strong ensemble baselines by 1.3 BLEU points on both language families. Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5{\%} of the total training data is accessible. Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems.",
     }

372. M. Yuan, P. Xia, C. May, B. Van Durme, and J. Boyd-Graber, “Adapting Coreference Resolution Models through Active Learning,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 7533–7549. doi:10.18653/v1/2022.acl-long.519
     [BibTeX] [Abstract] [Link]
     

     Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.

     @inproceedings{yuan-etal-2022-adapting,
     title = "Adapting Coreference Resolution Models through Active Learning",
     author = "Yuan, Michelle and
     Xia, Patrick and
     May, Chandler and
     Van Durme, Benjamin and
     Boyd-Graber, Jordan",
     editor = "Muresan, Smaranda and
     Nakov, Preslav and
     Villavicencio, Aline",
     booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
     month = may,
     year = "2022",
     address = "Dublin, Ireland",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.acl-long.519",
     doi = "10.18653/v1/2022.acl-long.519",
     pages = "7533--7549",
     abstract = "Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.",
     }

373. J. Zhou, J. Eisner, M. Newman, E. A. Platanios, and S. Thomson, “Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue,” in Proceedings of the Association for Computational Linguistics (ACL), Dublin, 2022, p. 1554–1576. doi:10.18653/v1/2022.acl-long.110
     [BibTeX] [Link]

     @InProceedings{zhou-et-al-2022,
     aclid = "2022.acl-long.110",
     doi = "10.18653/v1/2022.acl-long.110",
     author = "Jiawei Zhou and Jason Eisner and Michael Newman and
     Emmanouil Anthony Platanios and Sam Thomson",
     title = "Online Semantic Parsing for Latency Reduction in
     Task-Oriented Dialogue",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     pages = "1554--1576",
     year = "2022",
     month = may,
     address = "Dublin",
     URL = "http://cs.jhu.edu/~jason/papers/#zhou-et-al-2022",
     }

374. R. Cotterell and J. Eisner, “A Functionalist Account of Vowel System Typology,” in Proceedings of the Association for Computational Linguistics (ACL), Dublin, 2022.
     [BibTeX] [Link]

     @InProceedings{cotterell-eisner-2022,
     author = "Ryan Cotterell and Jason Eisner",
     title = "A Functionalist Account of Vowel System Typology",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2022",
     month = may,
     address = "Dublin",
     note = "Paper was accepted, but we withdrew it in order to add
     more experiments and analysis before publication.",
     URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2022",
     }

375. C. Yang, H. Mei, and J. Eisner, “Transformer Embeddings of Irregularly Spaced Events and Their Participants,” in Proceedings of the Tenth International Conference on Learning Representations (ICLR), 2022.
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     @InProceedings{yang-et-al-2022-iclr,
     author = "Chenghao Yang and Hongyuan Mei and Jason Eisner",
     title = "Transformer Embeddings of Irregularly Spaced Events
     and Their Participants",
     booktitle = "Proceedings of the Tenth International Conference on
     Learning Representations (ICLR)",
     year = "2022",
     month = apr,
     note = "9 pages plus appendices",
     URL = "http://cs.jhu.edu/~jason/papers/#yang-et-al-2022-iclr",
     }

376. M. Naphade, Shuo Wang, D. Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, R. Chellappa, and Pranamesh Chakraborty, “The 6th AI City Challenge,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022.
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     @inproceedings{216914509,
     title = {The 6th AI City Challenge},
     author = {{M. Naphade} and {Shuo Wang} and {D. Anastasiu} and {Zheng Tang} and {Ming-Ching Chang} and {Xiaodong Yang} and {Liang Zheng} and {Anuj Sharma} and {R. Chellappa} and {Pranamesh Chakraborty}},
     year = 2022,
     month = {4},
     booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
     url = {https://www.semanticscholar.org/paper/7f489232a16a54fa2b11d5758101f078f9db797c},
     }

377. Magdalena Rybicka, J. Villalba, N. Dehak, and K. Kowalczyk, “End-to-End Neural Speaker Diarization with an Iterative Refinement of Non-Autoregressive Attention-based Attractors,” in Interspeech, 2022.
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     @inproceedings{252346611,
     title = {End-to-End Neural Speaker Diarization with an Iterative Refinement of Non-Autoregressive Attention-based Attractors},
     author = {{Magdalena Rybicka} and {J. Villalba} and {N. Dehak} and {K. Kowalczyk}},
     year = 2022,
     month = {9},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/916cfa98c48af9931559fe0d8953bcaf7bdf7f2c},
     }

378. J. Sadeghi, Kevin Duh, G. Sugiyama, V. Patel, G. Coppa, and R. Barrera, “Pre-hospital caloric deficits in surgical patients.,” in Nutrition and Health, 2022.
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     @inproceedings{208391943,
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     author = {{J. Sadeghi} and {Kevin Duh} and {G. Sugiyama} and {V. Patel} and {G. Coppa} and {R. Barrera}},
     year = 2022,
     month = {7},
     booktitle = {Nutrition and Health},
     url = {https://www.semanticscholar.org/paper/cfee21939b8a016ed3d947607940dc9a0ccf8b0c},
     }

379. Suraj Nair, Eugene Yang, Dawn J Lawrie, Kevin Duh, Paul McNamee, Kenton Murray, J. Mayfield, and Douglas W. Oard, “Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models,” in European Conference on Information Retrieval, 2022.
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     @inproceedings{246210468,
     title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
     author = {{Suraj Nair} and {Eugene Yang} and {Dawn J Lawrie} and {Kevin Duh} and {Paul McNamee} and {Kenton Murray} and {J. Mayfield} and {Douglas W. Oard}},
     year = 2022,
     month = {1},
     booktitle = {European Conference on Information Retrieval},
     url = {https://www.semanticscholar.org/paper/d1ccffb8eb1b7a99cd586723074b82fa5399bdd2},
     }

380. Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, and N. Dehak, “Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification,” in Interspeech, 2022.
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     @inproceedings{247839251,
     title = {Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification},
     author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {N. Dehak}},
     year = 2022,
     month = {3},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/d58ebbc34e8ea987da5dda1bb132823b3e9105d3},
     }

381. Hexin Liu, Leibny Paola García Perera, Andy W. H. Khong, J. Dauwels, S. Styles, and S. Khudanpur, “Enhance Language Identification using Dual-mode Model with Knowledge Distillation,” in arXiv.org, 2022.
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     @inproceedings{247291930,
     title = {Enhance Language Identification using Dual-mode Model with Knowledge Distillation},
     author = {{Hexin Liu} and {Leibny Paola García Perera} and {Andy W. H. Khong} and {J. Dauwels} and {S. Styles} and {S. Khudanpur}},
     year = 2022,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/b238287cad4bf831f1f7600207e967b95620017d},
     }

382. A. Hussein, S. A. Chowdhury, Ahmed Abdelali, N. Dehak, and Ahmed M. Ali, “Code-Switching Text Augmentation for Multilingual Speech Processing,” in arXiv.org, 2022.
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     @inproceedings{245827791,
     title = {Code-Switching Text Augmentation for Multilingual Speech Processing},
     author = {{A. Hussein} and {S. A. Chowdhury} and {Ahmed Abdelali} and {N. Dehak} and {Ahmed M. Ali}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/be5074a85ef8166fc173cb51971a2e3f79685134},
     }

383. Zengle Zhu, Mintong Kang, A. Yuille, and Zongwei Zhou, “Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Late 2019.” 2022.
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     @inproceedings{259840482,
     title = {Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Late 2019},
     author = {{Zengle Zhu} and {Mintong Kang} and {A. Yuille} and {Zongwei Zhou}},
     year = 2022,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/5e9e11dbe87d01e44fc3a4e68d151f2a2809f261},
     }

384. Nllb team, M. Costa-jussà, James Cross, Onur cCelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Alison Youngblood, Bapi Akula, Loïc Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon L. Spruit, C. Tran, Pierre Yves Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzm’an, Philipp Koehn, Alexandre Mourachko, C. Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang, “No Language Left Behind: Scaling Human-Centered Machine Translation,” in arXiv.org, 2022.
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     @inproceedings{250425961,
     title = {No Language Left Behind: Scaling Human-Centered Machine Translation},
     author = {{Nllb team} and {M. Costa-jussà} and {James Cross} and {Onur cCelebi} and {Maha Elbayad} and {Kenneth Heafield} and {Kevin Heffernan} and {Elahe Kalbassi} and {Janice Lam} and {Daniel Licht} and {Jean Maillard} and {Anna Sun} and {Skyler Wang} and {Guillaume Wenzek} and {Alison Youngblood} and {Bapi Akula} and {Loïc Barrault} and {Gabriel Mejia Gonzalez} and {Prangthip Hansanti} and {John Hoffman} and {Semarley Jarrett} and {Kaushik Ram Sadagopan} and {Dirk Rowe} and {Shannon L. Spruit} and {C. Tran} and {Pierre Yves Andrews} and {Necip Fazil Ayan} and {Shruti Bhosale} and {Sergey Edunov} and {Angela Fan} and {Cynthia Gao} and {Vedanuj Goswami} and {Francisco Guzm'an} and {Philipp Koehn} and {Alexandre Mourachko} and {C. Ropers} and {Safiyyah Saleem} and {Holger Schwenk} and {Jeff Wang}},
     year = 2022,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/e19b54ad4c1c8af045069e9cac350ffc2ce60e1a},
     }

385. W. G. C. Bandara and Vishal M. Patel, “A Transformer-Based Siamese Network for Change Detection,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
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     @inproceedings{245668909,
     title = {A Transformer-Based Siamese Network for Change Detection},
     author = {{W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2022,
     month = {1},
     booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
     url = {https://www.semanticscholar.org/paper/ef3b15260a610473c95662f5df2c195ac19f64d6},
     }

386. Nithin Gopalakrishnan Nair, Kangfu Mei, and Vishal M. Patel, “A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration,” in International Conference on Information Photonics, 2022.
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     @inproceedings{248239720,
     title = {A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration},
     author = {{Nithin Gopalakrishnan Nair} and {Kangfu Mei} and {Vishal M. Patel}},
     year = 2022,
     month = {4},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/be3eb6827c645f176e204dffb5d740e5281dd67c},
     }

387. Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, and A. Yuille, “VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis,” in International Conference on Learning Representations, 2022.
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     @inproceedings{249209554,
     title = {VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis},
     author = {{Angtian Wang} and {Peng Wang} and {Jian Sun} and {Adam Kortylewski} and {A. Yuille}},
     year = 2022,
     month = {5},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/31e79b62a9483dcdf2575603469e6ff888e7f234},
     }

388. Yu Zeng, Zhe Lin, and Vishal M. Patel, “Shape-guided Object Inpainting,” in arXiv.org, 2022.
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     @inproceedings{248228101,
     title = {Shape-guided Object Inpainting},
     author = {{Yu Zeng} and {Zhe Lin} and {Vishal M. Patel}},
     year = 2022,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/69286603f2dd6037634921e1247543e30fe1756d},
     }

389. Cheng Peng and R. Chellappa, “PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images,” in AAAI Conference on Artificial Intelligence, 2022.
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     @inproceedings{251622408,
     title = {PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images},
     author = {{Cheng Peng} and {R. Chellappa}},
     year = 2022,
     month = {8},
     booktitle = {AAAI Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/c900f690fdab5d17b0253d4362e7f1a7d9d2d495},
     }

390. A. Hussein, S. A. Chowdhury, Ahmed Abdelali, N. Dehak, Ahmed M. Ali, and S. Khudanpur, “Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition,” in Spoken Language Technology Workshop, 2022.
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     @inproceedings{255595965,
     title = {Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition},
     author = {{A. Hussein} and {S. A. Chowdhury} and {Ahmed Abdelali} and {N. Dehak} and {Ahmed M. Ali} and {S. Khudanpur}},
     year = 2022,
     month = {1},
     booktitle = {Spoken Language Technology Workshop},
     url = {https://www.semanticscholar.org/paper/3c00e6cc82b49f046b5f36e5d5f8aa4af68cad5a},
     }

391. Pengfei Guo, Yiqun Mei, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer,” in IEEE Transactions on Medical Imaging, 2022.
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     @inproceedings{246240294,
     title = {ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer},
     author = {{Pengfei Guo} and {Yiqun Mei} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
     year = 2022,
     month = {1},
     booktitle = {IEEE Transactions on Medical Imaging},
     url = {https://www.semanticscholar.org/paper/a54467e6d5df7727c1c0338dd79449dcc78c406e},
     }

392. B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, Gary S. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, B. Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. Mccradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Author Correction: Regenerative and restorative medicine for eye disease,” in Nature Network Boston, 2022.
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     title = {Author Correction: Regenerative and restorative medicine for eye disease},
     author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {Gary S. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {B. Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. Mccradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
     year = 2022,
     month = {8},
     booktitle = {Nature Network Boston},
     url = {https://www.semanticscholar.org/paper/a22215acadb4ad4ec04624025021023acf7261d6},
     }

393. Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, A. Yuille, Yuyin Zhou, and Cihang Xie, “A Simple Data Mixing Prior for Improving Self-Supervised Learning,” in Computer Vision and Pattern Recognition, 2022.
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     @inproceedings{249037240,
     title = {A Simple Data Mixing Prior for Improving Self-Supervised Learning},
     author = {{Sucheng Ren} and {Huiyu Wang} and {Zhengqi Gao} and {Shengfeng He} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
     year = 2022,
     month = {6},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/5ed5dcb0763af9e6283dcdcf4af75248d9d19c95},
     }

394. B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, Gary S. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, B. Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. Mccradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in Nature Network Boston, 2022.
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     @inproceedings{248890002,
     title = {Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI},
     author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {Gary S. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {B. Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. Mccradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
     year = 2022,
     month = {5},
     booktitle = {Nature Network Boston},
     url = {https://www.semanticscholar.org/paper/83b6a76ba5112d27bdbfca3efd2ed918d8e73db5},
     }

395. A. Kala, E. McCollum, and Mounya Elhilali, “Implications of clinical variability on computer-aided lung auscultation classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022.
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     @inproceedings{252165718,
     title = {Implications of clinical variability on computer-aided lung auscultation classification},
     author = {{A. Kala} and {E. McCollum} and {Mounya Elhilali}},
     year = 2022,
     month = {7},
     booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
     url = {https://www.semanticscholar.org/paper/f97aa46f0602e85f4254933ad709f8fd1a4ab35f},
     }

396. R. Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel, “Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN,” in International Conference on Pattern Recognition, 2022.
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     @inproceedings{248377080,
     title = {Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN},
     author = {{R. Yasarla} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
     year = 2022,
     month = {4},
     booktitle = {International Conference on Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/ee48b57139e1d84c60926796195f5f77c2d8b1db},
     }

397. Suzanna Sia, Kokil Jaidka, Niyati Chayya, and Kevin Duh, “Modeling Constraints Can Identify Winning Arguments in Multi-Party Interactions (Student Abstract),” in AAAI Conference on Artificial Intelligence, 2022.
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     @inproceedings{250298720,
     title = {Modeling Constraints Can Identify Winning Arguments in Multi-Party Interactions (Student Abstract)},
     author = {{Suzanna Sia} and {Kokil Jaidka} and {Niyati Chayya} and {Kevin Duh}},
     year = 2022,
     month = {6},
     booktitle = {AAAI Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/da88a7e2b2187fc230b61f36752dbf396be9ce32},
     }

398. Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, Elvira Stellato, G. Carrafiello, Vishal M. Patel, and H. Roth, “Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation,” in European Conference on Computer Vision, 2022.
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     @inproceedings{247447734,
     title = {Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation},
     author = {{Pengfei Guo} and {Dong Yang} and {Ali Hatamizadeh} and {An Xu} and {Ziyue Xu} and {Wenqi Li} and {Can Zhao} and {Daguang Xu} and {S. Harmon} and {E. Turkbey} and {B. Turkbey} and {B. Wood} and {F. Patella} and {Elvira Stellato} and {G. Carrafiello} and {Vishal M. Patel} and {H. Roth}},
     year = 2022,
     month = {3},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/ea8889c3bbca75fcdd71ba60068df014dfb7d861},
     }

399. Shraman Pramanick, E. Nowara, Joshua Gleason, C. Castillo, and R. Chellappa, “Where in the World is this Image? Transformer-based Geo-localization in the Wild,” in European Conference on Computer Vision, 2022.
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     @inproceedings{248476325,
     title = {Where in the World is this Image? Transformer-based Geo-localization in the Wild},
     author = {{Shraman Pramanick} and {E. Nowara} and {Joshua Gleason} and {C. Castillo} and {R. Chellappa}},
     year = 2022,
     month = {4},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/1889dfb7c30f2b9f8e9d4026ca71ec10caa449af},
     }

400. Sangwook Park and Mounya Elhilali, “Time-Balanced Focal Loss for Audio Event Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
     [BibTeX] [Link]

     @inproceedings{249437208,
     title = {Time-Balanced Focal Loss for Audio Event Detection},
     author = {{Sangwook Park} and {Mounya Elhilali}},
     year = 2022,
     month = {5},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/62b7aa0300a9ebc3d494629579a4a051874b82a8},
     }

401. Piotr Żelasko, Siyuan Feng, Laureano Moro Velázquez, A. Abavisani, Saurabhchand Bhati, O. Scharenborg, M. Hasegawa-Johnson, and N. Dehak, “Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition,” in Computer Speech and Language, 2022.
     [BibTeX] [Link]

     @inproceedings{246294754,
     title = {Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition},
     author = {{Piotr Żelasko} and {Siyuan Feng} and {Laureano Moro Velázquez} and {A. Abavisani} and {Saurabhchand Bhati} and {O. Scharenborg} and {M. Hasegawa-Johnson} and {N. Dehak}},
     year = 2022,
     month = {1},
     booktitle = {Computer Speech and Language},
     url = {https://www.semanticscholar.org/paper/9da09ca7192a7546728575b2c0dfb923a36f110f},
     }

402. Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{249605363,
     title = {Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models},
     author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2022,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/c6480d46777da8f0e5fa6e65760f0adec31e4bff},
     }

403. R. Wicks and K. Duh, “The Effects of Language Token Prefixing for Multilingual Machine Translation,” in Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Online only, 2022, p. 148–153.
     [BibTeX] [Abstract] [Link]
     

     Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models either translating from or into many languages. The model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. The location and content of the prefix can vary and many use different approaches without much justification towards one approach or another. As a guidance to future researchers and directions for future work, we present a series of experiments that show how the positioning and type of a target language prefix token effects translation performance. We show that source side prefixes improve performance. Further, we find that the best language information to denote via tokens depends on the supported language set.

     @inproceedings{wicks-duh-2022-effects,
     title = "The Effects of Language Token Prefixing for Multilingual Machine Translation",
     author = "Wicks, Rachel and
     Duh, Kevin",
     editor = "He, Yulan and
     Ji, Heng and
     Li, Sujian and
     Liu, Yang and
     Chang, Chua-Hui",
     booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
     month = nov,
     year = "2022",
     address = "Online only",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2022.aacl-short.19",
     pages = "148--153",
     abstract = "Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models either translating from or into many languages. The model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. The location and content of the prefix can vary and many use different approaches without much justification towards one approach or another. As a guidance to future researchers and directions for future work, we present a series of experiments that show how the positioning and type of a target language prefix token effects translation performance. We show that source side prefixes improve performance. Further, we find that the best language information to denote via tokens depends on the supported language set.",
     }

404. Qihao Liu, Yi Zhang, S. Bai, and A. Yuille, “Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation,” in European Conference on Computer Vision, 2022.
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     @inproceedings{251223772,
     title = {Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation},
     author = {{Qihao Liu} and {Yi Zhang} and {S. Bai} and {A. Yuille}},
     year = 2022,
     month = {7},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/79743618fd9bc1e249e8b9df6ddde77b5e29e84f},
     }

405. W. G. C. Bandara, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection.” 2022.
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     @inproceedings{249953553,
     title = {DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection},
     author = {{W. G. C. Bandara} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
     year = 2022,
     month = {6},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/f278eaffb6ee792858ecdcb7209986542d018269},
     }

406. Ayah Zirikly, Bart Desmet, Denis R. Newman-Griffis, E. Marfeo, C. McDonough, Howard Goldman, and L. Chan, “Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case,” in JMIR Medical Informatics, 2022.
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     @inproceedings{247520370,
     title = {Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case},
     author = {{Ayah Zirikly} and {Bart Desmet} and {Denis R. Newman-Griffis} and {E. Marfeo} and {C. McDonough} and {Howard Goldman} and {L. Chan}},
     year = 2022,
     month = {3},
     booktitle = {JMIR Medical Informatics},
     url = {https://www.semanticscholar.org/paper/66ce3e5f86256fb9b54ab94457b3aa6a0080e6b2},
     }

407. Gerardo Flores, George H. Chen, T. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce Ho, and Tristan Naumann, “Conference on Health, Inference, and Learning (CHIL) 2022,” in ACM Conference on Health, Inference, and Learning, 2022.
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     @inproceedings{261102271,
     title = {Conference on Health, Inference, and Learning (CHIL) 2022},
     author = {{Gerardo Flores} and {George H. Chen} and {T. Pollard} and {Ayah Zirikly} and {Michael C. Hughes} and {Tasmie Sarker} and {Joyce Ho} and {Tristan Naumann}},
     year = 2022,
     booktitle = {ACM Conference on Health, Inference, and Learning},
     url = {https://www.semanticscholar.org/paper/20d7a0ea43dfc3c086fd41ca90f8885ea892f965},
     }

408. Junfeng Wu, Qihao Liu, Yi Jiang, S. Bai, A. Yuille, and Xiang Bai, “In Defense of Online Models for Video Instance Segmentation,” in European Conference on Computer Vision, 2022.
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     @inproceedings{250918749,
     title = {In Defense of Online Models for Video Instance Segmentation},
     author = {{Junfeng Wu} and {Qihao Liu} and {Yi Jiang} and {S. Bai} and {A. Yuille} and {Xiang Bai}},
     year = 2022,
     month = {7},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/65edfa85e5e665d51540a2c7ae1bcb6381793f68},
     }

409. Xiangyu Zhang, Zhanhong He, Shuyu Li, R. Togneri, and Leibny Paola García-Perera, “Investigating self-supervised learning for lyrics recognition,” in arXiv.org, 2022.
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     @inproceedings{252531266,
     title = {Investigating self-supervised learning for lyrics recognition},
     author = {{Xiangyu Zhang} and {Zhanhong He} and {Shuyu Li} and {R. Togneri} and {Leibny Paola García-Perera}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/6632436fd0a465c7b1399c503396233eb9d88b0e},
     }

410. Orion Weller, Aleem Khan, Nathaniel Weir, Dawn J Lawrie, and Benjamin Van Durme, “Defending Against Poisoning Attacks in Open-Domain Question Answering,” in arXiv.org, 2022.
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     @inproceedings{254877071,
     title = {Defending Against Poisoning Attacks in Open-Domain Question Answering},
     author = {{Orion Weller} and {Aleem Khan} and {Nathaniel Weir} and {Dawn J Lawrie} and {Benjamin Van Durme}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7e44002c4f78458987a90dc7a0408d60dd5cdb7c},
     }

411. Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, A. Yuille, and Tao Kong, “Image BERT Pre-training with Online Tokenizer,” in International Conference on Learning Representations, 2022.
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     @inproceedings{251647228,
     title = {Image BERT Pre-training with Online Tokenizer},
     author = {{Jinghao Zhou} and {Chen Wei} and {Huiyu Wang} and {Wei Shen} and {Cihang Xie} and {A. Yuille} and {Tao Kong}},
     year = 2022,
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/ff169d09a933756e8798021dbf9e24a0bbfd9b38},
     }

412. Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser,” in Interspeech, 2022.
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     @inproceedings{252346818,
     title = {Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser},
     author = {{Sonal Joshi} and {Saurabh Kataria} and {Yiwen Shao} and {Piotr Żelasko} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}},
     year = 2022,
     month = {9},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/b8c3c97f239a1048b460d659a14110cc7f7a499e},
     }

413. R. Yasarla, Renliang Weng, Wongun Choi, Vishal M. Patel, and Amir Sadeghian, “3SD: Self-Supervised Saliency Detection With No Labels,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
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     @inproceedings{247318765,
     title = {3SD: Self-Supervised Saliency Detection With No Labels},
     author = {{R. Yasarla} and {Renliang Weng} and {Wongun Choi} and {Vishal M. Patel} and {Amir Sadeghian}},
     year = 2022,
     month = {3},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/2a78e1c0412cbcc851ba60224c15c501debe2049},
     }

414. Jeya Maria Jose Valanarasu, Pengfei Guo, VS Vibashan, and Vishal M. Patel, “On-the-Fly Test-time Adaptation for Medical Image Segmentation,” in International Conference on Medical Imaging with Deep Learning, 2022.
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     @inproceedings{247411201,
     title = {On-the-Fly Test-time Adaptation for Medical Image Segmentation},
     author = {{Jeya Maria Jose Valanarasu} and {Pengfei Guo} and {VS Vibashan} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {International Conference on Medical Imaging with Deep Learning},
     url = {https://www.semanticscholar.org/paper/3b8c4a2a005df6dc7e9fb0b9e2e81a887ace5a6c},
     }

415. Jaejin Cho, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Non-Contrastive Self-Supervised Learning for Utterance-Level Information Extraction From Speech,” in IEEE Journal on Selected Topics in Signal Processing, 2022.
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     @inproceedings{251462729,
     title = {Non-Contrastive Self-Supervised Learning for Utterance-Level Information Extraction From Speech},
     author = {{Jaejin Cho} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2022,
     month = {8},
     booktitle = {IEEE Journal on Selected Topics in Signal Processing},
     url = {https://www.semanticscholar.org/paper/7504aeee4c344c4cf9c6fc071dcc4b4b34d124cc},
     }

416. Mo Zhou and Vishal M. Patel, “On Trace of PGD-Like Adversarial Attacks,” in arXiv.org, 2022.
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     @inproceedings{248887310,
     title = {On Trace of PGD-Like Adversarial Attacks},
     author = {{Mo Zhou} and {Vishal M. Patel}},
     year = 2022,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/90d02089aaf88b621880a036a2cc4c5924f7102c},
     }

417. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Auditing Predictive Uncertainty Under Covariate Shift,” in Neural Information Processing Systems, 2022.
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     @inproceedings{254017908,
     title = {JAWS: Auditing Predictive Uncertainty Under Covariate Shift},
     author = {{Drew Prinster} and {Anqi Liu} and {S. Saria}},
     year = 2022,
     month = {7},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/4fb13897dad166844ca020e3cef1563b8dc81775},
     }

418. Jian Xue, Peidong Wang, Jinyu Li, Matt Post, and Yashesh Gaur, “Large-Scale Streaming End-to-End Speech Translation with Neural Transducers,” in Interspeech, 2022.
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     @inproceedings{248118691,
     title = {Large-Scale Streaming End-to-End Speech Translation with Neural Transducers},
     author = {{Jian Xue} and {Peidong Wang} and {Jinyu Li} and {Matt Post} and {Yashesh Gaur}},
     year = 2022,
     month = {4},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/5a5704382fd8c980937e10618713d641c846b313},
     }

419. Kelly Marchisio, Conghao Xiong, and Philipp Koehn, “Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings,” in Conference of the Association for Machine Translation in the Americas, 2022.
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     @inproceedings{252186400,
     title = {Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings},
     author = {{Kelly Marchisio} and {Conghao Xiong} and {Philipp Koehn}},
     year = 2022,
     booktitle = {Conference of the Association for Machine Translation in the Americas},
     url = {https://www.semanticscholar.org/paper/4768c7f83f1c4fbb4fd98d9b4237ab483a8bc4b2},
     }

420. Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi, “COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics,” in Neural Information Processing Systems, 2022.
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     @inproceedings{247058662,
     title = {COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics},
     author = {{Lianhui Qin} and {Sean Welleck} and {Daniel Khashabi} and {Yejin Choi}},
     year = 2022,
     month = {2},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/4a6a65968a8eb8c09ffb57a7774ddabb596565b1},
     }

421. Geoffrey M. Gray, L. Ahumada, Ayah Zirikly, Masoud Rouhizadeh, Tom M. Richards, and E. Hatef, “Application of Natural Language Processing to Identify Social Needs from The Electronic Health Record’s Free-Text Notes,” in American Medical Informatics Association Annual Symposium, 2022.
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     @inproceedings{256665944,
     title = {Application of Natural Language Processing to Identify Social Needs from The Electronic Health Record's Free-Text Notes},
     author = {{Geoffrey M. Gray} and {L. Ahumada} and {Ayah Zirikly} and {Masoud Rouhizadeh} and {Tom M. Richards} and {E. Hatef}},
     year = 2022,
     booktitle = {American Medical Informatics Association Annual Symposium},
     url = {https://www.semanticscholar.org/paper/9b579eeb9351a75c1c491f22f28ae36bdadded28},
     }

422. R. Yasarla, C. Priebe, and Vishal M. Patel, “ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images,” in European Conference on Computer Vision, 2022.
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     title = {ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images},
     author = {{R. Yasarla} and {C. Priebe} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/0bf4fd83f0f17b0fa94c18631a28d52ce5ea6042},
     }

423. Kangfu Mei, Yiqun Mei, and Vishal M. Patel, “Thermal to Visible Image Synthesis Under Atmospheric Turbulence,” in International Conference on Information Photonics, 2022.
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     @inproceedings{248006370,
     title = {Thermal to Visible Image Synthesis Under Atmospheric Turbulence},
     author = {{Kangfu Mei} and {Yiqun Mei} and {Vishal M. Patel}},
     year = 2022,
     month = {4},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/0a123eb1a768cc151ff9ebb004cc2461414a53a3},
     }

424. Jonah P. Sengupta, M. Villemur, P. Pouliquen, P. Julián, and A. Andreou, “Embedded Processing Pipeline Exploration For Neuromorphic Event Based Perceptual Systems,” in International Symposium on Circuits and Systems, 2022.
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     title = {Embedded Processing Pipeline Exploration For Neuromorphic Event Based Perceptual Systems},
     author = {{Jonah P. Sengupta} and {M. Villemur} and {P. Pouliquen} and {P. Julián} and {A. Andreou}},
     year = 2022,
     month = {5},
     booktitle = {International Symposium on Circuits and Systems},
     url = {https://www.semanticscholar.org/paper/42845a69a8efd8e8dc7b697c3ce0a4a8f6dfae86},
     }

425. Matthew Maciejewski, Jing Shi, Shinji Watanabe, and S. Khudanpur, “A dilemma of ground truth in noisy speech separation and an approach to lessen the impact of imperfect training data,” in Computer Speech and Language, 2022.
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     title = {A dilemma of ground truth in noisy speech separation and an approach to lessen the impact of imperfect training data},
     author = {{Matthew Maciejewski} and {Jing Shi} and {Shinji Watanabe} and {S. Khudanpur}},
     year = 2022,
     month = {6},
     booktitle = {Computer Speech and Language},
     url = {https://www.semanticscholar.org/paper/e3b6ab2d2e1a0e734bf505fbb34dc6fe723ab37e},
     }

426. Nathaniel Weir and Benjamin Van Durme, “NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning.” 2022.
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     title = {NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning},
     author = {{Nathaniel Weir} and {Benjamin Van Durme}},
     year = 2022,
     month = {9},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/52aa0f1347459ab9dec1655fc8fa29866919f624},
     }

427. Bruce Y Lee, J. Ordovás, E. Parks, Cheryl Anderson, A. Barabási, S. Clinton, K. de la Haye, V. Duffy, P. Franks, Elizabeth M Ginexi, K. Hammond, Erin C. Hanlon, Michael Hittle, E. Ho, A. Horn, R. Isaacson, P. Mabry, S. Malone, Corby K. Martin, J. Mattei, S. Meydani, Lorene M. Nelson, M. Neuhouser, Brendan Parent, N. Pronk, H. Roche, S. Saria, F. Scheer, E. Segal, M. Sevick, T. Spector, Linda Van Horn, K. Varady, V. S. Voruganti, and Marie F Martinez, “Research gaps and opportunities in precision nutrition: an NIH workshop report.,” in American Journal of Clinical Nutrition, 2022.
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     title = {Research gaps and opportunities in precision nutrition: an NIH workshop report.},
     author = {{Bruce Y Lee} and {J. Ordovás} and {E. Parks} and {Cheryl Anderson} and {A. Barabási} and {S. Clinton} and {K. de la Haye} and {V. Duffy} and {P. Franks} and {Elizabeth M Ginexi} and {K. Hammond} and {Erin C. Hanlon} and {Michael Hittle} and {E. Ho} and {A. Horn} and {R. Isaacson} and {P. Mabry} and {S. Malone} and {Corby K. Martin} and {J. Mattei} and {S. Meydani} and {Lorene M. Nelson} and {M. Neuhouser} and {Brendan Parent} and {N. Pronk} and {H. Roche} and {S. Saria} and {F. Scheer} and {E. Segal} and {M. Sevick} and {T. Spector} and {Linda Van Horn} and {K. Varady} and {V. S. Voruganti} and {Marie F Martinez}},
     year = 2022,
     month = {9},
     booktitle = {American Journal of Clinical Nutrition},
     url = {https://www.semanticscholar.org/paper/31b65b7ccc0ed5ba975753c8b0ba8da8df28a09c},
     }

428. Christos Sapsanis, M. Sophocleous, A. Andreou, and J. Georgiou, “Trade-Offs in Sensor Systems Design: A Tutorial,” in IEEE Sensors Journal, 2022.
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     @inproceedings{246805990,
     title = {Trade-Offs in Sensor Systems Design: A Tutorial},
     author = {{Christos Sapsanis} and {M. Sophocleous} and {A. Andreou} and {J. Georgiou}},
     year = 2022,
     month = {6},
     booktitle = {IEEE Sensors Journal},
     url = {https://www.semanticscholar.org/paper/07cfa0c80e6ef73a2aa5fab377c2f698ed476341},
     }

429. R. Arora, Raef Bassily, Tom’as Gonz’alez, Crist’obal Guzm’an, Michael Menart, and Enayat Ullah, “Faster Rates of Convergence to Stationary Points in Differentially Private Optimization,” in International Conference on Machine Learning, 2022.
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     @inproceedings{249282662,
     title = {Faster Rates of Convergence to Stationary Points in Differentially Private Optimization},
     author = {{R. Arora} and {Raef Bassily} and {Tom'as Gonz'alez} and {Crist'obal Guzm'an} and {Michael Menart} and {Enayat Ullah}},
     year = 2022,
     month = {6},
     booktitle = {International Conference on Machine Learning},
     url = {https://www.semanticscholar.org/paper/6f85ad4e04fc157ed5b499e348972f188a39cd10},
     }

430. Yunjuan Wang, Enayat Ullah, Poorya Mianjy, and R. Arora, “Adversarial Robustness is at Odds with Lazy Training,” in Neural Information Processing Systems, 2022.
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     @inproceedings{250243820,
     title = {Adversarial Robustness is at Odds with Lazy Training},
     author = {{Yunjuan Wang} and {Enayat Ullah} and {Poorya Mianjy} and {R. Arora}},
     year = 2022,
     month = {6},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/e2100da66c556f6ce3fbe904696fb0cec2aca2bf},
     }

431. A. Shelton, E. Davis, Cathryn S. Cortesa, Jonathan D. Jones, Gregory Hager, S. Khudanpur, and B. Landau, “Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability,” in Cognitive Sciences, 2022.
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     @inproceedings{246239741,
     title = {Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability},
     author = {{A. Shelton} and {E. Davis} and {Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {B. Landau}},
     year = 2022,
     month = {1},
     booktitle = {Cognitive Sciences},
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432. Pirazh Khorramshahi, Vineet Shenoy, M. Pack, and R. Chellappa, “Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking,” in arXiv.org, 2022.
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433. VS Vibashan, Poojan Oza, and Vishal M. Patel, “Towards Online Domain Adaptive Object Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
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434. Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, H. Lai, I. Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, M. Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi, Noah A. Smith, and Daniel Khashabi, “Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks,” in arXiv.org, 2022.
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435. Sandeep Reddy Kothinti and Mounya Elhilali, “Temporal Contrastive-Loss for Audio Event Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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437. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning,” in International Journal of Computer Vision, 2022.
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438. W. G. C. Bandara and Vishal M. Patel, “HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening,” in Computer Vision and Pattern Recognition, 2022.
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440. Alex Troy Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi, “When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories,” in arXiv.org, 2022.
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441. Zhuowan Li, Cihang Xie, Benjamin Van Durme, Alan Yuille Johns Hopkins University, U. California, and Santa Cruz, “Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?,” in arXiv.org, 2022.
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442. H. Kayser, H. Hermansky, and B. Meyer, “Spatial speech detection for binaural hearing aids using deep phoneme classifiers,” in Acta acustica. European Acoustics Association, 2022.
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443. VS Vibashan, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Target and Task specific Source-Free Domain Adaptive Image Segmentation,” in arXiv.org, 2022.
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444. Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens, R. Arora, and I-J. Wang, “A Risk-Sensitive Approach to Policy Optimization,” in AAAI Conference on Artificial Intelligence, 2022.
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445. H. E. Echo Wang, M. Landers, R. Adams, Adarsh Subbaswamy, Hadi Kharrazi, D. Gaskin, and S. Saria, “A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models,” in J. Am. Medical Informatics Assoc., 2022.
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446. Shao-Yuan Lo, Wei Wang, Jim Thomas, Jingjing Zheng, Vishal M. Patel, and Cheng-Hao Kuo, “Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation,” in IEEE/RJS International Conference on Intelligent RObots and Systems, 2022.
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447. Tom B. Brown, Benjamin Mann, Nick Ryder, Jared D Subbiah, Prafulla Kaplan, A. Dhariwal, Chris Callison-Burch, Miles Osborne, J. Devlin, Ming-Wei Chang, Kenton Lee, Daniel Khashabi, Sewon Min, and Tushar Khot, “A Study of Pre-trained Language Models for Analogy Generation.” 2022.
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448. Hexin Liu, Leibny Paola García Perera, Andy W. H. Khong, S. Styles, and S. Khudanpur, “PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification,” in Interspeech, 2022.
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451. Shota Horiguchi, Shinji Watanabe, Leibny Paola García-Perera, Yuki Takashima, and Y. Kawaguchi, “Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2022.
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454. Wufei Ma, Angtian Wang, A. Yuille, and Adam Kortylewski, “Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features,” in European Conference on Computer Vision, 2022.
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456. Samik Sadhu and H. Hermansky, “Blind Signal Dereverberation for Machine Speech Recognition,” in arXiv.org, 2022.
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457. Nils Holzenberger, Yunmo Chen, and Benjamin Van Durme, “Asking the Right Questions in Low Resource Template Extraction,” in arXiv.org, 2022.
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458. W. G. C. Bandara and Vishal M. Patel, “Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images,” in arXiv.org, 2022.
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459. Subhro Roy, Sam Thomson, Tongfei Chen, Richard Shin, Adam Pauls, J. Eisner, and Benjamin Van Durme, “BenchCLAMP: A Benchmark for Evaluating Language Models on Semantic Parsing,” in arXiv.org, 2022.
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464. Mo Zhou and Vishal M. Patel, “Enhancing Adversarial Robustness for Deep Metric Learning,” in Computer Vision and Pattern Recognition, 2022.
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465. Samik Sadhu and H. Hermansky, “Importance of Different Temporal Modulations of Speech: a Tale of two Perspectives,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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     title = {Importance of Different Temporal Modulations of Speech: a Tale of two Perspectives},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2022,
     month = {3},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/3ce501d4d81d9a78c2e506df7f6de0d79ca91a5b},
     }

466. Jieneng Chen, Shuyang Sun, Ju He, Philip H. S. Torr, A. Yuille, and Song Bai, “Supplementary Materials: ”TransMix: Attend to Mix for Vision Transformers”.” 2022.
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     year = 2022,
     booktitle = {},
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     }

467. Nupoor Gandhi, Anjalie Field, and Emma Strubell, “Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution,” in arXiv.org, 2022.
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     }

468. Pirazh Khorramshahi, Vineet Shenoy, and R. Chellappa, “Scalable Vehicle Re-Identification via Self-Supervision,” in arXiv.org, 2022.
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469. Chan Young Park, Julia Mendelsohn, Anjalie Field, and Yulia Tsvetkov, “VoynaSlov: A Data Set of Russian Social Media Activity during the 2022 Ukraine-Russia War,” in arXiv.org, 2022.
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470. R. Yasarla and Vishal M. Patel, “CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2022.
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     author = {{R. Yasarla} and {Vishal M. Patel}},
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     month = {4},
     booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
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471. Nithin Gopalakrishnan Nair and Vishal M. Patel, “T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2022.
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     author = {{Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
     year = 2022,
     month = {9},
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     }

472. H. Goldman, Julia Porcino, Guy Divita, Ayah Zirikly, Bart Desmet, Maryanne Sacco, E. Marfeo, Christine M. McDonough, E. Rasch, and L. Chan, “Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.,” in Psychiatric Services, 2022.
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     author = {{H. Goldman} and {Julia Porcino} and {Guy Divita} and {Ayah Zirikly} and {Bart Desmet} and {Maryanne Sacco} and {E. Marfeo} and {Christine M. McDonough} and {E. Rasch} and {L. Chan}},
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473. Anastasia Razdaibiedina, A. Khetan, Zohar S. Karnin, Daniel Khashabi, Vishaal Kapoor, and V. Madan, “Representation Projection Invariance Mitigates Representation Collapse,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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474. Samik Sadhu and H. Hermansky, “Complex Frequency Domain Linear Prediction: A Tool to Compute Modulation Spectrum of Speech,” in Interspeech, 2022.
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475. Cheng Peng, Pengfei Guo, S. K. Zhou, Vishal M. Patel, and Ramalingam Chellappa, “Towards performant and reliable undersampled MR reconstruction via diffusion model sampling,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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476. Yukun Feng, Feng Li, Ziang Song, Boyuan Zheng, and Philipp Koehn, “Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation,” in NAACL-HLT, 2022.
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477. Thomas Wolf, Lysandre Debut, Julien Victor Sanh, Clement Chaumond, Anthony Delangue, Pier-339 Moi, Clara ric Cistac, Yacine Ma, Julien Jernite, Plu, Teven Xu, Sylvain Le Scao, Gugger, Mariama, Quentin Drame, M. LhoestAlexander, Rush, Michael Miller Yoder, Sopan Khosla, Qinlan Shen, Ben Zhou, Qiang Ning, Daniel Khashabi, Kyle Richardson, and Tushar Khot, “Cross-Lingual Speaker Identification from Weak Local Evidence.” 2022.
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478. G. Botev, A. D. McCarthy, W. Wu, and D. Yarowsky, “Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages,” in Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, 2022, p. 5309–5326.
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     This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.

     @inproceedings{botev-etal-2022-deciphering,
     title = "Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages",
     author = "Botev, Georgie and
     McCarthy, Arya D. and
     Wu, Winston and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     Huang, Chu-Ren and
     Kim, Hansaem and
     Pustejovsky, James and
     Wanner, Leo and
     Choi, Key-Sun and
     Ryu, Pum-Mo and
     Chen, Hsin-Hsi and
     Donatelli, Lucia and
     Ji, Heng and
     Kurohashi, Sadao and
     Paggio, Patrizia and
     Xue, Nianwen and
     Kim, Seokhwan and
     Hahm, Younggyun and
     He, Zhong and
     Lee, Tony Kyungil and
     Santus, Enrico and
     Bond, Francis and
     Na, Seung-Hoon",
     booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
     month = oct,
     year = "2022",
     address = "Gyeongju, Republic of Korea",
     publisher = "International Committee on Computational Linguistics",
     url = "https://aclanthology.org/2022.coling-1.472",
     pages = "5309--5326",
     abstract = "This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.",
     }

479. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in arXiv.org, 2022.
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     author = {{Drew Prinster} and {Anqi Liu} and {S. Saria}},
     year = 2022,
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     }

480. Jaejin Cho, R. Pappagari, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations,” in Interspeech, 2022.
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     year = 2022,
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     }

481. VS Vibashan, Poojan Oza, and Vishal M. Patel, “Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection,” in Computer Vision and Pattern Recognition, 2022.
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     author = {{VS Vibashan} and {Poojan Oza} and {Vishal M. Patel}},
     year = 2022,
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     }

482. Jeya Maria Jose Valanarasu, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, J. Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, and Vishal M. Patel, “Interactive Portrait Harmonization,” in International Conference on Learning Representations, 2022.
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483. Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, A. Yuille, Yuyin Zhou, and Cihang Xie, “Masked Autoencoders Enable Efficient Knowledge Distillers,” in Computer Vision and Pattern Recognition, 2022.
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484. Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation,” in Computer Vision and Pattern Recognition, 2022.
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485. Aimon Rahman, Jeya Maria Jose Valanarasu, I. Hacihaliloglu, and Vishal M. Patel, “Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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486. Subhro Roy, Sam Thomson, Tongfei Chen, Richard Shin, Adam Pauls, Jason Eisner, Benjamin Van Durme, Microsoft Semantic Machines, and Scaled Cognition, “BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing,” in Neural Information Processing Systems, 2022.
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487. Haoran Xu and Kenton Murray, “Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer,” in NAACL-HLT, 2022.
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     }

488. K. Henry, R. Adams, Cassandra Parent, Hossein Soleimani, A. Sridharan, Lauren Johnson, D. Hager, S. Cosgrove, Andrew Markowski, E. Klein, E. Chen, M. Saheed, Maureen Henley, S. Miranda, Katrina Houston, Robert C. Linton, Anushree R Ahluwalia, Albert W. Wu, and S. Saria, “Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing,” in Nature Network Boston, 2022.
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489. Saksham Suri, Saketh Rambhatla, R. Chellappa, and Abhinav Shrivastava, “R-SSL: Region based Semi-Supervised Learning for Sparsely Annotated Object Detection.” 2022.
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490. Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser,” in arXiv.org, 2022.
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491. Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, Piotr Żelasko, and N. Dehak, “Time-Domain Speech Super-Resolution With GAN Based Modeling for Telephony Speaker Verification,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2022.
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     author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {Piotr Żelasko} and {N. Dehak}},
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492. Yiqun Mei, Pengfei Guo, and Vishal M. Patel, “Supplementary File: Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination.” 2022.
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493. Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, A. Yuille, and Cihang Xie, “Fast AdvProp,” in International Conference on Learning Representations, 2022.
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494. J. Villalba, B. J. Borgstrom, Saurabh Kataria, Magdalena Rybicka, C. Castillo, Jaejin Cho, Leibny Paola García-Perera, P. Torres-Carrasquillo, and N. Dehak, “Advances in Cross-Lingual and Cross-Source Audio-Visual Speaker Recognition: The JHU-MIT System for NIST SRE21,” in The Speaker and Language Recognition Workshop, 2022.
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505. Emma Bigelow, S. Saria, B. Piening, B. Curti, A. Dowdell, R. Weerasinghe, C. Bifulco, W. Urba, N. Finkelstein, E. Fertig, A. Baras, N. Zaidi, E. Jaffee, and M. Yarchoan, “A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy,” in Cancer Informatics, 2022.
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506. Malsha V. Perera, W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SAR Despeckling Using Overcomplete Convolutional Networks,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
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507. Yutong Bai, Xinlei Chen, Alexander Kirillov, A. Yuille, and A. Berg, “Point-Level Region Contrast for Object Detection Pre-Training,” in Computer Vision and Pattern Recognition, 2022.
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508. Nithin Gopalakrishnan Nair, Kangfu Mei, and Vishal M. Patel, “AT-DDPM: Restoring Faces Degraded by Atmospheric Turbulence Using Denoising Diffusion Probabilistic Models,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
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509. Harminder Singh, R. Sharma, and R. Arora, “A Novel Dual-band filtenna for 2.4 and 5.8 GHz Wireless Local Area for Network Applications,” in 2022 Interdisciplinary Research in Technology and Management (IRTM), 2022.
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510. Yiwen Shao, J. Villalba, Sonal Joshi, Saurabh Kataria, S. Khudanpur, and N. Dehak, “Chunking Defense for Adversarial Attacks on ASR,” in Interspeech, 2022.
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511. Boyuan Zheng, Patrick Xia, M. Yarmohammadi, and Benjamin Van Durme, “Multilingual Coreference Resolution in Multiparty Dialogue,” in Transactions of the Association for Computational Linguistics, 2022.
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512. Anjalie Field, Chan Young Park, Antônio Theóphilo, J. Watson-Daniels, and Yulia Tsvetkov, “An analysis of emotions and the prominence of positivity in #BlackLivesMatter tweets,” in Proceedings of the National Academy of Sciences of the United States of America, 2022.
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513. Sonal Joshi, Saurabh Kataria, J. Villalba, and N. Dehak, “AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification,” in Interspeech, 2022.
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514. Subhrajit Roy, S. Pfohl, G. Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, and Emily Alsentzer, “Machine Learning for Health (ML4H) 2022,” in ML4H@NeurIPS, 2022.
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515. Aimon Rahman, W. G. C. Bandara, Jeya Maria Jose Valanarasu, I. Hacihaliloglu, and Vishal M. Patel, “Orientation-guided Graph Convolutional Network for Bone Surface Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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516. Shao-Yuan Lo and Vishal M. Patel, “Exploring Adversarially Robust Training for Unsupervised Domain Adaptation,” in Asian Conference on Computer Vision, 2022.
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517. Bruce Y Lee, J. Ordovás, E. J. Parks, Cheryl AM Anderson, A. Barabási, S. Clinton, K. Haye, V. Duffy, P. Franks, Elizabeth M Ginexi, K. Hammond, Erin C. Hanlon, Michael Hittle, Emily Ho, A. Horn, R. Isaacson, P. Mabry, Susan E. Malone, Corby K. Martin, J. Mattei, S. Meydani, Lorene M. Nelson, M. Neuhouser, N. Pronk, S. Saria, Frank Ajl Scheer, E. Segal, M. Sevick, T. Spector, Linda B Van Horn, K. Varady, V. S. Voruganti, and Marie F Martinez, “Microsoft Word-nqac237.docx.” 2022.
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518. Sangwook Park, Sandeep Reddy Kothinti, and Mounya Elhilali, “Temporal coding with magnitude-phase regularization for sound event detection,” in Interspeech, 2022.
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519. Martin Sustek, Samik Sadhu, and H. Hermansky, “Dealing with Unknowns in Continual Learning for End-to-end Automatic Speech Recognition,” in Interspeech, 2022.
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520. R. Adams, K. Henry, A. Sridharan, Hossein Soleimani, A. Zhan, Nishi Rawat, Lauren Johnson, D. Hager, S. Cosgrove, Andrew Markowski, E. Klein, E. Chen, M. Saheed, Maureen Henley, S. Miranda, Katrina Houston, Robert C. Linton, Anushree R Ahluwalia, Albert W. Wu, and S. Saria, “Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis,” in Nature Network Boston, 2022.
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521. A. Javaid, Fawzi Zghyer, Chang H Kim, Erin M. Spaulding, Nino Isakadze, Jie Ding, Daniel Kargillis, Yumin Gao, Faisal Rahman, Donald E. Brown, S. Saria, Seth S. Martin, C. Kramer, R. Blumenthal, and F. Marvel, “Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology,” in American Journal of Preventive Cardiology, 2022.
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522. R. Adams, K. Henry, and S. Saria, “Addressing the ‘coin flip model’ and the role of ‘process of care’ variables in the analysis of TREWS,” in medRxiv, 2022.
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523. J. Villalba, B. J. Borgstrom, Saurabh Kataria, Jaejin Cho, P. Torres-Carrasquillo, and N. Dehak, “Advances in Speaker Recognition for Multilingual Conversational Telephone Speech: The JHU-MIT System for NIST SRE20 CTS Challenge,” in The Speaker and Language Recognition Workshop, 2022.
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524. Nathaniel Weir and Benjamin Van Durme, “Dynamic Generation of Interpretable Inference Rules in a Neuro-Symbolic Expert System,” in arXiv.org, 2022.
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525. Christos Sapsanis, M. Villemur, and A. Andreou, “Real Number Modeling of a SAR ADC behavior using SystemVerilog,” in International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, 2022.
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526. Daniel Khashabi, Yeganeh Kordi, and Hannaneh Hajishirzi, “UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training,” in arXiv.org, 2022.
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527. K. Henry, R. Kornfield, A. Sridharan, Robert C. Linton, Catherine Groh, Tony Wang, Albert W. Wu, Bilge Mutlu, and S. Saria, “Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system,” in npj Digital Medicine, 2022.
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528. Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo-Xun Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, A. Yuille, and Mingxing Tan, “DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection,” in Computer Vision and Pattern Recognition, 2022.
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529. Vipul Gupta, Zhuowan Li, Adam Kortylewski, Chenyu Zhang, Yingwei Li, and A. Yuille, “SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering,” in Computer Vision and Pattern Recognition, 2022.
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530. Tasnim Mohiuddin, Philipp Koehn, Vishrav Chaudhary, James Cross, Shruti Bhosale, and Shafiq R. Joty, “Data Selection Curriculum for Neural Machine Translation,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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531. Ramraj Chandradevan, Eugene Yang, M. Yarmohammadi, and Eugene Agichtein, “Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
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532. Daniel E Park, Nora L. Watson, Christopher Focht, D. Feikin, Laura L Hammit, W. A. Brooks, S. Howie, K. Kotloff, O. Levine, S. Madhi, D. Murdoch, K. O’Brien, J. Scott, D. Thea, Tussanee Amorninthapichet, J. Awori, C. Bunthi, B. Ebruke, Mounya Elhilali, Melissa M Higdon, L. Hossain, Y. Jahan, D. Moore, J. Mulindwa, L. Mwananyanda, Sathapana Naorat, Christine Prosperi, S. Thamthitiwat, C. Verwey, K. Jablonski, M. Power, H. Young, M. Deloria Knoll, and E. McCollum, “Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study,” in BMJ Open Respiratory Research, 2022.
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     year = 2022,
     month = {5},
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     url = {https://www.semanticscholar.org/paper/dfedb313d8718de8aa162813060af3e24e8cbe28},
     }

533. Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “kMaX-DeepLab: k-means Mask Transformer.” 2022.
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534. Weiting Tan and Philipp Koehn, “Bitext Mining for Low-Resource Languages via Contrastive Learning,” in arXiv.org, 2022.
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535. Yuyin Zhou, D. Dreizin, Yan Wang, Fengze Liu, Wei-lei Shen, and A. Yuille, “External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data,” in IEEE Transactions on Medical Imaging, 2021.
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536. Kangfu Mei and Vishal M. Patel, “LTT-GAN: Looking Through Turbulence by Inverting GANs,” in IEEE Journal on Selected Topics in Signal Processing, 2021.
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537. Zach Wood-Doughty, Isabel Cachola, and Mark Dredze, “Proxy Model Explanations for Time Series RNNs,” in International Conference on Machine Learning and Applications, 2021.
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538. Chen Wei, Haoqi Fan, Saining Xie, Chaoxia Wu, A. Yuille, and Christoph Feichtenhofer, “Masked Feature Prediction for Self-Supervised Visual Pre-Training,” in Computer Vision and Pattern Recognition, 2021.
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539. Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe L. Lin, and A. Yuille, “Lite Vision Transformer with Enhanced Self-Attention,” in Computer Vision and Pattern Recognition, 2021.
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540. Jingye Chen, Jieneng Chen, Zongwei Zhou, Bin Li, A. Yuille, and Yongyi Lu, “MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification,” in arXiv.org, 2021.
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541. Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jieneng Chen, Shuai Liu, Cheng Yang, and A. Yuille, “PartImageNet: A Large, High-Quality Dataset of Parts,” in European Conference on Computer Vision, 2021.
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542. Ryan T. Scott, E. Antonsen, L. Sanders, Jaden J. A. Hastings, Seung-min Park, Graham Mackintosh, R. Reynolds, A. Hoarfrost, A. Sawyer, C. Greene, Benjamin S. Glicksberg, C. Theriot, D. Berrios, Jack M. Miller, Joel Babdor, Richard Barker, S. Baranzini, A. Beheshti, S. Chalk, Guillermo M. Delgado-Aparicio, M. Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, John Kalantari, Kia Khezeli, S. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, H. Martín, Christopher E. Mason, Mona Matar, G. Mias, J. Myers, Jr., Charlotte A. Nelson, Jonathan Oribello, P. Parsons-Wingerter, R. Prabhu, A. Qutub, J. Rask, Amanda M. Saravia-Butler, S. Saria, N. Singh, Frank Soboczenski, M. Snyder, Karthik Soman, D. V. Valen, K. Venkateswaran, L. Warren, Liz Worthey, Jason H. Yang, M. Zitnik, S. V. C. Kbr, Space Biosciences Division, N. R. Center, M. Field, Ca, USA., Department of Preventive Medicine, Center for Individualized Medicine, Baylor College of Medicine, Houston, Tx, Blue Marble Space Institute of Science, D. Physiology, Biophysics, Weill Cornell Medicine, New York., Ny, Department of Urology, D. Radiology, S. Medicine, Stanford, Bay Area Environmental Research Institute, Mortality ResearchConsulting, Inc., Universities Space Research Association, UC Space Health, D. Surgery, U. California, San Francisco, AI CenterforHealth, D. Biochemistry, Molecular Genetics, U. Medicine, Anschutz Medical Campus, Aurora, Co, Hasso Plattner Institute for Digital Health at Mount Sinai, Department of Genetics, Genomic Sciences, I. A. Sinai, Department of Preventive Medicine, C. Health, Utmb, Galveston, Human Health, Performance Directorate, NASAMarshall Space Flight Center, D. Microbiology, Immunology, Department of Otolaryngology, Head, N. Surgery, University of California San Francisco, The Gilroy AstroBiology Research Group, The University of Wisconsin, Madison, Wi, Weill Institute for Neurosciences, D. Neurology, D. Chemistry, U. Florida, Jacksonville, Fl, D. Analytics, G. I. O. Technology, Lima, Perú, Department of Neuroscience, U. Minnesota, Minneapolis, Mn, Department of Materials Science, College of Materials Science, San Diego State University, San José, Biorelate, Manchester, United Kingdom., Center for Individualized Medicine, D. Surgery, Department of Mathematical Sciences, Mayo Clinic, Rochester, Faculty of Veterinary Medicine, Oral Health Sciences, McGill University, Montreal., Quebec., Canada., Faculty of Veterinary Medicine, Cancer, I. -. London, London, SymbioSeq Llc, Ashburn, Va, Center for Data Driven Discovery, C. I. O. Technology., Pasadena, Waitt Advanced Biophotonics Center, Chan-Zuckerberg Imaging Scientist Fellow, Salk Institute for Biological Studies, La Jolla, Biological Systems, Engineering Division, Lawrence Berkeley National Lab., Berkeley, Doe Agile BioFoundry, Emeryville, Joint BioEnergy Institute, Human Research Program Cross-cutting Computational Model Project, N. R. Center, Cleveland, Oh, Institute for Quantum Science, Engineering, M. Biology, M. University, E. Lansing., Mi, Low Exploration Gravity Technology, AI Matrix Consortium, Department of Biomedical Engineering, U. Texas, San Antonio, UT Health Sciences, Office of the Director, Logyx, Computer Science, Statistics, Health Policy, J. University, Baltimore., Md, Ml, Ai, H. Lab, B. Health, Biotechnology, Planetary Protection Group, Jet propulsion Laboratory, Sphes, Medical Faculty, King’s College London, S. Medicine, Department of Medical Biology, Iss National Laboratory, Center for Space, Melbourne, Uab Center for Computational Biology, Data Science, U. Alabama, Birmingham, Al, Center for Emerging, Re-Emerging Pathogens, Biochemistry, Rutgers New Jersey Medical School, Newark, Nj, Department of Biomedical Informatics, H. School, Harvard Data Science, Broad Institute of Mit, Harvard, Harvard University, Boston, and Ma., “Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health,” in arXiv.org, 2021.
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     @inproceedings{245425181,
     title = {Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health},
     author = {{Ryan T. Scott} and {E. Antonsen} and {L. Sanders} and {Jaden J. A. Hastings} and {Seung-min Park} and {Graham Mackintosh} and {R. Reynolds} and {A. Hoarfrost} and {A. Sawyer} and {C. Greene} and {Benjamin S. Glicksberg} and {C. Theriot} and {D. Berrios} and {Jack M. Miller} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {S. Chalk} and {Guillermo M. Delgado-Aparicio} and {M. Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {John Kalantari} and {Kia Khezeli} and {S. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {H. Martín} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {J. Myers} and {Jr.} and {Charlotte A. Nelson} and {Jonathan Oribello} and {P. Parsons-Wingerter} and {R. Prabhu} and {A. Qutub} and {J. Rask} and {Amanda M. Saravia-Butler} and {S. Saria} and {N. Singh} and {Frank Soboczenski} and {M. Snyder} and {Karthik Soman} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {Jason H. Yang} and {M. Zitnik} and {S. V. C. Kbr} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Tx} and {Blue Marble Space Institute of Science} and {D. Physiology} and {Biophysics} and {Weill Cornell Medicine} and {New York.} and {Ny} and {Department of Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Bay Area Environmental Research Institute} and {Mortality ResearchConsulting} and {Inc.} and {Universities Space Research Association} and {UC Space Health} and {D. Surgery} and {U. California} and {San Francisco} and {AI CenterforHealth} and {D. Biochemistry} and {Molecular Genetics} and {U. Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Human Health} and {Performance Directorate} and {NASAMarshall Space Flight Center} and {D. Microbiology} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of California San Francisco} and {The Gilroy AstroBiology Research Group} and {The University of Wisconsin} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Perú} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Mn} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {Center for Individualized Medicine} and {D. Surgery} and {Department of Mathematical Sciences} and {Mayo Clinic} and {Rochester} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {McGill University} and {Montreal.} and {Quebec.} and {Canada.} and {Faculty of Veterinary Medicine} and {Cancer} and {I. -. London} and {London} and {SymbioSeq Llc} and {Ashburn} and {Va} and {Center for Data Driven Discovery} and {C. I. O. Technology.} and {Pasadena} and {Waitt Advanced Biophotonics Center} and {Chan-Zuckerberg Imaging Scientist Fellow} and {Salk Institute for Biological Studies} and {La Jolla} and {Biological Systems} and {Engineering Division} and {Lawrence Berkeley National Lab.} and {Berkeley} and {Doe Agile BioFoundry} and {Emeryville} and {Joint BioEnergy Institute} and {Human Research Program Cross-cutting Computational Model Project} and {N. R. Center} and {Cleveland} and {Oh} and {Institute for Quantum Science} and {Engineering} and {M. Biology} and {M. University} and {E. Lansing.} and {Mi} and {Low Exploration Gravity Technology} and {AI Matrix Consortium} and {Department of Biomedical Engineering} and {U. Texas} and {San Antonio} and {UT Health Sciences} and {Office of the Director} and {Logyx} and {Computer Science} and {Statistics} and {Health Policy} and {J. University} and {Baltimore.} and {Md} and {Ml} and {Ai} and {H. Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {Jet propulsion Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {Department of Medical Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {Data Science} and {U. Alabama} and {Birmingham} and {Al} and {Center for Emerging} and {Re-Emerging Pathogens} and {Biochemistry} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Department of Biomedical Informatics} and {H. School} and {Harvard Data Science} and {Broad Institute of Mit} and {Harvard} and {Harvard University} and {Boston} and {Ma.}},
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     }

543. R. Adams, K. Henry, Hossein Soleimani, Nishi Rawat, M. Saheed, E. Chen, Albert W. Wu, and S. Saria, “1405: ASSESSING CLINICAL USE AND PERFORMANCE OF A MACHINE LEARNING SEPSIS ALERT FOR SEX AND RACIAL BIAS,” in Critical Care Medicine, 2021.
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     @inproceedings{245295789,
     title = {1405: ASSESSING CLINICAL USE AND PERFORMANCE OF A MACHINE LEARNING SEPSIS ALERT FOR SEX AND RACIAL BIAS},
     author = {{R. Adams} and {K. Henry} and {Hossein Soleimani} and {Nishi Rawat} and {M. Saheed} and {E. Chen} and {Albert W. Wu} and {S. Saria}},
     year = 2021,
     month = {12},
     booktitle = {Critical Care Medicine},
     url = {https://www.semanticscholar.org/paper/b3c964ad654a01ccd25db4eb89129b7e8fb6bed1},
     }

544. S. Saria, K. Henry, Hossein Soleimani, R. Adams, A. Zhan, Nishi Rawat, E. Chen, and Albert W. Wu, “1429: LEAD TIME AND ACCURACY OF TREWS, A MACHINE LEARNING-BASED SEPSIS ALERT,” in Critical Care Medicine, 2021.
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     @inproceedings{245284251,
     title = {1429: LEAD TIME AND ACCURACY OF TREWS, A MACHINE LEARNING-BASED SEPSIS ALERT},
     author = {{S. Saria} and {K. Henry} and {Hossein Soleimani} and {R. Adams} and {A. Zhan} and {Nishi Rawat} and {E. Chen} and {Albert W. Wu}},
     year = 2021,
     month = {12},
     booktitle = {Critical Care Medicine},
     url = {https://www.semanticscholar.org/paper/66869b66e3beb408ffbccc97721678dc8c38963d},
     }

545. Zili Huang, Marc Delcroix, Leibny Paola García-Perera, Shinji Watanabe, Desh Raj, and S. Khudanpur, “Joint speaker diarization and speech recognition based on region proposal networks,” in Computer Speech and Language, 2021.
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     @inproceedings{244107471,
     title = {Joint speaker diarization and speech recognition based on region proposal networks},
     author = {{Zili Huang} and {Marc Delcroix} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Desh Raj} and {S. Khudanpur}},
     year = 2021,
     month = {11},
     booktitle = {Computer Speech and Language},
     url = {https://www.semanticscholar.org/paper/9bb9b23823b45ba7521d872bb3e970ede4aafb8a},
     }

546. M. A. Gordon, K. Duh, and J. Kaplan, “Data and Parameter Scaling Laws for Neural Machine Translation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 5915–5922. doi:10.18653/v1/2021.emnlp-main.478
     [BibTeX] [Abstract] [Link]
     

     We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.

     @inproceedings{gordon-etal-2021-data,
     title = "Data and Parameter Scaling Laws for Neural Machine Translation",
     author = "Gordon, Mitchell A and
     Duh, Kevin and
     Kaplan, Jared",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.478",
     doi = "10.18653/v1/2021.emnlp-main.478",
     pages = "5915--5922",
     abstract = "We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.",
     }

547. Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, A. Yuille, and Tao Kong, “iBOT: Image BERT Pre-Training with Online Tokenizer,” in arXiv.org, 2021.
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     @inproceedings{244117494,
     title = {iBOT: Image BERT Pre-Training with Online Tokenizer},
     author = {{Jinghao Zhou} and {Chen Wei} and {Huiyu Wang} and {Wei Shen} and {Cihang Xie} and {A. Yuille} and {Tao Kong}},
     year = 2021,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/9653c070724e44f023e8cc3ec79f0b9e6d59480d},
     }

548. Yutong Bai, Jieru Mei, A. Yuille, and Cihang Xie, “Are Transformers More Robust Than CNNs?,” in Neural Information Processing Systems, 2021.
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     @inproceedings{243938451,
     title = {Are Transformers More Robust Than CNNs?},
     author = {{Yutong Bai} and {Jieru Mei} and {A. Yuille} and {Cihang Xie}},
     year = 2021,
     month = {11},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/35c0800e657faa18cf3fc3629bdbeafbb976b006},
     }

549. Jeya Maria Jose Valanarasu, R. Yasarla, and Vishal M. Patel, “TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions,” in Computer Vision and Pattern Recognition, 2021.
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     @inproceedings{244714491,
     title = {TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions},
     author = {{Jeya Maria Jose Valanarasu} and {R. Yasarla} and {Vishal M. Patel}},
     year = 2021,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/b27d3be4264dcd06f990b44968f4382526f24f1e},
     }

550. Pengfei Guo and Vishal M. Patel, “Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme,” in arXiv.org, 2021.
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     @inproceedings{244345634,
     title = {Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme},
     author = {{Pengfei Guo} and {Vishal M. Patel}},
     year = 2021,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7bab95180b52749d2b018d120d8f04bba520ee0f},
     }

551. Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge J. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge,” in NeurIPS Datasets and Benchmarks, 2021.
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     @inproceedings{244117621,
     title = {Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge},
     author = {{Jiyang Qi} and {Yan Gao} and {Yao Hu} and {Xinggang Wang} and {Xiaoyu Liu} and {Xiang Bai} and {Serge J. Belongie} and {A. Yuille} and {Philip H. S. Torr} and {S. Bai}},
     year = 2021,
     month = {11},
     booktitle = {NeurIPS Datasets and Benchmarks},
     url = {https://www.semanticscholar.org/paper/60b137e3b5f378e50d7875bb5ad0390d107374bb},
     }

552. S. Ding, M. Junczys-Dowmunt, M. Post, and P. Koehn, “Levenshtein Training for Word-level Quality Estimation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 6724–6733. doi:10.18653/v1/2021.emnlp-main.539
     [BibTeX] [Abstract] [Link]
     

     We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision. To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human post-editing data. We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference. Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.

     @inproceedings{ding-etal-2021-levenshtein,
     title = "{L}evenshtein Training for Word-level Quality Estimation",
     author = "Ding, Shuoyang and
     Junczys-Dowmunt, Marcin and
     Post, Matt and
     Koehn, Philipp",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.539",
     doi = "10.18653/v1/2021.emnlp-main.539",
     pages = "6724--6733",
     abstract = "We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision. To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human post-editing data. We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference. Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.",
     }

553. Huaijin Pi, Huiyu Wang, Yingwei Li, Zizhang Li, and A. Yuille, “Searching for TrioNet: Combining Convolution with Local and Global Self-Attention,” in British Machine Vision Conference, 2021.
     [BibTeX] [Link]

     @inproceedings{244117374,
     title = {Searching for TrioNet: Combining Convolution with Local and Global Self-Attention},
     author = {{Huaijin Pi} and {Huiyu Wang} and {Yingwei Li} and {Zizhang Li} and {A. Yuille}},
     year = 2021,
     month = {11},
     booktitle = {British Machine Vision Conference},
     url = {https://www.semanticscholar.org/paper/2ecdb624c2a87624e27c34e3af388b559a0ba06c},
     }

554. A. Buczak, Benjamin D. Baugher, Christine S. Martin, Meg W. Keiley-Listermann, J. Howard, Nathan H. Parrish, Anton Q. Stalick, D. S. Berman, and Mark Dredze, “Crystal Cube: Forecasting Disruptive Events,” in Applied Artificial Intelligence, 2021.
     [BibTeX] [Link]

     @inproceedings{244096848,
     title = {Crystal Cube: Forecasting Disruptive Events},
     author = {{A. Buczak} and {Benjamin D. Baugher} and {Christine S. Martin} and {Meg W. Keiley-Listermann} and {J. Howard} and {Nathan H. Parrish} and {Anton Q. Stalick} and {D. S. Berman} and {Mark Dredze}},
     year = 2021,
     month = {11},
     booktitle = {Applied Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/3168dec5c6a5c1441f258c14d05f8520f20ecbaf},
     }

555. E. Salesky, D. Etter, and M. Post, “Robust Open-Vocabulary Translation from Visual Text Representations,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 7235–7252. doi:10.18653/v1/2021.emnlp-main.576
     [BibTeX] [Abstract] [Link]
     

     Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an {`}open vocabulary.{‘} This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German{–}English task where subword models degrade to 1.9.

     @inproceedings{salesky-etal-2021-robust,
     title = "Robust Open-Vocabulary Translation from Visual Text Representations",
     author = "Salesky, Elizabeth and
     Etter, David and
     Post, Matt",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.576",
     doi = "10.18653/v1/2021.emnlp-main.576",
     pages = "7235--7252",
     abstract = "Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an {`}open vocabulary.{'} This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German{--}English task where subword models degrade to 1.9.",
     }

556. S. Ding, M. Junczys-Dowmunt, M. Post, C. Federmann, and P. Koehn, “The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task,” in Proceedings of the Sixth Conference on Machine Translation, Online, 2021, p. 904–910.
     [BibTeX] [Abstract] [Link]
     

     This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.

     @inproceedings{ding-etal-2021-jhu,
     title = "The {JHU}-{M}icrosoft Submission for {WMT}21 Quality Estimation Shared Task",
     author = "Ding, Shuoyang and
     Junczys-Dowmunt, Marcin and
     Post, Matt and
     Federmann, Christian and
     Koehn, Philipp",
     editor = "Barrault, Loic and
     Bojar, Ondrej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-jussa, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Kocmi, Tom and
     Martins, Andre and
     Morishita, Makoto and
     Monz, Christof",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.94",
     pages = "904--910",
     abstract = "This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.",
     }

557. G. Kumar, P. Koehn, and S. Khudanpur, “Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora,” in Proceedings of the Sixth Conference on Machine Translation, Online, 2021, p. 1100–1109.
     [BibTeX] [Abstract] [Link]
     

     Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to deal with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.

     @inproceedings{kumar-etal-2021-learning-feature,
     title = "Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora",
     author = "Kumar, Gaurav and
     Koehn, Philipp and
     Khudanpur, Sanjeev",
     editor = "Barrault, Loic and
     Bojar, Ondrej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-jussa, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Kocmi, Tom and
     Martins, Andre and
     Morishita, Makoto and
     Monz, Christof",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.118",
     pages = "1100--1109",
     abstract = "Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to deal with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.",
     }

558. F. Akhbardeh, A. Arkhangorodsky, M. Biesialska, O. Bojar, R. Chatterjee, V. Chaudhary, M. R. Costa-jussa, C. España-Bonet, A. Fan, C. Federmann, M. Freitag, Y. Graham, R. Grundkiewicz, B. Haddow, L. Harter, K. Heafield, C. Homan, M. Huck, K. Amponsah-Kaakyire, J. Kasai, D. Khashabi, K. Knight, T. Kocmi, P. Koehn, N. Lourie, C. Monz, M. Morishita, M. Nagata, A. Nagesh, T. Nakazawa, M. Negri, S. Pal, A. A. Tapo, M. Turchi, V. Vydrin, and M. Zampieri, “Findings of the 2021 Conference on Machine Translation (WMT21),” in Proceedings of the Sixth Conference on Machine Translation, Online, 2021, p. 1–88.
     [BibTeX] [Abstract] [Link]
     

     This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.

     @inproceedings{akhbardeh-etal-2021-findings,
     title = "Findings of the 2021 Conference on Machine Translation ({WMT}21)",
     author = "Akhbardeh, Farhad and
     Arkhangorodsky, Arkady and
     Biesialska, Magdalena and
     Bojar, Ond{\v{r}}ej and
     Chatterjee, Rajen and
     Chaudhary, Vishrav and
     Costa-jussa, Marta R. and
     Espa{\~n}a-Bonet, Cristina and
     Fan, Angela and
     Federmann, Christian and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Haddow, Barry and
     Harter, Leonie and
     Heafield, Kenneth and
     Homan, Christopher and
     Huck, Matthias and
     Amponsah-Kaakyire, Kwabena and
     Kasai, Jungo and
     Khashabi, Daniel and
     Knight, Kevin and
     Kocmi, Tom and
     Koehn, Philipp and
     Lourie, Nicholas and
     Monz, Christof and
     Morishita, Makoto and
     Nagata, Masaaki and
     Nagesh, Ajay and
     Nakazawa, Toshiaki and
     Negri, Matteo and
     Pal, Santanu and
     Tapo, Allahsera Auguste and
     Turchi, Marco and
     Vydrin, Valentin and
     Zampieri, Marcos",
     editor = "Barrault, Loic and
     Bojar, Ondrej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-jussa, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Kocmi, Tom and
     Martins, Andre and
     Morishita, Makoto and
     Monz, Christof",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.1",
     pages = "1--88",
     abstract = "This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021.In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories. The taskwas also opened up to additional test suites toprobe specific aspects of translation.",
     }

559. Junfei Xiao, Longlong Jing, Lin Zhang, Ju He, Qi She, Zongwei Zhou, A. Yuille, and Yingwei Li, “Learning from Temporal Gradient for Semi-supervised Action Recognition,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{244709803,
     title = {Learning from Temporal Gradient for Semi-supervised Action Recognition},
     author = {{Junfei Xiao} and {Longlong Jing} and {Lin Zhang} and {Ju He} and {Qi She} and {Zongwei Zhou} and {A. Yuille} and {Yingwei Li}},
     year = 2021,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/069e9bb3c9674441c6872767f33ae5d9a4931cd3},
     }

560. M. Yarmohammadi, S. Wu, M. Marone, H. Xu, S. Ebner, G. Qin, Y. Chen, J. Guo, C. Harman, K. Murray, A. S. White, M. Dredze, and B. Van Durme, “Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 1950–1967. doi:10.18653/v1/2021.emnlp-main.149
     [BibTeX] [Abstract] [Link]
     

     Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of {“}train on English, run on any language{”}, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.

     @inproceedings{yarmohammadi-etal-2021-everything,
     title = "Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction",
     author = "Yarmohammadi, Mahsa and
     Wu, Shijie and
     Marone, Marc and
     Xu, Haoran and
     Ebner, Seth and
     Qin, Guanghui and
     Chen, Yunmo and
     Guo, Jialiang and
     Harman, Craig and
     Murray, Kenton and
     White, Aaron Steven and
     Dredze, Mark and
     Van Durme, Benjamin",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.149",
     doi = "10.18653/v1/2021.emnlp-main.149",
     pages = "1950--1967",
     abstract = "Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of {``}train on English, run on any language{''}, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.",
     }

561. Bingchen Zhao, Shaozuo Yu, Wufei Ma, M. Yu, Shenxiao Mei, Angtian Wang, Ju He, A. Yuille, and Adam Kortylewski, “OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images,” in European Conference on Computer Vision, 2021.
     [BibTeX] [Link]

     @inproceedings{251041144,
     title = {OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images},
     author = {{Bingchen Zhao} and {Shaozuo Yu} and {Wufei Ma} and {M. Yu} and {Shenxiao Mei} and {Angtian Wang} and {Ju He} and {A. Yuille} and {Adam Kortylewski}},
     year = 2021,
     month = {11},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/8f693bc2219607316e143ba543ae0e7abca6a4b1},
     }

562. A. Chinta, J. Zhang, A. DeLucia, M. Dredze, and A. L. Buczak, “Study of Manifestation of Civil Unrest on Twitter,” in Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), Online, 2021, p. 396–409. doi:10.18653/v1/2021.wnut-1.44
     [BibTeX] [Abstract] [Link]
     

     Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating \textit{how} civil unrest manifests on Twitter across countries and events. We present two in-depth case studies for two specific large-scale events, one in a country with high (English) Twitter usage (Johannesburg riots in South Africa) and one in a country with low Twitter usage (Burayu massacre protests in Ethiopia). We show that while there is event signal during the events, there is little signal leading up to the events. In addition to the case studies, we train Ngram-based models on a larger set of Twitter civil unrest data across time, events, and countries and use machine learning explainability tools (SHAP) to identify important features. The models were able to find words indicative of civil unrest that generalized across countries. The 42 countries span Africa, Middle East, and Southeast Asia and the events range occur between 2014 and 2019.

     @inproceedings{chinta-etal-2021-study,
     title = "Study of Manifestation of Civil Unrest on {T}witter",
     author = "Chinta, Abhinav and
     Zhang, Jingyu and
     DeLucia, Alexandra and
     Dredze, Mark and
     Buczak, Anna L.",
     editor = "Xu, Wei and
     Ritter, Alan and
     Baldwin, Tim and
     Rahimi, Afshin",
     booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wnut-1.44",
     doi = "10.18653/v1/2021.wnut-1.44",
     pages = "396--409",
     abstract = "Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating \textit{how} civil unrest manifests on Twitter across countries and events. We present two in-depth case studies for two specific large-scale events, one in a country with high (English) Twitter usage (Johannesburg riots in South Africa) and one in a country with low Twitter usage (Burayu massacre protests in Ethiopia). We show that while there is event signal during the events, there is little signal leading up to the events. In addition to the case studies, we train Ngram-based models on a larger set of Twitter civil unrest data across time, events, and countries and use machine learning explainability tools (SHAP) to identify important features. The models were able to find words indicative of civil unrest that generalized across countries. The 42 countries span Africa, Middle East, and Southeast Asia and the events range occur between 2014 and 2019.",
     }

563. M. M. I. Alam, I. Kvapil{‘i}ková, A. Anastasopoulos, L. Besacier, G. Dinu, M. Federico, M. Gallé, K. Jung, P. Koehn, and V. Nikoulina, “Findings of the WMT Shared Task on Machine Translation Using Terminologies,” in Proceedings of the Sixth Conference on Machine Translation, Online, 2021, p. 652–663.
     [BibTeX] [Abstract] [Link]
     

     Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.

     @inproceedings{alam-etal-2021-findings,
     title = "Findings of the {WMT} Shared Task on Machine Translation Using Terminologies",
     author = "Alam, Md Mahfuz Ibn and
     Kvapil{\'\i}kov{\'a}, Ivana and
     Anastasopoulos, Antonios and
     Besacier, Laurent and
     Dinu, Georgiana and
     Federico, Marcello and
     Gall{\'e}, Matthias and
     Jung, Kweonwoo and
     Koehn, Philipp and
     Nikoulina, Vassilina",
     editor = "Barrault, Loic and
     Bojar, Ondrej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-jussa, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Kocmi, Tom and
     Martins, Andre and
     Morishita, Makoto and
     Monz, Christof",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.69",
     pages = "652--663",
     abstract = "Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.",
     }

564. Yu Zeng, Zhe L. Lin, and Vishal M. Patel, “SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{244729626,
     title = {SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches},
     author = {{Yu Zeng} and {Zhe L. Lin} and {Vishal M. Patel}},
     year = 2021,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/378aa9ad054989663c6db5f2fe90d6982340e28b},
     }

565. Tiange Xiang, Yixiao Zhang, Yongyi Lu, A. Yuille, Chaoyi Zhang, Weidong (Tom) Cai, and Zongwei Zhou, “SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{257766829,
     title = {SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection},
     author = {{Tiange Xiang} and {Yixiao Zhang} and {Yongyi Lu} and {A. Yuille} and {Chaoyi Zhang} and {Weidong (Tom) Cai} and {Zongwei Zhou}},
     year = 2021,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/e2977c67f55b8a2a58ff1c232c96bed25002f8a2},
     }

566. P. Xia and B. Van Durme, “Moving on from OntoNotes: Coreference Resolution Model Transfer,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 5241–5256. doi:10.18653/v1/2021.emnlp-main.425
     [BibTeX] [Abstract] [Link]
     

     Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset. However, real-world applications of coref depend on the annotation guidelines and the domain of the target dataset, which often differ from those of OntoNotes. We aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. We examine eleven target datasets and find that continued training is consistently effective and especially beneficial when there are few target documents. We establish new benchmarks across several datasets, including state-of-the-art results on PreCo.

     @inproceedings{xia-van-durme-2021-moving,
     title = "Moving on from {O}nto{N}otes: Coreference Resolution Model Transfer",
     author = "Xia, Patrick and
     Van Durme, Benjamin",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.425",
     doi = "10.18653/v1/2021.emnlp-main.425",
     pages = "5241--5256",
     abstract = "Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset. However, real-world applications of coref depend on the annotation guidelines and the domain of the target dataset, which often differ from those of OntoNotes. We aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. We examine eleven target datasets and find that continued training is consistently effective and especially beneficial when there are few target documents. We establish new benchmarks across several datasets, including state-of-the-art results on PreCo.",
     }

567. R. Shin, C. Lin, S. Thomson, C. Chen, S. Roy, E. A. Platanios, A. Pauls, D. Klein, J. Eisner, and B. Van Durme, “Constrained Language Models Yield Few-Shot Semantic Parsers,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 7699–7715. doi:10.18653/v1/2021.emnlp-main.608
     [BibTeX] [Abstract] [Link]
     

     We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.

     @inproceedings{shin-etal-2021-constrained,
     title = "Constrained Language Models Yield Few-Shot Semantic Parsers",
     author = "Shin, Richard and
     Lin, Christopher and
     Thomson, Sam and
     Chen, Charles and
     Roy, Subhro and
     Platanios, Emmanouil Antonios and
     Pauls, Adam and
     Klein, Dan and
     Eisner, Jason and
     Van Durme, Benjamin",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.608",
     doi = "10.18653/v1/2021.emnlp-main.608",
     pages = "7699--7715",
     abstract = "We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.",
     }

568. H. Xu, B. Van Durme, and K. Murray, “BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021, p. 6663–6675. doi:10.18653/v1/2021.emnlp-main.534
     [BibTeX] [Abstract] [Link]
     

     The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT{‘}14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT{‘}14 dataset, which exceeds all published numbers.

     @inproceedings{xu-etal-2021-bert,
     title = "{BERT}, m{BERT}, or {B}i{BERT}? A Study on Contextualized Embeddings for Neural Machine Translation",
     author = "Xu, Haoran and
     Van Durme, Benjamin and
     Murray, Kenton",
     editor = "Moens, Marie-Francine and
     Huang, Xuanjing and
     Specia, Lucia and
     Yih, Scott Wen-tau",
     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
     month = nov,
     year = "2021",
     address = "Online and Punta Cana, Dominican Republic",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.emnlp-main.534",
     doi = "10.18653/v1/2021.emnlp-main.534",
     pages = "6663--6675",
     abstract = "The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT{'}14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT{'}14 dataset, which exceeds all published numbers.",
     }

569. Jieneng Chen, Shuyang Sun, Ju He, Philip H. S. Torr, A. Yuille, and S. Bai, “TransMix: Attend to Mix for Vision Transformers,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{244346829,
     title = {TransMix: Attend to Mix for Vision Transformers},
     author = {{Jieneng Chen} and {Shuyang Sun} and {Ju He} and {Philip H. S. Torr} and {A. Yuille} and {S. Bai}},
     year = 2021,
     month = {11},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/b39495876b494412e0918898db8f988e9f5fd69d},
     }

570. C. Tran, S. Bhosale, J. Cross, P. Koehn, S. Edunov, and A. Fan, “Facebook AI’s WMT21 News Translation Task Submission,” in Proceedings of the Sixth Conference on Machine Translation, Online, 2021, p. 205–215.
     [BibTeX] [Abstract] [Link]
     

     We describe Facebook{‘}s multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models. We utilize data from all available sources {–-} WMT, large-scale data mining, and in-domain backtranslation {–-} to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all eight languages. Our final submission is an ensemble of dense and sparse Mixture-of-Expert multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year{‘}s winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.

     @inproceedings{tran-etal-2021-facebook,
     title = "{F}acebook {AI}{'}s {WMT}21 News Translation Task Submission",
     author = "Tran, Chau and
     Bhosale, Shruti and
     Cross, James and
     Koehn, Philipp and
     Edunov, Sergey and
     Fan, Angela",
     editor = "Barrault, Loic and
     Bojar, Ondrej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-jussa, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Freitag, Markus and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Kocmi, Tom and
     Martins, Andre and
     Morishita, Makoto and
     Monz, Christof",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.19",
     pages = "205--215",
     abstract = "We describe Facebook{'}s multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models. We utilize data from all available sources {---} WMT, large-scale data mining, and in-domain backtranslation {---} to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all eight languages. Our final submission is an ensemble of dense and sparse Mixture-of-Expert multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year{'}s winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.",
     }

571. T. Vieira, R. Cotterell, and J. Eisner, “Searching for More Efficient Dynamic Programs,” in Findings of EMNLP’21, Punta Cana, 2021, p. 3812–3830. doi:10.18653/v1/2021.findings-emnlp.322
     [BibTeX] [Link]

     @InProceedings{vieira-et-al-2021-emnlp,
     aclid = "2021.findings-emnlp.322",
     doi = "10.18653/v1/2021.findings-emnlp.322",
     author = "Tim Vieira and Ryan Cotterell and Jason Eisner",
     title = "Searching for More Efficient Dynamic Programs",
     booktitle = "Findings of EMNLP'21",
     pages = "3812--3830",
     year = "2021",
     month = nov,
     address = "Punta Cana",
     URL = "http://cs.jhu.edu/~jason/papers/#vieira-et-al-2021-emnlp",
     }

572. R. Shin, C. H. Lin, S. Thomson, C. Chen, S. Roy, E. Antonios Platanios, A. Pauls, D. Klein, J. Eisner, and B. V. Durme, “Constrained Language Models Yield Few-Shot Semantic Parsers,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, 2021, p. 7699–7715. doi:10.18653/v1/2021.emnlp-main.608
     [BibTeX] [Link]

     @InProceedings{semanticmachines-2021-emnlp,
     aclid = "2021.emnlp-main.608",
     doi = "10.18653/v1/2021.emnlp-main.608",
     author = "Richard Shin and Christopher H. Lin and Sam Thomson
     and Charles Chen and Subhro Roy and Emmanouil Antonios
     Platanios and Adam Pauls and Dan Klein and Jason Eisner
     and Benjamin Van Durme",
     title = "Constrained Language Models Yield Few-Shot Semantic
     Parsers",
     booktitle = "Proceedings of the 2021 Conference on Empirical
     Methods in Natural Language Processing",
     pages = "7699--7715",
     year = "2021",
     month = nov,
     address = "Punta Cana",
     URL = "http://cs.jhu.edu/~jason/papers/#semanticmachines-2021-emnlp",
     }

573. Joseph P. Robinson, Can Qin, Ming Shao, Matthew A. Turk, R. Chellappa, and Y. Fu, “The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{244728315,
     title = {The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces},
     author = {{Joseph P. Robinson} and {Can Qin} and {Ming Shao} and {Matthew A. Turk} and {R. Chellappa} and {Y. Fu}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/9f260bdd4030af5297a9c1cbb817c75701ac8c83},
     }

574. Shota Horiguchi, Yuki Takashima, Leibny Paola García-Perera, Shinji Watanabe, and Y. Kawaguchi, “Multi-Channel End-To-End Neural Diarization with Distributed Microphones,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{238583387,
     title = {Multi-Channel End-To-End Neural Diarization with Distributed Microphones},
     author = {{Shota Horiguchi} and {Yuki Takashima} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Y. Kawaguchi}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/04b44c518b145be625ff270af56cfd2e37900137},
     }

575. Angtian Wang, Shenxiao Mei, A. Yuille, and Adam Kortylewski, “Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose,” in Neural Information Processing Systems, 2021.
     [BibTeX] [Link]

     @inproceedings{239998658,
     title = {Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose},
     author = {{Angtian Wang} and {Shenxiao Mei} and {A. Yuille} and {Adam Kortylewski}},
     year = 2021,
     month = {10},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/f47d7c69997ba460133410eef2309be4eb29322c},
     }

576. Xinyue Wei, Weichao Qiu, Yi Zhang, Zihao Xiao, and A. Yuille, “Nuisance-Label Supervision: Robustness Improvement by Free Labels,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021.
     [BibTeX] [Link]

     @inproceedings{238857299,
     title = {Nuisance-Label Supervision: Robustness Improvement by Free Labels},
     author = {{Xinyue Wei} and {Weichao Qiu} and {Yi Zhang} and {Zihao Xiao} and {A. Yuille}},
     year = 2021,
     month = {10},
     booktitle = {2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
     url = {https://www.semanticscholar.org/paper/0d8768aab838ec5c1af063fc95d22796fac05acf},
     }

577. VS Vibashan, Domenick Poster, Suya You, Shuowen Hu, and Vishal M. Patel, “Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
     [BibTeX] [Link]

     @inproceedings{238419143,
     title = {Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning},
     author = {{VS Vibashan} and {Domenick Poster} and {Suya You} and {Shuowen Hu} and {Vishal M. Patel}},
     year = 2021,
     month = {10},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/b9e3bd4e032adcdb4093a0cad5ae21d9eabbcee9},
     }

578. Shraman Pramanick, A. Roy, and Vishal M. Patel, “Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
     [BibTeX] [Link]

     @inproceedings{239049720,
     title = {Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection},
     author = {{Shraman Pramanick} and {A. Roy} and {Vishal M. Patel}},
     year = 2021,
     month = {10},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/204d5d9362533247df9a9303b44114c503236cdd},
     }

579. Aishan Liu, Xinyun Chen, Yingwei Li, Chaowei Xiao, Xun Yang, Xianglong Liu, D. Song, D. Tao, A. Yuille, and Anima Anandkumar, “ADVM’21: 1st International Workshop on Adversarial Learning for Multimedia,” in ACM Multimedia, 2021.
     [BibTeX] [Link]

     @inproceedings{239011990,
     title = {ADVM'21: 1st International Workshop on Adversarial Learning for Multimedia},
     author = {{Aishan Liu} and {Xinyun Chen} and {Yingwei Li} and {Chaowei Xiao} and {Xun Yang} and {Xianglong Liu} and {D. Song} and {D. Tao} and {A. Yuille} and {Anima Anandkumar}},
     year = 2021,
     month = {10},
     booktitle = {ACM Multimedia},
     url = {https://www.semanticscholar.org/paper/943215bcb7866a6c6fe25944b14f41d5e2bd72b9},
     }

580. Rui Shao, Bochao Zhang, P. Yuen, and Vishal M. Patel, “Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{239768813,
     title = {Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation},
     author = {{Rui Shao} and {Bochao Zhang} and {P. Yuen} and {Vishal M. Patel}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/3f3258ebf13c912d7de8df8a5a9446a702cd614c},
     }

581. Sandeep Reddy Kothinti, Nicholas Huang, and Mounya Elhilali, “Auditory salience using natural scenes: An online study,” in Journal of the Acoustical Society of America, 2021.
     [BibTeX] [Link]

     @inproceedings{239454688,
     title = {Auditory salience using natural scenes: An online study},
     author = {{Sandeep Reddy Kothinti} and {Nicholas Huang} and {Mounya Elhilali}},
     year = 2021,
     month = {10},
     booktitle = {Journal of the Acoustical Society of America},
     url = {https://www.semanticscholar.org/paper/06ae11378419c01df4297c03d962459aefb3c054},
     }

582. Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, and R. Chellappa, “Identification of Attack-Specific Signatures in Adversarial Examples,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{238743967,
     title = {Identification of Attack-Specific Signatures in Adversarial Examples},
     author = {{Hossein Souri} and {Pirazh Khorramshahi} and {Chun Pong Lau} and {Micah Goldblum} and {R. Chellappa}},
     year = 2021,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7cfeca9f831e4f2d31114215abaa5078a98d1656},
     }

583. K. Allen, Angeles Salles, Sanwook Park, Mounya Elhilali, and C. Moss, “Effect of background clutter on neural discrimination in the bat auditory midbrain.,” in Journal of Neurophysiology, 2021.
     [BibTeX] [Link]

     @inproceedings{239051966,
     title = {Effect of background clutter on neural discrimination in the bat auditory midbrain.},
     author = {{K. Allen} and {Angeles Salles} and {Sanwook Park} and {Mounya Elhilali} and {C. Moss}},
     year = 2021,
     month = {10},
     booktitle = {Journal of Neurophysiology},
     url = {https://www.semanticscholar.org/paper/1652bdf2674f195b97aee0f1f32926f1c7b9aced},
     }

584. Piotr Żelasko, Daniel Povey, J. Trmal, and S. Khudanpur, “Lhotse: a speech data representation library for the modern deep learning ecosystem,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{239768221,
     title = {Lhotse: a speech data representation library for the modern deep learning ecosystem},
     author = {{Piotr Żelasko} and {Daniel Povey} and {J. Trmal} and {S. Khudanpur}},
     year = 2021,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/18394264fe8b4c05527117c5d15a1d19e52c2687},
     }

585. Zhuowan Li, Elias Stengel-Eskin, Yixiao Zhang, Cihang Xie, Q. Tran, Benjamin Van Durme, and A. Yuille, “Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images,” in IEEE International Conference on Computer Vision, 2021.
     [BibTeX] [Link]

     @inproceedings{238253118,
     title = {Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images},
     author = {{Zhuowan Li} and {Elias Stengel-Eskin} and {Yixiao Zhang} and {Cihang Xie} and {Q. Tran} and {Benjamin Van Durme} and {A. Yuille}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/40b065eb3aa5c5a54962aee78ebe30943beaabb1},
     }

586. M. Sophocleous, J. Georgiou, A. Andreou, Yosi Shacham-Diamand, Theerawit Wilaiprasitporn, J. Atkinson, Paddy J. French, E. García-Breijo, and Mohammad Russel, “Guest Editorial Special Issue on Sensors Tutorials: A Vigorous Dive Into the Vast Sea of Sensor- Related Knowledge—Part I,” in IEEE Sensors Journal, 2021.
     [BibTeX] [Link]

     @inproceedings{245002248,
     title = {Guest Editorial Special Issue on Sensors Tutorials: A Vigorous Dive Into the Vast Sea of Sensor- Related Knowledge—Part I},
     author = {{M. Sophocleous} and {J. Georgiou} and {A. Andreou} and {Yosi Shacham-Diamand} and {Theerawit Wilaiprasitporn} and {J. Atkinson} and {Paddy J. French} and {E. García-Breijo} and {Mohammad Russel}},
     year = 2021,
     month = {10},
     booktitle = {IEEE Sensors Journal},
     url = {https://www.semanticscholar.org/paper/72e190cfe76cde934943ae35908bff346d4c970d},
     }

587. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Unsupervised Speech Segmentation and Variable Rate Representation Learning Using Segmental Contrastive Predictive Coding,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{238408084,
     title = {Unsupervised Speech Segmentation and Variable Rate Representation Learning Using Segmental Contrastive Predictive Coding},
     author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2021,
     month = {10},
     booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
     url = {https://www.semanticscholar.org/paper/3c2502b6d82ba4fca35fb871e7ed697fb4952f23},
     }

588. Yu Zeng, Zhe L. Lin, Huchuan Lu, and Vishal M. Patel, “CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction,” in IEEE International Conference on Computer Vision, 2021.
     [BibTeX] [Link]

     @inproceedings{244072324,
     title = {CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction},
     author = {{Yu Zeng} and {Zhe L. Lin} and {Huchuan Lu} and {Vishal M. Patel}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/2f1103a039c4511a111b506fdbe980a4f34b6709},
     }

589. Wenpin Hou, Mingyu Zhang, Yuelong Ji, X. Hong, Guoying Wang, L. Liang, Hongkai Ji, S. Saria, and Xiaobin Wang, “In-Utero Exposure to Cigarette Smoking on Child Long-Term Risk of Obesity: Concordance of Self-Report, Maternal and Cord Blood Biomarkers.” 2021.
     [BibTeX] [Link]

     @inproceedings{240189255,
     title = {In-Utero Exposure to Cigarette Smoking on Child Long-Term Risk of Obesity: Concordance of Self-Report, Maternal and Cord Blood Biomarkers},
     author = {{Wenpin Hou} and {Mingyu Zhang} and {Yuelong Ji} and {X. Hong} and {Guoying Wang} and {L. Liang} and {Hongkai Ji} and {S. Saria} and {Xiaobin Wang}},
     year = 2021,
     month = {10},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/eb17d81e0fdd641f07329cd202064e60db1aa2a3},
     }

590. Matthew Wiesner, Desh Raj, and S. Khudanpur, “Injecting Text and Cross-Lingual Supervision in Few-Shot Learning from Self-Supervised Models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{238583266,
     title = {Injecting Text and Cross-Lingual Supervision in Few-Shot Learning from Self-Supervised Models},
     author = {{Matthew Wiesner} and {Desh Raj} and {S. Khudanpur}},
     year = 2021,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/047ce1b1f4dfec2d5f53de955f5e0f645ddc929c},
     }

591. Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, B. Green, E. Engle, Guillermo Almodovar, Ryan Walk, Sigfredo Soto-Diaz, J. Taube, A. Szalay, and A. Yuille, “A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation,” in MOVI@MICCAI, 2021.
     [BibTeX] [Link]

     @inproceedings{252440016,
     title = {A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation},
     author = {{Yixiao Zhang} and {Adam Kortylewski} and {Qing Liu} and {Seyoun Park} and {B. Green} and {E. Engle} and {Guillermo Almodovar} and {Ryan Walk} and {Sigfredo Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
     year = 2021,
     month = {10},
     booktitle = {MOVI@MICCAI},
     url = {https://www.semanticscholar.org/paper/4795bf843f77bfd891e34729609c194b85b72a4d},
     }

592. W. Wu and D. Yarowsky, “On Pronunciations in Wiktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction,” in Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021), Online (Virtual Mode), 2021, p. 68–74.
     [BibTeX] [Abstract] [Link]
     

     We constructed parsers for five non-English editions of Wiktionary, which combined with pronunciations from the English edition, comprises over 5.3 million IPA pronunciations, the largest pronunciation lexicon of its kind. This dataset is a unique comparable corpus of IPA pronunciations annotated from multiple sources. We analyze the dataset, noting the presence of machine-generated pronunciations. We develop a novel visualization method to quantify syllabification. We experiment on the new combined task of multilingual IPA syllabification and stress prediction, finding that training a massively multilingual neural sequence-to-sequence model with copy attention can improve performance on both high- and low-resource languages, and multi-task training on stress prediction helps with syllabification.

     @inproceedings{wu-yarowsky-2021-pronunciations,
     title = "On Pronunciations in {W}iktionary: Extraction and Experiments on Multilingual Syllabification and Stress Prediction",
     author = "Wu, Winston and
     Yarowsky, David",
     editor = "Rapp, Reinhard and
     Sharoff, Serge and
     Zweigenbaum, Pierre",
     booktitle = "Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)",
     month = sep,
     year = "2021",
     address = "Online (Virtual Mode)",
     publisher = "INCOMA Ltd.",
     url = "https://aclanthology.org/2021.bucc-1.9",
     pages = "68--74",
     abstract = "We constructed parsers for five non-English editions of Wiktionary, which combined with pronunciations from the English edition, comprises over 5.3 million IPA pronunciations, the largest pronunciation lexicon of its kind. This dataset is a unique comparable corpus of IPA pronunciations annotated from multiple sources. We analyze the dataset, noting the presence of machine-generated pronunciations. We develop a novel visualization method to quantify syllabification. We experiment on the new combined task of multilingual IPA syllabification and stress prediction, finding that training a massively multilingual neural sequence-to-sequence model with copy attention can improve performance on both high- and low-resource languages, and multi-task training on stress prediction helps with syllabification.",
     }

593. T. Q. Nguyen, K. Murray, and D. Chiang, “Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution,” in Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand (online), 2021, p. 287–293. doi:10.18653/v1/2021.iwslt-1.33
     [BibTeX] [Abstract] [Link]
     

     In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments suggest that discourse context is unlikely the cause for concatenation improving BLEU by about +1 across four language pairs. Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.

     @inproceedings{nguyen-etal-2021-data,
     title = "Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution",
     author = "Nguyen, Toan Q. and
     Murray, Kenton and
     Chiang, David",
     editor = "Federico, Marcello and
     Waibel, Alex and
     Costa-juss{\`a}, Marta R. and
     Niehues, Jan and
     Stuker, Sebastian and
     Salesky, Elizabeth",
     booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
     month = aug,
     year = "2021",
     address = "Bangkok, Thailand (online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.iwslt-1.33",
     doi = "10.18653/v1/2021.iwslt-1.33",
     pages = "287--293",
     abstract = "In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments suggest that discourse context is unlikely the cause for concatenation improving BLEU by about +1 across four language pairs. Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.",
     }

594. E. Stengel-Eskin, J. Guallar-Blasco, and B. Van Durme, “Human-Model Divergence in the Handling of Vagueness,” in Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language, Online, 2021, p. 43–57. doi:10.18653/v1/2021.unimplicit-1.6
     [BibTeX] [Abstract] [Link]
     

     While aggregate performance metrics can generate valuable insights at a large scale, their dominance means more complex and nuanced language phenomena, such as vagueness, may be overlooked. Focusing on vague terms (e.g. sunny, cloudy, young, etc.) we inspect the behavior of visually grounded and text-only models, finding systematic divergences from human judgments even when a model{‘}s overall performance is high. To help explain this disparity, we identify two assumptions made by the datasets and models examined and, guided by the philosophy of vagueness, isolate cases where they do not hold.

     @inproceedings{stengel-eskin-etal-2021-human,
     title = "Human-Model Divergence in the Handling of Vagueness",
     author = "Stengel-Eskin, Elias and
     Guallar-Blasco, Jimena and
     Van Durme, Benjamin",
     editor = "Roth, Michael and
     Tsarfaty, Reut and
     Goldberg, Yoav",
     booktitle = "Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.unimplicit-1.6",
     doi = "10.18653/v1/2021.unimplicit-1.6",
     pages = "43--57",
     abstract = "While aggregate performance metrics can generate valuable insights at a large scale, their dominance means more complex and nuanced language phenomena, such as vagueness, may be overlooked. Focusing on vague terms (e.g. sunny, cloudy, young, etc.) we inspect the behavior of visually grounded and text-only models, finding systematic divergences from human judgments even when a model{'}s overall performance is high. To help explain this disparity, we identify two assumptions made by the datasets and models examined and, guided by the philosophy of vagueness, isolate cases where they do not hold.",
     }

595. G. Kumar, P. Koehn, and S. Khudanpur, “Learning Curricula for Multilingual Neural Machine Translation Training,” in Proceedings of Machine Translation Summit XVIII: Research Track, Virtual, 2021, p. 1–9.
     [BibTeX] [Abstract] [Link]
     

     Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula {–} orderings of the multilingual training data {–} which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.

     @inproceedings{kumar-etal-2021-learning-curricula,
     title = "Learning Curricula for Multilingual Neural Machine Translation Training",
     author = "Kumar, Gaurav and
     Koehn, Philipp and
     Khudanpur, Sanjeev",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of Machine Translation Summit XVIII: Research Track",
     month = aug,
     year = "2021",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2021.mtsummit-research.1",
     pages = "1--9",
     abstract = "Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula {--} orderings of the multilingual training data {--} which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.",
     }

596. K. Marchisio, P. Koehn, and C. Xiong, “An Alignment-Based Approach to Semi-Supervised Bilingual Lexicon Induction with Small Parallel Corpora,” in Proceedings of Machine Translation Summit XVIII: Research Track, Virtual, 2021, p. 293–304.
     [BibTeX] [Abstract] [Link]
     

     Aimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While interesting and fully unsupervised settings are unrealistic; small amounts of bilingual data are usually available due to the existence of massively multilingual parallel corpora and or linguists can create small amounts of parallel data. In this work and we demonstrate an effective bootstrapping approach for semi-supervised bilingual lexicon induction that capitalizes upon the complementary strengths of two disparate methods for inducing bilingual lexicons. Whereas statistical methods are highly effective at inducing correct translation pairs for words frequently occurring in a parallel corpus and monolingual embedding spaces have the advantage of having been trained on large amounts of data and and therefore may induce accurate translations for words absent from the small corpus. By combining these relative strengths and our method achieves state-of-the-art results on 3 of 4 language pairs in the challenging VecMap test set using minimal amounts of parallel data and without the need for a translation dictionary. We release our implementation at www.blind-review.code.

     @inproceedings{marchisio-etal-2021-alignment,
     title = "An Alignment-Based Approach to Semi-Supervised Bilingual Lexicon Induction with Small Parallel Corpora",
     author = "Marchisio, Kelly and
     Koehn, Philipp and
     Xiong, Conghao",
     editor = "Duh, Kevin and
     Guzm{\'a}n, Francisco",
     booktitle = "Proceedings of Machine Translation Summit XVIII: Research Track",
     month = aug,
     year = "2021",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2021.mtsummit-research.24",
     pages = "293--304",
     abstract = "Aimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While interesting and fully unsupervised settings are unrealistic; small amounts of bilingual data are usually available due to the existence of massively multilingual parallel corpora and or linguists can create small amounts of parallel data. In this work and we demonstrate an effective bootstrapping approach for semi-supervised bilingual lexicon induction that capitalizes upon the complementary strengths of two disparate methods for inducing bilingual lexicons. Whereas statistical methods are highly effective at inducing correct translation pairs for words frequently occurring in a parallel corpus and monolingual embedding spaces have the advantage of having been trained on large amounts of data and and therefore may induce accurate translations for words absent from the small corpus. By combining these relative strengths and our method achieves state-of-the-art results on 3 of 4 language pairs in the challenging VecMap test set using minimal amounts of parallel data and without the need for a translation dictionary. We release our implementation at www.blind-review.code.",
     }

597. J. Ou, N. Weir, A. Belyy, F. Yu, and B. Van Durme, “InFillmore: Frame-Guided Language Generation with Bidirectional Context,” in Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, Online, 2021, p. 129–142. doi:10.18653/v1/2021.starsem-1.12
     [BibTeX] [Abstract] [Link]
     

     We propose a structured extension to bidirectional-context conditional language generation, or {“}infilling,{”} inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.

     @inproceedings{ou-etal-2021-infillmore,
     title = "{I}n{F}illmore: Frame-Guided Language Generation with Bidirectional Context",
     author = "Ou, Jiefu and
     Weir, Nathaniel and
     Belyy, Anton and
     Yu, Felix and
     Van Durme, Benjamin",
     editor = "Ku, Lun-Wei and
     Nastase, Vivi and
     Vuli{\'c}, Ivan",
     booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.starsem-1.12",
     doi = "10.18653/v1/2021.starsem-1.12",
     pages = "129--142",
     abstract = "We propose a structured extension to bidirectional-context conditional language generation, or {``}infilling,{''} inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.",
     }

598. N. Holzenberger and B. Van Durme, “Factoring Statutory Reasoning as Language Understanding Challenges,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, 2021, p. 2742–2758. doi:10.18653/v1/2021.acl-long.213
     [BibTeX] [Abstract] [Link]
     

     Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.

     @inproceedings{holzenberger-van-durme-2021-factoring,
     title = "Factoring Statutory Reasoning as Language Understanding Challenges",
     author = "Holzenberger, Nils and
     Van Durme, Benjamin",
     editor = "Zong, Chengqing and
     Xia, Fei and
     Li, Wenjie and
     Navigli, Roberto",
     booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.acl-long.213",
     doi = "10.18653/v1/2021.acl-long.213",
     pages = "2742--2758",
     abstract = "Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.",
     }

599. L. Zhou, L. Ding, K. Duh, S. Watanabe, R. Sasano, and K. Takeda, “Self-Guided Curriculum Learning for Neural Machine Translation,” in Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand (online), 2021, p. 206–214. doi:10.18653/v1/2021.iwslt-1.25
     [BibTeX] [Abstract] [Link]
     

     In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model {“}knows{”} how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.

     @inproceedings{zhou-etal-2021-self,
     title = "Self-Guided Curriculum Learning for Neural Machine Translation",
     author = "Zhou, Lei and
     Ding, Liang and
     Duh, Kevin and
     Watanabe, Shinji and
     Sasano, Ryohei and
     Takeda, Koichi",
     editor = "Federico, Marcello and
     Waibel, Alex and
     Costa-juss{\`a}, Marta R. and
     Niehues, Jan and
     Stuker, Sebastian and
     Salesky, Elizabeth",
     booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
     month = aug,
     year = "2021",
     address = "Bangkok, Thailand (online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.iwslt-1.25",
     doi = "10.18653/v1/2021.iwslt-1.25",
     pages = "206--214",
     abstract = "In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model {``}knows{''} how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.",
     }

600. W. Wu, K. Duh, and D. Yarowsky, “Sequence Models for Computational Etymology of Borrowings,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, 2021, p. 4032–4037. doi:10.18653/v1/2021.findings-acl.353
     [BibTeX] [Link]

     @inproceedings{wu-etal-2021-sequence,
     title = "Sequence Models for Computational Etymology of Borrowings",
     author = "Wu, Winston and
     Duh, Kevin and
     Yarowsky, David",
     editor = "Zong, Chengqing and
     Xia, Fei and
     Li, Wenjie and
     Navigli, Roberto",
     booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.findings-acl.353",
     doi = "10.18653/v1/2021.findings-acl.353",
     pages = "4032--4037",
     }

601. H. Inaguma, B. Yan, S. Dalmia, P. Guo, J. Shi, K. Duh, and S. Watanabe, “ESPnet-ST IWSLT 2021 Offline Speech Translation System,” in Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand (online), 2021, p. 100–109. doi:10.18653/v1/2021.iwslt-1.10
     [BibTeX] [Abstract] [Link]
     

     This paper describes the ESPnet-ST group{‘}s IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audio segmentation. On the data side, we investigated sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech translation. Specifically, we used multi-referenced SeqKD from multiple teachers trained on different amounts of bitext. On the architecture side, we adopted the Conformer encoder and the Multi-Decoder architecture, which equips dedicated decoders for speech recognition and translation tasks in a unified encoder-decoder model and enables search in both source and target language spaces during inference. We also significantly improved audio segmentation by using the pyannote.audio toolkit and merging multiple short segments for long context modeling. Experimental evaluations showed that each of them contributed to large improvements in translation performance. Our best E2E system combined all the above techniques with model ensembling and achieved 31.4 BLEU on the 2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of tst2021.

     @inproceedings{inaguma-etal-2021-espnet,
     title = "{ESP}net-{ST} {IWSLT} 2021 Offline Speech Translation System",
     author = "Inaguma, Hirofumi and
     Yan, Brian and
     Dalmia, Siddharth and
     Guo, Pengcheng and
     Shi, Jiatong and
     Duh, Kevin and
     Watanabe, Shinji",
     editor = "Federico, Marcello and
     Waibel, Alex and
     Costa-juss{\`a}, Marta R. and
     Niehues, Jan and
     Stuker, Sebastian and
     Salesky, Elizabeth",
     booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
     month = aug,
     year = "2021",
     address = "Bangkok, Thailand (online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.iwslt-1.10",
     doi = "10.18653/v1/2021.iwslt-1.10",
     pages = "100--109",
     abstract = "This paper describes the ESPnet-ST group{'}s IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audio segmentation. On the data side, we investigated sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech translation. Specifically, we used multi-referenced SeqKD from multiple teachers trained on different amounts of bitext. On the architecture side, we adopted the Conformer encoder and the Multi-Decoder architecture, which equips dedicated decoders for speech recognition and translation tasks in a unified encoder-decoder model and enables search in both source and target language spaces during inference. We also significantly improved audio segmentation by using the pyannote.audio toolkit and merging multiple short segments for long context modeling. Experimental evaluations showed that each of them contributed to large improvements in translation performance. Our best E2E system combined all the above techniques with model ensembling and achieved 31.4 BLEU on the 2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of tst2021.",
     }

602. R. Wicks and M. Post, “A unified approach to sentence segmentation of punctuated text in many languages,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, 2021, p. 3995–4007. doi:10.18653/v1/2021.acl-long.309
     [BibTeX] [Abstract] [Link]
     

     The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is \textit{sentence segmentation}. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.

     @inproceedings{wicks-post-2021-unified,
     title = "A unified approach to sentence segmentation of punctuated text in many languages",
     author = "Wicks, Rachel and
     Post, Matt",
     editor = "Zong, Chengqing and
     Xia, Fei and
     Li, Wenjie and
     Navigli, Roberto",
     booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.acl-long.309",
     doi = "10.18653/v1/2021.acl-long.309",
     pages = "3995--4007",
     abstract = "The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is \textit{sentence segmentation}. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.",
     }

603. E. Schumacher, J. Mayfield, and M. Dredze, “Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, 2021, p. 583–595. doi:10.18653/v1/2021.findings-acl.52
     [BibTeX] [Link]

     @inproceedings{schumacher-etal-2021-cross,
     title = "Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking",
     author = "Schumacher, Elliot and
     Mayfield, James and
     Dredze, Mark",
     editor = "Zong, Chengqing and
     Xia, Fei and
     Li, Wenjie and
     Navigli, Roberto",
     booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.findings-acl.52",
     doi = "10.18653/v1/2021.findings-acl.52",
     pages = "583--595",
     }

604. G. Coppersmith, A. Fine, P. Crutchley, and J. Carroll, “Individual Differences in the Movement-Mood Relationship in Digital Life Data,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 25–31. doi:10.18653/v1/2021.clpsych-1.3
     [BibTeX] [Abstract] [Link]
     

     Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood, and wellbeing. Such {“}digital life data{”} provides crucial insight into the lives of patients outside the healthcare setting that has long been lacking, from a better understanding of mundane patterns of exercise and sleep routines to harbingers of emotional crisis. Moreover, information about individual differences and personalities is encoded in digital life data. In this paper we examine the relationship between mood and movement using linguistic and biometric data, respectively. Does increased physical activity (movement) have an effect on a person{‘}s mood (or vice-versa)? We find that weak group-level relationships between movement and mood mask interesting and often strong relationships between the two for individuals within the group. We describe these individual differences, and argue that individual variability in the relationship between movement and mood is one of many such factors that ought be taken into account in wellbeing-focused apps and AI systems.

     @inproceedings{coppersmith-etal-2021-individual,
     title = "Individual Differences in the Movement-Mood Relationship in Digital Life Data",
     author = "Coppersmith, Glen and
     Fine, Alex and
     Crutchley, Patrick and
     Carroll, Joshua",
     editor = "Goharian, Nazli and
     Resnik, Philip and
     Yates, Andrew and
     Ireland, Molly and
     Niederhoffer, Kate and
     Resnik, Rebecca",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.3",
     doi = "10.18653/v1/2021.clpsych-1.3",
     pages = "25--31",
     abstract = "Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood, and wellbeing. Such {``}digital life data{''} provides crucial insight into the lives of patients outside the healthcare setting that has long been lacking, from a better understanding of mundane patterns of exercise and sleep routines to harbingers of emotional crisis. Moreover, information about individual differences and personalities is encoded in digital life data. In this paper we examine the relationship between mood and movement using linguistic and biometric data, respectively. Does increased physical activity (movement) have an effect on a person{'}s mood (or vice-versa)? We find that weak group-level relationships between movement and mood mask interesting and often strong relationships between the two for individuals within the group. We describe these individual differences, and argue that individual variability in the relationship between movement and mood is one of many such factors that ought be taken into account in wellbeing-focused apps and AI systems.",
     }

605. T. Lippincott and B. Van Durme, “Active learning and negative evidence for language identification,” in Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, Online, 2021, p. 47–51. doi:10.18653/v1/2021.dash-1.8
     [BibTeX] [Abstract] [Link]
     

     Language identification (LID), the task of determining the natural language of a given text, is an essential first step in most NLP pipelines. While generally a solved problem for documents of sufficient length and languages with ample training data, the proliferation of microblogs and other social media has made it increasingly common to encounter use-cases that *don{‘}t* satisfy these conditions. In these situations, the fundamental difficulty is the lack of, and cost of gathering, labeled data: unlike some annotation tasks, no single {“}expert{”} can quickly and reliably identify more than a handful of languages. This leads to a natural question: can we gain useful information when annotators are only able to *rule out* languages for a given document, rather than supply a positive label? What are the optimal choices for gathering and representing such *negative evidence* as a model is trained? In this paper, we demonstrate that using negative evidence can improve the performance of a simple neural LID model. This improvement is sensitive to policies of how the evidence is represented in the loss function, and for deciding which annotators to employ given the instance and model state. We consider simple policies and report experimental results that indicate the optimal choices for this task. We conclude with a discussion of future work to determine if and how the results generalize to other classification tasks.

     @inproceedings{lippincott-van-durme-2021-active,
     title = "Active learning and negative evidence for language identification",
     author = "Lippincott, Thomas and
     Van Durme, Ben",
     editor = "Dragut, Eduard and
     Li, Yunyao and
     Popa, Lucian and
     Vucetic, Slobodan",
     booktitle = "Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.dash-1.8",
     doi = "10.18653/v1/2021.dash-1.8",
     pages = "47--51",
     abstract = "Language identification (LID), the task of determining the natural language of a given text, is an essential first step in most NLP pipelines. While generally a solved problem for documents of sufficient length and languages with ample training data, the proliferation of microblogs and other social media has made it increasingly common to encounter use-cases that *don{'}t* satisfy these conditions. In these situations, the fundamental difficulty is the lack of, and cost of gathering, labeled data: unlike some annotation tasks, no single {``}expert{''} can quickly and reliably identify more than a handful of languages. This leads to a natural question: can we gain useful information when annotators are only able to *rule out* languages for a given document, rather than supply a positive label? What are the optimal choices for gathering and representing such *negative evidence* as a model is trained? In this paper, we demonstrate that using negative evidence can improve the performance of a simple neural LID model. This improvement is sensitive to policies of how the evidence is represented in the loss function, and for deciding which annotators to employ given the instance and model state. We consider simple policies and report experimental results that indicate the optimal choices for this task. We conclude with a discussion of future work to determine if and how the results generalize to other classification tasks.",
     }

606. S. MacAvaney, A. Mittu, G. Coppersmith, J. Leintz, and P. Resnik, “Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 70–80. doi:10.18653/v1/2021.clpsych-1.7
     [BibTeX] [Abstract] [Link]
     

     Progress on NLP for mental health {–-} indeed, for healthcare in general {–-} is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.

     @inproceedings{macavaney-etal-2021-community,
     title = "Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the {CLP}sych 2021 Shared Task",
     author = "MacAvaney, Sean and
     Mittu, Anjali and
     Coppersmith, Glen and
     Leintz, Jeff and
     Resnik, Philip",
     editor = "Goharian, Nazli and
     Resnik, Philip and
     Yates, Andrew and
     Ireland, Molly and
     Niederhoffer, Kate and
     Resnik, Rebecca",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.7",
     doi = "10.18653/v1/2021.clpsych-1.7",
     pages = "70--80",
     abstract = "Progress on NLP for mental health {---} indeed, for healthcare in general {---} is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.",
     }

607. Z. Wood-Doughty, P. Xu, X. Liu, and M. Dredze, “Using Noisy Self-Reports to Predict Twitter User Demographics,” in Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, Online, 2021, p. 123–137. doi:10.18653/v1/2021.socialnlp-1.11
     [BibTeX] [Abstract] [Link]
     

     Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.

     @inproceedings{wood-doughty-etal-2021-using,
     title = "Using Noisy Self-Reports to Predict {T}witter User Demographics",
     author = "Wood-Doughty, Zach and
     Xu, Paiheng and
     Liu, Xiao and
     Dredze, Mark",
     editor = "Ku, Lun-Wei and
     Li, Cheng-Te",
     booktitle = "Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.socialnlp-1.11",
     doi = "10.18653/v1/2021.socialnlp-1.11",
     pages = "123--137",
     abstract = "Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.",
     }

608. K. Harrigian, C. Aguirre, and M. Dredze, “On the State of Social Media Data for Mental Health Research,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 15–24. doi:10.18653/v1/2021.clpsych-1.2
     [BibTeX] [Abstract] [Link]
     

     Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.

     @inproceedings{harrigian-etal-2021-state,
     title = "On the State of Social Media Data for Mental Health Research",
     author = "Harrigian, Keith and
     Aguirre, Carlos and
     Dredze, Mark",
     editor = "Goharian, Nazli and
     Resnik, Philip and
     Yates, Andrew and
     Ireland, Molly and
     Niederhoffer, Kate and
     Resnik, Rebecca",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.2",
     doi = "10.18653/v1/2021.clpsych-1.2",
     pages = "15--24",
     abstract = "Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.",
     }

609. C. Lin, A. Jaech, X. Li, Matt Gormley, and J. Eisner, “Limitations of Autoregressive Models and Their Alternatives,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Online, 2021, p. 5147–5173. doi:10.18653/v1/2021.naacl-main.405
     [BibTeX] [Link]

     @InProceedings{lin-et-al-2021-naacl,
     aclid = "2021.naacl-main.405",
     doi = "10.18653/v1/2021.naacl-main.405",
     author = "Chu-Cheng Lin and Aaron Jaech and Xin Li and Matt
     Gormley and Jason Eisner",
     title = "Limitations of Autoregressive Models and Their
     Alternatives",
     booktitle = "Proceedings of the 2021 Conference of the North
     American Chapter of the Association for Computational
     Linguistics: Human Language Technologies (NAACL-HLT)",
     pages = "5147--5173",
     year = "2021",
     month = jun,
     address = "Online",
     URL = "http://cs.jhu.edu/~jason/papers/#lin-et-al-2021-naacl",
     }

610. G. Qin and J. Eisner, “Learning How To Ask: Querying LMs with Mixtures of Soft Prompts,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Online, 2021, p. 5203–5212. doi:10.18653/v1/2021.naacl-main.410
     [BibTeX] [Link]

     @InProceedings{qin-eisner-2021,
     aclid = "2021.naacl-main.410",
     doi = "10.18653/v1/2021.naacl-main.410",
     author = "Guanghui Qin and Jason Eisner",
     title = "Learning How To Ask: Querying {LM}s with Mixtures of
     Soft Prompts",
     booktitle = "Proceedings of the 2021 Conference of the North
     American Chapter of the Association for Computational
     Linguistics: Human Language Technologies (NAACL-HLT)",
     pages = "5203--5212",
     year = "2021",
     month = jun,
     address = "Online",
     note = "Best Short Paper Award.",
     URL = "http://cs.jhu.edu/~jason/papers/#qin-eisner-2021",
     }

611. S. Sia and K. Duh, “Adaptive Mixed Component LDA for Low Resource Topic Modeling,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, p. 2451–2469. doi:10.18653/v1/2021.eacl-main.209
     [BibTeX] [Abstract] [Link]
     

     Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods. In this challenging resource constrained setting, we explore mixture models which interpolate between the discrete and continuous topic-word distributions that utilise pre-trained embeddings to improve topic coherence. We introduce an automatic trade-off between the discrete and continuous representations via an adaptive mixture coefficient, which places greater weight on the discrete representation when the corpus statistics are more reliable. The adaptive mixture coefficient takes into account global corpus statistics, and the uncertainty in each topic{‘}s continuous distributions. Our approach outperforms the fully discrete, fully continuous, and static mixture model on topic coherence in low resource settings. We additionally demonstrate the generalisability of our method by extending it to handle multilingual document collections.

     @inproceedings{sia-duh-2021-adaptive,
     title = "Adaptive Mixed Component {LDA} for Low Resource Topic Modeling",
     author = "Sia, Suzanna and
     Duh, Kevin",
     editor = "Merlo, Paola and
     Tiedemann, Jorg and
     Tsarfaty, Reut",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-main.209",
     doi = "10.18653/v1/2021.eacl-main.209",
     pages = "2451--2469",
     abstract = "Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods. In this challenging resource constrained setting, we explore mixture models which interpolate between the discrete and continuous topic-word distributions that utilise pre-trained embeddings to improve topic coherence. We introduce an automatic trade-off between the discrete and continuous representations via an adaptive mixture coefficient, which places greater weight on the discrete representation when the corpus statistics are more reliable. The adaptive mixture coefficient takes into account global corpus statistics, and the uncertainty in each topic{'}s continuous distributions. Our approach outperforms the fully discrete, fully continuous, and static mixture model on topic coherence in low resource settings. We additionally demonstrate the generalisability of our method by extending it to handle multilingual document collections.",
     }

612. X. Huang, M. J. Paul, F. Dernoncourt, R. Burke, and M. Dredze, “User Factor Adaptation for User Embedding via Multitask Learning,” in Proceedings of the Second Workshop on Domain Adaptation for NLP, Kyiv, Ukraine, 2021, p. 172–182.
     [BibTeX] [Abstract] [Link]
     

     Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.

     @inproceedings{huang-etal-2021-user,
     title = "User Factor Adaptation for User Embedding via Multitask Learning",
     author = "Huang, Xiaolei and
     Paul, Michael J. and
     Dernoncourt, Franck and
     Burke, Robin and
     Dredze, Mark",
     editor = "Ben-David, Eyal and
     Cohen, Shay and
     McDonald, Ryan and
     Plank, Barbara and
     Reichart, Roi and
     Rotman, Guy and
     Ziser, Yftah",
     booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
     month = apr,
     year = "2021",
     address = "Kyiv, Ukraine",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.adaptnlp-1.18",
     pages = "172--182",
     abstract = "Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.",
     }

613. P. Xia, G. Qin, S. Vashishtha, Y. Chen, T. Chen, C. May, C. Harman, K. Rawlins, A. S. White, and B. Van Durme, “LOME: Large Ontology Multilingual Extraction,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, Online, 2021, p. 149–159. doi:10.18653/v1/2021.eacl-demos.19
     [BibTeX] [Abstract] [Link]
     

     We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.

     @inproceedings{xia-etal-2021-lome,
     title = "{LOME}: Large Ontology Multilingual Extraction",
     author = "Xia, Patrick and
     Qin, Guanghui and
     Vashishtha, Siddharth and
     Chen, Yunmo and
     Chen, Tongfei and
     May, Chandler and
     Harman, Craig and
     Rawlins, Kyle and
     White, Aaron Steven and
     Van Durme, Benjamin",
     editor = "Gkatzia, Dimitra and
     Seddah, Djam{\'e}",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-demos.19",
     doi = "10.18653/v1/2021.eacl-demos.19",
     pages = "149--159",
     abstract = "We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.",
     }

614. H. Xu, S. Ebner, M. Yarmohammadi, A. S. White, B. Van Durme, and K. Murray, “Gradual Fine-Tuning for Low-Resource Domain Adaptation,” in Proceedings of the Second Workshop on Domain Adaptation for NLP, Kyiv, Ukraine, 2021, p. 214–221.
     [BibTeX] [Abstract] [Link]
     

     Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.

     @inproceedings{xu-etal-2021-gradual,
     title = "Gradual Fine-Tuning for Low-Resource Domain Adaptation",
     author = "Xu, Haoran and
     Ebner, Seth and
     Yarmohammadi, Mahsa and
     White, Aaron Steven and
     Van Durme, Benjamin and
     Murray, Kenton",
     editor = "Ben-David, Eyal and
     Cohen, Shay and
     McDonald, Ryan and
     Plank, Barbara and
     Reichart, Roi and
     Rotman, Guy and
     Ziser, Yftah",
     booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
     month = apr,
     year = "2021",
     address = "Kyiv, Ukraine",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.adaptnlp-1.22",
     pages = "214--221",
     abstract = "Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.",
     }

615. J. Shi, J. D. Amith, R. Castillo Garc{‘i}a, E. Guadalupe Sierra, K. Duh, and S. Watanabe, “Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, p. 1134–1145. doi:10.18653/v1/2021.eacl-main.96
     [BibTeX] [Abstract] [Link]
     

     {“}Transcription bottlenecks{”}, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yoloxóchitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.

     @inproceedings{shi-etal-2021-leveraging,
     title = "Leveraging End-to-End {ASR} for Endangered Language Documentation: An Empirical Study on Yol{\'o}xochitl {M}ixtec",
     author = "Shi, Jiatong and
     Amith, Jonathan D. and
     Castillo Garc{\'\i}a, Rey and
     Guadalupe Sierra, Esteban and
     Duh, Kevin and
     Watanabe, Shinji",
     editor = "Merlo, Paola and
     Tiedemann, Jorg and
     Tsarfaty, Reut",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-main.96",
     doi = "10.18653/v1/2021.eacl-main.96",
     pages = "1134--1145",
     abstract = "{``}Transcription bottlenecks{''}, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yolox{\'o}chitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.",
     }

616. Yuki Takashima, Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Leibny Paola García-Perera, and Kenji Nagamatsu, “End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection,” in Spoken Language Technology Workshop, 2021.
     [BibTeX] [Link]

     @inproceedings{232413801,
     title = {End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection},
     author = {{Yuki Takashima} and {Yusuke Fujita} and {Shinji Watanabe} and {Shota Horiguchi} and {Leibny Paola García-Perera} and {Kenji Nagamatsu}},
     year = 2021,
     month = {1},
     booktitle = {Spoken Language Technology Workshop},
     url = {https://www.semanticscholar.org/paper/cbf9a2560eac548e7b3d5eb7074c40b7bb861909},
     }

617. Fengze Liu, K. Yan, Adam P. Harrison, Dazhou Guo, Le Lu, A. Yuille, Lingyun Huang, G. Xie, Jing Xiao, X. Ye, and D. Jin, “SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
     [BibTeX] [Link]

     @inproceedings{237621545,
     title = {SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings},
     author = {{Fengze Liu} and {K. Yan} and {Adam P. Harrison} and {Dazhou Guo} and {Le Lu} and {A. Yuille} and {Lingyun Huang} and {G. Xie} and {Jing Xiao} and {X. Ye} and {D. Jin}},
     year = 2021,
     month = {9},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/2950acc069210c93d5d25f615b82bdc403241046},
     }

618. Yao Sun, R. Kaur, Shubham Gupta, Rahul Paul, Ritu Das, S. Cho, Saket Anand, J. Boutilier, S. Saria, J. Palma, S. Saluja, R. McAdams, A. Kaur, Gautam Yadav, and Harpreet Singh, “Development and validation of high definition phenotype-based mortality prediction in critical care units,” in JAMIA Open, 2021.
     [BibTeX] [Link]

     @inproceedings{232479139,
     title = {Development and validation of high definition phenotype-based mortality prediction in critical care units},
     author = {{Yao Sun} and {R. Kaur} and {Shubham Gupta} and {Rahul Paul} and {Ritu Das} and {S. Cho} and {Saket Anand} and {J. Boutilier} and {S. Saria} and {J. Palma} and {S. Saluja} and {R. McAdams} and {A. Kaur} and {Gautam Yadav} and {Harpreet Singh}},
     year = 2021,
     month = {1},
     booktitle = {JAMIA Open},
     url = {https://www.semanticscholar.org/paper/6077d1afa94008aceb63e81b4bdd6ad08e98f3d8},
     }

619. Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “Over-and-Under Complete Convolutional RNN for MRI Reconstruction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
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     @inproceedings{235446931,
     title = {Over-and-Under Complete Convolutional RNN for MRI Reconstruction},
     author = {{Pengfei Guo} and {Jeya Maria Jose Valanarasu} and {Puyang Wang} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
     year = 2021,
     month = {6},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/4e5095ca6e280b068aa572c6d4afc32d6b246492},
     }

620. Kelly Marchisio, Conghao Xiong, and Philipp Koehn, “Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{260435370,
     title = {Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry},
     author = {{Kelly Marchisio} and {Conghao Xiong} and {Philipp Koehn}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/f7bb9e4c44356182bff28bfca99404c7d9c04d2b},
     }

621. Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, and S. Khudanpur, “Wake Word Detection with Streaming Transformers,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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     @inproceedings{231855282,
     title = {Wake Word Detection with Streaming Transformers},
     author = {{Yiming Wang} and {Hang Lv} and {Daniel Povey} and {Lei Xie} and {S. Khudanpur}},
     year = 2021,
     month = {2},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/ca4b945ad7d109c3cbc2170a942ca3b0ecf6fcf5},
     }

622. G. Kumar, Philipp Koehn, and S. Khudanpur, “Learning Policies for Multilingual Training of Neural Machine Translation Systems,” in arXiv.org, 2021.
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     @inproceedings{232222906,
     title = {Learning Policies for Multilingual Training of Neural Machine Translation Systems},
     author = {{G. Kumar} and {Philipp Koehn} and {S. Khudanpur}},
     year = 2021,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/04bc96a2380bccb884cf2568e06d6e726247032b},
     }

623. Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, A. Yuille, and Wei Shen, “Glance-and-Gaze Vision Transformer,” in Neural Information Processing Systems, 2021.
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     @inproceedings{235352495,
     title = {Glance-and-Gaze Vision Transformer},
     author = {{Qihang Yu} and {Yingda Xia} and {Yutong Bai} and {Yongyi Lu} and {A. Yuille} and {Wei Shen}},
     year = 2021,
     month = {6},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/ac74a160e0ca53d3ffb15f79f0b9d3911df2fc28},
     }

624. X. Zhang and K. Duh, “Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task,” in Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL), Virtual, 2021, p. 60–70.
     [BibTeX] [Abstract] [Link]
     

     A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.

     @inproceedings{zhang-duh-2021-approaching,
     title = "Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task",
     author = "Zhang, Xuan and
     Duh, Kevin",
     editor = "Shterionov, Dimitar",
     booktitle = "Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)",
     month = aug,
     year = "2021",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2021.mtsummit-at4ssl.7",
     pages = "60--70",
     abstract = "A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.",
     }

625. J. Villalba, Sonal Joshi, Piotr Żelasko, and N. Dehak, “Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems,” in Interspeech, 2021.
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     @inproceedings{235790692,
     title = {Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems},
     author = {{J. Villalba} and {Sonal Joshi} and {Piotr Żelasko} and {N. Dehak}},
     year = 2021,
     month = {7},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/8abbc820db608654c4ba10203245c191566e7286},
     }

626. Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Leibny Paola García-Perera, and Kenji Nagamatsu, “Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers,” in arXiv.org, 2021.
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     @inproceedings{231662389,
     title = {Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers},
     author = {{Yawen Xue} and {Shota Horiguchi} and {Yusuke Fujita} and {Yuki Takashima} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Kenji Nagamatsu}},
     year = 2021,
     month = {1},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/8ca58f3f6e59a6d243f3da6c196e9f730e6e9993},
     }

627. Ju He, Adam Kortylewski, and A. Yuille, “COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{231719786,
     title = {COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification},
     author = {{Ju He} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/9941da89d24b2537a88f615d12f163fd432ebfda},
     }

628. Hongru Zhu, A. Yuille, and D. Kersten, “Three-dimensional pose discrimination in natural images of humans,” in Annual Meeting of the Cognitive Science Society, 2021.
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     @inproceedings{239122639,
     title = {Three-dimensional pose discrimination in natural images of humans},
     author = {{Hongru Zhu} and {A. Yuille} and {D. Kersten}},
     year = 2021,
     month = {7},
     booktitle = {Annual Meeting of the Cognitive Science Society},
     url = {https://www.semanticscholar.org/paper/b11a13a4118fc032cb995ca601b01fe481c75665},
     }

629. Seyoun Park, J. Sham, S. Kawamoto, A. Blair, N. Rozich, D. Fouladi, S. Shayesteh, R. Hruban, Jin He, elliot k fishman, A. Yuille, E. Fishman, and L. Chu, “CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.,” in AJR. American journal of roentgenology, 2021.
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     @inproceedings{237375333,
     title = {CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.},
     author = {{Seyoun Park} and {J. Sham} and {S. Kawamoto} and {A. Blair} and {N. Rozich} and {D. Fouladi} and {S. Shayesteh} and {R. Hruban} and {Jin He} and {elliot k fishman} and {A. Yuille} and {E. Fishman} and {L. Chu}},
     year = 2021,
     month = {9},
     booktitle = {AJR. American journal of roentgenology},
     url = {https://www.semanticscholar.org/paper/c68fbb8e0ba372d01ed9c4c797369668274dc89d},
     }

630. Sangwook Park, Angeles Salles, K. Allen, C. Moss, and Mounya Elhilali, “Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks,” in eNeuro, 2021.
     [BibTeX] [Link]

     @inproceedings{233744082,
     title = {Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks},
     author = {{Sangwook Park} and {Angeles Salles} and {K. Allen} and {C. Moss} and {Mounya Elhilali}},
     year = 2021,
     month = {5},
     booktitle = {eNeuro},
     url = {https://www.semanticscholar.org/paper/33056958f57d7a3bdf0c28bafb4932e6443579a8},
     }

631. Prithviraj Dhar, Joshua Gleason, A. Roy, C. Castillo, and R. Chellappa, “Supplementary Material – PASS.” 2021.
     [BibTeX] [Link]

     @inproceedings{244312236,
     title = {Supplementary Material - PASS},
     author = {{Prithviraj Dhar} and {Joshua Gleason} and {A. Roy} and {C. Castillo} and {R. Chellappa}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/ee50fa46cd195e4b59330297d4285877906583b5},
     }

632. Chenglin Yang, Siyuan Qiao, Adam Kortylewski, and A. Yuille, “Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{235829300,
     title = {Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms},
     author = {{Chenglin Yang} and {Siyuan Qiao} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/cca5a070dac2f434a10bcc12bd1377b8c7356e21},
     }

633. Domenick Poster, Matthew D. Thielke, R. Nguyen, Srinivasan Rajaraman, Xing Di, Cedric Nimpa Fondje, Vishal M. Patel, Nathan J. Short, B. Riggan, N. Nasrabadi, and Shuowen Hu, “A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
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     @inproceedings{230125224,
     title = {A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset},
     author = {{Domenick Poster} and {Matthew D. Thielke} and {R. Nguyen} and {Srinivasan Rajaraman} and {Xing Di} and {Cedric Nimpa Fondje} and {Vishal M. Patel} and {Nathan J. Short} and {B. Riggan} and {N. Nasrabadi} and {Shuowen Hu}},
     year = 2021,
     month = {1},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/d88d4a05e076a070e1209245a40d57a0e9c211a2},
     }

634. Jejin Cho, J. Villalba, and N. Dehak, “The JHU submission to VoxSRC-21: Track 3,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{238198440,
     title = {The JHU submission to VoxSRC-21: Track 3},
     author = {{Jejin Cho} and {J. Villalba} and {N. Dehak}},
     year = 2021,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ed2065a9cb6f31806aba9a70a4148b99225782a3},
     }

635. W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction,” in IEEE Transactions on Geoscience and Remote Sensing, 2021.
     [BibTeX] [Link]

     @inproceedings{235743099,
     title = {Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction},
     author = {{W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2021,
     month = {7},
     booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
     url = {https://www.semanticscholar.org/paper/6dffdd9ad229900de79646f53cc73715ad261508},
     }

636. Sai Saketh Rambhatla, Michael Jones, and R. Chellappa, “To Boost or not to Boost: On the Limits of Boosted Neural Networks,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{236493542,
     title = {To Boost or not to Boost: On the Limits of Boosted Neural Networks},
     author = {{Sai Saketh Rambhatla} and {Michael Jones} and {R. Chellappa}},
     year = 2021,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/cf94610981c556cc8e8930c6f71f88f2186d446f},
     }

637. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Label-Assemble: Leveraging Multiple Datasets with Partial Labels,” in IEEE International Symposium on Biomedical Imaging, 2021.
     [BibTeX] [Link]

     @inproceedings{247245043,
     title = {Label-Assemble: Leveraging Multiple Datasets with Partial Labels},
     author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
     year = 2021,
     month = {9},
     booktitle = {IEEE International Symposium on Biomedical Imaging},
     url = {https://www.semanticscholar.org/paper/ace00da928797186bf3c6e48e79149f4b8886418},
     }

638. Tiago Pimentel, Maria Ryskina, Sabrina J. Mielke, Shijie Wu, Eleanor Chodroff, Brian Leonard, Garrett Nicolai, Yustinus Ghanggo Ate, Salam Khalifa, Charbel El-Khaissi, Omer Goldman, M. Gasser, William Lane, M. Coler, Arturo Oncevay, Jaime Rafael Montoya Samame, Gema Celeste Silva Villegas, Adam Ek, Jean-Philippe Bernardy, A. Shcherbakov, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, E. Klyachko, A. Salehi, A. A. Krizhanovsky, Natalia Krizhanovsky, Clara Vania, Sardana Ivanova, A. Salchak, Christopher A. Straughn, Zoey Liu, J. North, Duygu Ataman, Witold Kieraś, Marcin Woliński, T. Suhardijanto, Niklas Stoehr, Z. Nuriah, S. Ratan, Francis M. Tyers, E. M. Ponti, Grant Aiton, R. Hatcher, Ritesh Kumar, Mans Hulden, B. Barta, Dorina Lakatos, Gábor Szolnok, Judit Ács, Mohith S Raj, David Yarowsky, Ryan Cotterell, Ben Ambridge, and Ekaterina Vylomova, “SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages,” in Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, 2021.
     [BibTeX] [Link]

     @inproceedings{244460160,
     title = {SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages},
     author = {{Tiago Pimentel} and {Maria Ryskina} and {Sabrina J. Mielke} and {Shijie Wu} and {Eleanor Chodroff} and {Brian Leonard} and {Garrett Nicolai} and {Yustinus Ghanggo Ate} and {Salam Khalifa} and {Charbel El-Khaissi} and {Omer Goldman} and {M. Gasser} and {William Lane} and {M. Coler} and {Arturo Oncevay} and {Jaime Rafael Montoya Samame} and {Gema Celeste Silva Villegas} and {Adam Ek} and {Jean-Philippe Bernardy} and {A. Shcherbakov} and {Karina Sheifer} and {Sofya Ganieva} and {Matvey Plugaryov} and {E. Klyachko} and {A. Salehi} and {A. A. Krizhanovsky} and {Natalia Krizhanovsky} and {Clara Vania} and {Sardana Ivanova} and {A. Salchak} and {Christopher A. Straughn} and {Zoey Liu} and {J. North} and {Duygu Ataman} and {Witold Kieraś} and {Marcin Woliński} and {T. Suhardijanto} and {Niklas Stoehr} and {Z. Nuriah} and {S. Ratan} and {Francis M. Tyers} and {E. M. Ponti} and {Grant Aiton} and {R. Hatcher} and {Ritesh Kumar} and {Mans Hulden} and {B. Barta} and {Dorina Lakatos} and {Gábor Szolnok} and {Judit Ács} and {Mohith S Raj} and {David Yarowsky} and {Ryan Cotterell} and {Ben Ambridge} and {Ekaterina Vylomova}},
     year = 2021,
     booktitle = {Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology},
     url = {https://www.semanticscholar.org/paper/136235d2a3dc4f1c995eaf977aec9c42114da850},
     }

639. Mounya Elhilali, “Adaptive Listening to Everyday Soundscapes,” in Interspeech, 2021.
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     @inproceedings{247439732,
     title = {Adaptive Listening to Everyday Soundscapes},
     author = {{Mounya Elhilali}},
     year = 2021,
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/fe2ecaf07328112ffbfd40c932b6356c41262198},
     }

640. Aviad Shtrosberg, J. Villalba, N. Dehak, Azaria Cohen, and Bar Ben-Yair, “Invariant Representation Learning for Robust Far-Field Speaker Recognition,” in International Conference on Statistical Language and Speech Processing, 2021.
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     @inproceedings{239039731,
     title = {Invariant Representation Learning for Robust Far-Field Speaker Recognition},
     author = {{Aviad Shtrosberg} and {J. Villalba} and {N. Dehak} and {Azaria Cohen} and {Bar Ben-Yair}},
     year = 2021,
     booktitle = {International Conference on Statistical Language and Speech Processing},
     url = {https://www.semanticscholar.org/paper/f157b429553c4a6165856783ec879cd8d0f6a4cd},
     }

641. Pramuditha Perera, Poojan Oza, and Vishal M. Patel, “One-Class Classification: A Survey,” in arXiv.org, 2021.
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     @inproceedings{231418911,
     title = {One-Class Classification: A Survey},
     author = {{Pramuditha Perera} and {Poojan Oza} and {Vishal M. Patel}},
     year = 2021,
     month = {1},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/bd6262ebdd1a865e8e6859ab7dd8dc576d2a90e6},
     }

642. Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, and Jamie Callan, “Complement Lexical Retrieval Model with Semantic Residual Embeddings,” in European Conference on Information Retrieval, 2021.
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     @inproceedings{232423090,
     title = {Complement Lexical Retrieval Model with Semantic Residual Embeddings},
     author = {{Luyu Gao} and {Zhuyun Dai} and {Tongfei Chen} and {Zhen Fan} and {Benjamin Van Durme} and {Jamie Callan}},
     year = 2021,
     booktitle = {European Conference on Information Retrieval},
     url = {https://www.semanticscholar.org/paper/1e4b28465d3166dd4fedeb5f23d4c768c170e859},
     }

643. Prithviraj Dhar, Joshua Gleason, A. Roy, C. Castillo, and R. Chellappa, “PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition,” in IEEE International Conference on Computer Vision, 2021.
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     @inproceedings{236956411,
     title = {PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition},
     author = {{Prithviraj Dhar} and {Joshua Gleason} and {A. Roy} and {C. Castillo} and {R. Chellappa}},
     year = 2021,
     month = {8},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/5451ff6ea2e7bb3d40bb61889bb3494cf0eebb3e},
     }

644. S. Schwarcz and R. Chellappa, “Finding Facial Forgery Artifacts with Parts-Based Detectors,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
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     @inproceedings{235703532,
     title = {Finding Facial Forgery Artifacts with Parts-Based Detectors},
     author = {{S. Schwarcz} and {R. Chellappa}},
     year = 2021,
     month = {6},
     booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
     url = {https://www.semanticscholar.org/paper/eb752fd572ca2c984b56a06c9974fdfdf951acb6},
     }

645. Md Mahfuz Ibn Alam, Antonios Anastasopoulos, L. Besacier, James Cross, Matthias Gallé, Philipp Koehn, and Vassilina Nikoulina, “On the Evaluation of Machine Translation for Terminology Consistency,” in arXiv.org, 2021.
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     @inproceedings{235593078,
     title = {On the Evaluation of Machine Translation for Terminology Consistency},
     author = {{Md Mahfuz Ibn Alam} and {Antonios Anastasopoulos} and {L. Besacier} and {James Cross} and {Matthias Gallé} and {Philipp Koehn} and {Vassilina Nikoulina}},
     year = 2021,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/365d30a104d03acee14530327eeaf7b66baa3421},
     }

646. E. Stengel-Eskin, J. Guallar-Blasco, and B. Van Durme, “Human-Model Divergence in the Handling of Vagueness,” in Proceedings of the Society for Computation in Linguistics 2021, Online, 2021, p. 390–393.
     [BibTeX] [Link]

     @inproceedings{stengel-eskin-etal-2021-human-model,
     title = "Human-Model Divergence in the Handling of Vagueness",
     author = "Stengel-Eskin, Elias and
     Guallar-Blasco, Jimena and
     Van Durme, Benjamin",
     editor = "Ettinger, Allyson and
     Pavlick, Ellie and
     Prickett, Brandon",
     booktitle = "Proceedings of the Society for Computation in Linguistics 2021",
     month = feb,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.scil-1.42",
     pages = "390--393",
     }

647. Junfei Xiao, Lequan Yu, Lei Xing, A. Yuille, and Yuyin Zhou, “DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{232417893,
     title = {DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation},
     author = {{Junfei Xiao} and {Lequan Yu} and {Lei Xing} and {A. Yuille} and {Yuyin Zhou}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/c624668efef8a707b2e7122f6cad648296b254a8},
     }

648. R. Pappagari, Piotr Żelasko, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Beyond Isolated Utterances: Conversational Emotion Recognition,” in Automatic Speech Recognition & Understanding, 2021.
     [BibTeX] [Link]

     @inproceedings{237492280,
     title = {Beyond Isolated Utterances: Conversational Emotion Recognition},
     author = {{R. Pappagari} and {Piotr Żelasko} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2021,
     month = {9},
     booktitle = {Automatic Speech Recognition & Understanding},
     url = {https://www.semanticscholar.org/paper/6b39bd717627d97c7e69e46801fdbb38ef4eb946},
     }

649. Shota Horiguchi, Shinji Watanabe, Leibny Paola García-Perera, Yawen Xue, Yuki Takashima, and Y. Kawaguchi, “Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors,” in Automatic Speech Recognition & Understanding, 2021.
     [BibTeX] [Link]

     @inproceedings{235732166,
     title = {Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors},
     author = {{Shota Horiguchi} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Yawen Xue} and {Yuki Takashima} and {Y. Kawaguchi}},
     year = 2021,
     month = {7},
     booktitle = {Automatic Speech Recognition & Understanding},
     url = {https://www.semanticscholar.org/paper/6f173939f6defe3ebae8fb12f19349ba96b7b5c4},
     }

650. Piotr Żelasko, Sonal Joshi, Yiwen Shao, J. Villalba, J. Trmal, N. Dehak, and S. Khudanpur, “Adversarial Attacks and Defenses for Speech Recognition Systems,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{232427815,
     title = {Adversarial Attacks and Defenses for Speech Recognition Systems},
     author = {{Piotr Żelasko} and {Sonal Joshi} and {Yiwen Shao} and {J. Villalba} and {J. Trmal} and {N. Dehak} and {S. Khudanpur}},
     year = 2021,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/9d15685433a067c5beca67e5f6cc612b3dc29f66},
     }

651. Yingda Xia, Dong Yang, Wenqi Li, A. Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, P. An, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, Elvira Stellato, G. Carrafiello, A. Ierardi, A. Yuille, and H. Roth, “Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{233324290,
     title = {Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation},
     author = {{Yingda Xia} and {Dong Yang} and {Wenqi Li} and {A. Myronenko} and {Daguang Xu} and {Hirofumi Obinata} and {Hitoshi Mori} and {P. An} and {S. Harmon} and {E. Turkbey} and {B. Turkbey} and {B. Wood} and {F. Patella} and {Elvira Stellato} and {G. Carrafiello} and {A. Ierardi} and {A. Yuille} and {H. Roth}},
     year = 2021,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7ba0e1864f094da06410af52166dc6c4e7a74adf},
     }

652. D. Dreizin, Tina Chen, Yuanyuan Liang, Yuyin Zhou, Fabio M. Paes, Yan Wang, A. Yuille, Patrick Roth, Kathryn Champ, Guang Li, Ashley McLenithan, and J. Morrison, “Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis,” in Abdominal Radiology, 2021.
     [BibTeX] [Link]

     @inproceedings{231651448,
     title = {Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis},
     author = {{D. Dreizin} and {Tina Chen} and {Yuanyuan Liang} and {Yuyin Zhou} and {Fabio M. Paes} and {Yan Wang} and {A. Yuille} and {Patrick Roth} and {Kathryn Champ} and {Guang Li} and {Ashley McLenithan} and {J. Morrison}},
     year = 2021,
     month = {1},
     booktitle = {Abdominal Radiology},
     url = {https://www.semanticscholar.org/paper/7f5547253cf023c093b2cd3c9f9412e53c58578e},
     }

653. Magdalena Rybicka, J. Villalba, Piotr Żelasko, N. Dehak, and K. Kowalczyk, “Spine2Net: SpineNet with Res2Net and Time-Squeeze-and-Excitation Blocks for Speaker Recognition,” in Interspeech, 2021.
     [BibTeX] [Link]

     @inproceedings{239671591,
     title = {Spine2Net: SpineNet with Res2Net and Time-Squeeze-and-Excitation Blocks for Speaker Recognition},
     author = {{Magdalena Rybicka} and {J. Villalba} and {Piotr Żelasko} and {N. Dehak} and {K. Kowalczyk}},
     year = 2021,
     month = {8},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/62a007787bdf51bb58668d2a88df18850c4e9e28},
     }

654. Xinyue Wei, Weichao Qiu, Yi Zhang, Zihao Xiao, and A. Yuille, “Supplementary Materials of NLS.” 2021.
     [BibTeX] [Link]

     @inproceedings{248067452,
     title = {Supplementary Materials of NLS},
     author = {{Xinyue Wei} and {Weichao Qiu} and {Yi Zhang} and {Zihao Xiao} and {A. Yuille}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/d31880f8e3aa03e9366ff5f582cbc70427a10783},
     }

655. Jieneng Chen, K. Yan, Yu-Dong Zhang, Youbao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, A. Yuille, Ya Zhang, and Le Lu, “Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
     [BibTeX] [Link]

     @inproceedings{232168577,
     title = {Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining},
     author = {{Jieneng Chen} and {K. Yan} and {Yu-Dong Zhang} and {Youbao Tang} and {Xun Xu} and {Shuwen Sun} and {Qiuping Liu} and {Lingyun Huang} and {Jing Xiao} and {A. Yuille} and {Ya Zhang} and {Le Lu}},
     year = 2021,
     month = {3},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/f3bbab69d8da5835868497409c9129d111ccf919},
     }

656. Samik Sadhu and H. Hermansky, “FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition.” 2021.
     [BibTeX] [Link]

     @inproceedings{232380365,
     title = {FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/e0a963ee0038b6cbe3e2aa90770080056d8555e6},
     }

657. M. Martindale, K. Duh, and M. Carpuat, “Machine Translation Believability,” in Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing, Online, 2021, p. 88–95.
     [BibTeX] [Abstract] [Link]
     

     Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.

     @inproceedings{martindale-etal-2021-machine,
     title = "Machine Translation Believability",
     author = "Martindale, Marianna and
     Duh, Kevin and
     Carpuat, Marine",
     editor = "Blodgett, Su Lin and
     Madaio, Michael and
     O'Connor, Brendan and
     Wallach, Hanna and
     Yang, Qian",
     booktitle = "Proceedings of the First Workshop on Bridging Human{--}Computer Interaction and Natural Language Processing",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.hcinlp-1.14",
     pages = "88--95",
     abstract = "Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.",
     }

658. Zach Wood-Doughty, I. Shpitser, and Mark Dredze, “Generating Synthetic Text Data to Evaluate Causal Inference Methods,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{231861828,
     title = {Generating Synthetic Text Data to Evaluate Causal Inference Methods},
     author = {{Zach Wood-Doughty} and {I. Shpitser} and {Mark Dredze}},
     year = 2021,
     month = {2},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/9adc1a3307c05ff3c9b0ae595cb57b1de041713f},
     }

659. Sangwook Park, Ashwin Bellur, D. Han, and Mounya Elhilali, “Self-Training for Sound Event Detection in Audio Mixtures,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{235400502,
     title = {Self-Training for Sound Event Detection in Audio Mixtures},
     author = {{Sangwook Park} and {Ashwin Bellur} and {D. Han} and {Mounya Elhilali}},
     year = 2021,
     month = {6},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/1c8465fc6210e5daeccad968e84259cff8185cb5},
     }

660. Faisal Rahman, N. Finkelstein, A. Alyakin, N. Gilotra, Jeff Trost, S. Schulman, and S. Saria, “Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure,” in Journal of the Society for Cardiovascular Angiography & Interventions, 2021.
     [BibTeX] [Link]

     @inproceedings{236623062,
     title = {Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure},
     author = {{Faisal Rahman} and {N. Finkelstein} and {A. Alyakin} and {N. Gilotra} and {Jeff Trost} and {S. Schulman} and {S. Saria}},
     year = 2021,
     month = {4},
     booktitle = {Journal of the Society for Cardiovascular Angiography & Interventions},
     url = {https://www.semanticscholar.org/paper/d3d1d3e4e14e7810e94de4c11eda135bc17bd41f},
     }

661. Yunjuan Wang, Poorya Mianjy, and R. Arora, “Robust Learning for Data Poisoning Attacks,” in International Conference on Machine Learning, 2021.
     [BibTeX] [Link]

     @inproceedings{235826166,
     title = {Robust Learning for Data Poisoning Attacks},
     author = {{Yunjuan Wang} and {Poorya Mianjy} and {R. Arora}},
     year = 2021,
     booktitle = {International Conference on Machine Learning},
     url = {https://www.semanticscholar.org/paper/c541fa104bc5297f3ebf967855d582ab9a37291d},
     }

662. Anshul B. Shah, Shlok Kumar Mishra, Ankan Bansal, Jun-Cheng Chen, R. Chellappa, and Abhinav Shrivastava, “Pose and Joint-Aware Action Recognition – Supplementary Material.” 2021.
     [BibTeX] [Link]

     @inproceedings{247112044,
     title = {Pose and Joint-Aware Action Recognition - Supplementary Material},
     author = {{Anshul B. Shah} and {Shlok Kumar Mishra} and {Ankan Bansal} and {Jun-Cheng Chen} and {R. Chellappa} and {Abhinav Shrivastava}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/7400177a4165c13d22da45a242ab8180e32a3d38},
     }

663. Desh Raj and S. Khudanpur, “Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem,” in Interspeech, 2021.
     [BibTeX] [Link]

     @inproceedings{233025093,
     title = {Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem},
     author = {{Desh Raj} and {S. Khudanpur}},
     year = 2021,
     month = {4},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/88dfc766aeff22a4e5fbdb81ce6161994c745039},
     }

664. Navaneeth Bodla, G. Shrivastava, R. Chellappa, and Abhinav Shrivastava, “Supplementary: Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions.” 2021.
     [BibTeX] [Link]

     @inproceedings{235691818,
     title = {Supplementary: Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions},
     author = {{Navaneeth Bodla} and {G. Shrivastava} and {R. Chellappa} and {Abhinav Shrivastava}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/302e4537b277384542d7f0b5cdc4db33abbaa1db},
     }

665. L. Chu, Seyoun Park, S. Kawamoto, A. Yuille, R. Hruban, and E. Fishman, “Current Status of Radiomics and Deep Learning in Liver Imaging,” in Journal of computer assisted tomography, 2021.
     [BibTeX] [Link]

     @inproceedings{235128460,
     title = {Current Status of Radiomics and Deep Learning in Liver Imaging},
     author = {{L. Chu} and {Seyoun Park} and {S. Kawamoto} and {A. Yuille} and {R. Hruban} and {E. Fishman}},
     year = 2021,
     booktitle = {Journal of computer assisted tomography},
     url = {https://www.semanticscholar.org/paper/3ed6aa987299d52aad39a6e8339f57dc27c81980},
     }

666. H. Inaguma, Yosuke Higuchi, Kevin Duh, Tatsuya Kawahara, and Shinji Watanabe, “Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{237453587,
     title = {Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring},
     author = {{H. Inaguma} and {Yosuke Higuchi} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
     year = 2021,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/d79b613a67cf79740e1c08037f7d054585a12284},
     }

667. Ankan Bansal, Jingxiao Zheng, and R. Chellappa, “Face Recognition from Still Images and Video,” in Encyclopedia of Cryptography, Security and Privacy, 2021.
     [BibTeX] [Link]

     @inproceedings{243104926,
     title = {Face Recognition from Still Images and Video},
     author = {{Ankan Bansal} and {Jingxiao Zheng} and {R. Chellappa}},
     year = 2021,
     booktitle = {Encyclopedia of Cryptography, Security and Privacy},
     url = {https://www.semanticscholar.org/paper/eddee7bdc03d5973cd98303c0d5850bc433069c1},
     }

668. Haoran Xu and Philipp Koehn, “Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{236133964,
     title = {Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions},
     author = {{Haoran Xu} and {Philipp Koehn}},
     year = 2021,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/f38b33d863f9554a00cd9798484e0cc8b0236579},
     }

669. Pirazh Khorramshahi, Sai Saketh Rambhatla, and R. Chellappa, “Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
     [BibTeX] [Link]

     @inproceedings{235657291,
     title = {Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems},
     author = {{Pirazh Khorramshahi} and {Sai Saketh Rambhatla} and {R. Chellappa}},
     year = 2021,
     month = {6},
     booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
     url = {https://www.semanticscholar.org/paper/8be99c2d0802d6222e233dd67d2927c75a0bed24},
     }

670. Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, and A. Yuille, “Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{235266081,
     title = {Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning},
     author = {{Ju He} and {Adam Kortylewski} and {Shaokang Yang} and {Shuai Liu} and {Cheng Yang} and {Changhu Wang} and {A. Yuille}},
     year = 2021,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/1c9b186efe4529493bad1b89cac8d837c5f121ee},
     }

671. M. Landers, R. Dorsey, and S. Saria, “Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption,” in Digital Biomarkers, 2021.
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     @inproceedings{239134009,
     title = {Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption},
     author = {{M. Landers} and {R. Dorsey} and {S. Saria}},
     year = 2021,
     month = {9},
     booktitle = {Digital Biomarkers},
     url = {https://www.semanticscholar.org/paper/a79a3ea8141ac2d9f780abbcd1ee0b2bfbd78ead},
     }

672. Neehar Peri, Joshua Gleason, C. Castillo, T. Bourlai, Vishal M. Patel, and R. Chellappa, “A Synthesis-Based Approach for Thermal-to-Visible Face Verification,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{237266437,
     title = {A Synthesis-Based Approach for Thermal-to-Visible Face Verification},
     author = {{Neehar Peri} and {Joshua Gleason} and {C. Castillo} and {T. Bourlai} and {Vishal M. Patel} and {R. Chellappa}},
     year = 2021,
     month = {8},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/edcfc2e222d08c51a9f1087fb29252b659d9b071},
     }

673. Hang Lv, Zhehuai Chen, Hainan Xu, Daniel Povey, Lei Xie, and S. Khudanpur, “An Asynchronous WFST-Based Decoder for Automatic Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{232237963,
     title = {An Asynchronous WFST-Based Decoder for Automatic Speech Recognition},
     author = {{Hang Lv} and {Zhehuai Chen} and {Hainan Xu} and {Daniel Povey} and {Lei Xie} and {S. Khudanpur}},
     year = 2021,
     month = {3},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/ea221ef54fd47b3d1487a6f686871b2bccdc94c6},
     }

674. R. Yasarla, Hamid Reza Vaezi Joze, and Vishal M. Patel, “Network Architecture Search for Face Enhancement,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{234681236,
     title = {Network Architecture Search for Face Enhancement},
     author = {{R. Yasarla} and {Hamid Reza Vaezi Joze} and {Vishal M. Patel}},
     year = 2021,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/02c7dcedee24ae9ca55a96180fae7b7000009ad0},
     }

675. VS Vibashan, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, and Vishal M. Patel, “MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{232147762,
     title = {MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection},
     author = {{VS Vibashan} and {Vikram Gupta} and {Poojan Oza} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
     year = 2021,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/7b90fd1b397e0bbc4dc168673806c043ff690287},
     }

676. Shao-Yuan Lo, Poojan Oza, and Vishal M. Patel, “Adversarially Robust One-Class Novelty Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
     [BibTeX] [Link]

     @inproceedings{237291787,
     title = {Adversarially Robust One-Class Novelty Detection},
     author = {{Shao-Yuan Lo} and {Poojan Oza} and {Vishal M. Patel}},
     year = 2021,
     month = {8},
     booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/75892061a60651e9f5db894ea0b5a4b1f6c272c1},
     }

677. Poojan Oza, Vishwanath A. Sindagi, Vibashan Vs, and Vishal M. Patel, “Unsupervised Domain Adaptation of Object Detectors: A Survey,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
     [BibTeX] [Link]

     @inproceedings{235247842,
     title = {Unsupervised Domain Adaptation of Object Detectors: A Survey},
     author = {{Poojan Oza} and {Vishwanath A. Sindagi} and {Vibashan Vs} and {Vishal M. Patel}},
     year = 2021,
     month = {5},
     booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/96bb5e0ee886b499e5d6d64b4636bc7be343ccc0},
     }

678. Hui Che, J. Radbel, J. Sunderram, J. Nosher, Vishal M. Patel, and I. Hacihaliloglu, “Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021.
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679. Xing Di, He Zhang, and Vishal M. Patel, “Thermal-to-Visible Face Synthesis and Recognition,” in Advances in Computer Vision and Pattern Recognition, 2021.
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680. Yingda Xia, Jiawen Yao, Le Lu, Lingyun Huang, G. Xie, Jing Xiao, A. Yuille, K. Cao, and Ling Zhang, “Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
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681. Matt Landers, S. Saria, and A. Espay, “Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?,” in Journal of Parkinson’s Disease, 2021.
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682. Alycen Wiacek, N. Dehak, and M. L. Lediju Bell, “Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses,” in IUS, 2021.
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683. Kevin Duh and Francisco Guzmán, “Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track),” in Machine Translation Summit, 2021.
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684. Chenxu Luo, Xiaodong Yang, and A. Yuille, “Exploring Simple 3D Multi-Object Tracking for Autonomous Driving,” in IEEE International Conference on Computer Vision, 2021.
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685. Jeya Maria Jose Valanarasu, Poojan Oza, I. Hacihaliloglu, and Vishal M. Patel, “Medical Transformer: Gated Axial-Attention for Medical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
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686. L. Moro-Velázquez, Jorge Andrés Gómez García, J. D. Arias-Londoño, N. Dehak, and Juan Ignacio Godino-Llorente, “Advances in Parkinson’s Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects,” in Biomedical Signal Processing and Control, 2021.
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687. Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, E. Adeli, Yan Wang, Le Lu, A. Yuille, and Yuyin Zhou, “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” in arXiv.org, 2021.
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688. A. Hussein, Shammur A. Chowdhury, N. Dehak, and Ahmed Ali, “Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition.” 2021.
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689. Jaejin Cho, Piotr Żelasko, J. Villalba, and N. Dehak, “Improving Reconstruction Loss Based Speaker Embedding in Unsupervised and Semi-Supervised Scenarios,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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690. Hexin Liu, Leibny Paola García Perera, Xinyi Zhang, J. Dauwels, Andy W. H. Khong, S. Khudanpur, and S. Styles, “End-to-End Language Diarization for Bilingual Code-Switching Speech,” in Interspeech, 2021.
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691. Nicholas Ichien, Qing Liu, Shuhao Fu, K. Holyoak, A. Yuille, and Hongjing Lu, “Visual analogy: Deep learning versus compositional models,” in Annual Meeting of the Cognitive Science Society, 2021.
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692. M. Illa, B. Halpern, R. V. Son, L. Moro-Velázquez, and O. Scharenborg, “Pathological voice adaptation with autoencoder-based voice conversion,” in 11th ISCA Speech Synthesis Workshop (SSW 11), 2021.
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693. Noah Weber, Anton Belyy, Nils Holzenberger, Rachel Rudinger, and Benjamin Van Durme, “Schema Curation via Causal Association Rule Mining,” in arXiv.org, 2021.
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694. Shota Horiguchi, Nelson Yalta, Leibny Paola García-Perera, Yuki Takashima, Yawen Xue, Desh Raj, Zili Huang, Yusuke Fujita, Shinji Watanabe, and S. Khudanpur, “The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap,” in arXiv.org, 2021.
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695. Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractors for End-to-End Neural Diarization,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2021.
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696. Angtian Wang, Adam Kortylewski, and A. Yuille, “NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation,” in International Conference on Learning Representations, 2021.
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697. Yonatan Belinkov, Adam Poliak, Stuart M. Shieber, Benjamin Van Durme, Alexander M. Rush, Samuel R. Bowman, Gabor Angeli, and Christopher Potts, “Combating Spurious Features by Distribution Difference Regularization.” 2021.
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698. Benjamin Skerritt-Davis and Mounya Elhilali, “Computational framework for investigating predictive processing in auditory perception,” in Journal of Neuroscience Methods, 2021.
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699. Adarsh Subbaswamy, Bryant Chen, and S. Saria, “T HE S TABILITY AND A CCURACY T RADEOFF U NDER D ATASET S HIFT : A C AUSAL G RAPHICAL A NALYSIS.” 2021.
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700. Nataniel Ruiz, Adam Kortylewski, Weichao Qiu, Cihang Xie, Sarah Adel Bargal, A. Yuille, and S. Sclaroff, “Simulated Adversarial Testing of Face Recognition Models,” in Computer Vision and Pattern Recognition, 2021.
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701. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels,” in arXiv.org, 2021.
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702. Ian McLane, Dimitra Emmanouilidou, James E. West, and Mounya Elhilali, “Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings,” in IEEE journal of biomedical and health informatics, 2021.
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703. Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, B. Green, Elizabeth L Engle, Guillermo Almodovar, Ryan Walk, Sigfredo Soto-Diaz, J. Taube, A. Szalay, and A. Yuille, “A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation,” in arXiv.org, 2021.
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704. Matthew Wiesner, Mousmita Sarma, Ashish Arora, Desh Raj, Dongji Gao, Ruizhe Huang, Supreet Preet, Moris Johnson, Zikra Iqbal, N. Goel, J. Trmal, Leibny Paola García Perera, and S. Khudanpur, “Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition,” in Interspeech, 2021.
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705. Nanxin Chen, Shinji Watanabe, J. Villalba, Piotr Żelasko, and N. Dehak, “Non-Autoregressive Transformer for Speech Recognition,” in IEEE Signal Processing Letters, 2021.
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706. Chen Wei, Kihyuk Sohn, Clayton Mellina, A. Yuille, and Fan Yang, “CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning,” in Computer Vision and Pattern Recognition, 2021.
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707. Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, and Benjamin Van Durme, “Joint Universal Syntactic and Semantic Parsing,” in Transactions of the Association for Computational Linguistics, 2021.
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708. Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning,” in Computer Vision and Pattern Recognition, 2021.
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711. Chun Pong Lau, C. Castillo, and R. Chellappa, “ATFaceGAN: Single Face Semantic Aware Image Restoration and Recognition From Atmospheric Turbulence,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2021.
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713. N. Balachandran, Jun-Cheng Chen, and R. Chellappa, “LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network,” in arXiv.org, 2021.
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714. Chenxu Luo, Xiaodong Yang, and A. Yuille, “Self-Supervised Pillar Motion Learning for Autonomous Driving,” in Computer Vision and Pattern Recognition, 2021.
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715. Hui Che, S. Ramanathan, D. Foran, J. Nosher, Vishal M. Patel, and I. Hacihaliloglu, “Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease,” in ASMUS@MICCAI, 2021.
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716. Ju He, Jieneng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, and A. Yuille, “TransFG: A Transformer Architecture for Fine-grained Recognition,” in AAAI Conference on Artificial Intelligence, 2021.
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717. Chenglin Yang, Siyuan Qiao, Adam Kortylewski, and A. Yuille, “Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms.” 2021.
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718. VS Vibashan, Jeya Maria Jose Valanarasu, Poojan Oza, and Vishal M. Patel, “Image Fusion Transformer,” in International Conference on Information Photonics, 2021.
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719. Ke Li, Daniel Povey, and S. Khudanpur, “A Parallelizable Lattice Rescoring Strategy with Neural Language Models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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720. Xing Di, Shuowen Hu, and Vishal M. Patel, “Heterogeneous Face Frontalization via Domain Agnostic Learning,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
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721. D. Kelly, Max Spaderna, Vedrana Hodzic, Glen A. Coppersmith, Shuo Chen, and P. Resnik, “Can language use in social media help in the treatment of severe mental illness?,” in Current research in psychiatry, 2021.
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722. Michelle Yuan, Patrick Xia, Benjamin Van Durme, and Jordan L. Boyd-Graber, “Adaptive Active Learning for Coreference Resolution,” in arXiv.org, 2021.
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723. S. Saria, Peter F. Schulam, Brian J. Yeh, Daniel Burke, S. Mooney, C. Fong, Jacob E. Sunshine, Dustin R. Long, and V. O’Reilly-Shah, “Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients,” in Critical Care Explorations, 2021.
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725. R. Yasarla, Federico Perazzi, and Vishal M. Patel, “Deblurring Face Images Using Deep Networks,” in Advances in Computer Vision and Pattern Recognition, 2021.
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726. Andrew Blair-Stanek and Benjamin Van Durme, “AI for Tax Analogies and Code Renumbering,” in Tax Law: Practitioner Series eJournal, 2021.
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727. Liming Wang, Xinsheng Wang, M. Hasegawa-Johnson, O. Scharenborg, and N. Dehak, “Align or attend? Toward More Efficient and Accurate Spoken Word Discovery Using Speech-to-Image Retrieval,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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728. Adarsh Subbaswamy, R. Adams, and S. Saria, “Evaluating Model Robustness and Stability to Dataset Shift,” in International Conference on Artificial Intelligence and Statistics, 2021.
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729. Yuval Pinter, A. Stent, Mark Dredze, and Jacob Eisenstein, “Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information,” in arXiv.org, 2021.
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730. Enayat Ullah, Tung Mai, Anup B. Rao, Ryan A. Rossi, and R. Arora, “Machine Unlearning via Algorithmic Stability,” in Annual Conference Computational Learning Theory, 2021.
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731. R. Yasarla and Vishal M. Patel, “Learning to Restore Images Degraded by Atmospheric Turbulence Using Uncertainty,” in International Conference on Information Photonics, 2021.
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732. Prakhar Kaushik, Alex Gain, Adam Kortylewski, and A. Yuille, “Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping,” in arXiv.org, 2021.
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733. Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization,” in arXiv.org, 2021.
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734. Sonal Joshi, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems,” in IEEE Transactions on Information Forensics and Security, 2021.
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735. Shao-Yuan Lo and Vishal M. Patel, “Error Diffusion Halftoning Against Adversarial Examples,” in International Conference on Information Photonics, 2021.
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736. S. Saria, M. Ghassemi, Z. Obermeyer, Karandeep Singh, P. Hsueh, and E. Topol, “Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery,” in American Medical Informatics Association Annual Symposium, 2021.
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737. Pramuditha Perera and Vishal M. Patel, “A Joint Representation Learning and Feature Modeling Approach for One-class Recognition,” in International Conference on Pattern Recognition, 2021.
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738. Pramuditha Perera and Vishal M. Patel, “Geometric Transformation-Based Network Ensemble for Open-Set Recognition,” in IEEE International Conference on Multimedia and Expo, 2021.
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739. Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge J. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: A Benchmark,” in International Journal of Computer Vision, 2021.
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740. Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Leibny Paola García-Perera, and Kenji Nagamatsu, “Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers,” in Interspeech, 2021.
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741. Jonah P. Sengupta, M. Villemur, and A. Andreou, “Efficient, event-driven feature extraction and unsupervised object tracking for embedded applications,” in Annual Conference on Information Sciences and Systems, 2021.
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742. Nanxin Chen, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition,” in Interspeech, 2021.
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743. Rakhil Immidisetti, Shuowen Hu, and Vishal M. Patel, “Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
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744. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation,” in Interspeech, 2021.
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745. Ehsan, Adeli, Yan Wang, Le Lu, A. Yuille, and Yuyin Zhou, “Download A Visual Segmentation Method For Temporal Smart Card Data.” 2021.
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746. Ayah Zirikly, Bart Desmet, Denis R. Newman-Griffis, Beth Marfeo, C. McDonough, H. Goldman, and L. Chan, “An Information Extraction Framework for Disability Determination: A mental Functioning Use-Case (Preprint).” 2021.
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747. Chun Pong Lau, Amit Kumar, and R. Chellappa, “Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images,” in IEEE Journal on Selected Topics in Signal Processing, 2021.
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748. Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, D. Cremers, L. Leal-Taixé, A. Yuille, Florian Schroff, Hartwig Adam, and Liang-Chieh Chen, “DeepLab2: A TensorFlow Library for Deep Labeling,” in arXiv.org, 2021.
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749. Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, and A. Yuille, “Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles,” in Computer Vision and Pattern Recognition, 2021.
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750. Xing Di and Vishal M. Patel, “Multimodal Face Synthesis From Visual Attributes,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2021.
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751. Ruizhi Li, Gregory Sell, and H. Hermansky, “Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream end-to-end ASR,” in Spoken Language Technology Workshop, 2021.
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752. Piotr Żelasko, R. Pappagari, and N. Dehak, “What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition,” in Transactions of the Association for Computational Linguistics, 2021.
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753. Velat Kilic, Deepti Hegde, Vishwanath A. Sindagi, A. Cooper, M. Foster, and Vishal M. Patel, “Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection,” in arXiv.org, 2021.
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754. Bingchen Zhao, Shaozuo Yu, Wufei Ma, M. Yu, Shenxiao Mei, Angtian Wang, Ju He, A. Yuille, and Adam Kortylewski, “ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts,” in arXiv.org, 2021.
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755. Jeya Maria Jose Valanarasu and Vishal M. Patel, “Fine-Context Shadow Detection using Shadow Removal,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
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756. Junfei Xiao, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, A. Yuille, and Yuyin Zhou, “CateNorm: Categorical Normalization for Robust Medical Image Segmentation,” in DART@MICCAI, 2021.
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757. Jiteng Mu, Weichao Qiu, Adam Kortylewski, A. Yuille, N. Vasconcelos, and Xiaolong Wang, “A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation,” in IEEE International Conference on Computer Vision, 2021.
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758. Jonah P. Sengupta, M. Villemur, and A. Andreou, “A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering,” in Annual Conference on Information Sciences and Systems, 2021.
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759. P. Resnik, M. de Choudhury, Katherine Musacchio Schafer, and Glen A. Coppersmith, “Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”,” in Journal of Medical Internet Research, 2021.
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760. Matthew Maciejewski, Shinji Watanabe, and S. Khudanpur, “Speaker Verification-Based Evaluation of Single-Channel Speech Separation,” in Interspeech, 2021.
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761. Lauren M Sanders, Jason H. Yang, Ryan T. Scott, A. Qutub, H. Martín, D. Berrios, Jaden J. A. Hastings, J. Rask, Graham Mackintosh, A. Hoarfrost, Stuart Chalk, John Kalantari, Kia Khezeli, E. Antonsen, Joel Babdor, Richard Barker, S. Baranzini, A. Beheshti, Guillermo M. Delgado-Aparicio, B. Glicksberg, C. Greene, M. Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, S. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Christopher E. Mason, Mona Matar, G. Mias, Jack M. Miller, J. Myers, Charlotte A. Nelson, Jonathan Oribello, Seung-min Park, P. Parsons-Wingerter, R. Prabhu, R. Reynolds, Amanda M. Saravia-Butler, S. Saria, A. Sawyer, N. Singh, Frank Soboczenski, Michael Snyder, Karthik Soman, C. Theriot, D. V. Valen, K. Venkateswaran, L. Warren, Liz Worthey, M. Zitnik, S. V. C. B. M. S. I. O. Science, Space Biosciences Division, N. R. Center, M. Field, Ca, USA., Center for Emerging, Re-Emerging Pathogens, D. Microbiology, Biochemistry, Molecular Genetics, Rutgers New Jersey Medical School, Newark, Nj, Kbr, AI Matrix Consortium, Department of Biomedical Engineering, U. Texas, San Antonio, UT Health Sciences, Tx, Biological Systems, Engineering Division, Lawrence Berkeley National Lab., Berkeley, Doe Agile BioFoundry, Emeryville, Joint BioEnergy Institute, D. Physiology, Biophysics, Weill Cornell Medicine, New York., Ny, Office of the Director, Bay Area Environmental Research Institute, Universities Space Research Association, D. Chemistry, U. Florida, Jacksonville, Fl, Center for Individualized Medicine, D. Surgery, Department of Mathematical Sciences, Mayo Clinic, Rochester, Mn, Department of Preventive Medicine, Center for Individualized Medicine, Baylor College of Medicine, Houston, Immunology, Department of Otolaryngology, Head, N. Surgery, University of California San Francisco, San Francisco, The Gilroy AstroBiology Research Group, Theodore Madison, Madison, Wi, Weill Institute for Neurosciences, D. Neurology, D. Analytics, G. I. O. Technology, Lima, Perú, Hasso Plattner Institute for Digital Health at Mount Sinai, Department of Genetics, Genomic Sciences, I. A. Sinai, AI CenterforHealth, D. Biochemistry, U. Medicine, Anschutz Medical Campus, Aurora, Co, Department of Neuroscience, U. Minnesota, Minneapolis, Department of Materials Science, College of Materials Science, San Diego State University, San José, Biorelate, Manchester, United Kingdom., UC Space Health, D. Surgery, U. California, Faculty of Veterinary Medicine, Oral Health Sciences, McGill University, Montreal., Quebec., Canada., Faculty of Veterinary Medicine, Dept of Surgery, Cancer, I. -. London, London, SymbioSeq Llc, NASAMarshall Space Flight Center, Ashburn, Va, Center for Data Driven Discovery, C. I. O. Technology., Pasadena, Waitt Advanced Biophotonics Center, Chan-Zuckerberg Imaging Scientist Fellow, Salk Institute for Biological Studies, La Jolla, Human Research Program Cross-cutting Computational Model Project, N. R. Center, Cleveland, Oh, Institute for Quantum Science, Engineering, M. Biology, M. University, E. Lansing., Mi, Department of Urology, D. Radiology, S. Medicine, Stanford, Low Exploration Gravity Technology, Human Research Program Cross-cutting Computational Model Project, Mortality ResearchConsulting, Inc., Logyx, Computer Science, Statistics, Health Policy, J. University, Baltimore., Md, Ml, Ai, H. Lab, B. Health, Biotechnology, Planetary Protection Group, Jet propulsion Laboratory, Sphes, Medical Faculty, King’s College London, S. Medicine, California Usa, Department of Preventive Medicine, C. Health, Utmb, Galveston, Tx Usa, Human Health, Performance Directorate, Department of Medical Biology, Iss National Laboratory, Center for Space, Melbourne, Uab Center for Computational Biology, Data Science, U. Alabama, Birmingham, Al, Department of Biomedical Informatics, H. School, Harvard Data Science, Broad Institute of Mit, Harvard, Harvard University, Boston, and Ma., “Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs,” in arXiv.org, 2021.
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Theriot} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {S. V. C. B. M. S. I. O. Science} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Center for Emerging} and {Re-Emerging Pathogens} and {D. Microbiology} and {Biochemistry} and {Molecular Genetics} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Kbr} and {AI Matrix Consortium} and {Department of Biomedical Engineering} and {U. Texas} and {San Antonio} and {UT Health Sciences} and {Tx} and {Biological Systems} and {Engineering Division} and {Lawrence Berkeley National Lab.} and {Berkeley} and {Doe Agile BioFoundry} and {Emeryville} and {Joint BioEnergy Institute} and {D. Physiology} and {Biophysics} and {Weill Cornell Medicine} and {New York.} and {Ny} and {Office of the Director} and {Bay Area Environmental Research Institute} and {Universities Space Research Association} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {Center for Individualized Medicine} and {D. Surgery} and {Department of Mathematical Sciences} and {Mayo Clinic} and {Rochester} and {Mn} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of California San Francisco} and {San Francisco} and {The Gilroy AstroBiology Research Group} and {Theodore Madison} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Perú} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {AI CenterforHealth} and {D. Biochemistry} and {U. Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {UC Space Health} and {D. Surgery} and {U. California} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {McGill University} and {Montreal.} and {Quebec.} and {Canada.} and {Faculty of Veterinary Medicine} and {Dept of Surgery} and {Cancer} and {I. -. London} and {London} and {SymbioSeq Llc} and {NASAMarshall Space Flight Center} and {Ashburn} and {Va} and {Center for Data Driven Discovery} and {C. I. O. Technology.} and {Pasadena} and {Waitt Advanced Biophotonics Center} and {Chan-Zuckerberg Imaging Scientist Fellow} and {Salk Institute for Biological Studies} and {La Jolla} and {Human Research Program Cross-cutting Computational Model Project} and {N. R. Center} and {Cleveland} and {Oh} and {Institute for Quantum Science} and {Engineering} and {M. Biology} and {M. University} and {E. Lansing.} and {Mi} and {Department of Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Low Exploration Gravity Technology} and {Human Research Program Cross-cutting Computational Model Project} and {Mortality ResearchConsulting} and {Inc.} and {Logyx} and {Computer Science} and {Statistics} and {Health Policy} and {J. University} and {Baltimore.} and {Md} and {Ml} and {Ai} and {H. Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {Jet propulsion Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {California Usa} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Tx Usa} and {Human Health} and {Performance Directorate} and {Department of Medical Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {Data Science} and {U. 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762. Jonah P. Sengupta, M. Villemur, Daniel R. Mendat, Gaspar Tognetti, and A. Andreou, “Architecture and Algorithm Co-Design Framework for Embedded Processors in Event-Based Cameras,” in International Symposium on Circuits and Systems, 2021.
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763. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Data-Assemble: Leveraging Multiple Datasets with Partial Labels.” 2021.
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764. Nathan C Higgins, Ambar Monjaras, Breanne D Yerkes, David F. Little, J. Nave-Blodgett, Mounya Elhilali, and J. Snyder, “Resetting of Auditory and Visual Segregation Occurs After Transient Stimuli of the Same Modality,” in Frontiers in Psychology, 2021.
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765. Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, and A. Yuille, “CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models,” in arXiv.org, 2021.
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766. L. Moro-Velázquez, J. Gómez-García, N. Dehak, and Juan Ignacio Godino-Llorente, “New tools for the differential evaluation of Parkinson’s disease using voice and speech processing,” in IberSPEECH Conference, 2021.
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767. K. Henry, R. Adams, C. Parent, A. Sridharan, L. Johnson, D. Hager, S. Cosgrove, A. Markowski, E. Klein, E. Chen, M. Henley, S. Miranda, K. Houston, R. C. Linton, A. Ahluwalia, A. Wu, and S. Saria, “Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System,” in medRxiv, 2021.
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768. Seyoun Park, E. Fishman, and A. Yuille, “Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations,” in arXiv.org, 2021.
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769. Daniel R. Mendat, Jonah P. Sengupta, Drake K. Foreman, and A. Andreou, “Parallel Computation of Event-Based Visual Features Using Relational Graphs,” in Annual Conference on Information Sciences and Systems, 2021.
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770. Sangwook Park, Woohyun Choi, and Mounya Elhilali, “Sound Event Detection with Cross-Referencing Self-Training.” 2021.
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771. R. Pappagari, Jaejin Cho, Sonal Joshi, L. Moro-Velázquez, Piotr Żelasko, J. Villalba, and N. Dehak, “Automatic Detection and Assessment of Alzheimer Disease Using Speech and Language Technologies in Low-Resource Scenarios,” in Interspeech, 2021.
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772. Tiange Xiang, Yongyi Lu, A. Yuille, Chaoyi Zhang, Weidong (Tom) Cai, and Zongwei Zhou, “In-painting Radiography Images for Unsupervised Anomaly Detection,” in arXiv.org, 2021.
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773. Benjamin Skerritt-Davis and Mounya Elhilali, “Neural Encoding of Auditory Statistics,” in Journal of Neuroscience, 2021.
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774. Poojan Oza and Vishal M. Patel, “Federated Learning-based Active Authentication on Mobile Devices,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
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775. Jeya Maria Jose Valanarasu, Christina Chen, and Vishal M. Patel, “R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection,” in arXiv.org, 2021.
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776. C. Castillo-Sanchez and Leibny Paola García-Perera, “The CLIR-CLSP System for the IberSPEECH-RTVE 2020 Speaker Diarization and Identity Assignment Challenge,” in IberSPEECH Conference, 2021.
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777. Kelly Marchisio, Youngser Park, Ali Saad-Eldin, A. Alyakin, Kevin Duh, C. Priebe, and Philipp Koehn, “An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces,” in Conference on Empirical Methods in Natural Language Processing, 2021.
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778. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Federated Generalized Face Presentation Attack Detection,” in IEEE Transactions on Neural Networks and Learning Systems, 2021.
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779. S. G. Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, I. Kohane, and S. Saria, “The Clinician and Dataset Shift in Artificial Intelligence.,” in New England Journal of Medicine, 2021.
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780. Hang Lv, Daniel Povey, M. Yarmohammadi, Ke Li, Yiming Wang, Lei Xie, and S. Khudanpur, “LET-Decoder: A WFST-Based Lazy-Evaluation Token-Group Decoder With Exact Lattice Generation,” in IEEE Signal Processing Letters, 2021.
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781. Zefan Li, Chenxi Liu, A. Yuille, Bingbing Ni, Wenjun Zhang, and Wen Gao, “Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement,” in Computer Vision and Pattern Recognition, 2021.
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782. Qing Liu, Vignesh Ramanathan, D. Mahajan, A. Yuille, and Zhenheng Yang, “Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency,” in Computer Vision and Pattern Recognition, 2021.
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783. Hao Ding, Siyuan Qiao, A. Yuille, and Wei Shen, “Deeply Shape-guided Cascade for Instance Segmentation,” in Computer Vision and Pattern Recognition, 2021.
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784. Guoguo Chen, Shuzhou Chai, Guan-Bo Wang, Jiayu Du, Weiqiang Zhang, Chao Weng, Dan Su, Daniel Povey, J. Trmal, Junbo Zhang, Mingjie Jin, S. Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, and Zhiyong Yan, “GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio,” in Interspeech, 2021.
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785. Adam Kortylewski, Ju He, Qing Liu, Christian Cosgrove, Chenglin Yang, and A. Yuille, “Compositional Generative Networks and Robustness to Perceptible Image Changes,” in Annual Conference on Information Sciences and Systems, 2021.
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786. Jiefu Ou, Nathaniel Weir, Anton Belyy, Felix X. Yu, and Benjamin Van Durme, “InFillmore: Neural Frame Lexicalization for Narrative Text Infilling,” in arXiv.org, 2021.
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787. Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, C. Qin, J. Duan, D. Rueckert, Jingu Lee, Nicola Pezzotti, E. Weerdt, Sahar Yousefi, M. Elmahdy, J. V. Gemert, C. Schülke, M. Doneva, T. Nielsen, Sergey Kastryulin, B. Lelieveldt, M. Osch, M. Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, Nikita Miriakov, B. Bakker, M. Caan, Max Welling, Matthew Muckley, and F. Knoll, “Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge,” in MLMIR@MICCAI, 2021.
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788. N. Mathioudakis, Mohammed S. Abusamaan, Ahmed F. Shakarchi, Sam Sokolinsky, Shamil Fayzullin, J. McGready, Mihail Zilbermint, S. Saria, and S. Golden, “Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients,” in JAMA Network Open, 2021.
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789. Jalaj Upadhyay, Sarvagya Upadhyay, and R. Arora, “Di ↵ erentially Private Analysis on Graph Streams.” 2021.
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790. Nanxin Chen, Yu Zhang, H. Zen, Ron J. Weiss, Mohammad Norouzi, N. Dehak, and William Chan, “WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis,” in Interspeech, 2021.
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791. Jorge Andrés Gómez García, L. Moro-Velázquez, J. D. Arias-Londoño, and Juan Ignacio Godino-Llorente, “On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies,” in Biomedical Signal Processing and Control, 2021.
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792. Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, E. Fishman, and A. Yuille, “Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction,” in IEEE Transactions on Medical Imaging, 2021.
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793. Sangwook Park, D. Han, and Mounya Elhilali, “Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures,” in IEEE transactions on multimedia, 2021.
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794. V. Rennoll, Ian McLane, Adebayo A. Eisape, Mounya Elhilali, and James E. West, “Evaluating the impact of acoustic impedance matching on the airborne noise rejection and sensitivity of an electrostatic transducer,” in Journal of the Acoustical Society of America, 2021.
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795. W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving,” in IEEE International Conference on Robotics and Automation, 2021.
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796. R. Pappagari, Piotr Żelasko, Agnieszka Mikołajczyk, Piotr Pęzik, and N. Dehak, “Joint Prediction of Truecasing and Punctuation for Conversational Speech in Low-Resource Scenarios,” in Automatic Speech Recognition & Understanding, 2021.
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797. Sonal Joshi, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Adversarial Attacks and Defenses for Speaker Identification Systems,” in arXiv.org, 2021.
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798. Saurabh Kataria, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification,” in Interspeech, 2021.
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799. Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman, R. Cattoni, Matteo Negri, Marco Turchi, Douglas W. Oard, and Matt Post, “The Multilingual TEDx Corpus for Speech Recognition and Translation,” in Interspeech, 2021.
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800. Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, F. Yu, Xianglong Liu, D. Song, A. Yuille, Philip H. S. Torr, and D. Tao, “RobustART: Benchmarking Robustness on Architecture Design and Training Techniques,” in arXiv.org, 2021.
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801. Xiaoding Yuan, Adam Kortylewski, Yihong Sun, and A. Yuille, “Robust Instance Segmentation through Reasoning about Multi-Object Occlusion,” in Computer Vision and Pattern Recognition, 2020.
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802. Cheng Peng, Haofu Liao, G. Wong, Jiebo Luo, S. Zhou, and R. Chellappa, “XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors,” in AAAI Conference on Artificial Intelligence, 2020.
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803. Harpreet Singh, S. Kusuda, R. McAdams, Shubham Gupta, Jayant Kalra, R. Kaur, Ritu Das, Saket Anand, Ashish Kumar Pandey, S. Cho, S. Saluja, J. Boutilier, S. Saria, J. Palma, A. Kaur, Gautam Yadav, and Yao Sun, “Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study,” in Children, 2020.
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     @inproceedings{229720553,
     title = {Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study},
     author = {{Harpreet Singh} and {S. Kusuda} and {R. McAdams} and {Shubham Gupta} and {Jayant Kalra} and {R. Kaur} and {Ritu Das} and {Saket Anand} and {Ashish Kumar Pandey} and {S. Cho} and {S. Saluja} and {J. Boutilier} and {S. Saria} and {J. Palma} and {A. Kaur} and {Gautam Yadav} and {Yao Sun}},
     year = 2020,
     month = {12},
     booktitle = {Children},
     url = {https://www.semanticscholar.org/paper/9279ffd9cc9c0753d2f737b204fff479e24bad42},
     }

804. Mengqi Guo, Yutong Bai, Zhishuai Zhang, Adam Kortylewski, and A. Yuille, “Unsupervised Part Discovery via Feature Alignment,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227239052,
     title = {Unsupervised Part Discovery via Feature Alignment},
     author = {{Mengqi Guo} and {Yutong Bai} and {Zhishuai Zhang} and {Adam Kortylewski} and {A. Yuille}},
     year = 2020,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/2f2879a07875a94e0e04bc59068807924ea17f97},
     }

805. N. Finkelstein, R. Adams, S. Saria, and I. Shpitser, “Partial Identifiability in Discrete Data With Measurement Error,” in Conference on Uncertainty in Artificial Intelligence, 2020.
     [BibTeX] [Link]

     @inproceedings{229363765,
     title = {Partial Identifiability in Discrete Data With Measurement Error},
     author = {{N. Finkelstein} and {R. Adams} and {S. Saria} and {I. Shpitser}},
     year = 2020,
     month = {12},
     booktitle = {Conference on Uncertainty in Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/6c0484885b9de17e2da47e1e73c5fa7416f08383},
     }

806. Thanh Thieu, Jonathan Camacho Maldonado, Pei-Shu Ho, Min Ding, Alex R Marr, D. Brandt, Denis R. Newman-Griffis, Ayah Zirikly, L. Chan, and E. Rasch, “A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling,” in Int. J. Medical Informatics, 2020.
     [BibTeX] [Link]

     @inproceedings{230784020,
     title = {A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling},
     author = {{Thanh Thieu} and {Jonathan Camacho Maldonado} and {Pei-Shu Ho} and {Min Ding} and {Alex R Marr} and {D. Brandt} and {Denis R. Newman-Griffis} and {Ayah Zirikly} and {L. Chan} and {E. Rasch}},
     year = 2020,
     month = {12},
     booktitle = {Int. J. Medical Informatics},
     url = {https://www.semanticscholar.org/paper/0380a40df2833b48c509af21ada2e755300d8389},
     }

807. Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zhe L. Lin, and A. Yuille, “Meticulous Object Segmentation,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{229153995,
     title = {Meticulous Object Segmentation},
     author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zhe L. Lin} and {A. Yuille}},
     year = 2020,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/67b4298db52b5082e851ff6bbd7fbcebeb1c33fc},
     }

808. Denis R. Newman-Griffis, Guy Divita, Bart Desmet, Ayah Zirikly, C. Rosé, and E. Fosler-Lussier, “Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets,” in J. Am. Medical Informatics Assoc., 2020.
     [BibTeX] [Link]

     @inproceedings{229173551,
     title = {Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets},
     author = {{Denis R. Newman-Griffis} and {Guy Divita} and {Bart Desmet} and {Ayah Zirikly} and {C. Rosé} and {E. Fosler-Lussier}},
     year = 2020,
     month = {12},
     booktitle = {J. Am. Medical Informatics Assoc.},
     url = {https://www.semanticscholar.org/paper/e38e5957a05b5bd21f7d18a41a56d15e6549d3c7},
     }

809. X. Ma, J. Pino, and P. Koehn, “SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation,” in Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, Suzhou, China, 2020, p. 582–587.
     [BibTeX] [Abstract] [Link]
     

     We investigate how to adapt simultaneous text translation methods such as wait-$k$ and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.

     @inproceedings{ma-etal-2020-simulmt,
     title = "{S}imul{MT} to {S}imul{ST}: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation",
     author = "Ma, Xutai and
     Pino, Juan and
     Koehn, Philipp",
     editor = "Wong, Kam-Fai and
     Knight, Kevin and
     Wu, Hua",
     booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
     month = dec,
     year = "2020",
     address = "Suzhou, China",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.aacl-main.58",
     pages = "582--587",
     abstract = "We investigate how to adapt simultaneous text translation methods such as wait-$k$ and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.",
     }

810. Shota Horiguchi, Leibny Paola García-Perera, Yusuke Fujita, Shinji Watanabe, and Kenji Nagamatsu, “End-To-End Speaker Diarization as Post-Processing,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
     [BibTeX] [Link]

     @inproceedings{229331970,
     title = {End-To-End Speaker Diarization as Post-Processing},
     author = {{Shota Horiguchi} and {Leibny Paola García-Perera} and {Yusuke Fujita} and {Shinji Watanabe} and {Kenji Nagamatsu}},
     year = 2020,
     month = {12},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/7374494ee88608ef76f74b58a8f8c26ab06adfb9},
     }

811. Huiyu Wang, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers,” in Computer Vision and Pattern Recognition, 2020.
     [BibTeX] [Link]

     @inproceedings{227248077,
     title = {MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers},
     author = {{Huiyu Wang} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
     year = 2020,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/787119e3c3f819244c82b7d97779473773e60696},
     }

812. Christian Cosgrove, Adam Kortylewski, Chenglin Yang, and A. Yuille, “Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227239366,
     title = {Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks},
     author = {{Christian Cosgrove} and {Adam Kortylewski} and {Chenglin Yang} and {A. Yuille}},
     year = 2020,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/53ae52ef49a8c2ffa4d893332fa0ea9ca7b20805},
     }

813. Ilya Kavalerov, Weilin Li, W. Czaja, and R. Chellappa, “3-D Fourier Scattering Transform and Classification of Hyperspectral Images,” in IEEE Transactions on Geoscience and Remote Sensing, 2020.
     [BibTeX] [Link]

     @inproceedings{234523589,
     title = {3-D Fourier Scattering Transform and Classification of Hyperspectral Images},
     author = {{Ilya Kavalerov} and {Weilin Li} and {W. Czaja} and {R. Chellappa}},
     year = 2020,
     month = {12},
     booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
     url = {https://www.semanticscholar.org/paper/74b6910c70e9990b06b6ec9a55b976765b238a16},
     }

814. Joshua C. Chang, P. Fletcher, Ju Han, Ted L.Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, and C. Chow, “Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization,” in International Conference on Learning Representations, 2020.
     [BibTeX] [Link]

     @inproceedings{227745021,
     title = {Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization},
     author = {{Joshua C. Chang} and {P. Fletcher} and {Ju Han} and {Ted L.Chang} and {Shashaank Vattikuti} and {Bart Desmet} and {Ayah Zirikly} and {C. Chow}},
     year = 2020,
     month = {12},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/302c5388dfc37671ce109d65349a3c8cf0746788},
     }

815. Pengfei Guo, Puyang Wang, R. Yasarla, Jinyuan Zhou, Vishal M. Patel, and Shanshan Jiang, “Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs,” in IEEE Transactions on Medical Imaging, 2020.
     [BibTeX] [Link]

     @inproceedings{229687263,
     title = {Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs},
     author = {{Pengfei Guo} and {Puyang Wang} and {R. Yasarla} and {Jinyuan Zhou} and {Vishal M. Patel} and {Shanshan Jiang}},
     year = 2020,
     month = {12},
     booktitle = {IEEE Transactions on Medical Imaging},
     url = {https://www.semanticscholar.org/paper/cfc1473fa1ee01d64a15cb12713b06797fd7d739},
     }

816. Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe L. Lin, N. Xu, Yutong Bai, and A. Yuille, “Mask Guided Matting via Progressive Refinement Network,” in Computer Vision and Pattern Recognition, 2020.
     [BibTeX] [Link]

     @inproceedings{229156417,
     title = {Mask Guided Matting via Progressive Refinement Network},
     author = {{Qihang Yu} and {Jianming Zhang} and {He Zhang} and {Yilin Wang} and {Zhe L. Lin} and {N. Xu} and {Yutong Bai} and {A. Yuille}},
     year = 2020,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/2dd4b5e8633a5587ce2ebf73284134f21d1bc6a9},
     }

817. D. Lewis, W. Wu, A. D. McCarthy, and D. Yarowsky, “Neural Transduction for Multilingual Lexical Translation,” in Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online), 2020, p. 4373–4384. doi:10.18653/v1/2020.coling-main.387
     [BibTeX] [Abstract] [Link]
     

     We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.

     @inproceedings{lewis-etal-2020-neural,
     title = "Neural Transduction for Multilingual Lexical Translation",
     author = "Lewis, Dylan and
     Wu, Winston and
     McCarthy, Arya D. and
     Yarowsky, David",
     editor = "Scott, Donia and
     Bel, Nuria and
     Zong, Chengqing",
     booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
     month = dec,
     year = "2020",
     address = "Barcelona, Spain (Online)",
     publisher = "International Committee on Computational Linguistics",
     url = "https://aclanthology.org/2020.coling-main.387",
     doi = "10.18653/v1/2020.coling-main.387",
     pages = "4373--4384",
     abstract = "We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.",
     }

818. Jonathan D. Jones, Cathryn S. Cortesa, A. Shelton, B. Landau, S. Khudanpur, and Gregory Hager, “Fine-Grained Activity Recognition for Assembly Videos,” in IEEE Robotics and Automation Letters, 2020.
     [BibTeX] [Link]

     @inproceedings{227247996,
     title = {Fine-Grained Activity Recognition for Assembly Videos},
     author = {{Jonathan D. Jones} and {Cathryn S. Cortesa} and {A. Shelton} and {B. Landau} and {S. Khudanpur} and {Gregory Hager}},
     year = 2020,
     month = {12},
     booktitle = {IEEE Robotics and Automation Letters},
     url = {https://www.semanticscholar.org/paper/b48e6990bda8f29bde11f0f3f6b7c1a9e0785312},
     }

819. Siyuan Qiao, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation,” in Computer Vision and Pattern Recognition, 2020.
     [BibTeX] [Link]

     @inproceedings{228083552,
     title = {ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation},
     author = {{Siyuan Qiao} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
     year = 2020,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/86f88bc71034122eb9d4f8ea16371ebd3efd42cc},
     }

820. Shao-Yuan Lo, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Overcomplete Representations Against Adversarial Videos,” in International Conference on Information Photonics, 2020.
     [BibTeX] [Link]

     @inproceedings{227739091,
     title = {Overcomplete Representations Against Adversarial Videos},
     author = {{Shao-Yuan Lo} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2020,
     month = {12},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/f045e09aa1a97cbb96560aa1c6a7647ceb2ab0e5},
     }

821. W. Wu and D. Yarowsky, “Wiktionary Normalization of Translations and Morphological Information,” in Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online), 2020, p. 4683–4692. doi:10.18653/v1/2020.coling-main.413
     [BibTeX] [Abstract] [Link]
     

     We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation{–-}clipping, contraction, and eye dialect{–-}and improve upon a standard attention baseline by using copy attention.

     @inproceedings{wu-yarowsky-2020-wiktionary,
     title = "{W}iktionary Normalization of Translations and Morphological Information",
     author = "Wu, Winston and
     Yarowsky, David",
     editor = "Scott, Donia and
     Bel, Nuria and
     Zong, Chengqing",
     booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
     month = dec,
     year = "2020",
     address = "Barcelona, Spain (Online)",
     publisher = "International Committee on Computational Linguistics",
     url = "https://aclanthology.org/2020.coling-main.413",
     doi = "10.18653/v1/2020.coling-main.413",
     pages = "4683--4692",
     abstract = "We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation{---}clipping, contraction, and eye dialect{---}and improve upon a standard attention baseline by using copy attention.",
     }

822. H. Mei, T. Wan, and J. Eisner, “Noise-Contrastive Estimation for Multivariate Point Processes,” in Advances in Neural Information Processing Systems (NeurIPS), 2020, p. 5204–5214.
     [BibTeX] [Link]

     @InProceedings{mei-wan-eisner-2020,
     author = "Hongyuan Mei and Tom Wan and Jason Eisner",
     title = "Noise-Contrastive Estimation for Multivariate Point
     Processes",
     booktitle = "Advances in Neural Information Processing Systems
     (NeurIPS)",
     pages = "5204--5214",
     year = "2020",
     month = dec,
     URL = "http://cs.jhu.edu/~jason/papers/#mei-wan-eisner-2020",
     }

823. A. Fine, P. Crutchley, J. Blase, J. Carroll, and G. Coppersmith, “Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data,” in Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Online, 2020, p. 50–54. doi:10.18653/v1/2020.nlpcss-1.6
     [BibTeX] [Abstract] [Link]
     

     Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.

     @inproceedings{fine-etal-2020-assessing,
     title = "Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using {NLP} applied to social media data",
     author = "Fine, Alex and
     Crutchley, Patrick and
     Blase, Jenny and
     Carroll, Joshua and
     Coppersmith, Glen",
     editor = "Bamman, David and
     Hovy, Dirk and
     Jurgens, David and
     O'Connor, Brendan and
     Volkova, Svitlana",
     booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.nlpcss-1.6",
     doi = "10.18653/v1/2020.nlpcss-1.6",
     pages = "50--54",
     abstract = "Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.",
     }

824. F. Koerner and P. Koehn, “Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 966–971.
     [BibTeX] [Abstract] [Link]
     

     This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year{‘}s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER{_}LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF{_}DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.

     @inproceedings{koerner-koehn-2020-dual,
     title = "Dual Conditional Cross Entropy Scores and {LASER} Similarity Scores for the {WMT}20 Parallel Corpus Filtering Shared Task",
     author = "Koerner, Felicia and
     Koehn, Philipp",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.109",
     pages = "966--971",
     abstract = "This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year{'}s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER{\_}LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF{\_}DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.",
     }

825. B. Thompson and M. Post, “Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 561–570.
     [BibTeX] [Abstract] [Link]
     

     Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.

     @inproceedings{thompson-post-2020-paraphrase,
     title = "Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity",
     author = "Thompson, Brian and
     Post, Matt",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.67",
     pages = "561--570",
     abstract = "Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.",
     }

826. Desh Raj, Leibny Paola García-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, A. Stolcke, and S. Khudanpur, “DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs,” in Spoken Language Technology Workshop, 2020.
     [BibTeX] [Link]

     @inproceedings{226246280,
     title = {DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs},
     author = {{Desh Raj} and {Leibny Paola García-Perera} and {Zili Huang} and {Shinji Watanabe} and {Daniel Povey} and {A. Stolcke} and {S. Khudanpur}},
     year = 2020,
     month = {11},
     booktitle = {Spoken Language Technology Workshop},
     url = {https://www.semanticscholar.org/paper/6c59a6ad00d82ca9f76fef92232ff3e2f3c1acc8},
     }

827. A. D. McCarthy, A. Williams, S. Liu, D. Yarowsky, and R. Cotterell, “Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5664–5675. doi:10.18653/v1/2020.emnlp-main.456
     [BibTeX] [Abstract] [Link]
     

     A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages{‘} gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.

     @inproceedings{mccarthy-etal-2020-measuring,
     title = "Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions",
     author = "McCarthy, Arya D. and
     Williams, Adina and
     Liu, Shijia and
     Yarowsky, David and
     Cotterell, Ryan",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.456",
     doi = "10.18653/v1/2020.emnlp-main.456",
     pages = "5664--5675",
     abstract = "A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages{'} gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.",
     }

828. L. Barrault, M. Biesialska, O. Bojar, M. R. Costa-jussà, C. Federmann, Y. Graham, R. Grundkiewicz, B. Haddow, M. Huck, E. Joanis, T. Kocmi, P. Koehn, C. Lo, N. Ljubešić, C. Monz, M. Morishita, M. Nagata, T. Nakazawa, S. Pal, M. Post, and M. Zampieri, “Findings of the 2020 Conference on Machine Translation (WMT20),” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 1–55.
     [BibTeX] [Abstract] [Link]
     

     This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

     @inproceedings{barrault-etal-2020-findings,
     title = "Findings of the 2020 Conference on Machine Translation ({WMT}20)",
     author = {Barrault, Lo{\"\i}c and
     Biesialska, Magdalena and
     Bojar, Ond{\v{r}}ej and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Graham, Yvette and
     Grundkiewicz, Roman and
     Haddow, Barry and
     Huck, Matthias and
     Joanis, Eric and
     Kocmi, Tom and
     Koehn, Philipp and
     Lo, Chi-kiu and
     Ljube{\v{s}}i{\'c}, Nikola and
     Monz, Christof and
     Morishita, Makoto and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Pal, Santanu and
     Post, Matt and
     Zampieri, Marcos},
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.1",
     pages = "1--55",
     abstract = "This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.",
     }

829. R. Adams, S. Saria, and Michael Rosenblum, “The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.,” in arXiv: Methodology, 2020.
     [BibTeX] [Link]

     @inproceedings{227238661,
     title = {The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.},
     author = {{R. Adams} and {S. Saria} and {Michael Rosenblum}},
     year = 2020,
     month = {11},
     booktitle = {arXiv: Methodology},
     url = {https://www.semanticscholar.org/paper/4fbea743d7e81b8a1cd48376a264ea30df9ea6f2},
     }

830. K. Kelly, A. Fine, and G. Coppersmith, “Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,” in Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Online, 2020, p. 184–192. doi:10.18653/v1/2020.nlpcss-1.20
     [BibTeX] [Abstract] [Link]
     

     In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans{‘} issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.

     @inproceedings{kelly-etal-2020-social,
     title = "Social media data as a lens onto care-seeking behavior among women veterans of the {US} armed forces",
     author = "Kelly, Kacie and
     Fine, Alex and
     Coppersmith, Glen",
     editor = "Bamman, David and
     Hovy, Dirk and
     Jurgens, David and
     O'Connor, Brendan and
     Volkova, Svitlana",
     booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.nlpcss-1.20",
     doi = "10.18653/v1/2020.nlpcss-1.20",
     pages = "184--192",
     abstract = "In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans{'} issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.",
     }

831. J. D. Arias-Londoño, J. Gómez-García, L. Moro-Velázquez, and Juan Ignacio Godino-Llorente, “Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach,” in IEEE Access, 2020.
     [BibTeX] [Link]

     @inproceedings{227228735,
     title = {Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach},
     author = {{J. D. Arias-Londoño} and {J. Gómez-García} and {L. Moro-Velázquez} and {Juan Ignacio Godino-Llorente}},
     year = 2020,
     month = {11},
     booktitle = {IEEE Access},
     url = {https://www.semanticscholar.org/paper/325a462076363f59ad76daff579666adfd1af3ea},
     }

832. H. Khayrallah, B. Thompson, M. Post, and P. Koehn, “Simulated multiple reference training improves low-resource machine translation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 82–89. doi:10.18653/v1/2020.emnlp-main.7
     [BibTeX] [Abstract] [Link]
     

     Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser{‘}s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.

     @inproceedings{khayrallah-etal-2020-simulated,
     title = "Simulated multiple reference training improves low-resource machine translation",
     author = "Khayrallah, Huda and
     Thompson, Brian and
     Post, Matt and
     Koehn, Philipp",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.7",
     doi = "10.18653/v1/2020.emnlp-main.7",
     pages = "82--89",
     abstract = "Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser{'}s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.",
     }

833. N. Weber, R. Rudinger, and B. Van Durme, “Causal Inference of Script Knowledge,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 7583–7596. doi:10.18653/v1/2020.emnlp-main.612
     [BibTeX] [Abstract] [Link]
     

     When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.

     @inproceedings{weber-etal-2020-causal,
     title = "Causal Inference of Script Knowledge",
     author = "Weber, Noah and
     Rudinger, Rachel and
     Van Durme, Benjamin",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.612",
     doi = "10.18653/v1/2020.emnlp-main.612",
     pages = "7583--7596",
     abstract = "When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.",
     }

834. He Zhang, Jianming Zhang, Federico Perazzi, Zhe L. Lin, and Vishal M. Patel, “Deep Image Compositing,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020.
     [BibTeX] [Link]

     @inproceedings{226246291,
     title = {Deep Image Compositing},
     author = {{He Zhang} and {Jianming Zhang} and {Federico Perazzi} and {Zhe L. Lin} and {Vishal M. Patel}},
     year = 2020,
     month = {11},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/313f77fec4a2a18e84eea1d9923bd94b732ec2b2},
     }

835. D. Dreizin, Yuyin Zhou, Shuhao Fu, Yan Wang, Guang Li, Kathryn Champ, E. Siegel, Ze Wang, Tina Chen, and A. Yuille, “A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.,” in Radiology: Artificial Intelligence, 2020.
     [BibTeX] [Link]

     @inproceedings{228930077,
     title = {A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.},
     author = {{D. Dreizin} and {Yuyin Zhou} and {Shuhao Fu} and {Yan Wang} and {Guang Li} and {Kathryn Champ} and {E. Siegel} and {Ze Wang} and {Tina Chen} and {A. Yuille}},
     year = 2020,
     month = {11},
     booktitle = {Radiology: Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/c194d760641dc8333dca3d5819e6664c25b5b53b},
     }

836. A. Singh, P. Xia, G. Qin, M. Yarmohammadi, and B. Van Durme, “CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models,” in Proceedings of the Fourth Workshop on Structured Prediction for NLP, Online, 2020, p. 11–16. doi:10.18653/v1/2020.spnlp-1.2
     [BibTeX] [Abstract] [Link]
     

     Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.

     @inproceedings{singh-etal-2020-copynext,
     title = "{C}opy{N}ext: Explicit Span Copying and Alignment in Sequence to Sequence Models",
     author = "Singh, Abhinav and
     Xia, Patrick and
     Qin, Guanghui and
     Yarmohammadi, Mahsa and
     Van Durme, Benjamin",
     editor = "Agrawal, Priyanka and
     Kozareva, Zornitsa and
     Kreutzer, Julia and
     Lampouras, Gerasimos and
     Martins, Andr{\'e} and
     Ravi, Sujith and
     Vlachos, Andreas",
     booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.spnlp-1.2",
     doi = "10.18653/v1/2020.spnlp-1.2",
     pages = "11--16",
     abstract = "Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.",
     }

837. Yuhui Xu, Lingxi Xie, Cihang Xie, Jieru Mei, Siyuan Qiao, Wei Shen, H. Xiong, and A. Yuille, “Batch Normalization with Enhanced Linear Transformation,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227228087,
     title = {Batch Normalization with Enhanced Linear Transformation},
     author = {{Yuhui Xu} and {Lingxi Xie} and {Cihang Xie} and {Jieru Mei} and {Siyuan Qiao} and {Wei Shen} and {H. Xiong} and {A. Yuille}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/95824133679061448b57ea746456f36f14796aa0},
     }

838. P. Koehn, V. Chaudhary, A. El-Kishky, N. Goyal, P. Chen, and F. Guzmán, “Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 726–742.
     [BibTeX] [Abstract] [Link]
     

     Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto{–}English and Khmer{–}English and also included the challenge of sentence alignment from document pairs.

     @inproceedings{koehn-etal-2020-findings,
     title = "Findings of the {WMT} 2020 Shared Task on Parallel Corpus Filtering and Alignment",
     author = "Koehn, Philipp and
     Chaudhary, Vishrav and
     El-Kishky, Ahmed and
     Goyal, Naman and
     Chen, Peng-Jen and
     Guzm{\'a}n, Francisco",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.78",
     pages = "726--742",
     abstract = "Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto{--}English and Khmer{--}English and also included the challenge of sentence alignment from document pairs.",
     }

839. Desh Raj, Zili Huang, and S. Khudanpur, “Multi-Class Spectral Clustering with Overlaps for Speaker Diarization,” in Spoken Language Technology Workshop, 2020.
     [BibTeX] [Link]

     @inproceedings{226254048,
     title = {Multi-Class Spectral Clustering with Overlaps for Speaker Diarization},
     author = {{Desh Raj} and {Zili Huang} and {S. Khudanpur}},
     year = 2020,
     month = {11},
     booktitle = {Spoken Language Technology Workshop},
     url = {https://www.semanticscholar.org/paper/43dadc5a85b3b6203f9b78d6eb985dd1f65b2dfc},
     }

840. Desh Raj, J. Villalba, Daniel Povey, and S. Khudanpur, “Frustratingly Easy Noise-aware Training of Acoustic Models,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{226246188,
     title = {Frustratingly Easy Noise-aware Training of Acoustic Models},
     author = {{Desh Raj} and {J. Villalba} and {Daniel Povey} and {S. Khudanpur}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/3b2eb1a573dcdb5a27103b857d32bd0c4d5ef60a},
     }

841. Jesús Antonio Villalba López, D. Garcia-Romero, Nanxin Chen, Gregory Sell, Jonas Borgstrom, A. McCree, Leibny Paola García-Perera, Saurabh Kataria, P. S. Nidadavolu, Pedro Torres-Carrasquiilo, and N. Dehak, “Advances in Speaker Recognition for Telephone and Audio-Visual Data: the JHU-MIT Submission for NIST SRE19,” in The Speaker and Language Recognition Workshop, 2020.
     [BibTeX] [Link]

     @inproceedings{219505334,
     title = {Advances in Speaker Recognition for Telephone and Audio-Visual Data: the JHU-MIT Submission for NIST SRE19},
     author = {{Jesús Antonio Villalba López} and {D. Garcia-Romero} and {Nanxin Chen} and {Gregory Sell} and {Jonas Borgstrom} and {A. McCree} and {Leibny Paola García-Perera} and {Saurabh Kataria} and {P. S. Nidadavolu} and {Pedro Torres-Carrasquiilo} and {N. Dehak}},
     year = 2020,
     month = {11},
     booktitle = {The Speaker and Language Recognition Workshop},
     url = {https://www.semanticscholar.org/paper/de00fffe4b64aef3797e05e74b5d3d07065b20ee},
     }

842. P. Xia, S. Wu, and B. Van Durme, “Which *BERT? A Survey Organizing Contextualized Encoders,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 7516–7533. doi:10.18653/v1/2020.emnlp-main.608
     [BibTeX] [Abstract] [Link]
     

     Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.

     @inproceedings{xia-etal-2020-bert,
     title = "Which *{BERT}? {A} Survey Organizing Contextualized Encoders",
     author = "Xia, Patrick and
     Wu, Shijie and
     Van Durme, Benjamin",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.608",
     doi = "10.18653/v1/2020.emnlp-main.608",
     pages = "7516--7533",
     abstract = "Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.",
     }

843. B. Thompson and P. Koehn, “Exploiting Sentence Order in Document Alignment,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5997–6007. doi:10.18653/v1/2020.emnlp-main.483
     [BibTeX] [Abstract] [Link]
     

     We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61{\%} relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala{–}English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.

     @inproceedings{thompson-koehn-2020-exploiting,
     title = "Exploiting Sentence Order in Document Alignment",
     author = "Thompson, Brian and
     Koehn, Philipp",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.483",
     doi = "10.18653/v1/2020.emnlp-main.483",
     pages = "5997--6007",
     abstract = "We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61{\%} relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala{--}English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.",
     }

844. Nanxin Chen, Piotr Żelasko, J. Villalba, and N. Dehak, “Focus on the Present: A Regularization Method for the ASR Source-Target Attention Layer,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
     [BibTeX] [Link]

     @inproceedings{226236802,
     title = {Focus on the Present: A Regularization Method for the ASR Source-Target Attention Layer},
     author = {{Nanxin Chen} and {Piotr Żelasko} and {J. Villalba} and {N. Dehak}},
     year = 2020,
     month = {11},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/f90f383a3f027bfa48fea68790d3cb77f7634b92},
     }

845. L. Specia, Z. Li, J. Pino, V. Chaudhary, F. Guzmán, G. Neubig, N. Durrani, Y. Belinkov, P. Koehn, H. Sajjad, P. Michel, and X. Li, “Findings of the WMT 2020 Shared Task on Machine Translation Robustness,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 76–91.
     [BibTeX] [Abstract] [Link]
     

     We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs {–} English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for {”}catastrophic errors{”}. We received 59 submissions by 11 participating teams from a variety of types of institutions.

     @inproceedings{specia-etal-2020-findings,
     title = "Findings of the {WMT} 2020 Shared Task on Machine Translation Robustness",
     author = "Specia, Lucia and
     Li, Zhenhao and
     Pino, Juan and
     Chaudhary, Vishrav and
     Guzm{\'a}n, Francisco and
     Neubig, Graham and
     Durrani, Nadir and
     Belinkov, Yonatan and
     Koehn, Philipp and
     Sajjad, Hassan and
     Michel, Paul and
     Li, Xian",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.4",
     pages = "76--91",
     abstract = "We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs {--} English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for {''}catastrophic errors{''}. We received 59 submissions by 11 participating teams from a variety of types of institutions.",
     }

846. P. Xia, J. Sedoc, and B. Van Durme, “Incremental Neural Coreference Resolution in Constant Memory,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 8617–8624. doi:10.18653/v1/2020.emnlp-main.695
     [BibTeX] [Abstract] [Link]
     

     We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity{‘}s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3{\%} relative loss in F1 on OntoNotes 5.0.

     @inproceedings{xia-etal-2020-incremental,
     title = "Incremental Neural Coreference Resolution in Constant Memory",
     author = "Xia, Patrick and
     Sedoc, Jo{\~a}o and
     Van Durme, Benjamin",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.695",
     doi = "10.18653/v1/2020.emnlp-main.695",
     pages = "8617--8624",
     abstract = "We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity{'}s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3{\%} relative loss in F1 on OntoNotes 5.0.",
     }

847. K. Harrigian, C. Aguirre, and M. Dredze, “Do Models of Mental Health Based on Social Media Data Generalize?,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 3774–3788. doi:10.18653/v1/2020.findings-emnlp.337
     [BibTeX] [Abstract] [Link]
     

     Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.

     @inproceedings{harrigian-etal-2020-models,
     title = "Do Models of Mental Health Based on Social Media Data Generalize?",
     author = "Harrigian, Keith and
     Aguirre, Carlos and
     Dredze, Mark",
     editor = "Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.findings-emnlp.337",
     doi = "10.18653/v1/2020.findings-emnlp.337",
     pages = "3774--3788",
     abstract = "Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.",
     }

848. Jeya Maria Jose Valanarasu and Vishal M. Patel, “Overcomplete Deep Subspace Clustering Networks,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020.
     [BibTeX] [Link]

     @inproceedings{226975634,
     title = {Overcomplete Deep Subspace Clustering Networks},
     author = {{Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2020,
     month = {11},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/ace30204c77e5aecf28fc26d2775b89e839cbe7e},
     }

849. R. Bawden, B. Zhang, L. Yankovskaya, A. Tättar, and M. Post, “A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 918–932. doi:10.18653/v1/2020.findings-emnlp.82
     [BibTeX] [Abstract] [Link]
     

     We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU{‘}s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.

     @inproceedings{bawden-etal-2020-study,
     title = "A Study in Improving {BLEU} Reference Coverage with Diverse Automatic Paraphrasing",
     author = {Bawden, Rachel and
     Zhang, Biao and
     Yankovskaya, Lisa and
     T{\"a}ttar, Andre and
     Post, Matt},
     editor = "Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.findings-emnlp.82",
     doi = "10.18653/v1/2020.findings-emnlp.82",
     pages = "918--932",
     abstract = "We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU{'}s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.",
     }

850. Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, V. Chandra, and A. Yuille, “Can Temporal Information Help with Contrastive Self-Supervised Learning?,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227209513,
     title = {Can Temporal Information Help with Contrastive Self-Supervised Learning?},
     author = {{Yutong Bai} and {Haoqi Fan} and {Ishan Misra} and {Ganesh Venkatesh} and {Yongyi Lu} and {Yuyin Zhou} and {Qihang Yu} and {V. Chandra} and {A. Yuille}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/c8993a95dac7a0bf86fb96ee30cf653a57755783},
     }

851. Yu Zeng, Zhe L. Lin, Huchuan Lu, and Vishal M. Patel, “Image Inpainting with Contextual Reconstruction Loss,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227162482,
     title = {Image Inpainting with Contextual Reconstruction Loss},
     author = {{Yu Zeng} and {Zhe L. Lin} and {Huchuan Lu} and {Vishal M. Patel}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/590c81fe445551cca14e6e7b66a64534fdb454f8},
     }

852. S. Sun and K. Duh, “CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 4160–4170. doi:10.18653/v1/2020.emnlp-main.340
     [BibTeX] [Abstract] [Link]
     

     We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139×138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.

     @inproceedings{sun-duh-2020-clirmatrix,
     title = "{CLIRM}atrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval",
     author = "Sun, Shuo and
     Duh, Kevin",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.340",
     doi = "10.18653/v1/2020.emnlp-main.340",
     pages = "4160--4170",
     abstract = "We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.",
     }

853. S. Wu and M. Dredze, “Do Explicit Alignments Robustly Improve Multilingual Encoders?,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 4471–4482. doi:10.18653/v1/2020.emnlp-main.362
     [BibTeX] [Abstract] [Link]
     

     Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.

     @inproceedings{wu-dredze-2020-explicit,
     title = "Do Explicit Alignments Robustly Improve Multilingual Encoders?",
     author = "Wu, Shijie and
     Dredze, Mark",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.362",
     doi = "10.18653/v1/2020.emnlp-main.362",
     pages = "4471--4482",
     abstract = "Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.",
     }

854. B. Thompson and M. Post, “Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 90–121. doi:10.18653/v1/2020.emnlp-main.8
     [BibTeX] [Abstract] [Link]
     

     We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser{‘}s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric{–-}conditioning on the source instead of the reference{–-}and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.

     @inproceedings{thompson-post-2020-automatic,
     title = "Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing",
     author = "Thompson, Brian and
     Post, Matt",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.8",
     doi = "10.18653/v1/2020.emnlp-main.8",
     pages = "90--121",
     abstract = "We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser{'}s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric{---}conditioning on the source instead of the reference{---}and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.",
     }

855. S. Vashishtha, A. Poliak, Y. K. Lal, B. Van Durme, and A. S. White, “Temporal Reasoning in Natural Language Inference,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 4070–4078. doi:10.18653/v1/2020.findings-emnlp.363
     [BibTeX] [Abstract] [Link]
     

     We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration{–-}how long an event lasts{–-}and event ordering{–-}how events are temporally arranged{–-}into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.

     @inproceedings{vashishtha-etal-2020-temporal,
     title = "Temporal Reasoning in Natural Language Inference",
     author = "Vashishtha, Siddharth and
     Poliak, Adam and
     Lal, Yash Kumar and
     Van Durme, Benjamin and
     White, Aaron Steven",
     editor = "Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.findings-emnlp.363",
     doi = "10.18653/v1/2020.findings-emnlp.363",
     pages = "4070--4078",
     abstract = "We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration{---}how long an event lasts{---}and event ordering{---}how events are temporally arranged{---}into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.",
     }

856. K. Marchisio, K. Duh, and P. Koehn, “When Does Unsupervised Machine Translation Work?,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 571–583.
     [BibTeX] [Abstract] [Link]
     

     Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.

     @inproceedings{marchisio-etal-2020-unsupervised,
     title = "When Does Unsupervised Machine Translation Work?",
     author = "Marchisio, Kelly and
     Duh, Kevin and
     Koehn, Philipp",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.68",
     pages = "571--583",
     abstract = "Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.",
     }

857. N. Weir, J. Sedoc, and B. Van Durme, “COD3S: Diverse Generation with Discrete Semantic Signatures,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5199–5211. doi:10.18653/v1/2020.emnlp-main.421
     [BibTeX] [Abstract] [Link]
     

     We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition{‘}s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.

     @inproceedings{weir-etal-2020-cod3s,
     title = "{COD3S}: Diverse Generation with Discrete Semantic Signatures",
     author = "Weir, Nathaniel and
     Sedoc, Jo{\~a}o and
     Van Durme, Benjamin",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.421",
     doi = "10.18653/v1/2020.emnlp-main.421",
     pages = "5199--5211",
     abstract = "We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition{'}s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.",
     }

858. R. Bawden, B. Zhang, A. Tättar, and M. Post, “ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 887–894.
     [BibTeX] [Abstract] [Link]
     

     We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.

     @inproceedings{bawden-etal-2020-parbleu,
     title = "{P}ar{BLEU}: Augmenting Metrics with Automatic Paraphrases for the {WMT}{'}20 Metrics Shared Task",
     author = {Bawden, Rachel and
     Zhang, Biao and
     T{\"a}ttar, Andre and
     Post, Matt},
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.98",
     pages = "887--894",
     abstract = "We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.",
     }

859. Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory Hager, and A. Yuille, “Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{227239264,
     title = {Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation},
     author = {{Qihao Liu} and {Weichao Qiu} and {Weiyao Wang} and {Gregory Hager} and {A. Yuille}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/287a96966040d6dd5aa84d329cb08929de624135},
     }

860. Y. Chen, T. Chen, and B. Van Durme, “Joint Modeling of Arguments for Event Understanding,” in Proceedings of the First Workshop on Computational Approaches to Discourse, Online, 2020, p. 96–101. doi:10.18653/v1/2020.codi-1.10
     [BibTeX] [Abstract] [Link]
     

     We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.

     @inproceedings{chen-etal-2020-joint-modeling,
     title = "Joint Modeling of Arguments for Event Understanding",
     author = "Chen, Yunmo and
     Chen, Tongfei and
     Van Durme, Benjamin",
     editor = "Braud, Chlo{\'e} and
     Hardmeier, Christian and
     Li, Junyi Jessy and
     Louis, Annie and
     Strube, Michael",
     booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.codi-1.10",
     doi = "10.18653/v1/2020.codi-1.10",
     pages = "96--101",
     abstract = "We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.",
     }

861. A. Kejriwal and P. Koehn, “An exploratory approach to the Parallel Corpus Filtering shared task WMT20,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 959–965.
     [BibTeX] [Abstract] [Link]
     

     In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.

     @inproceedings{kejriwal-koehn-2020-exploratory,
     title = "An exploratory approach to the Parallel Corpus Filtering shared task {WMT}20",
     author = "Kejriwal, Ankur and
     Koehn, Philipp",
     editor = {Barrault, Lo{\"\i}c and
     Bojar, Ond{\v{r}}ej and
     Bougares, Fethi and
     Chatterjee, Rajen and
     Costa-juss{\`a}, Marta R. and
     Federmann, Christian and
     Fishel, Mark and
     Fraser, Alexander and
     Graham, Yvette and
     Guzman, Paco and
     Haddow, Barry and
     Huck, Matthias and
     Yepes, Antonio Jimeno and
     Koehn, Philipp and
     Martins, Andr{\'e} and
     Morishita, Makoto and
     Monz, Christof and
     Nagata, Masaaki and
     Nakazawa, Toshiaki and
     Negri, Matteo},
     booktitle = "Proceedings of the Fifth Conference on Machine Translation",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.wmt-1.108",
     pages = "959--965",
     abstract = "In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.",
     }

862. Rachel Dorn, A. Nobles, Masoud Rouhizadeh, and Mark Dredze, “Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{226975724,
     title = {Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data},
     author = {{Rachel Dorn} and {A. Nobles} and {Masoud Rouhizadeh} and {Mark Dredze}},
     year = 2020,
     month = {11},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/3d35c0aec777f6c180d4bf61a2443ec35230bfd2},
     }

863. J. Bremerman, H. Khayrallah, D. Oard, and M. Post, “On the Evaluation of Machine Translation n-best Lists,” in Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, Online, 2020, p. 60–68. doi:10.18653/v1/2020.eval4nlp-1.7
     [BibTeX] [Abstract] [Link]
     

     The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a {`}good{‘} n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.

     @inproceedings{bremerman-etal-2020-evaluation,
     title = "On the Evaluation of Machine Translation n-best Lists",
     author = "Bremerman, Jacob and
     Khayrallah, Huda and
     Oard, Douglas and
     Post, Matt",
     editor = "Eger, Steffen and
     Gao, Yang and
     Peyrard, Maxime and
     Zhao, Wei and
     Hovy, Eduard",
     booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.eval4nlp-1.7",
     doi = "10.18653/v1/2020.eval4nlp-1.7",
     pages = "60--68",
     abstract = "The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a {`}good{'} n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.",
     }

864. Y. Graham, B. Haddow, and P. Koehn, “Statistical Power and Translationese in Machine Translation Evaluation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 72–81. doi:10.18653/v1/2020.emnlp-main.6
     [BibTeX] [Abstract] [Link]
     

     The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.

     @inproceedings{graham-etal-2020-statistical,
     title = "Statistical Power and Translationese in Machine Translation Evaluation",
     author = "Graham, Yvette and
     Haddow, Barry and
     Koehn, Philipp",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.6",
     doi = "10.18653/v1/2020.emnlp-main.6",
     pages = "72--81",
     abstract = "The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.",
     }

865. M. Yuan, M. Zhang, B. Van Durme, L. Findlater, and J. Boyd-Graber, “Interactive Refinement of Cross-Lingual Word Embeddings,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5984–5996. doi:10.18653/v1/2020.emnlp-main.482
     [BibTeX] [Abstract] [Link]
     

     Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.

     @inproceedings{yuan-etal-2020-interactive,
     title = "Interactive Refinement of Cross-Lingual Word Embeddings",
     author = "Yuan, Michelle and
     Zhang, Mozhi and
     Van Durme, Benjamin and
     Findlater, Leah and
     Boyd-Graber, Jordan",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.482",
     doi = "10.18653/v1/2020.emnlp-main.482",
     pages = "5984--5996",
     abstract = "Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.",
     }

866. A. El-Kishky, V. Chaudhary, F. Guzmán, and P. Koehn, “CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5960–5969. doi:10.18653/v1/2020.emnlp-main.480
     [BibTeX] [Abstract] [Link]
     

     Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.

     @inproceedings{el-kishky-etal-2020-ccaligned,
     title = "{CCA}ligned: A Massive Collection of Cross-Lingual Web-Document Pairs",
     author = "El-Kishky, Ahmed and
     Chaudhary, Vishrav and
     Guzm{\'a}n, Francisco and
     Koehn, Philipp",
     editor = "Webber, Bonnie and
     Cohn, Trevor and
     He, Yulan and
     Liu, Yang",
     booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.emnlp-main.480",
     doi = "10.18653/v1/2020.emnlp-main.480",
     pages = "5960--5969",
     abstract = "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.",
     }

867. Y. Chen, T. Chen, S. Ebner, A. S. White, and B. Van Durme, “Reading the Manual: Event Extraction as Definition Comprehension,” in Proceedings of the Fourth Workshop on Structured Prediction for NLP, Online, 2020, p. 74–83. doi:10.18653/v1/2020.spnlp-1.9
     [BibTeX] [Abstract] [Link]
     

     We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, {“}Some person was born in some location at some time.{”} We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.

     @inproceedings{chen-etal-2020-reading,
     title = "Reading the Manual: Event Extraction as Definition Comprehension",
     author = "Chen, Yunmo and
     Chen, Tongfei and
     Ebner, Seth and
     White, Aaron Steven and
     Van Durme, Benjamin",
     editor = "Agrawal, Priyanka and
     Kozareva, Zornitsa and
     Kreutzer, Julia and
     Lampouras, Gerasimos and
     Martins, Andr{\'e} and
     Ravi, Sujith and
     Vlachos, Andreas",
     booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
     month = nov,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.spnlp-1.9",
     doi = "10.18653/v1/2020.spnlp-1.9",
     pages = "74--83",
     abstract = "We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, {``}Some person was born in some location at some time.{''} We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.",
     }

868. J. Villalba, Yuekai Zhang, and N. Dehak, “x-Vectors Meet Adversarial Attacks: Benchmarking Adversarial Robustness in Speaker Verification,” in Interspeech, 2020.
     [BibTeX] [Link]

     @inproceedings{226200412,
     title = {x-Vectors Meet Adversarial Attacks: Benchmarking Adversarial Robustness in Speaker Verification},
     author = {{J. Villalba} and {Yuekai Zhang} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/1b305dbfb789a19013d7ab8fa4f26ab33d99f6ed},
     }

869. Xutai Ma, Yongqiang Wang, M. Dousti, Philipp Koehn, and J. Pino, “Streaming Simultaneous Speech Translation with Augmented Memory Transformer,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
     [BibTeX] [Link]

     @inproceedings{226227140,
     title = {Streaming Simultaneous Speech Translation with Augmented Memory Transformer},
     author = {{Xutai Ma} and {Yongqiang Wang} and {M. Dousti} and {Philipp Koehn} and {J. Pino}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/0d85f33d43ef7dbac3e559b94aea2fd8f5e64f7f},
     }

870. Siyuan Feng, Piotr Żelasko, Laureano Moro-Vel’azquez, A. Abavisani, M. Hasegawa-Johnson, O. Scharenborg, and N. Dehak, “How Phonotactics Affect Multilingual and Zero-Shot ASR Performance,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
     [BibTeX] [Link]

     @inproceedings{225062469,
     title = {How Phonotactics Affect Multilingual and Zero-Shot ASR Performance},
     author = {{Siyuan Feng} and {Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {A. Abavisani} and {M. Hasegawa-Johnson} and {O. Scharenborg} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/2fb642dc5d724c32d3b4cfa2359432968d591287},
     }

871. Mahdi Abavisani and Vishal M. Patel, “Deep Multimodal Sparse Representation-Based Classification,” in International Conference on Information Photonics, 2020.
     [BibTeX] [Link]

     @inproceedings{224883209,
     title = {Deep Multimodal Sparse Representation-Based Classification},
     author = {{Mahdi Abavisani} and {Vishal M. Patel}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/3244860c13dbeef339a92a6a37f0975891c539ca},
     }

872. D. Kelly, Max Spaderna, Vedrana Hodzic, Suraj Nair, Christopher Kitchen, A. Werkheiser, Megan M. Powell, Fang Liu, Glen A. Coppersmith, Shuo Chen, and P. Resnik, “Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls,” in Psychiatry Research, 2020.
     [BibTeX] [Link]

     @inproceedings{222119577,
     title = {Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls},
     author = {{D. Kelly} and {Max Spaderna} and {Vedrana Hodzic} and {Suraj Nair} and {Christopher Kitchen} and {A. Werkheiser} and {Megan M. Powell} and {Fang Liu} and {Glen A. Coppersmith} and {Shuo Chen} and {P. Resnik}},
     year = 2020,
     month = {10},
     booktitle = {Psychiatry Research},
     url = {https://www.semanticscholar.org/paper/dbfbee8705b0f3f172963bc22d1b145cfdec0f55},
     }

873. David A. Broniatowski, Amelia M. Jamison, N. Johnson, N. Velásquez, R. Leahy, N. J. Restrepo, Mark Dredze, and S. Quinn, “Facebook Pages, the “Disneyland” Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.,” in American Journal of Public Health, 2020.
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     @inproceedings{222154979,
     title = {Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.},
     author = {{David A. Broniatowski} and {Amelia M. Jamison} and {N. Johnson} and {N. Velásquez} and {R. Leahy} and {N. J. Restrepo} and {Mark Dredze} and {S. Quinn}},
     year = 2020,
     month = {10},
     booktitle = {American Journal of Public Health},
     url = {https://www.semanticscholar.org/paper/77f452950894994f55dae8a4cfbdf4cd1980fc59},
     }

874. Yihong Sun, Adam Kortylewski, and A. Yuille, “Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model,” in Computer Vision and Pattern Recognition, 2020.
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     @inproceedings{244728395,
     title = {Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model},
     author = {{Yihong Sun} and {Adam Kortylewski} and {A. Yuille}},
     year = 2020,
     month = {10},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/941e8b25be33bb961182c9ecbb95815d8e62eee6},
     }

875. J. Naradowsky, X. Zhang, and K. Duh, “Machine Translation System Selection from Bandit Feedback,” in Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Virtual, 2020, p. 50–63.
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     @inproceedings{naradowsky-etal-2020-machine,
     title = "Machine Translation System Selection from Bandit Feedback",
     author = "Naradowsky, Jason and
     Zhang, Xuan and
     Duh, Kevin",
     editor = "Denkowski, Michael and
     Federmann, Christian",
     booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
     month = oct,
     year = "2020",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2020.amta-research.5",
     pages = "50--63",
     }

876. Saurabh Kataria, J. Villalba, and N. Dehak, “Perceptual Loss Based Speech Denoising with an Ensemble of Audio Pattern Recognition and Self-Supervised Models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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     @inproceedings{225039829,
     title = {Perceptual Loss Based Speech Denoising with an Ensemble of Audio Pattern Recognition and Self-Supervised Models},
     author = {{Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/af803a305d5f1b079bb55a9f0ceeb5acf3726a1a},
     }

877. Puyang Wang, M. Vives, Vishal M. Patel, and I. Hacihaliloglu, “Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{222136412,
     title = {Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition},
     author = {{Puyang Wang} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/5a6acd6c8daf2468b27bca9042e94e337dfb4f1b},
     }

878. Ahmed Z. Alsinan, Charles Rule, M. Vives, Vishal M. Patel, and I. Hacihaliloglu, “GAN-Based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{222137553,
     title = {GAN-Based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation},
     author = {{Ahmed Z. Alsinan} and {Charles Rule} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/b77d0fd091ddc9b6d23f1b246ea5cbf333c6be24},
     }

879. R. Pappagari, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “CopyPaste: An Augmentation Method for Speech Emotion Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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     @inproceedings{225094487,
     title = {CopyPaste: An Augmentation Method for Speech Emotion Recognition},
     author = {{R. Pappagari} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/f620d71fccdf3efad7be1748d40eaadea5c9d6dd},
     }

880. R. Yasarla, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Exploring Overcomplete Representations for Single Image Deraining Using CNNs,” in IEEE Journal on Selected Topics in Signal Processing, 2020.
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     @inproceedings{224814251,
     title = {Exploring Overcomplete Representations for Single Image Deraining Using CNNs},
     author = {{R. Yasarla} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2020,
     month = {10},
     booktitle = {IEEE Journal on Selected Topics in Signal Processing},
     url = {https://www.semanticscholar.org/paper/051a024dcf5b7c81f22504dc317a8de9d1940020},
     }

881. Yihong Sun, Adam Kortylewski, and A. Yuille, “Weakly-Supervised Amodal Instance Segmentation with Compositional Priors,” in arXiv.org, 2020.
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     @inproceedings{225067102,
     title = {Weakly-Supervised Amodal Instance Segmentation with Compositional Priors},
     author = {{Yihong Sun} and {Adam Kortylewski} and {A. Yuille}},
     year = 2020,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/f3edb5d5a1c825949578c2f9bfacc3141180ec43},
     }

882. H. Inaguma, Yosuke Higuchi, Kevin Duh, Tatsuya Kawahara, and Shinji Watanabe, “ORTHROS: non-autoregressive end-to-end speech translation With dual-decoder,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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     @inproceedings{225069289,
     title = {ORTHROS: non-autoregressive end-to-end speech translation With dual-decoder},
     author = {{H. Inaguma} and {Yosuke Higuchi} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/589e651c69251ee20a89e075d015eb03b35cf17d},
     }

883. Jeremias Sulam, Ramchandran Muthumukar, and R. Arora, “Adversarial Robustness of Supervised Sparse Coding,” in Neural Information Processing Systems, 2020.
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     @inproceedings{225062156,
     title = {Adversarial Robustness of Supervised Sparse Coding},
     author = {{Jeremias Sulam} and {Ramchandran Muthumukar} and {R. Arora}},
     year = 2020,
     month = {10},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/07cc4408d5fa28007db9135fceb73943a713a962},
     }

884. Yuekai Zhang, Ziyan Jiang, J. Villalba, and N. Dehak, “Black-Box Attacks on Spoofing Countermeasures Using Transferability of Adversarial Examples,” in Interspeech, 2020.
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     @inproceedings{226202223,
     title = {Black-Box Attacks on Spoofing Countermeasures Using Transferability of Adversarial Examples},
     author = {{Yuekai Zhang} and {Ziyan Jiang} and {J. Villalba} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/cf1e3bf91fa9989981e5ed3e00331ff0dbe3d56f},
     }

885. Poorya Mianjy and R. Arora, “On Convergence and Generalization of Dropout Training,” in Neural Information Processing Systems, 2020.
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     @inproceedings{225068823,
     title = {On Convergence and Generalization of Dropout Training},
     author = {{Poorya Mianjy} and {R. Arora}},
     year = 2020,
     month = {10},
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/2d9dc4b6228ca78f395bd55be79b26e02fcb608b},
     }

886. Samik Sadhu and H. Hermansky, “Continual Learning in Automatic Speech Recognition,” in Interspeech, 2020.
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     @inproceedings{226200343,
     title = {Continual Learning in Automatic Speech Recognition},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/802731161ad5db91bac8569307d8e75019d9ffdd},
     }

887. Amelia M. Jamison, David A. Broniatowski, Michael C. Smith, Kajal Parikh, Adeena Malik, Mark Dredze, and S. Quinn, “Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter.,” in American Journal of Public Health, 2020.
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     @inproceedings{222152020,
     title = {Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter.},
     author = {{Amelia M. Jamison} and {David A. Broniatowski} and {Michael C. Smith} and {Kajal Parikh} and {Adeena Malik} and {Mark Dredze} and {S. Quinn}},
     year = 2020,
     month = {10},
     booktitle = {American Journal of Public Health},
     url = {https://www.semanticscholar.org/paper/8aceab6f7c62f65667094060b79b7ac735ae7f3a},
     }

888. E. Leas, E. M. Hendrickson, A. Nobles, R. Todd, Davey M. Smith, Mark Dredze, and J. Ayers, “Self-reported Cannabidiol (CBD) Use for Conditions With Proven Therapies,” in JAMA Network Open, 2020.
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     @inproceedings{222834475,
     title = {Self-reported Cannabidiol (CBD) Use for Conditions With Proven Therapies},
     author = {{E. Leas} and {E. M. Hendrickson} and {A. Nobles} and {R. Todd} and {Davey M. Smith} and {Mark Dredze} and {J. Ayers}},
     year = 2020,
     month = {10},
     booktitle = {JAMA Network Open},
     url = {https://www.semanticscholar.org/paper/43da600949c62a5cb2a54f427ddfa468167a3243},
     }

889. Jaime S. Cardoso, H. Nguyen, N. Heller, P. Abreu, I. Išgum, W. Silva, Ricardo Cruz, J. P. Amorim, Vishal M. Patel, B. Roysam, Kevin Zhou, Steve Jiang, Ngan T. H. Le, Khoa Luu, R. Sznitman, V. Cheplygina, D. Mateus, E. Trucco, and Samaneh Abbasi-Sureshjani, “Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing,” in iMIMIC/MIL3ID/LABELS@MICCAI, 2020.
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     @inproceedings{226238222,
     title = {Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing},
     author = {{Jaime S. Cardoso} and {H. Nguyen} and {N. Heller} and {P. Abreu} and {I. Išgum} and {W. Silva} and {Ricardo Cruz} and {J. P. Amorim} and {Vishal M. Patel} and {B. Roysam} and {Kevin Zhou} and {Steve Jiang} and {Ngan T. H. Le} and {Khoa Luu} and {R. Sznitman} and {V. Cheplygina} and {D. Mateus} and {E. Trucco} and {Samaneh Abbasi-Sureshjani}},
     year = 2020,
     month = {10},
     booktitle = {iMIMIC/MIL3ID/LABELS@MICCAI},
     url = {https://www.semanticscholar.org/paper/ec323495610daede5d9fb1143be4b07c14971f7e},
     }

890. Chen Wei, Huiyu Wang, Wei Shen, and A. Yuille, “CO2: Consistent Contrast for Unsupervised Visual Representation Learning,” in International Conference on Learning Representations, 2020.
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     @inproceedings{222140947,
     title = {CO2: Consistent Contrast for Unsupervised Visual Representation Learning},
     author = {{Chen Wei} and {Huiyu Wang} and {Wei Shen} and {A. Yuille}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/b6f54f6d3a0cb9d3f1244c63773c40b0f5a1e224},
     }

891. Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, E. Fishman, and A. Yuille, “Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples,” in Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, 2020.
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     @inproceedings{202760086,
     title = {Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples},
     author = {{Yingwei Li} and {Zhuotun Zhu} and {Yuyin Zhou} and {Yingda Xia} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
     year = 2020,
     month = {10},
     booktitle = {Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics},
     url = {https://www.semanticscholar.org/paper/71ec4e16c6a313dc04ece50aed94554aebe41b1f},
     }

892. Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, I. Hacihaliloglu, and Vishal M. Patel, “KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation,” in IEEE Transactions on Medical Imaging, 2020.
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     @inproceedings{222133837,
     title = {KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation},
     author = {{Jeya Maria Jose Valanarasu} and {Vishwanath A. Sindagi} and {I. Hacihaliloglu} and {Vishal M. Patel}},
     year = 2020,
     month = {10},
     booktitle = {IEEE Transactions on Medical Imaging},
     url = {https://www.semanticscholar.org/paper/380f9376e00ae9e56c79c1bef7e4e3a10ae75365},
     }

893. Matthew Maciejewski, Jing Shi, Shinji Watanabe, and S. Khudanpur, “Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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     @inproceedings{225062331,
     title = {Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step},
     author = {{Matthew Maciejewski} and {Jing Shi} and {Shinji Watanabe} and {S. Khudanpur}},
     year = 2020,
     month = {10},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/085072963b33367b842369b9ce81394d32ac8843},
     }

894. T. Yip, S. Saria, M. Petri, and L. Magder, “Predictors of the start of declining eGFR in patients with systemic lupus erythematosus,” in Lupus, 2020.
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     @inproceedings{226037987,
     title = {Predictors of the start of declining eGFR in patients with systemic lupus erythematosus},
     author = {{T. Yip} and {S. Saria} and {M. Petri} and {L. Magder}},
     year = 2020,
     month = {10},
     booktitle = {Lupus},
     url = {https://www.semanticscholar.org/paper/dc049aa6be740ffbb5e03be04c9c7f3c8f56eb5b},
     }

895. Pegah Ghahramani, Hossein Hadian, Daniel Povey, H. Hermansky, and S. Khudanpur, “An Alternative to MFCCs for ASR,” in Interspeech, 2020.
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     @inproceedings{226201790,
     title = {An Alternative to MFCCs for ASR},
     author = {{Pegah Ghahramani} and {Hossein Hadian} and {Daniel Povey} and {H. Hermansky} and {S. Khudanpur}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/ce6fca70a2e54733a501648f9f7f3b346a57096a},
     }

896. Puyang Wang, Pengfei Guo, Jianhua Lu, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{222137241,
     title = {Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images},
     author = {{Puyang Wang} and {Pengfei Guo} and {Jianhua Lu} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/7f010fac05da563967298618e663effdcafe3e9d},
     }

897. Jaejin Cho, Piotr Żelasko, J. Villalba, Shinji Watanabe, and N. Dehak, “Learning Speaker Embedding from Text-to-Speech,” in Interspeech, 2020.
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     @inproceedings{225039997,
     title = {Learning Speaker Embedding from Text-to-Speech},
     author = {{Jaejin Cho} and {Piotr Żelasko} and {J. Villalba} and {Shinji Watanabe} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/faf494d0aa25a17aa25930ffb4c750fa59c44849},
     }

898. Ian McLane, V. Rennoll, Adebayo A. Eisape, Mounya Elhilali, and J. West, “Flexible electrostatic transducer with tuned acoustic impedance for improved sensing of body-and water-borne sounds,” in Journal of the Acoustical Society of America, 2020.
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     @inproceedings{229518417,
     title = {Flexible electrostatic transducer with tuned acoustic impedance for improved sensing of body-and water-borne sounds},
     author = {{Ian McLane} and {V. Rennoll} and {Adebayo A. Eisape} and {Mounya Elhilali} and {J. West}},
     year = 2020,
     month = {10},
     booktitle = {Journal of the Acoustical Society of America},
     url = {https://www.semanticscholar.org/paper/5b105c7c8ccef29c54b4f8b1f3d9e8ca8f8da7e4},
     }

899. V. Rennoll, Ian McLane, Mounya Elhilali, and J. West, “Characterizing the acoustic impedance and attenuation of biocompatible elastomers: An optimal design of experiments approach,” in Journal of the Acoustical Society of America, 2020.
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     @inproceedings{229510453,
     title = {Characterizing the acoustic impedance and attenuation of biocompatible elastomers: An optimal design of experiments approach},
     author = {{V. Rennoll} and {Ian McLane} and {Mounya Elhilali} and {J. West}},
     year = 2020,
     month = {10},
     booktitle = {Journal of the Acoustical Society of America},
     url = {https://www.semanticscholar.org/paper/7212311ad0f267e90414a56b95f4f4ead2753645},
     }

900. R. Pappagari, Jaejin Cho, L. Moro-Velázquez, and N. Dehak, “Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer’s Disease and Assess its Severity,” in Interspeech, 2020.
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     @inproceedings{226203271,
     title = {Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer's Disease and Assess its Severity},
     author = {{R. Pappagari} and {Jaejin Cho} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2020,
     month = {10},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/4c25acf91e0b0b475e69cb9ab9f0041d16bc7c7d},
     }

901. Adarsh Subbaswamy, R. Adams, and S. Saria, “Evaluating Model Robustness to Dataset Shift,” in arXiv.org, 2020.
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     @inproceedings{225094283,
     title = {Evaluating Model Robustness to Dataset Shift},
     author = {{Adarsh Subbaswamy} and {R. Adams} and {S. Saria}},
     year = 2020,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/d814c4f5d1e5a0b134638344d43b30645144f9b4},
     }

902. Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, A. Yuille, and Cihang Xie, “Shape-Texture Debiased Neural Network Training,” in International Conference on Learning Representations, 2020.
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     @inproceedings{222310549,
     title = {Shape-Texture Debiased Neural Network Training},
     author = {{Yingwei Li} and {Qihang Yu} and {Mingxing Tan} and {Jieru Mei} and {Peng Tang} and {Wei Shen} and {A. Yuille} and {Cihang Xie}},
     year = 2020,
     month = {10},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/33ccec80b42f624cec07f0ab485c04de14886fe5},
     }

903. J. Ayers, B. Althouse, Adam Poliak, E. Leas, A. Nobles, Mark Dredze, and Davey M. Smith, “Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd’s Death,” in Journal of Medical Internet Research, 2020.
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     @inproceedings{224829138,
     title = {Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd’s Death},
     author = {{J. Ayers} and {B. Althouse} and {Adam Poliak} and {E. Leas} and {A. Nobles} and {Mark Dredze} and {Davey M. Smith}},
     year = 2020,
     month = {10},
     booktitle = {Journal of Medical Internet Research},
     url = {https://www.semanticscholar.org/paper/53dfb4e46c47b98a11ca5fc94db5dc55c42243ee},
     }

904. E. Leas, A. Nobles, Theodore L. Caputi, Mark Dredze, Shu-Hong Zhu, Joanna E. Cohen, and J. Ayers, “News coverage of the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak and internet searches for vaping cessation,” in Tobacco Control, 2020.
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     @inproceedings{222354379,
     title = {News coverage of the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak and internet searches for vaping cessation},
     author = {{E. Leas} and {A. Nobles} and {Theodore L. Caputi} and {Mark Dredze} and {Shu-Hong Zhu} and {Joanna E. Cohen} and {J. Ayers}},
     year = 2020,
     month = {10},
     booktitle = {Tobacco Control},
     url = {https://www.semanticscholar.org/paper/22c3117fc4fa28bef30d00843035d604ee1dc0c4},
     }

905. T. Chen, Y. Chen, and B. Van Durme, “Hierarchical Entity Typing via Multi-level Learning to Rank,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8465–8475. doi:10.18653/v1/2020.acl-main.749
     [BibTeX] [Abstract] [Link]
     

     We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.

     @inproceedings{chen-etal-2020-hierarchical,
     title = "Hierarchical Entity Typing via Multi-level Learning to Rank",
     author = "Chen, Tongfei and
     Chen, Yunmo and
     Van Durme, Benjamin",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.749",
     doi = "10.18653/v1/2020.acl-main.749",
     pages = "8465--8475",
     abstract = "We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.",
     }

906. H. Inaguma, S. Kiyono, K. Duh, S. Karita, N. Yalta, T. Hayashi, and S. Watanabe, “ESPnet-ST: All-in-One Speech Translation Toolkit,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Online, 2020, p. 302–311. doi:10.18653/v1/2020.acl-demos.34
     [BibTeX] [Abstract] [Link]
     

     We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at \url{https://github.com/espnet/espnet}.

     @inproceedings{inaguma-etal-2020-espnet,
     title = "{ESP}net-{ST}: All-in-One Speech Translation Toolkit",
     author = "Inaguma, Hirofumi and
     Kiyono, Shun and
     Duh, Kevin and
     Karita, Shigeki and
     Yalta, Nelson and
     Hayashi, Tomoki and
     Watanabe, Shinji",
     editor = "Celikyilmaz, Asli and
     Wen, Tsung-Hsien",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-demos.34",
     doi = "10.18653/v1/2020.acl-demos.34",
     pages = "302--311",
     abstract = "We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at \url{https://github.com/espnet/espnet}.",
     }

907. E. Schumacher, A. Mulyar, and M. Dredze, “Clinical Concept Linking with Contextualized Neural Representations,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8585–8592. doi:10.18653/v1/2020.acl-main.760
     [BibTeX] [Abstract] [Link]
     

     In traditional approaches to entity linking, linking decisions are based on three sources of information {–} the similarity of the mention string to an entity{‘}s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.

     @inproceedings{schumacher-etal-2020-clinical,
     title = "Clinical Concept Linking with Contextualized Neural Representations",
     author = "Schumacher, Elliot and
     Mulyar, Andriy and
     Dredze, Mark",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.760",
     doi = "10.18653/v1/2020.acl-main.760",
     pages = "8585--8592",
     abstract = "In traditional approaches to entity linking, linking decisions are based on three sources of information {--} the similarity of the mention string to an entity{'}s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.",
     }

908. H. Khayrallah, J. Bremerman, A. D. McCarthy, K. Murray, W. Wu, and M. Post, “The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education,” in Proceedings of the Fourth Workshop on Neural Generation and Translation, Online, 2020, p. 188–197. doi:10.18653/v1/2020.ngt-1.22
     [BibTeX] [Abstract] [Link]
     

     This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric{–-}weighted macro F1 of an n-best list.

     @inproceedings{khayrallah-etal-2020-jhu,
     title = "The {JHU} Submission to the 2020 {D}uolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education",
     author = "Khayrallah, Huda and
     Bremerman, Jacob and
     McCarthy, Arya D. and
     Murray, Kenton and
     Wu, Winston and
     Post, Matt",
     editor = "Birch, Alexandra and
     Finch, Andrew and
     Hayashi, Hiroaki and
     Heafield, Kenneth and
     Junczys-Dowmunt, Marcin and
     Konstas, Ioannis and
     Li, Xian and
     Neubig, Graham and
     Oda, Yusuke",
     booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.ngt-1.22",
     doi = "10.18653/v1/2020.ngt-1.22",
     pages = "188--197",
     abstract = "This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric{---}weighted macro F1 of an n-best list.",
     }

909. S. Wu and M. Dredze, “Are All Languages Created Equal in Multilingual BERT?,” in Proceedings of the 5th Workshop on Representation Learning for NLP, Online, 2020, p. 120–130. doi:10.18653/v1/2020.repl4nlp-1.16
     [BibTeX] [Abstract] [Link]
     

     Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data.

     @inproceedings{wu-dredze-2020-languages,
     title = "Are All Languages Created Equal in Multilingual {BERT}?",
     author = "Wu, Shijie and
     Dredze, Mark",
     editor = "Gella, Spandana and
     Welbl, Johannes and
     Rei, Marek and
     Petroni, Fabio and
     Lewis, Patrick and
     Strubell, Emma and
     Seo, Minjoon and
     Hajishirzi, Hannaneh",
     booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.repl4nlp-1.16",
     doi = "10.18653/v1/2020.repl4nlp-1.16",
     pages = "120--130",
     abstract = "Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data.",
     }

910. S. Sun, S. Sia, and K. Duh, “CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Online, 2020, p. 134–141. doi:10.18653/v1/2020.acl-demos.18
     [BibTeX] [Abstract] [Link]
     

     We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the {“}metrics shared task{”} of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at \url{https://github.com/ssun32/CLIReval}.

     @inproceedings{sun-etal-2020-clireval,
     title = "{CLIR}eval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task",
     author = "Sun, Shuo and
     Sia, Suzanna and
     Duh, Kevin",
     editor = "Celikyilmaz, Asli and
     Wen, Tsung-Hsien",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-demos.18",
     doi = "10.18653/v1/2020.acl-demos.18",
     pages = "134--141",
     abstract = "We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the {``}metrics shared task{''} of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at \url{https://github.com/ssun32/CLIReval}.",
     }

911. A. Belyy and B. Van Durme, “Script Induction as Association Rule Mining,” in Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, Online, 2020, p. 55–62. doi:10.18653/v1/2020.nuse-1.7
     [BibTeX] [Abstract] [Link]
     

     We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.

     @inproceedings{belyy-van-durme-2020-script,
     title = "Script Induction as Association Rule Mining",
     author = "Belyy, Anton and
     Van Durme, Benjamin",
     editor = "Bonial, Claire and
     Caselli, Tommaso and
     Chaturvedi, Snigdha and
     Clark, Elizabeth and
     Huang, Ruihong and
     Iyyer, Mohit and
     Jaimes, Alejandro and
     Ji, Heng and
     Martin, Lara J. and
     Miller, Ben and
     Mitamura, Teruko and
     Peng, Nanyun and
     Tetreault, Joel",
     booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.nuse-1.7",
     doi = "10.18653/v1/2020.nuse-1.7",
     pages = "55--62",
     abstract = "We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.",
     }

912. M. Gordon and K. Duh, “Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation,” in Proceedings of the Fourth Workshop on Neural Generation and Translation, Online, 2020, p. 110–118. doi:10.18653/v1/2020.ngt-1.12
     [BibTeX] [Abstract] [Link]
     

     We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.

     @inproceedings{gordon-duh-2020-distill,
     title = "Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation",
     author = "Gordon, Mitchell and
     Duh, Kevin",
     editor = "Birch, Alexandra and
     Finch, Andrew and
     Hayashi, Hiroaki and
     Heafield, Kenneth and
     Junczys-Dowmunt, Marcin and
     Konstas, Ioannis and
     Li, Xian and
     Neubig, Graham and
     Oda, Yusuke",
     booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.ngt-1.12",
     doi = "10.18653/v1/2020.ngt-1.12",
     pages = "110--118",
     abstract = "We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.",
     }

913. D. Mueller, N. Andrews, and M. Dredze, “Sources of Transfer in Multilingual Named Entity Recognition,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8093–8104. doi:10.18653/v1/2020.acl-main.720
     [BibTeX] [Abstract] [Link]
     

     Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider \textit{polyglot} named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are \textit{fine-tuned} on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.

     @inproceedings{mueller-etal-2020-sources,
     title = "Sources of Transfer in Multilingual Named Entity Recognition",
     author = "Mueller, David and
     Andrews, Nicholas and
     Dredze, Mark",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.720",
     doi = "10.18653/v1/2020.acl-main.720",
     pages = "8093--8104",
     abstract = "Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider \textit{polyglot} named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are \textit{fine-tuned} on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.",
     }

914. M. Gordon, K. Duh, and N. Andrews, “Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning,” in Proceedings of the 5th Workshop on Representation Learning for NLP, Online, 2020, p. 143–155. doi:10.18653/v1/2020.repl4nlp-1.18
     [BibTeX] [Abstract] [Link]
     

     Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40{\%}) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.

     @inproceedings{gordon-etal-2020-compressing,
     title = "Compressing {BERT}: Studying the Effects of Weight Pruning on Transfer Learning",
     author = "Gordon, Mitchell and
     Duh, Kevin and
     Andrews, Nicholas",
     editor = "Gella, Spandana and
     Welbl, Johannes and
     Rei, Marek and
     Petroni, Fabio and
     Lewis, Patrick and
     Strubell, Emma and
     Seo, Minjoon and
     Hajishirzi, Hannaneh",
     booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.repl4nlp-1.18",
     doi = "10.18653/v1/2020.repl4nlp-1.18",
     pages = "143--155",
     abstract = "Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40{\%}) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.",
     }

915. E. Stengel-Eskin, A. S. White, S. Zhang, and B. Van Durme, “Universal Decompositional Semantic Parsing,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8427–8439. doi:10.18653/v1/2020.acl-main.746
     [BibTeX] [Abstract] [Link]
     

     We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.

     @inproceedings{stengel-eskin-etal-2020-universal,
     title = "Universal Decompositional Semantic Parsing",
     author = "Stengel-Eskin, Elias and
     White, Aaron Steven and
     Zhang, Sheng and
     Van Durme, Benjamin",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.746",
     doi = "10.18653/v1/2020.acl-main.746",
     pages = "8427--8439",
     abstract = "We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.",
     }

916. S. Ebner, P. Xia, R. Culkin, K. Rawlins, and B. Van Durme, “Multi-Sentence Argument Linking,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8057–8077. doi:10.18653/v1/2020.acl-main.718
     [BibTeX] [Abstract] [Link]
     

     We present a novel document-level model for finding argument spans that fill an event{‘}s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.

     @inproceedings{ebner-etal-2020-multi,
     title = "Multi-Sentence Argument Linking",
     author = "Ebner, Seth and
     Xia, Patrick and
     Culkin, Ryan and
     Rawlins, Kyle and
     Van Durme, Benjamin",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.718",
     doi = "10.18653/v1/2020.acl-main.718",
     pages = "8057--8077",
     abstract = "We present a novel document-level model for finding argument spans that fill an event{'}s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.",
     }

917. O. Adams, M. Wiesner, J. Trmal, G. Nicolai, and D. Yarowsky, “Induced Inflection-Set Keyword Search in Speech,” in Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Online, 2020, p. 210–216. doi:10.18653/v1/2020.sigmorphon-1.25
     [BibTeX] [Abstract] [Link]
     

     We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.

     @inproceedings{adams-etal-2020-induced,
     title = "Induced Inflection-Set Keyword Search in Speech",
     author = "Adams, Oliver and
     Wiesner, Matthew and
     Trmal, Jan and
     Nicolai, Garrett and
     Yarowsky, David",
     editor = "Nicolai, Garrett and
     Gorman, Kyle and
     Cotterell, Ryan",
     booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.sigmorphon-1.25",
     doi = "10.18653/v1/2020.sigmorphon-1.25",
     pages = "210--216",
     abstract = "We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.",
     }

918. T. Chen, Z. Jiang, A. Poliak, K. Sakaguchi, and B. Van Durme, “Uncertain Natural Language Inference,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, p. 8772–8779. doi:10.18653/v1/2020.acl-main.774
     [BibTeX] [Abstract] [Link]
     

     We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.

     @inproceedings{chen-etal-2020-uncertain,
     title = "Uncertain Natural Language Inference",
     author = "Chen, Tongfei and
     Jiang, Zhengping and
     Poliak, Adam and
     Sakaguchi, Keisuke and
     Van Durme, Benjamin",
     editor = "Jurafsky, Dan and
     Chai, Joyce and
     Schluter, Natalie and
     Tetreault, Joel",
     booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
     month = jul,
     year = "2020",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2020.acl-main.774",
     doi = "10.18653/v1/2020.acl-main.774",
     pages = "8772--8779",
     abstract = "We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.",
     }

919. H. Mei, G. Qin, M. Xu, and Jason Eisner, “Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification,” in Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
     [BibTeX] [Link]

     @InProceedings{mei-et-al-2020-icml,
     author = "Hongyuan Mei and Guanghui Qin and Minjie Xu and Jason
     Eisner",
     title = "Neural {D}atalog Through Time: Informed Temporal
     Modeling via Logical Specification",
     booktitle = "Proceedings of the 37th International Conference on
     Machine Learning (ICML)",
     year = "2020",
     month = jul,
     URL = "http://cs.jhu.edu/~jason/papers/#mei-et-al-2020-icml",
     }

920. E. Salesky, E. Chodroff, Tiago Pimentel, M. Wiesner, R. Cotterell, A. W. Black, and J. Eisner, “A Corpus for Large-Scale Phonetic Typology,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020, p. 2388–2397. doi:10.18653/v1/2020.acl-main.415
     [BibTeX] [Link]

     @InProceedings{salesky-et-al-2020,
     aclid = "2020.acl-main.415",
     doi = "10.18653/v1/2020.acl-main.415",
     author = "Elizabeth Salesky and Eleanor Chodroff and Tiago
     Pimentel and Matthew Wiesner and Ryan Cotterell and
     Alan W. Black and Jason Eisner",
     title = "A Corpus for Large-Scale Phonetic Typology",
     booktitle = "Proceedings of the 58th Annual Meeting of the
     Association for Computational Linguistics (ACL)",
     pages = "2388--2397",
     year = "2020",
     month = jul,
     URL = "http://cs.jhu.edu/~jason/papers/#salesky-et-al-2020",
     }

921. G. Nicolai, D. Lewis, A. D. McCarthy, A. Mueller, W. Wu, and D. Yarowsky, “Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 3963–3972.
     [BibTeX] [Abstract] [Link]
     

     Exploiting the broad translation of the Bible into the world{‘}s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike

     @inproceedings{nicolai-etal-2020-fine,
     title = "Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages",
     author = "Nicolai, Garrett and
     Lewis, Dylan and
     McCarthy, Arya D. and
     Mueller, Aaron and
     Wu, Winston and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.488",
     pages = "3963--3972",
     abstract = "Exploiting the broad translation of the Bible into the world{'}s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

922. W. Wu, G. Nicolai, and D. Yarowsky, “Multilingual Dictionary Based Construction of Core Vocabulary,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 4211–4217.
     [BibTeX] [Abstract] [Link]
     

     We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high overlap with existing widely utilized core vocabulary lists targeted at applications such as first and second language learning or field linguistics. Our in-depth analysis illustrates multiple desirable properties of our newly proposed core vocabulary set, including their non-compositionality. We employ a cognate prediction method to recover missing coverage of this core vocabulary in massively multilingual dictionary construction, and we argue that this core vocabulary should be prioritized for elicitation when creating new dictionaries for low-resource languages for multiple downstream tasks including machine translation and language learning.

     @inproceedings{wu-etal-2020-multilingual,
     title = "Multilingual Dictionary Based Construction of Core Vocabulary",
     author = "Wu, Winston and
     Nicolai, Garrett and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.519",
     pages = "4211--4217",
     abstract = "We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high overlap with existing widely utilized core vocabulary lists targeted at applications such as first and second language learning or field linguistics. Our in-depth analysis illustrates multiple desirable properties of our newly proposed core vocabulary set, including their non-compositionality. We employ a cognate prediction method to recover missing coverage of this core vocabulary in massively multilingual dictionary construction, and we argue that this core vocabulary should be prioritized for elicitation when creating new dictionaries for low-resource languages for multiple downstream tasks including machine translation and language learning.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

923. K. Duh, P. McNamee, M. Post, and B. Thompson, “Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 2667–2675.
     [BibTeX] [Abstract] [Link]
     

     Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.

     @inproceedings{duh-etal-2020-benchmarking,
     title = "Benchmarking Neural and Statistical Machine Translation on Low-Resource {A}frican Languages",
     author = "Duh, Kevin and
     McNamee, Paul and
     Post, Matt and
     Thompson, Brian",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.325",
     pages = "2667--2675",
     abstract = "Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

924. B. Desmet, J. Porcino, A. Zirikly, D. Newman-Griffis, G. Divita, and E. Rasch, “Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the U.S.,” in Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov), Marseille, France, 2020, p. 1–6.
     [BibTeX] [Abstract] [Link]
     

     The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA{‘}s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.

     @inproceedings{desmet-etal-2020-development,
     title = "Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the {U}.{S}.",
     author = "Desmet, Bart and
     Porcino, Julia and
     Zirikly, Ayah and
     Newman-Griffis, Denis and
     Divita, Guy and
     Rasch, Elizabeth",
     editor = "Samy, Doaa and
     P{\'e}rez-Fern{\'a}ndez, David and
     Arenas-Garc{\'\i}a, Jer{\'o}nimo",
     booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lt4gov-1.1",
     pages = "1--6",
     abstract = "The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA{'}s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.",
     language = "English",
     ISBN = "979-10-95546-62-7",
     }

925. A. Mueller, G. Nicolai, A. D. McCarthy, D. Lewis, W. Wu, and D. Yarowsky, “An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 3710–3718.
     [BibTeX] [Abstract] [Link]
     

     In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance{–-}the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one{‘}s approach to the source language and its typology.

     @inproceedings{mueller-etal-2020-analysis,
     title = "An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages",
     author = "Mueller, Aaron and
     Nicolai, Garrett and
     McCarthy, Arya D. and
     Lewis, Dylan and
     Wu, Winston and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.458",
     pages = "3710--3718",
     abstract = "In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance{---}the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one{'}s approach to the source language and its typology.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

926. A. D. McCarthy, R. Wicks, D. Lewis, A. Mueller, W. Wu, O. Adams, G. Nicolai, M. Post, and D. Yarowsky, “The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 2884–2892.
     [BibTeX] [Abstract] [Link]
     

     We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world{‘}s languages. We catalog this by showing highly similar proportions of representation of Ethnologue{‘}s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.

     @inproceedings{mccarthy-etal-2020-johns,
     title = "The {J}ohns {H}opkins {U}niversity {B}ible Corpus: 1600+ Tongues for Typological Exploration",
     author = "McCarthy, Arya D. and
     Wicks, Rachel and
     Lewis, Dylan and
     Mueller, Aaron and
     Wu, Winston and
     Adams, Oliver and
     Nicolai, Garrett and
     Post, Matt and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.352",
     pages = "2884--2892",
     abstract = "We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world{'}s languages. We catalog this by showing highly similar proportions of representation of Ethnologue{'}s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

927. W. Wu and D. Yarowsky, “Computational Etymology and Word Emergence,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 3252–3259.
     [BibTeX] [Abstract] [Link]
     

     We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field.

     @inproceedings{wu-yarowsky-2020-computational,
     title = "Computational Etymology and Word Emergence",
     author = "Wu, Winston and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.397",
     pages = "3252--3259",
     abstract = "We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

928. A. D. McCarthy, C. Kirov, M. Grella, A. Nidhi, P. Xia, K. Gorman, E. Vylomova, S. J. Mielke, G. Nicolai, M. Silfverberg, T. Arkhangelskiy, N. Krizhanovsky, A. Krizhanovsky, E. Klyachko, A. Sorokin, J. Mansfield, V. Ernštreits, Y. Pinter, C. L. Jacobs, R. Cotterell, M. Hulden, and D. Yarowsky, “UniMorph 3.0: Universal Morphology,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 3922–3931.
     [BibTeX] [Abstract] [Link]
     

     The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.

     @inproceedings{mccarthy-etal-2020-unimorph,
     title = "{U}ni{M}orph 3.0: {U}niversal {M}orphology",
     author = "McCarthy, Arya D. and
     Kirov, Christo and
     Grella, Matteo and
     Nidhi, Amrit and
     Xia, Patrick and
     Gorman, Kyle and
     Vylomova, Ekaterina and
     Mielke, Sabrina J. and
     Nicolai, Garrett and
     Silfverberg, Miikka and
     Arkhangelskiy, Timofey and
     Krizhanovsky, Nataly and
     Krizhanovsky, Andrew and
     Klyachko, Elena and
     Sorokin, Alexey and
     Mansfield, John and
     Ern{\v{s}}treits, Valts and
     Pinter, Yuval and
     Jacobs, Cassandra L. and
     Cotterell, Ryan and
     Hulden, Mans and
     Yarowsky, David",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.483",
     pages = "3922--3931",
     abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

929. A. S. White, E. Stengel-Eskin, S. Vashishtha, V. S. Govindarajan, D. A. Reisinger, T. Vieira, K. Sakaguchi, S. Zhang, F. Ferraro, R. Rudinger, K. Rawlins, and B. Van Durme, “The Universal Decompositional Semantics Dataset and Decomp Toolkit,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France, 2020, p. 5698–5707.
     [BibTeX] [Abstract] [Link]
     

     We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification{–-}with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at \url{http://decomp.io}.

     @inproceedings{white-etal-2020-universal,
     title = "The Universal Decompositional Semantics Dataset and Decomp Toolkit",
     author = "White, Aaron Steven and
     Stengel-Eskin, Elias and
     Vashishtha, Siddharth and
     Govindarajan, Venkata Subrahmanyan and
     Reisinger, Dee Ann and
     Vieira, Tim and
     Sakaguchi, Keisuke and
     Zhang, Sheng and
     Ferraro, Francis and
     Rudinger, Rachel and
     Rawlins, Kyle and
     Van Durme, Benjamin",
     editor = "Calzolari, Nicoletta and
     B{\'e}chet, Fr{\'e}d{\'e}ric and
     Blache, Philippe and
     Choukri, Khalid and
     Cieri, Christopher and
     Declerck, Thierry and
     Goggi, Sara and
     Isahara, Hitoshi and
     Maegaard, Bente and
     Mariani, Joseph and
     Mazo, H{\'e}l{\`e}ne and
     Moreno, Asuncion and
     Odijk, Jan and
     Piperidis, Stelios",
     booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
     month = may,
     year = "2020",
     address = "Marseille, France",
     publisher = "European Language Resources Association",
     url = "https://aclanthology.org/2020.lrec-1.699",
     pages = "5698--5707",
     abstract = "We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification{---}with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at \url{http://decomp.io}.",
     language = "English",
     ISBN = "979-10-95546-34-4",
     }

930. M. Francis-Landau, T. Vieira, and Jason Eisner, “Evaluation of Logic Programs with Built-Ins and Aggregation: A Calculus for Bag Relations,” in 13th International Workshop on Rewriting Logic and Its Applications, 2020, p. 49–63.
     [BibTeX] [Link]

     @InProceedings{francislandau-vieira-eisner-2020-wrla,
     author = "Matthew Francis-Landau and Tim Vieira and Jason
     Eisner",
     title = "Evaluation of Logic Programs with Built-Ins and
     Aggregation: {A} Calculus for Bag Relations",
     booktitle = "13th International Workshop on Rewriting Logic and Its
     Applications",
     pages = "49--63",
     year = "2020",
     month = apr,
     note = "Extended version (27 pages) available on arXiv,
     October 2020.",
     URL = "http://cs.jhu.edu/~jason/papers/#francislandau-vieira-eisner-2020-wrla",
     }

931. Poojan Oza and Vishal M. Patel, “Utilizing Patch-Level Category Activation Patterns for Multiple Class Novelty Detection,” in European Conference on Computer Vision, 2020.
     [BibTeX] [Link]

     @inproceedings{226291937,
     title = {Utilizing Patch-Level Category Activation Patterns for Multiple Class Novelty Detection},
     author = {{Poojan Oza} and {Vishal M. Patel}},
     year = 2020,
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/f1924faec5822797e5c06a6470eabd586a1886bd},
     }

932. Zhongyang Li, Xiao Ding, Ting Liu, J. E. Hu, and Benjamin Van Durme, “Guided Generation of Cause and Effect,” in International Joint Conference on Artificial Intelligence, 2020.
     [BibTeX] [Link]

     @inproceedings{220485081,
     title = {Guided Generation of Cause and Effect},
     author = {{Zhongyang Li} and {Xiao Ding} and {Ting Liu} and {J. E. Hu} and {Benjamin Van Durme}},
     year = 2020,
     month = {7},
     booktitle = {International Joint Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/b39074c68ac53f258fb340d3d24ef868c514310b},
     }

933. Patrick Xia, João Sedoc, and Benjamin Van Durme, “Revisiting Memory-Efficient Incremental Coreference Resolution,” in arXiv.org, 2020.
     [BibTeX] [Link]

     @inproceedings{218470195,
     title = {Revisiting Memory-Efficient Incremental Coreference Resolution},
     author = {{Patrick Xia} and {João Sedoc} and {Benjamin Van Durme}},
     year = 2020,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ff800ed01cbef800c79706c69a728b30a6e7fb24},
     }

934. Sandeep Reddy Kothinti, Benjamin Skerritt-Davis, Aditya Nair, and Mounya Elhilali, “Synthesizing Engaging Music Using Dynamic Models of Statistical Surprisal,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
     [BibTeX] [Link]

     @inproceedings{216325563,
     title = {Synthesizing Engaging Music Using Dynamic Models of Statistical Surprisal},
     author = {{Sandeep Reddy Kothinti} and {Benjamin Skerritt-Davis} and {Aditya Nair} and {Mounya Elhilali}},
     year = 2020,
     month = {5},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/91b6f64dbb8175ac9fca7088481d794dea34389c},
     }

935. Michael Liu, Theodore L. Caputi, Mark Dredze, A. Kesselheim, and J. Ayers, “Internet Searches for Unproven COVID-19 Therapies in the United States.,” in JAMA Internal Medicine, 2020.
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     @inproceedings{216647640,
     title = {Internet Searches for Unproven COVID-19 Therapies in the United States.},
     author = {{Michael Liu} and {Theodore L. Caputi} and {Mark Dredze} and {A. Kesselheim} and {J. Ayers}},
     year = 2020,
     month = {4},
     booktitle = {JAMA Internal Medicine},
     url = {https://www.semanticscholar.org/paper/4838dc8eef22add083218846480835f94e60647a},
     }

936. Ankan Bansal, Rajeev Ranjan, C. Castillo, and R. Chellappa, “Deep CNN-Based Face Recognition,” in Computer Vision, 2020.
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     @inproceedings{215931996,
     title = {Deep CNN-Based Face Recognition},
     author = {{Ankan Bansal} and {Rajeev Ranjan} and {C. Castillo} and {R. Chellappa}},
     year = 2020,
     booktitle = {Computer Vision},
     url = {https://www.semanticscholar.org/paper/d98e495f03f2daabb65dfeb32de54e1cd8e3be30},
     }

937. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Open-set Adversarial Defense,” in European Conference on Computer Vision, 2020.
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     @inproceedings{221446916,
     title = {Open-set Adversarial Defense},
     author = {{Rui Shao} and {Pramuditha Perera} and {P. Yuen} and {Vishal M. Patel}},
     year = 2020,
     month = {9},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/525de09d3a184c2455ff68dabfd7d427a5b2e68e},
     }

938. Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, I. Hacihaliloglu, and Vishal M. Patel, “KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{219558823,
     title = {KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations},
     author = {{Jeya Maria Jose Valanarasu} and {Vishwanath A. Sindagi} and {I. Hacihaliloglu} and {Vishal M. Patel}},
     year = 2020,
     month = {6},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/095ef3e09bf8bc036a79b4270a828ffc1451331c},
     }

939. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Federated Face Anti-spoofing,” in arXiv.org, 2020.
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     @inproceedings{219124005,
     title = {Federated Face Anti-spoofing},
     author = {{Rui Shao} and {Pramuditha Perera} and {P. Yuen} and {Vishal M. Patel}},
     year = 2020,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/9f10a930ac4d7805d0baecf86cece7ac8df4d1f7},
     }

940. Yutong Bai, Angtian Wang, Adam Kortylewski, and A. Yuille, “CoKe: Localized Contrastive Learning for Robust Keypoint Detection,” in arXiv.org, 2020.
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     @inproceedings{260440662,
     title = {CoKe: Localized Contrastive Learning for Robust Keypoint Detection},
     author = {{Yutong Bai} and {Angtian Wang} and {Adam Kortylewski} and {A. Yuille}},
     year = 2020,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/03ba217f604c8d05cf3051b15025bab752aa715f},
     }

941. Sangwook Park, Ashwin Bellur, Sandeep Reddy Kothinti, Masoumeh Heidari Kapourchali, and Mounya Elhilali, “JOINT ACOUSTIC AND SUPERVISED INFERENCE FOR SOUND EVENT DETECTION Technical.” 2020.
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     @inproceedings{221094134,
     title = {JOINT ACOUSTIC AND SUPERVISED INFERENCE FOR SOUND EVENT DETECTION Technical},
     author = {{Sangwook Park} and {Ashwin Bellur} and {Sandeep Reddy Kothinti} and {Masoumeh Heidari Kapourchali} and {Mounya Elhilali}},
     year = 2020,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/2bced494f7eb73b7781090c3ec4b52f7450aac5f},
     }

942. Paiheng Xu, Mark Dredze, and David A. Broniatowski, “The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets,” in Journal of Medical Internet Research, 2020.
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     title = {The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets},
     author = {{Paiheng Xu} and {Mark Dredze} and {David A. Broniatowski}},
     year = 2020,
     month = {6},
     booktitle = {Journal of Medical Internet Research},
     url = {https://www.semanticscholar.org/paper/48214dc2d0e5d13f9e2127d4149e154a3dcfd3aa},
     }

943. Stephanie Graceffo, A. Husain, Salahuddin Ahmed, E. McCollum, and Mounya Elhilali, “Validation of Auscultation Technologies using Objective and Clinical Comparisons,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2020.
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     @inproceedings{221387782,
     title = {Validation of Auscultation Technologies using Objective and Clinical Comparisons},
     author = {{Stephanie Graceffo} and {A. Husain} and {Salahuddin Ahmed} and {E. McCollum} and {Mounya Elhilali}},
     year = 2020,
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     booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
     url = {https://www.semanticscholar.org/paper/b1bbe8261381c334190c2dbdcab2f5fc677815bf},
     }

944. Pengfei Guo, Puyang Wang, Jinyuan Zhou, Vishal M. Patel, and Shanshan Jiang, “Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{220128156,
     title = {Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN},
     author = {{Pengfei Guo} and {Puyang Wang} and {Jinyuan Zhou} and {Vishal M. Patel} and {Shanshan Jiang}},
     year = 2020,
     month = {6},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/fdd3b466b9d2aee84ffe603350b6b36afe4b74b9},
     }

945. Joseph P. Robinson, Yu Yin, Zaid Khan, Ming Shao, Siyu Xia, Michael Stopa, Samson Timoner, Matthew A. Turk, R. Chellappa, and Y. Fu, “Recognizing Families In the Wild (RFIW): The 4th Edition,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2020.
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     @inproceedings{211132726,
     title = {Recognizing Families In the Wild (RFIW): The 4th Edition},
     author = {{Joseph P. Robinson} and {Yu Yin} and {Zaid Khan} and {Ming Shao} and {Siyu Xia} and {Michael Stopa} and {Samson Timoner} and {Matthew A. Turk} and {R. Chellappa} and {Y. Fu}},
     year = 2020,
     month = {2},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/8a9e437b2e2d813b402ac560c852ef0ab2f1cd3c},
     }

946. Hanwen Cao, Yongyi Lu, Cewu Lu, Bo Pang, Gongshen Liu, and A. Yuille, “ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation,” in British Machine Vision Conference, 2020.
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     @inproceedings{221103848,
     title = {ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation},
     author = {{Hanwen Cao} and {Yongyi Lu} and {Cewu Lu} and {Bo Pang} and {Gongshen Liu} and {A. Yuille}},
     year = 2020,
     month = {8},
     booktitle = {British Machine Vision Conference},
     url = {https://www.semanticscholar.org/paper/c47acec51880b981ad302487586d42aaeca2bc1c},
     }

947. L. Moro-Velázquez, J. Villalba, and N. Dehak, “Using X-Vectors to Automatically Detect Parkinson’s Disease from Speech,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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     @inproceedings{216471277,
     title = {Using X-Vectors to Automatically Detect Parkinson’s Disease from Speech},
     author = {{L. Moro-Velázquez} and {J. Villalba} and {N. Dehak}},
     year = 2020,
     month = {5},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/fe52caa985bcf9ad5f2789ddcd1adeaa21a1740e},
     }

948. Ashwin Bellur and Mounya Elhilali, “Audio object classification using distributed beliefs and attention,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2020.
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     title = {Audio object classification using distributed beliefs and attention},
     author = {{Ashwin Bellur} and {Mounya Elhilali}},
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     url = {https://www.semanticscholar.org/paper/e57e8fe67d3c7e3e4c825ab2cc7d431892513660},
     }

949. Chenxu Luo, Lin Sun, D. Dabiri, and A. Yuille, “Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles,” in IEEE/RJS International Conference on Intelligent RObots and Systems, 2020.
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     @inproceedings{220363790,
     title = {Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles},
     author = {{Chenxu Luo} and {Lin Sun} and {D. Dabiri} and {A. Yuille}},
     year = 2020,
     month = {7},
     booktitle = {IEEE/RJS International Conference on Intelligent RObots and Systems},
     url = {https://www.semanticscholar.org/paper/04aa924481893b30e5f8036eeb8247cb201d7713},
     }

950. Zhe Ren, Junchi Yan, Xiaokang Yang, A. Yuille, and H. Zha, “Unsupervised learning of optical flow with patch consistency and occlusion estimation,” in Pattern Recognition, 2020.
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     author = {{Zhe Ren} and {Junchi Yan} and {Xiaokang Yang} and {A. Yuille} and {H. Zha}},
     year = 2020,
     month = {7},
     booktitle = {Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/3b79183bde6d1cf0308a0a7d7b508ab1b60edc2e},
     }

951. Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, K. Yan, T. Ho, Jinzheng Cai, Adam P. Harrison, X. Ye, Jing Xiao, A. Yuille, Min Sun, Le Lu, and D. Jin, “Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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     @inproceedings{221376586,
     title = {Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network},
     author = {{Chun-Hung Chao} and {Zhuotun Zhu} and {Dazhou Guo} and {K. Yan} and {T. Ho} and {Jinzheng Cai} and {Adam P. Harrison} and {X. Ye} and {Jing Xiao} and {A. Yuille} and {Min Sun} and {Le Lu} and {D. Jin}},
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     url = {https://www.semanticscholar.org/paper/2a6eb4241e62f94fd7e269f7c3403882a4a599d0},
     }

952. Dazhou Guo, D. Jin, Zhuotun Zhu, T. Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, A. Yuille, Chien-Yu Lin, and Le Lu, “Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search,” in Computer Vision and Pattern Recognition, 2020.
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     title = {Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search},
     author = {{Dazhou Guo} and {D. Jin} and {Zhuotun Zhu} and {T. Ho} and {Adam P. Harrison} and {Chun-Hung Chao} and {Jing Xiao} and {A. Yuille} and {Chien-Yu Lin} and {Le Lu}},
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     }

953. E. Dorsey, L. Omberg, Emma M Waddell, Jamie L. Adams, R. Adams, M. R. Ali, Katherine Amodeo, Abigail Arky, E. Augustine, K. Dinesh, Ehsan Hoque, Alistair Glidden, Stella Jensen-Roberts, Zachary Kabelac, D. Katabi, K. Kieburtz, Daniel R. Kinel, Max A. Little, K. Lizarraga, Taylor Myers, Sara Riggare, S. Rosero, S. Saria, G. Schifitto, Ruth B. Schneider, G. Sharma, I. Shoulson, E. A. Stevenson, Christopher G. Tarolli, Jiebo Luo, and M. Mcdermott, “Deep Phenotyping of Parkinson’s Disease,” in Journal of Parkinson’s Disease, 2020.
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     title = {Deep Phenotyping of Parkinson’s Disease},
     author = {{E. Dorsey} and {L. Omberg} and {Emma M Waddell} and {Jamie L. Adams} and {R. Adams} and {M. R. Ali} and {Katherine Amodeo} and {Abigail Arky} and {E. Augustine} and {K. Dinesh} and {Ehsan Hoque} and {Alistair Glidden} and {Stella Jensen-Roberts} and {Zachary Kabelac} and {D. Katabi} and {K. Kieburtz} and {Daniel R. Kinel} and {Max A. Little} and {K. Lizarraga} and {Taylor Myers} and {Sara Riggare} and {S. Rosero} and {S. Saria} and {G. Schifitto} and {Ruth B. Schneider} and {G. Sharma} and {I. Shoulson} and {E. A. Stevenson} and {Christopher G. Tarolli} and {Jiebo Luo} and {M. Mcdermott}},
     year = 2020,
     month = {5},
     booktitle = {Journal of Parkinson's Disease},
     url = {https://www.semanticscholar.org/paper/c2fa8d5141c13a61e3474b10b2cb75953af6f78e},
     }

954. Pramuditha Perera, Julian Fierrez, and Vishal M. Patel, “Quickest Intruder Detection For Multiple User Active Authentication,” in International Conference on Information Photonics, 2020.
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     @inproceedings{219966052,
     title = {Quickest Intruder Detection For Multiple User Active Authentication},
     author = {{Pramuditha Perera} and {Julian Fierrez} and {Vishal M. Patel}},
     year = 2020,
     month = {6},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/abb871d121dd93ac374e094e5b8c0dbe1d344be9},
     }

955. Nathaniel Weir, Adam Poliak, and Benjamin Van Durme, “On the Existence of Tacit Assumptions in Contextualized Language Models,” in arXiv.org, 2020.
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     @inproceedings{215737345,
     title = {On the Existence of Tacit Assumptions in Contextualized Language Models},
     author = {{Nathaniel Weir} and {Adam Poliak} and {Benjamin Van Durme}},
     year = 2020,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/885e52a5b1a4fb36fae0e3fe31e9d4da21e03b41},
     }

956. Prithviraj Dhar, Joshua Gleason, Hossein Souri, C. Castillo, and R. Chellappa, “Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition,” in arXiv: Computer Vision and Pattern Recognition, 2020.
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     @inproceedings{221771158,
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     author = {{Prithviraj Dhar} and {Joshua Gleason} and {Hossein Souri} and {C. Castillo} and {R. Chellappa}},
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957. Ankan Bansal, Yuting Zhang, and R. Chellappa, “Visual Question Answering on Image Sets,” in European Conference on Computer Vision, 2020.
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     author = {{Ankan Bansal} and {Yuting Zhang} and {R. Chellappa}},
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     url = {https://www.semanticscholar.org/paper/18ba4e542a5206a40e308f54ceffc6786b7d94d2},
     }

958. Vishwanath A. Sindagi, R. Yasarla, and Vishal M. Patel, “JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
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     @inproceedings{215415929,
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     author = {{Vishwanath A. Sindagi} and {R. Yasarla} and {Vishal M. Patel}},
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     url = {https://www.semanticscholar.org/paper/6eb6c3c4285983eb49e005a8b6b49da11ea19e5d},
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959. Zolt’an Tuske, H. Ney, and H. Hermansky, “Discriminative feature modeling for statistical speech recognition.” 2020.
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     @inproceedings{242407348,
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960. V. Rennoll, Ian McLane, Dimitra Emmanouilidou, James E. West, and Mounya Elhilali, “Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope,” in IEEE journal of biomedical and health informatics, 2020.
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     @inproceedings{221466749,
     title = {Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope},
     author = {{V. Rennoll} and {Ian McLane} and {Dimitra Emmanouilidou} and {James E. West} and {Mounya Elhilali}},
     year = 2020,
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961. Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, S. Bai, and A. Yuille, “Neural Architecture Search for Lightweight Non-Local Networks,” in Computer Vision and Pattern Recognition, 2020.
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962. Emine Merve Kaya, Nicolas Huang, and Mounya Elhilali, “Pitch, Timbre and Intensity Interdependently Modulate Neural Responses to Salient Sounds,” in Neuroscience, 2020.
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963. Juan Ignacio Godino-Llorente, D. O’Shaughnessy, Tan Lee, N. Dehak, and C. Manfredi, “Introduction to the Issue on Automatic Assessment of Health Disorders Based on Voice, Speech, and Language Processing,” in IEEE Journal on Selected Topics in Signal Processing, 2020.
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964. Zhuowan Li, Quan Hung Tran, Long Mai, Zhe L. Lin, and A. Yuille, “Context-Aware Group Captioning via Self-Attention and Contrastive Features,” in Computer Vision and Pattern Recognition, 2020.
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965. P. Galiatsatos, A. Follin, F. Alghanim, Melissa K Sherry, C. Sylvester, Y. Daniel, A. Chanmugam, J. Townsend, S. Saria, A. Kind, and E. Chen, “The Association Between Neighborhood Socioeconomic Disadvantage and Readmissions for Patients Hospitalized With Sepsis,” in Critical Care Medicine, 2020.
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966. Shao-Yuan Lo and Vishal M. Patel, “Defending Against Multiple and Unforeseen Adversarial Videos,” in IEEE Transactions on Image Processing, 2020.
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967. S. Saria and K. Henry, “Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?,” in Critical Care Medicine, 2020.
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968. Ashwin Bellur and Mounya Elhilali, “Bio-Mimetic Attentional Feedback in Music Source Separation,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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969. B. Bosworth, Iskandar Atakhodjaev, M. Kossey, Brian C. Grubel, Daniel S. Vresilovic, J. R. Stroud, Neil Macfarlane, J. Villalba, N. Dehak, A. B. Cooper, Mark A. Foster, and A. Foster, “Unclonable photonic keys hardened against machine learning attacks.” 2020.
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970. Pirazh Khorramshahi, Neehar Peri, Jun-Cheng Chen, and R. Chellappa, “The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification,” in European Conference on Computer Vision, 2020.
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971. L. Chu, Seyoun Park, S. Kawamoto, A. Yuille, R. Hruban, and E. Fishman, “Pancreatic Cancer Imaging: A New Look at an Old Problem.,” in Current problems in diagnostic radiology, 2020.
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972. R. Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel, “Supplementary for Syn2Real Transfer Learning for Image Deraining using Gaussian Processes.” 2020.
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973. Guy Divita, Bart Desmet, Ayah Zirikly, Luke Breitfeller, and C. Rosé, “Angelfish: Building a Structurally Diverse Clinical Document Corpus,” in American Medical Informatics Association Annual Symposium, 2020.
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974. Fengze Liu, Jinzheng Cai, Yuankai Huo, Chi-Tung Cheng, Ashwin Raju, D. Jin, Jing Xiao, A. Yuille, Le Lu, Chien-Hung Liao, and Adam P. Harrison, “JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans,” in European Conference on Computer Vision, 2020.
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975. S. Park, L. Chu, E. Fishman, A. Yuille, B. Vogelstein, K. Kinzler, K. Horton, R. Hruban, E. Zinreich, D. Fouladi, S. Shayesteh, J. Graves, and S. Kawamoto, “Erratum to “Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation” [Diagn. Interv. Imaging. 101 (2020) 35-44].,” in Diagnostic and Interventional Imaging, 2020.
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976. Ahmed Z. Alsinan, Vishal M. Patel, and I. Hacihaliloglu, “Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN,” in International Journal of Computer Assisted Radiology and Surgery, 2020.
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977. Yixiao Zhang, Xiaosong Wang, Ziyue Xu, Qihang Yu, A. Yuille, and Daguang Xu, “When Radiology Report Generation Meets Knowledge Graph,” in AAAI Conference on Artificial Intelligence, 2020.
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978. Beau Norgeot, Giorgio Quer, Brett K. Beaulieu-Jones, A. Torkamani, Raquel Dias, M. Gianfrancesco, Rima Arnaout, I. Kohane, S. Saria, E. Topol, Z. Obermeyer, Bin Yu, and A. Butte, “Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist,” in Nature Network Boston, 2020.
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979. Xuan Zhang and Kevin Duh, “Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems,” in Transactions of the Association for Computational Linguistics, 2020.
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980. C. Castillo-Sanchez, Leibny Paola García-Perera, and A. Martín-González, “DNN Speaker Tracking with Embeddings,” in arXiv.org, 2020.
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981. R. Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel, “Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes,” in Computer Vision and Pattern Recognition, 2020.
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982. Cihang Xie, Mingxing Tan, Boqing Gong, A. Yuille, and Quoc V. Le, “Smooth Adversarial Training,” in arXiv.org, 2020.
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983. R. Arora, T. V. Marinov, and Enayat Ullah, “Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives,” in arXiv.org, 2020.
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984. Thomas Lippincott, “StarCoder: A general neural ensemble technique to support traditional scholarship, illustrated with a study of the post-Atlantic slave trade,” in Digital Humanities Conference, 2020.
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985. Xiaohui Zhang, Daniel Povey, and S. Khudanpur, “OOV Recovery with Efficient 2nd Pass Decoding and Open-vocabulary Word-level RNNLM Rescoring for Hybrid ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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986. Nicholas Huang and Mounya Elhilali, “Author response: Push-pull competition between bottom-up and top-down auditory attention to natural soundscapes,” in eLife, 2020.
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987. Zhe Ren, Wenhan Luo, Junchi Yan, Wenlong Liao, Xiaokang Yang, A. Yuille, and H. Zha, “STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels,” in IEEE Transactions on Image Processing, 2020.
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988. Xing Di, B. Riggan, Shuowen Hu, Nathan J. Short, and Vishal M. Patel, “Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2020.
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989. Rachel Bawden, Biao Zhang, Lisa Yankovskaya, Andre Tattar, and Matt Post, “Explicit Representation of the Translation Space: Automatic Paraphrasing for Machine Translation Evaluation,” in arXiv.org, 2020.
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990. E. Weisberg, L. Chu, S. Park, A. Yuille, K. Kinzler, B. Vogelstein, and E. Fishman, “Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis.,” in Diagnostic and Interventional Imaging, 2020.
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991. Ke Li, Daniel Povey, and S. Khudanpur, “Neural Language Modeling with Implicit Cache Pointers,” in Interspeech, 2020.
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992. Yiwen Shao, Yiming Wang, Daniel Povey, and S. Khudanpur, “PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR,” in Interspeech, 2020.
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993. S. Shamma and Mounya Elhilali, “Temporal Coherence Principle in Scene Analysis.” 2020.
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994. Chenxi Liu, Piotr Doll’ar, Kaiming He, Ross B. Girshick, A. Yuille, and Saining Xie, “Are Labels Necessary for Neural Architecture Search?,” in European Conference on Computer Vision, 2020.
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995. Shuo Sun and Kevin Duh, “Modeling Document Interactions for Learning to Rank with Regularized Self-Attention,” in arXiv.org, 2020.
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996. D. Bates, A. Auerbach, Peter F. Schulam, A. Wright, and S. Saria, “Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence,” in Annals of Internal Medicine, 2020.
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997. Pengyu Yuan, Aryan Mobiny, J. Jahanipour, Xiaoyang Li, P. Cicalese, B. Roysam, Vishal M. Patel, Maric Dragan, and H. Nguyen, “Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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998. Shuhao Fu, Yongyi Lu, Yan Wang, Yuyin Zhou, Wei Shen, E. Fishman, and A. Yuille, “Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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999. Theodore L. Caputi, J. Ayers, Mark Dredze, Nicholas Suplina, and S. Burd-Sharps, “Collateral Crises of Gun Preparation and the COVID-19 Pandemic: Infodemiology Study,” in JMIR Public Health and Surveillance, 2020.
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1000. A. Kala, A. Husain, E. McCollum, and Mounya Elhilali, “An objective measure of signal quality for pediatric lung auscultations,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2020.
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1001. Pengfei Guo, Puyang Wang, R. Yasarla, Jinyuan Zhou, Vishal M. Patel, and Shanshan Jiang, “Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas,” in arXiv.org, 2020.
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1002. David A. Broniatowski, S. Quinn, Mark Dredze, and Amelia M. Jamison, “Vaccine Communication as Weaponized Identity Politics.,” in American Journal of Public Health, 2020.
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1003. Nils Holzenberger, Andrew Blair-Stanek, and Benjamin Van Durme, “A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering,” in NLLP@KDD, 2020.
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1004. Yashasvi Baweja, Poojan Oza, Pramuditha Perera, and Vishal M. Patel, “Anomaly Detection-Based Unknown Face Presentation Attack Detection,” in 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020.
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1005. M. Naphade, Shuo Wang, D. Anastasiu, Zhenghang Tang, Ming-Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, R. Chellappa, and Pranamesh Chakraborty, “The 4th AI City Challenge,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020.
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1006. Boyu Lu, Jun-Cheng Chen, and R. Chellappa, “UID-GAN: Unsupervised Image Deblurring via Disentangled Representations,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2020.
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1007. Mahdi Abavisani, Alireza Naghizadeh, Dimitris N. Metaxas, and Vishal M. Patel, “Deep Subspace Clustering with Data Augmentation,” in Neural Information Processing Systems, 2020.
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1008. Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, and A. Yuille, “Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation,” in European Conference on Computer Vision, 2020.
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1009. Estefanía Hernández-García, L. Moro-Velázquez, Ramón González-Herranz, Juan Ignacio Godino-Llorente, and G. Plaza, “Effect of Functional Endoscopic Sinus Surgery on Voice and Speech Recognition.,” in Journal of Voice, 2020.
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1010. Saurabh Kataria, P. S. Nidadavolu, J. Villalba, and N. Dehak, “Analysis of Deep Feature Loss based Enhancement for Speaker Verification,” in The Speaker and Language Recognition Workshop, 2020.
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1011. Yutong Bai, Angtian Wang, Adam Kortylewski, and A. Yuille, “CoKe: Contrastive Learning for Robust Keypoint Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020.
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1012. Haley Grant, Yifan Zhang, Lu Li, Yan Wang, S. Kawamoto, Sophie Pénisson, D. Fouladi, S. Shayesteh, A. Blanco, Saeed Ghandili, E. Zinreich, J. Graves, Seyoun Park, S. Kern, J. Hooper, A. Yuille, E. Fishman, L. Chu, and C. Tomasetti, “Larger organ size caused by obesity is a mechanism for higher cancer risk,” in bioRxiv, 2020.
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1013. Qi Chen, Lin Sun, Ernest C. H. Cheung, and A. Yuille, “Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization,” in Neural Information Processing Systems, 2020.
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1014. Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, E. Fishman, and A. Yuille, “Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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1015. Vishal M. Patel and H. Nguyen, “Domain Adaptation Using Dictionaries,” in Computer Vision, 2020.
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1016. Qihang Yu, Yingwei Li, Jieru Mei, Yuyin Zhou, and A. Yuille, “CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Network,” in AAAI Conference on Artificial Intelligence, 2020.
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1017. D. Dreizin, Yuyin Zhou, Tina Chen, Guang Li, A. Yuille, Ashley McLenithan, and J. Morrison, “Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.,” in Journal of Trauma and Acute Care Surgery, 2020.
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1018. Zach Wood-Doughty, I. Shpitser, and Mark Dredze, “Sensitivity Analyses for Incorporating Machine Learning Predictions into Causal Estimates.” 2020.
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1019. Xiaodong Liu, Kevin Duh, Liyuan Liu, and Jianfeng Gao, “Very Deep Transformers for Neural Machine Translation,” in arXiv.org, 2020.
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1020. Kacie Kelly, Alex B. Fine, and Glen A. Coppersmith, “Gender differences in social media usage related to mental health and veterans’ topics among US military veterans: An exploratory analysis (Preprint).” 2020.
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1021. P. S. Nidadavolu, Saurabh Kataria, Leibny Paola García-Perera, J. Villalba, and N. Dehak, “Single Channel Far Field Feature Enhancement For Speaker Verification In The Wild,” in arXiv.org, 2020.
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1022. Angeles Salles, Sangwook Park, Harshavardhan Sundar, S. Macias, Mounya Elhilali, and C. Moss, “Neural Response Selectivity to Natural Sounds in the Bat Midbrain,” in Neuroscience, 2020.
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1023. Ryan Culkin, J. E. Hu, Elias Stengel-Eskin, Guanghui Qin, and Benjamin Van Durme, “Iterative Paraphrastic Augmentation with Discriminative Span Alignment,” in Transactions of the Association for Computational Linguistics, 2020.
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1024. Lingxi Xie, Qihang Yu, Yuyin Zhou, Yan Wang, E. Fishman, and A. Yuille, “Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans,” in IEEE Transactions on Medical Imaging, 2020.
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1025. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Supplementary Material for Open-set Adversarial Defense.” 2020.
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1026. Siyuan Qiao, Liang-Chieh Chen, and A. Yuille, “DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution,” in Computer Vision and Pattern Recognition, 2020.
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1027. L. Moro-Velázquez and N. Dehak, “A Review of the Use of Prosodic Aspects of Speech for the Automatic Detection and Assessment of Parkinson’s Disease.” 2020.
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1028. Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, and R. Chellappa, “Spatial Priming for Detecting Human-Object Interactions,” in arXiv.org, 2020.
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1029. Ashish Arora, Desh Raj, A. Subramanian, Ke Li, Bar Ben-Yair, Matthew Maciejewski, Piotr Żelasko, Leibny Paola García-Perera, Shinji Watanabe, and S. Khudanpur, “The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge,” in 6th International Workshop on Speech Processing in Everyday Environments (CHiME 2020), 2020.
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1030. Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, and A. Yuille, “Detecting Pancreatic Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble,” in arXiv.org, 2020.
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      year = 2020,
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1031. A. Masys, T. Bourlai, P. Karampelas, and Vishal M. Patel, “Securing Social Identity in Mobile Platforms: Technologies for Security, Privacy and Identity Management,” in Securing Social Identity in Mobile Platforms, 2020.
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1032. J. Villalba, Nanxin Chen, David Snyder, D. Garcia-Romero, A. McCree, Gregory Sell, Jonas Borgstrom, Leibny Paola García-Perera, Fred Richardson, Réda Dehak, P. Torres-Carrasquillo, and N. Dehak, “State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and Speakers in the Wild evaluations,” in Computer Speech and Language, 2020.
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1033. Dian Hu, Christine Martin, Mark Dredze, and David A. Broniatowski, “Chinese social media suggest decreased vaccine acceptance in China: An observational study on Weibo following the 2018 Changchun Changsheng vaccine incident.,” in Vaccine, 2020.
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1034. R. Arora, P. Bartlett, Poorya Mianjy, and N. Srebro, “Dropout: Explicit Forms and Capacity Control,” in International Conference on Machine Learning, 2020.
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1035. Jonah P. Sengupta, R. Kubendran, E. Neftci, and A. Andreou, “High-Speed, Real-Time, Spike-Based Object Tracking and Path Prediction on Google Edge TPU,” in International Conference on Artificial Intelligence Circuits and Systems, 2020.
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1036. Ke Li, Zhe Liu, Tianxing He, Hongzhao Huang, Fuchun Peng, Daniel Povey, and S. Khudanpur, “An Empirical Study of Transformer-Based Neural Language Model Adaptation,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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1037. T. Strat, R. Chellappa, and Vishal M. Patel, “Vision and Robotics,” in The AI Magazine, 2020.
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1038. R. Knowles and Matt Post, “Domain Adaptation,” in Machine Learning for Speaker Recognition, 2020.
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1039. R. Yasarla and Vishal M. Patel, “Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs,” in arXiv.org, 2020.
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1040. Angtian Wang, Yihong Sun, Adam Kortylewski, and A. Yuille, “Robust Object Detection Under Occlusion With Context-Aware CompositionalNets,” in Computer Vision and Pattern Recognition, 2020.
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1041. Xiaolei Huang, Amelia M. Jamison, David A. Broniatowski, S. Quinn, and Mark Dredze, “Coronavirus Twitter Data: A collection of COVID-19 tweets with automated annotations.” 2020.
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1042. R. Pappagari, Tianzi Wang, J. Villalba, Nanxin Chen, and N. Dehak, “X-Vectors Meet Emotions: A Study On Dependencies Between Emotion and Speaker Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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1043. Chenglin Yang, Adam Kortylewski, Cihang Xie, Yinzhi Cao, and A. Yuille, “PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning,” in European Conference on Computer Vision, 2020.
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1044. Paiheng Xu, Mark Dredze, and David A. Broniatowski, “The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets,” in arXiv.org, 2020.
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1045. Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation,” in European Conference on Computer Vision, 2020.
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1046. Yingda Xia, D. Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, A. Yuille, and H. Roth, “Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation,” in Medical Image Anal., 2020.
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1047. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, and N. Dehak, “Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery,” in Interspeech, 2020.
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1048. Puyang Wang, M. Vives, Vishal M. Patel, and I. Hacihaliloglu, “Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN,” in International Journal of Computer Assisted Radiology and Surgery, 2020.
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1049. Prithviraj Dhar, Joshua Gleason, Hossein Souri, C. Castillo, and R. Chellappa, “An adversarial learning algorithm for mitigating gender bias in face recognition,” in arXiv.org, 2020.
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1050. Zili Huang, Shinji Watanabe, Yusuke Fujita, Leibny Paola García-Perera, Yiwen Shao, Daniel Povey, and S. Khudanpur, “Speaker Diarization with Region Proposal Network,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
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1051. Pramuditha Perera, Julian Fierrez, and Vishal M. Patel, “Quickest Multiple User Active Authentication,” in Advanced Sciences and Technologies for Security Applications, 2020.
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1052. Vishwanath A. Sindagi, R. Yasarla, Deepak Sam Babu, R. Venkatesh Babu, and Vishal M. Patel, “Learning to Count in the Crowd from Limited Labeled Data,” in European Conference on Computer Vision, 2020.
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1053. Tomas Figliolia and A. Andreou, “An FPGA multiprocessor architecture for Bayesian online change point detection using stochastic computation,” in Microprocessors and microsystems, 2020.
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1054. Nathaniel Weir, Adam Poliak, and Benjamin Van Durme, “Probing Neural Language Models for Human Tacit Assumptions,” in Annual Meeting of the Cognitive Science Society, 2020.
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1055. Zhuotun Zhu, K. Yan, D. Jin, Jinzheng Cai, T. Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, X. Ye, Jing Xiao, A. Yuille, and Le Lu, “Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification,” in arXiv.org, 2020.
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1056. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Federated Face Presentation Attack Detection,” in arXiv: Computer Vision and Pattern Recognition, 2020.
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1057. Jonathan Camacho Maldonado, Pei-Shu Ho, R. J. Silva, Alex R Marr, Julia Porcino, Maryanne Sacco, Ayah Zirikly, L. Chan, and E. Rasch, “Experiences and Challenges in Manual Annotation of Functioning Information in Medical Records,” in American Medical Informatics Association Annual Symposium, 2020.
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1058. Piotr Żelasko, Laureano Moro-Vel’azquez, M. Hasegawa-Johnson, O. Scharenborg, and N. Dehak, “That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages,” in Interspeech, 2020.
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1059. M. Villemur, P. Julián, Tomas Figliolia, and A. Andreou, “7 TOPS/W Cellular Neural Network Processor Core for Intelligent Internet-of-Things,” in IEEE Transactions on Circuits and Systems – II – Express Briefs, 2020.
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1060. Juan Ignacio Godino-Llorente, L. Moro-Velázquez, J. Gómez-García, Jeung-Yoon Choi, N. Dehak, and S. Shattuck-Hufnagel, “Approaches to Evaluate Parkinsonian Speech Using Artificial Models.” 2020.
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1061. Soumyadeep Ghosh, Richa Singh, Mayank Vatsa, N. Ratha, and Vishal M. Patel, “Domain Adaptation for Visual Understanding,” in Domain Adaptation for Visual Understanding, 2020.
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1062. L. Chu, Berkan Solmaz, Seyoun Park, S. Kawamoto, A. Yuille, R. Hruban, and E. Fishman, “Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls,” in Abdominal Radiology, 2020.
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1063. Ruizhe Huang, Ke Li, Ashish Arora, Daniel Povey, and S. Khudanpur, “Efficient MDI Adaptation for n-gram Language Models,” in Interspeech, 2020.
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1064. J. Ayers, E. Leas, Derek C. Johnson, Adam Poliak, B. Althouse, Mark Dredze, and A. Nobles, “Internet Searches for Acute Anxiety During the Early Stages of the COVID-19 Pandemic.,” in JAMA Internal Medicine, 2020.
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1065. R. Arora, T. V. Marinov, and M. Mohri, “Corralling Stochastic Bandit Algorithms,” in International Conference on Artificial Intelligence and Statistics, 2020.
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1066. Jingxiao Zheng and R. Chellappa, “Video-Based Face Recognition,” in Computer Vision, 2020.
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1067. Haley Grant Yifan Zhang, Lu Li, Yan Wang, S. Kawamoto, Sophie P’enisson, D. Fouladi, S. Shayesteh, A. Blanco, Saeed Ghandili, E. Zinreich, J. Graves, Seyoun Park, S. Kern, J. Hooper, A. Yuille, E. Fishman, L. Chu, and C. Tomasetti, “Organ size increases with obesity and correlates with cancer risk,” in arXiv: Tissues and Organs, 2020.
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1068. Sangwook Park, Mounya Elhilali, D. Han, and Hanseok Ko, “Amphibian Sounds Generating Network Based on Adversarial Learning,” in IEEE Signal Processing Letters, 2020.
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1069. Deepak Babu Sam, Abhinav Agarwalla, Jimmy Joseph, Vishwanath A. Sindagi, R. Venkatesh Babu, and Vishal M. Patel, “Completely Self-Supervised Crowd Counting via Distribution Matching,” in European Conference on Computer Vision, 2020.
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1070. Shao-Yuan Lo and Vishal M. Patel, “MultAV: Multiplicative Adversarial Videos,” in Advanced Video and Signal Based Surveillance, 2020.
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1071. P. Motlícek, H. Hermansky, S. Madikeri, Amrutha Prasad, and Sriram Ganapathy, “AM-FM DECOMPOSITION OF SPEECH SIGNAL: APPLICATIONS FOR SPEECH PRIVACY AND DIAGNOSIS.” 2020.
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1072. D. Cate, Helen V. Hsieh, Veronika A. Glukhova, J. Bishop, H. Hermansky, B. Barrios-Lopez, Benjamin D Grant, Caitlin E. Anderson, Ethan Spencer, S. Kuhn, Ryan Gallagher, Rafael Rivera, Crissa Bennett, S. Byrnes, J. Connelly, P. Dewan, David S. Boyle, B. Weigl, and K. P. Nichols, “Antibody Screening Results for Anti-Nucleocapsid Antibodies Towards the Development of a SARS-CoV-2 Nucleocapsid Protein Antigen Detecting Lateral Flow Assay.” 2020.
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1073. Pramuditha Perera, Vlad I. Morariu, R. Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, and Vishal M. Patel, “Generative-Discriminative Feature Representations for Open-Set Recognition,” in Computer Vision and Pattern Recognition, 2020.
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1074. Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, and S. Khudanpur, “Wake Word Detection with Alignment-Free Lattice-Free MMI,” in Interspeech, 2020.
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1075. Adarsh Subbaswamy and S. Saria, “I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models,” in arXiv.org, 2020.
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1076. Seyoun Park, Linda C Chu, R. Hruban, Bert Vogelstein, B. Vogelstein, K. Kinzler, K. Kinzler, A. Yuille, D. Fouladi, S. Shayesteh, Saeed Ghandili, elliot k fishman, R. Burkhart, Jin He, Elliott K. Fishman, and S. Kawamoto, “Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.,” in Diagnostic and Interventional Imaging, 2020.
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1077. Zhen Zhang, Chaokun Chang, Haibin Lin, Yida Wang, R. Arora, and Xin Jin, “Is Network the Bottleneck of Distributed Training?,” in NetAI@SIGCOMM, 2020.
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1078. Laureano Moro-Veláquez, Estefanía Hernández-García, J. Gómez-García, Juan Ignacio Godino-Llorente, and N. Dehak, “Analysis of the Effects of Supraglottal Tract Surgical Procedures in Automatic Speaker Recognition Performance,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2020.
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1079. R. Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel, “Semi-Supervised Image Deraining Using Gaussian Processes,” in IEEE Transactions on Image Processing, 2020.
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1080. M. Adrian, J. Coifman, M. Pullmann, J. Blossom, C. Chandler, Glen A. Coppersmith, P. Thompson, and Aaron R. Lyon, “Implementation Determinants and Outcomes of a Technology-Enabled Service Targeting Suicide Risk in High Schools: Mixed Methods Study,” in JMIR Mental Health, 2020.
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1081. Adam Kortylewski, Ju He, Qing Liu, and A. Yuille, “Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion,” in Computer Vision and Pattern Recognition, 2020.
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1082. Nicholas Huang and Mounya Elhilali, “Push-pull competition between bottom-up and top-down auditory attention to natural soundscapes,” in eLife, 2020.
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1083. Zhuotun Zhu, Yongyi Lu, Wei Shen, E. Fishman, and A. Yuille, “Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2020.
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1084. Poojan Oza, H. Nguyen, and Vishal M. Patel, “Multiple Class Novelty Detection Under Data Distribution Shift,” in European Conference on Computer Vision, 2020.
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1085. Jiamin Xie, Suzanna Sia, Leibny Paola García-Perera, Daniel Povey, and S. Khudanpur, “Mixture of Speaker-type PLDAs for Children’s Speech Diarization,” in arXiv: Audio and Speech Processing, 2020.
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1086. Hui Ding, Peng Zhou, and R. Chellappa, “Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition,” in 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020.
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1087. T. Shinozaki, Shinji Watanabe, and Kevin Duh, “Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms,” in Deep Neural Evolution, 2020.
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1088. Lukasz Augustyniak, Piotr Szymański, Mikolaj Morzy, Piotr Żelasko, Adrian Szymczak, Jan Mizgajski, Yishay Carmiel, and N. Dehak, “Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?,” in Interspeech, 2020.
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1089. Cheng Peng, Wei-An Lin, Haofu Liao, R. Chellappa, and S. Zhou, “SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis,” in Computer Vision and Pattern Recognition, 2020.
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1090. Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, and A. Yuille, “Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion,” in International Journal of Computer Vision, 2020.
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1091. Zhuotun Zhu, D. Jin, K. Yan, T. Ho, X. Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, A. Yuille, and Le Lu, “Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
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1092. Adam Poliak, Max Fleming, Cash Costello, Kenton W. Murray, M. Yarmohammadi, Shivani R. Pandya, Darius Irani, Milind Agarwal, Udit Sharma, Shuo Sun, Nicola Ivanov, Lingxi Shang, Kaushik Srinivasan, Seolhwa Lee, Xu Han, Smisha Agarwal, and João Sedoc, “Collecting Verified COVID-19 Question Answer Pairs,” in NLP4COVID@EMNLP, 2020.
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1093. R. J. Silva, Chunxiao Zhou, Bart Desmet, Ayah Zirikly, Alex R Marr, Maryanne Sacco, Pei-Shu Ho, Jonathan Camacho Maldonado, Julia Porcino, Guy Divita, and E. Rasch, “Curating Annotated Corpora for Functioning Information,” in American Medical Informatics Association Annual Symposium, 2020.
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1094. Puyang Wang, Eric Z. Chen, Terrence Chen, Vishal M. Patel, and Shanhui Sun, “Pyramid Convolutional RNN for MRI Reconstruction,” in arXiv.org, 2019.
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1095. Jialing Lyu, Weichao Qiu, Xinyue Wei, Yi Zhang, A. Yuille, and Zhengjun Zha, “Identity Preserve Transform: Understand What Activity Classification Models Have Learnt,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
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1096. Leibny Paola García-Perera, J. Villalba, H. Bredin, Jun Du, Diego Castán, Alejandrina Cristia, Latané Bullock, Ling Guo, K. Okabe, P. S. Nidadavolu, Saurabh Kataria, Sizhu Chen, Léo Galmant, Marvin Lavechin, Lei Sun, Marie-Philippe Gill, Bar Ben-Yair, Sajjad Abdoli, Xin Wang, Wassim Bouaziz, Hadrien Titeux, Emmanuel Dupoux, Kong Aik LEE, and N. Dehak, “Speaker detection in the wild: Lessons learned from JSALT 2019,” in The Speaker and Language Recognition Workshop, 2019.
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1097. Zhehuai Chen, M. Yarmohammadi, Hainan Xu, Hang Lv, Lei Xie, Daniel Povey, and S. Khudanpur, “Incremental Lattice Determinization for WFST Decoders,” in Automatic Speech Recognition & Understanding, 2019.
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      @inproceedings{210706835,
      title = {Incremental Lattice Determinization for WFST Decoders},
      author = {{Zhehuai Chen} and {M. Yarmohammadi} and {Hainan Xu} and {Hang Lv} and {Lei Xie} and {Daniel Povey} and {S. Khudanpur}},
      year = 2019,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/920238fedadcfb835c808c039a44d3ccf8ebab69},
      }

1098. Nils Holzenberger and R. Arora, “Multiview Representation Learning for a Union of Subspaces,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{209515968,
      title = {Multiview Representation Learning for a Union of Subspaces},
      author = {{Nils Holzenberger} and {R. Arora}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/68941236b9ea941350180427fe60aa1f3644ae75},
      }

1099. Qi Chen, Lin Sun, Zhixin Wang, K. Jia, and A. Yuille, “Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots,” in European Conference on Computer Vision, 2019.
      [BibTeX] [Link]

      @inproceedings{209515687,
      title = {Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots},
      author = {{Qi Chen} and {Lin Sun} and {Zhixin Wang} and {K. Jia} and {A. Yuille}},
      year = 2019,
      month = {12},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/78ea7c49d28a904ec2a5af3326f0752949101983},
      }

1100. Jonathan D. Jones, Tae Soo Kim, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, A. Yuille, and Gregory Hager, “DAZSL: Dynamic Attributes for Zero-Shot Learning.” 2019.
      [BibTeX] [Link]

      @inproceedings{212658043,
      title = {DAZSL: Dynamic Attributes for Zero-Shot Learning},
      author = {{Jonathan D. Jones} and {Tae Soo Kim} and {Michael Peven} and {Zihao Xiao} and {Jin Bai} and {Yi Zhang} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
      year = 2019,
      month = {12},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a7a5f48b87051eb4ca6e112da976cc3874d4a363},
      }

1101. Matthew Wiesner, Oliver Adams, David Yarowsky, J. Trmal, and S. Khudanpur, “Zero-Shot Pronunciation Lexicons for Cross-Language Acoustic Model Transfer,” in Automatic Speech Recognition & Understanding, 2019.
      [BibTeX] [Link]

      @inproceedings{211243176,
      title = {Zero-Shot Pronunciation Lexicons for Cross-Language Acoustic Model Transfer},
      author = {{Matthew Wiesner} and {Oliver Adams} and {David Yarowsky} and {J. Trmal} and {S. Khudanpur}},
      year = 2019,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/9972dc80b863f64a51ba2e0cf44bb40659ecf853},
      }

1102. Yi Zhang, Xinyue Wei, Weichao Qiu, Zihao Xiao, Gregory Hager, and A. Yuille, “RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208547825,
      title = {RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition},
      author = {{Yi Zhang} and {Xinyue Wei} and {Weichao Qiu} and {Zihao Xiao} and {Gregory Hager} and {A. Yuille}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/b3260a2fb397b9a04d96546f0823ce7b84ba8e3d},
      }

1103. Jonathan D. Jones, Tae Soo Kim, Michael Peven, Jin Bai, Zihao Xiao, Yi Zhang, Weichao Qiu, A. Yuille, and Gregory Hager, “Zero-shot Recognition of Complex Action Sequences,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208910119,
      title = {Zero-shot Recognition of Complex Action Sequences},
      author = {{Jonathan D. Jones} and {Tae Soo Kim} and {Michael Peven} and {Jin Bai} and {Zihao Xiao} and {Yi Zhang} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/3aa1c9750ccf9da321db7b893776c060a1d0a7b3},
      }

1104. L. Moro-Velázquez, J. Gómez-García, Juan Ignacio Godino-Llorente, F. Grandas-Pérez, S. Shattuck-Hufnagel, V. Yagüe-Jiménez, and N. Dehak, “Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s Disease,” in Scientific Reports, 2019.
      [BibTeX] [Link]

      @inproceedings{209331418,
      title = {Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s Disease},
      author = {{L. Moro-Velázquez} and {J. Gómez-García} and {Juan Ignacio Godino-Llorente} and {F. Grandas-Pérez} and {S. Shattuck-Hufnagel} and {V. Yagüe-Jiménez} and {N. Dehak}},
      year = 2019,
      month = {12},
      booktitle = {Scientific Reports},
      url = {https://www.semanticscholar.org/paper/bc4156658cd9330fb18dfdb577e5913a7f7878c3},
      }

1105. Xing Di and Vishal M. Patel, “Facial Synthesis From Visual Attributes via Sketch Using Multiscale Generators,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2019.
      [BibTeX] [Link]

      @inproceedings{209444781,
      title = {Facial Synthesis From Visual Attributes via Sketch Using Multiscale Generators},
      author = {{Xing Di} and {Vishal M. Patel}},
      year = 2019,
      month = {12},
      booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
      url = {https://www.semanticscholar.org/paper/d043bf8f03ada1f9f3504a8084a452c7579b84d8},
      }

1106. Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, A. Yuille, and Jianchao Yang, “AtomNAS: Fine-Grained End-to-End Neural Architecture Search,” in International Conference on Learning Representations, 2019.
      [BibTeX] [Link]

      @inproceedings{209439665,
      title = {AtomNAS: Fine-Grained End-to-End Neural Architecture Search},
      author = {{Jieru Mei} and {Yingwei Li} and {Xiaochen Lian} and {Xiaojie Jin} and {Linjie Yang} and {A. Yuille} and {Jianchao Yang}},
      year = 2019,
      month = {12},
      booktitle = {International Conference on Learning Representations},
      url = {https://www.semanticscholar.org/paper/f5f35340893d550bd5d1a2711f04308525c6dcd2},
      }

1107. Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, E. Fishman, and A. Yuille, “Deep Distance Transform for Tubular Structure Segmentation in CT Scans,” in Computer Vision and Pattern Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{208910348,
      title = {Deep Distance Transform for Tubular Structure Segmentation in CT Scans},
      author = {{Yan Wang} and {Xu Wei} and {Fengze Liu} and {Jieneng Chen} and {Yuyin Zhou} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
      year = 2019,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/5e47365ed87009e78eb53eae2f5660e1cfd1df1d},
      }

1108. Jeya Maria Jose Valanarasu, R. Yasarla, Puyang Wang, I. Hacihaliloglu, and Vishal M. Patel, “Learning to Segment Brain Anatomy From 2D Ultrasound With Less Data,” in IEEE Journal on Selected Topics in Signal Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{209405109,
      title = {Learning to Segment Brain Anatomy From 2D Ultrasound With Less Data},
      author = {{Jeya Maria Jose Valanarasu} and {R. Yasarla} and {Puyang Wang} and {I. Hacihaliloglu} and {Vishal M. Patel}},
      year = 2019,
      month = {12},
      booktitle = {IEEE Journal on Selected Topics in Signal Processing},
      url = {https://www.semanticscholar.org/paper/de9d268cb1c518717bf6b5df1d807867cbb4a9a6},
      }

1109. Jiteng Mu, Weichao Qiu, Gregory Hager, and A. Yuille, “Learning From Synthetic Animals,” in Computer Vision and Pattern Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{209405066,
      title = {Learning From Synthetic Animals},
      author = {{Jiteng Mu} and {Weichao Qiu} and {Gregory Hager} and {A. Yuille}},
      year = 2019,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/5d5afcb1ca9a0c455ba291dee328cef23f014b26},
      }

1110. Nagaraj R. Mahajan, N. Mesgarani, and H. Hermansky, “General properties of auditory spectro-temporal receptive fields.,” in Journal of the Acoustical Society of America, 2019.
      [BibTeX] [Link]

      @inproceedings{209543410,
      title = {General properties of auditory spectro-temporal receptive fields.},
      author = {{Nagaraj R. Mahajan} and {N. Mesgarani} and {H. Hermansky}},
      year = 2019,
      month = {12},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/603bfa5b7d64978224862eda05135945af90c525},
      }

1111. Qihang Yu, Dong Yang, H. Roth, Yutong Bai, Yixiao Zhang, A. Yuille, and Daguang Xu, “C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation,” in Computer Vision and Pattern Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{209439815,
      title = {C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation},
      author = {{Qihang Yu} and {Dong Yang} and {H. Roth} and {Yutong Bai} and {Yixiao Zhang} and {A. Yuille} and {Daguang Xu}},
      year = 2019,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/eca36cc534f516db1c4ff94531ae458240141b9c},
      }

1112. Pengfei Li, Weichao Qiu, Michael Peven, Gregory Hager, and A. Yuille, “Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{209140669,
      title = {Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints},
      author = {{Pengfei Li} and {Weichao Qiu} and {Michael Peven} and {Gregory Hager} and {A. Yuille}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/3ec5d2fcf2b070401f1178326b66ab0f0c0059b1},
      }

1113. Tae Soo Kim, Jonathan D. Jones, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, A. Yuille, and Gregory Hager, “DASZL: Dynamic Action Signatures for Zero-shot Learning,” in AAAI Conference on Artificial Intelligence, 2019.
      [BibTeX] [Link]

      @inproceedings{227049234,
      title = {DASZL: Dynamic Action Signatures for Zero-shot Learning},
      author = {{Tae Soo Kim} and {Jonathan D. Jones} and {Michael Peven} and {Zihao Xiao} and {Jin Bai} and {Yi Zhang} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
      year = 2019,
      month = {12},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/707b023e6c25ed283f23625bec514da480fb90da},
      }

1114. Mitchell A. Gordon and Kevin Duh, “Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208910258,
      title = {Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation},
      author = {{Mitchell A. Gordon} and {Kevin Duh}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/d1ba8c532b954ea8b3a66d9c155e769fc2081af6},
      }

1115. Ilya Kavalerov, W. Czaja, and R. Chellappa, “cGANs with Multi-Hinge Loss,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208910107,
      title = {cGANs with Multi-Hinge Loss},
      author = {{Ilya Kavalerov} and {W. Czaja} and {R. Chellappa}},
      year = 2019,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c1d58def0becaeaf368e124f2898ccd89f0c74eb},
      }

1116. Wei-An Lin, Y. Balaji, Pouya Samangouei, and R. Chellappa, “Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208268082,
      title = {Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference},
      author = {{Wei-An Lin} and {Y. Balaji} and {Pouya Samangouei} and {R. Chellappa}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/34adff99c6ce47057b24c1bd1305adf292403fa7},
      }

1117. S. Ebner, F. Wang, and B. Van Durme, “Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning,” in Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), Hong Kong, China, 2019, p. 40–46. doi:10.18653/v1/D19-6105
      [BibTeX] [Abstract] [Link]
      

      Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.

      @inproceedings{ebner-etal-2019-bag,
      title = "Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning",
      author = "Ebner, Seth and
      Wang, Felicity and
      Van Durme, Benjamin",
      editor = "Cherry, Colin and
      Durrett, Greg and
      Foster, George and
      Haffari, Reza and
      Khadivi, Shahram and
      Peng, Nanyun and
      Ren, Xiang and
      Swayamdipta, Swabha",
      booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-6105",
      doi = "10.18653/v1/D19-6105",
      pages = "40--46",
      abstract = "Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.",
      }

1118. S. Zhang, X. Ma, K. Duh, and B. Van Durme, “Broad-Coverage Semantic Parsing as Transduction,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, p. 3786–3798. doi:10.18653/v1/D19-1392
      [BibTeX] [Abstract] [Link]
      

      We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks {–} AMR, SDP and UCCA {–} demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.

      @inproceedings{zhang-etal-2019-broad,
      title = "Broad-Coverage Semantic Parsing as Transduction",
      author = "Zhang, Sheng and
      Ma, Xutai and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Inui, Kentaro and
      Jiang, Jing and
      Ng, Vincent and
      Wan, Xiaojun",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-1392",
      doi = "10.18653/v1/D19-1392",
      pages = "3786--3798",
      abstract = "We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks {--} AMR, SDP and UCCA {--} demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.",
      }

1119. Saurabhchand Bhati, Chunxi Liu, J. Villalba, J. Trmal, S. Khudanpur, and N. Dehak, “Bottom-Up Unsupervised Word Discovery via Acoustic Units,” in IEEE Global Conference on Signal and Information Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{210972323,
      title = {Bottom-Up Unsupervised Word Discovery via Acoustic Units},
      author = {{Saurabhchand Bhati} and {Chunxi Liu} and {J. Villalba} and {J. Trmal} and {S. Khudanpur} and {N. Dehak}},
      year = 2019,
      month = {11},
      booktitle = {IEEE Global Conference on Signal and Information Processing},
      url = {https://www.semanticscholar.org/paper/2a626d33a9e7af638eac1660426a486288a489cc},
      }

1120. Vishwanath A. Sindagi, Poojan Oza, R. Yasarla, and Vishal M. Patel, “Prior-Based Domain Adaptive Object Detection for Hazy and Rainy Conditions,” in European Conference on Computer Vision, 2019.
      [BibTeX] [Link]

      @inproceedings{215867646,
      title = {Prior-Based Domain Adaptive Object Detection for Hazy and Rainy Conditions},
      author = {{Vishwanath A. Sindagi} and {Poojan Oza} and {R. Yasarla} and {Vishal M. Patel}},
      year = 2019,
      month = {11},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/4586b801c3c72a7ccb081867a56b0dff92a9c6d9},
      }

1121. Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, A. Yuille, and Quoc V. Le, “Adversarial Examples Improve Image Recognition,” in Computer Vision and Pattern Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{208201954,
      title = {Adversarial Examples Improve Image Recognition},
      author = {{Cihang Xie} and {Mingxing Tan} and {Boqing Gong} and {Jiang Wang} and {A. Yuille} and {Quoc V. Le}},
      year = 2019,
      month = {11},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/948839277bface5780896e8e8791906818aa41ac},
      }

1122. Hao Ding, Siyuan Qiao, A. Yuille, and Wei Shen, “Deeply Shape-guided Instance Segmentation,” in arXiv: Computer Vision and Pattern Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{219573546,
      title = {Deeply Shape-guided Instance Segmentation},
      author = {{Hao Ding} and {Siyuan Qiao} and {A. Yuille} and {Wei Shen}},
      year = 2019,
      month = {11},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/cd79f35857cdd9660994b249a49679f7cf5bd8a4},
      }

1123. Adarsh Subbaswamy and S. Saria, “From development to deployment: dataset shift, causality, and shift-stable models in health AI.,” in Biostatistics, 2019.
      [BibTeX] [Link]

      @inproceedings{208170863,
      title = {From development to deployment: dataset shift, causality, and shift-stable models in health AI.},
      author = {{Adarsh Subbaswamy} and {S. Saria}},
      year = 2019,
      month = {11},
      booktitle = {Biostatistics},
      url = {https://www.semanticscholar.org/paper/9fb10e0ee0e200d4298f3a146f81c12dce441179},
      }

1124. E. J. Hu, A. Singh, N. Holzenberger, M. Post, and B. Van Durme, “Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering,” in Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, 2019, p. 44–54. doi:10.18653/v1/K19-1005
      [BibTeX] [Abstract] [Link]
      

      Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering. We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.

      @inproceedings{hu-etal-2019-large,
      title = "Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering",
      author = "Hu, J. Edward and
      Singh, Abhinav and
      Holzenberger, Nils and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Bansal, Mohit and
      Villavicencio, Aline",
      booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K19-1005",
      doi = "10.18653/v1/K19-1005",
      pages = "44--54",
      abstract = "Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering. We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.",
      }

1125. Hao Ding, Siyuan Qiao, Wei Shen, and A. Yuille, “Shape-aware Feature Extraction for Instance Segmentation,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208291180,
      title = {Shape-aware Feature Extraction for Instance Segmentation},
      author = {{Hao Ding} and {Siyuan Qiao} and {Wei Shen} and {A. Yuille}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/e15761ef565af80f37e6ceddc62ef094c0af54fd},
      }

1126. A. D. McCarthy, W. Wu, A. Mueller, W. Watson, and D. Yarowsky, “Modeling Color Terminology Across Thousands of Languages,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, p. 2241–2250. doi:10.18653/v1/D19-1229
      [BibTeX] [Abstract] [Link]
      

      There is an extensive history of scholarship into what constitutes a {“}basic{”} color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design{–-}as well as their aggregation{–-}correlate strongly with both the Berlin and Kay basic/secondary color term partition (γ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.

      @inproceedings{mccarthy-etal-2019-modeling,
      title = "Modeling Color Terminology Across Thousands of Languages",
      author = "McCarthy, Arya D. and
      Wu, Winston and
      Mueller, Aaron and
      Watson, William and
      Yarowsky, David",
      editor = "Inui, Kentaro and
      Jiang, Jing and
      Ng, Vincent and
      Wan, Xiaojun",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-1229",
      doi = "10.18653/v1/D19-1229",
      pages = "2241--2250",
      abstract = "There is an extensive history of scholarship into what constitutes a {``}basic{''} color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design{---}as well as their aggregation{---}correlate strongly with both the Berlin and Kay basic/secondary color term partition (γ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.",
      }

1127. B. Thompson, R. Knowles, X. Zhang, H. Khayrallah, K. Duh, and P. Koehn, “HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, p. 1382–1387. doi:10.18653/v1/D19-1142
      [BibTeX] [Abstract] [Link]
      

      Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines – constrained decoding and continued training – and an improvement to continued training to address overfitting.

      @inproceedings{thompson-etal-2019-hablex,
      title = "{HABL}ex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation",
      author = "Thompson, Brian and
      Knowles, Rebecca and
      Zhang, Xuan and
      Khayrallah, Huda and
      Duh, Kevin and
      Koehn, Philipp",
      editor = "Inui, Kentaro and
      Jiang, Jing and
      Ng, Vincent and
      Wan, Xiaojun",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-1142",
      doi = "10.18653/v1/D19-1142",
      pages = "1382--1387",
      abstract = "Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines - constrained decoding and continued training - and an improvement to continued training to address overfitting.",
      }

1128. Saurabhchand Bhati, L. Moro-Velázquez, J. Villalba, and N. Dehak, “LSTM Siamese Network for Parkinson’s Disease Detection from Speech,” in IEEE Global Conference on Signal and Information Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{210971728,
      title = {LSTM Siamese Network for Parkinson’s Disease Detection from Speech},
      author = {{Saurabhchand Bhati} and {L. Moro-Velázquez} and {J. Villalba} and {N. Dehak}},
      year = 2019,
      month = {11},
      booktitle = {IEEE Global Conference on Signal and Information Processing},
      url = {https://www.semanticscholar.org/paper/e8c28555fe828a27a691a24608cd229c0359c8b1},
      }

1129. E. Stengel-Eskin, T. Su, M. Post, and B. Van Durme, “A Discriminative Neural Model for Cross-Lingual Word Alignment,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, p. 910–920. doi:10.18653/v1/D19-1084
      [BibTeX] [Abstract] [Link]
      

      We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K{–}5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11{–}27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.

      @inproceedings{stengel-eskin-etal-2019-discriminative,
      title = "A Discriminative Neural Model for Cross-Lingual Word Alignment",
      author = "Stengel-Eskin, Elias and
      Su, Tzu-ray and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Inui, Kentaro and
      Jiang, Jing and
      Ng, Vincent and
      Wan, Xiaojun",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-1084",
      doi = "10.18653/v1/D19-1084",
      pages = "910--920",
      abstract = "We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K{--}5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11{--}27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.",
      }

1130. A. Nobles, E. Leas, B. Althouse, Mark Dredze, C. Longhurst, Davey M. Smith, and J. Ayers, “Requests for Diagnoses of Sexually Transmitted Diseases on a Social Media Platform.,” in Journal of the American Medical Association (JAMA), 2019.
      [BibTeX] [Link]

      @inproceedings{207894590,
      title = {Requests for Diagnoses of Sexually Transmitted Diseases on a Social Media Platform.},
      author = {{A. Nobles} and {E. Leas} and {B. Althouse} and {Mark Dredze} and {C. Longhurst} and {Davey M. Smith} and {J. Ayers}},
      year = 2019,
      month = {11},
      booktitle = {Journal of the American Medical Association (JAMA)},
      url = {https://www.semanticscholar.org/paper/9d98c236bf7e729db2b31cace3328e335dc8a942},
      }

1131. Nanxin Chen, Shinji Watanabe, J. Villalba, and N. Dehak, “Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{214802613,
      title = {Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition},
      author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {N. Dehak}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/2de8019fd7d04e3d1305d5efaeeb591f0d966550},
      }

1132. Michelle Shu, Chenxi Liu, Weichao Qiu, and A. Yuille, “Identifying Model Weakness with Adversarial Examiner,” in AAAI Conference on Artificial Intelligence, 2019.
      [BibTeX] [Link]

      @inproceedings{208277443,
      title = {Identifying Model Weakness with Adversarial Examiner},
      author = {{Michelle Shu} and {Chenxi Liu} and {Weichao Qiu} and {A. Yuille}},
      year = 2019,
      month = {11},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/1a0d3c0fc3c87c801ce3667eec4987fd0901e19d},
      }

1133. Nanxin Chen, Shinji Watanabe, J. Villalba, and N. Dehak, “Non-Autoregressive Transformer Automatic Speech Recognition,” in arXiv: Audio and Speech Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{207863618,
      title = {Non-Autoregressive Transformer Automatic Speech Recognition},
      author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {N. Dehak}},
      year = 2019,
      month = {11},
      booktitle = {arXiv: Audio and Speech Processing},
      url = {https://www.semanticscholar.org/paper/49f657d704a1b80ce3dba0d8a9e5479ec1d703d4},
      }

1134. S. Wu and M. Dredze, “Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, p. 833–844. doi:10.18653/v1/D19-1077
      [BibTeX] [Abstract] [Link]
      

      Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.

      @inproceedings{wu-dredze-2019-beto,
      title = "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of {BERT}",
      author = "Wu, Shijie and
      Dredze, Mark",
      editor = "Inui, Kentaro and
      Jiang, Jing and
      Ng, Vincent and
      Wan, Xiaojun",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D19-1077",
      doi = "10.18653/v1/D19-1077",
      pages = "833--844",
      abstract = "Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.",
      }

1135. Amelia M. Jamison, David A. Broniatowski, Mark Dredze, Zach Wood-Doughty, DureAden Khan, and S. Quinn, “Vaccine-related advertising in the Facebook Ad Archive.,” in Vaccine, 2019.
      [BibTeX] [Link]

      @inproceedings{208063342,
      title = {Vaccine-related advertising in the Facebook Ad Archive.},
      author = {{Amelia M. Jamison} and {David A. Broniatowski} and {Mark Dredze} and {Zach Wood-Doughty} and {DureAden Khan} and {S. Quinn}},
      year = 2019,
      month = {11},
      booktitle = {Vaccine},
      url = {https://www.semanticscholar.org/paper/09d933efcaaaeedec22d08bceb09dc2b3e7b7efd},
      }

1136. S.R.P. van Hal, Matt Post, and K. Wendel, “Generating Commit Messages from Git Diffs,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208291427,
      title = {Generating Commit Messages from Git Diffs},
      author = {{S.R.P. van Hal} and {Matt Post} and {K. Wendel}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/ccff37de654ce48ce219239da0fe91ef23a8d7f3},
      }

1137. Elliot Schumacher and Mark Dredze, “Learning unsupervised contextual representations for medical synonym discovery,” in JAMIA Open, 2019.
      [BibTeX] [Link]

      @inproceedings{211033454,
      title = {Learning unsupervised contextual representations for medical synonym discovery},
      author = {{Elliot Schumacher} and {Mark Dredze}},
      year = 2019,
      month = {11},
      booktitle = {JAMIA Open},
      url = {https://www.semanticscholar.org/paper/cad8a6b9248227d041f35acbfb341ab870d8995f},
      }

1138. Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and A. Yuille, “Rethinking Normalization and Elimination Singularity in Neural Networks,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208247947,
      title = {Rethinking Normalization and Elimination Singularity in Neural Networks},
      author = {{Siyuan Qiao} and {Huiyu Wang} and {Chenxi Liu} and {Wei Shen} and {A. Yuille}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/557c8597b1cfecc0fdaf8c070024c19a89c3c7dd},
      }

1139. Vishwanath A. Sindagi, Poojan Oza, R. Yasarla, and Vishal M. Patel, “Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{208527596,
      title = {Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions},
      author = {{Vishwanath A. Sindagi} and {Poojan Oza} and {R. Yasarla} and {Vishal M. Patel}},
      year = 2019,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/8428ac16108c6ffa2e4a71325939f361dd31d8ed},
      }

1140. X. L. Li and J. Eisner, “Specializing Word Embeddings (for Parsing) by Information Bottleneck,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Hong Kong, 2019, p. 2744–2754. doi:10.18653/v1/D19-1276
      [BibTeX] [Link]

      @InProceedings{li-eisner-2019,
      aclid = "D19-1276",
      doi = "10.18653/v1/D19-1276",
      author = "Xiang Lisa Li and Jason Eisner",
      title = "Specializing Word Embeddings (for Parsing) by
      Information Bottleneck",
      booktitle = "Proceedings of the 2019 Conference on Empirical
      Methods in Natural Language Processing and 9th
      International Joint Conference on Natural Language
      Processing",
      pages = "2744--2754",
      year = "2019",
      month = nov,
      address = "Hong Kong",
      note = "Best Paper Award.",
      URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-2019",
      }

1141. A. Renduchintala, P. Koehn, and Jason Eisner, “Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Hong Kong, 2019, p. 6439–6444. doi:10.18653/v1/D19-1679
      [BibTeX] [Link]

      @InProceedings{renduchintala-et-al-2019-emnlp,
      aclid = "D19-1679",
      doi = "10.18653/v1/D19-1679",
      author = "Adithya Renduchintala and Philipp Koehn and Jason
      Eisner",
      title = "Spelling-Aware Construction of Macaronic Texts for
      Teaching Foreign-Language Vocabulary",
      booktitle = "Proceedings of the 2019 Conference on Empirical
      Methods in Natural Language Processing and 9th
      International Joint Conference on Natural Language
      Processing",
      pages = "6439--6444",
      year = "2019",
      month = nov,
      address = "Hong Kong",
      URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2019-emnlp",
      }

1142. E. Leas, A. Nobles, Theodore L. Caputi, Mark Dredze, Davey M. Smith, and J. Ayers, “Trends in Internet Searches for Cannabidiol (CBD) in the United States,” in JAMA Network Open, 2019.
      [BibTeX] [Link]

      @inproceedings{204848948,
      title = {Trends in Internet Searches for Cannabidiol (CBD) in the United States},
      author = {{E. Leas} and {A. Nobles} and {Theodore L. Caputi} and {Mark Dredze} and {Davey M. Smith} and {J. Ayers}},
      year = 2019,
      month = {10},
      booktitle = {JAMA Network Open},
      url = {https://www.semanticscholar.org/paper/30672fad20fa70024c7311140b7e702b8201974c},
      }

1143. Saurabh Kataria, P. S. Nidadavolu, J. Villalba, Nanxin Chen, Paola García, and N. Dehak, “Feature Enhancement with Deep Feature Losses for Speaker Verification,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{204976531,
      title = {Feature Enhancement with Deep Feature Losses for Speaker Verification},
      author = {{Saurabh Kataria} and {P. S. Nidadavolu} and {J. Villalba} and {Nanxin Chen} and {Paola García} and {N. Dehak}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/343fa6aea1bf71751f632be85fde936c66d21356},
      }

1144. P. S. Nidadavolu, Saurabh Kataria, J. Villalba, and N. Dehak, “Low-Resource Domain Adaptation for Speaker Recognition Using Cycle-Gans,” in Automatic Speech Recognition & Understanding, 2019.
      [BibTeX] [Link]

      @inproceedings{204976547,
      title = {Low-Resource Domain Adaptation for Speaker Recognition Using Cycle-Gans},
      author = {{P. S. Nidadavolu} and {Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
      year = 2019,
      month = {10},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/373acc04096d80a03dba238f73ce96930a3abb7b},
      }

1145. Li Liu, M. Pietikäinen, Jie Chen, Guoying Zhao, Xiaogang Wang, and R. Chellappa, “Guest Editors’ Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
      [BibTeX] [Link]

      @inproceedings{202550367,
      title = {Guest Editors' Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision},
      author = {{Li Liu} and {M. Pietikäinen} and {Jie Chen} and {Guoying Zhao} and {Xiaogang Wang} and {R. Chellappa}},
      year = 2019,
      month = {10},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/3c0f6d2b76c9d68da37e319cdae9802298ca7c44},
      }

1146. K. Henry, D. Hager, T. Osborn, A. Wu, and S. Saria, “Comparison of Automated Sepsis Identification Methods and Electronic Health Record–based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions,” in Critical Care Explorations, 2019.
      [BibTeX] [Link]

      @inproceedings{208462059,
      title = {Comparison of Automated Sepsis Identification Methods and Electronic Health Record–based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions},
      author = {{K. Henry} and {D. Hager} and {T. Osborn} and {A. Wu} and {S. Saria}},
      year = 2019,
      month = {10},
      booktitle = {Critical Care Explorations},
      url = {https://www.semanticscholar.org/paper/16d96c9ae82aff4ee357907311a9b4dd1cbf068d},
      }

1147. H. Inaguma, Kevin Duh, Tatsuya Kawahara, and Shinji Watanabe, “Multilingual End-to-End Speech Translation,” in Automatic Speech Recognition & Understanding, 2019.
      [BibTeX] [Link]

      @inproceedings{203610481,
      title = {Multilingual End-to-End Speech Translation},
      author = {{H. Inaguma} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
      year = 2019,
      month = {10},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/8b231737e0048a400527d89aa56c712e8b9bc690},
      }

1148. Andriy Mulyar, Elliot Schumacher, Masoud Rouhizadeh, and Mark Dredze, “Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{204961310,
      title = {Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models},
      author = {{Andriy Mulyar} and {Elliot Schumacher} and {Masoud Rouhizadeh} and {Mark Dredze}},
      year = 2019,
      month = {10},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c975d19e3861621b287a05bba31f6e1e3f0c4285},
      }

1149. Marvin Lavechin, Marie-Philippe Gill, Ruben Bousbib, H. Bredin, and Leibny Paola García-Perera, “End-to-End Domain-Adversarial Voice Activity Detection,” in Interspeech, 2019.
      [BibTeX] [Link]

      @inproceedings{204837862,
      title = {End-to-End Domain-Adversarial Voice Activity Detection},
      author = {{Marvin Lavechin} and {Marie-Philippe Gill} and {Ruben Bousbib} and {H. Bredin} and {Leibny Paola García-Perera}},
      year = 2019,
      month = {10},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/0694da1e2f5e1f62917a5c0f2a51d78981cd6f13},
      }

1150. Matthew Francis-Landau and Benjamin Van Durme, “Exact and/or Fast Nearest Neighbors,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{203837027,
      title = {Exact and/or Fast Nearest Neighbors},
      author = {{Matthew Francis-Landau} and {Benjamin Van Durme}},
      year = 2019,
      month = {10},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/198a2b07e71037e47b45b989a072418060ec4422},
      }

1151. Ahmed Z. Alsinan, M. Vives, Vishal M. Patel, and I. Hacihaliloglu, “Spine Surface Segmentation from Ultrasound Using Multi-feature Guided CNN.” 2019.
      [BibTeX] [Link]

      @inproceedings{210957413,
      title = {Spine Surface Segmentation from Ultrasound Using Multi-feature Guided CNN},
      author = {{Ahmed Z. Alsinan} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
      year = 2019,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/dc2f6a0fe64a6046f0f4c6d2808671de4aff83f9},
      }

1152. P. S. Nidadavolu, Saurabh Kataria, J. Villalba, Leibny Paola García-Perera, and N. Dehak, “Unsupervised Feature Enhancement for Speaker Verification,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{204907138,
      title = {Unsupervised Feature Enhancement for Speaker Verification},
      author = {{P. S. Nidadavolu} and {Saurabh Kataria} and {J. Villalba} and {Leibny Paola García-Perera} and {N. Dehak}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/df49e860305c871f5078bf7aa0b8cef7dcda11e7},
      }

1153. Matthew Maciejewski, Gregory Sell, Yusuke Fujita, Leibny Paola García-Perera, Shinji Watanabe, and S. Khudanpur, “Analysis of Robustness of Deep Single-Channel Speech Separation Using Corpora Constructed From Multiple Domains,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2019.
      [BibTeX] [Link]

      @inproceedings{209460517,
      title = {Analysis of Robustness of Deep Single-Channel Speech Separation Using Corpora Constructed From Multiple Domains},
      author = {{Matthew Maciejewski} and {Gregory Sell} and {Yusuke Fujita} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {S. Khudanpur}},
      year = 2019,
      month = {10},
      booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
      url = {https://www.semanticscholar.org/paper/e929c9b53c66d52ae5ea56f0dc2764aef4cc67f6},
      }

1154. Elias Stengel-Eskin, A. White, Sheng Zhang, and Benjamin Van Durme, “Transductive Parsing for Universal Decompositional Semantics,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{204824093,
      title = {Transductive Parsing for Universal Decompositional Semantics},
      author = {{Elias Stengel-Eskin} and {A. White} and {Sheng Zhang} and {Benjamin Van Durme}},
      year = 2019,
      month = {10},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/1eba3538d3b5be4b1a2e30da2e6eed08ff61008a},
      }

1155. Arthita Ghosh and R. Chellappa, “Single-Shot 3D Mesh Estimation via Adversarial Domain Adaptation,” in SN Computer Science, 2019.
      [BibTeX] [Link]

      @inproceedings{204539408,
      title = {Single-Shot 3D Mesh Estimation via Adversarial Domain Adaptation},
      author = {{Arthita Ghosh} and {R. Chellappa}},
      year = 2019,
      month = {10},
      booktitle = {SN Computer Science},
      url = {https://www.semanticscholar.org/paper/b80646f9b8d51090dfe383575680b00a268410a4},
      }

1156. Maneet Singh, M. Chawla, Richa Singh, Mayank Vatsa, and R. Chellappa, “Disguised Faces in the Wild 2019,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019.
      [BibTeX] [Link]

      @inproceedings{207901958,
      title = {Disguised Faces in the Wild 2019},
      author = {{Maneet Singh} and {M. Chawla} and {Richa Singh} and {Mayank Vatsa} and {R. Chellappa}},
      year = 2019,
      month = {10},
      booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
      url = {https://www.semanticscholar.org/paper/65e62791fc8df7d578991937533e41d5c4dc5263},
      }

1157. Latané Bullock, H. Bredin, and Leibny Paola García-Perera, “Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{204900778,
      title = {Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection},
      author = {{Latané Bullock} and {H. Bredin} and {Leibny Paola García-Perera}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/e483ff5e1f098398e619887bdf4dc8d3a003be78},
      }

1158. Zhishuai Zhang, Yuyin Zhou, Wei Shen, E. Fishman, and A. Yuille, “Lesion Detection by Efficiently Bridging 3D Context,” in MLMI@MICCAI, 2019.
      [BibTeX] [Link]

      @inproceedings{204539175,
      title = {Lesion Detection by Efficiently Bridging 3D Context},
      author = {{Zhishuai Zhang} and {Yuyin Zhou} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
      year = 2019,
      month = {10},
      booktitle = {MLMI@MICCAI},
      url = {https://www.semanticscholar.org/paper/2f659eeb4b0d97c8341042f46f6e6166160de811},
      }

1159. Chun Pong Lau, Hossein Souri, and R. Chellappa, “ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{203902626,
      title = {ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence},
      author = {{Chun Pong Lau} and {Hossein Souri} and {R. Chellappa}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/57fea03ab1b4e3d06fae5770a01875e7143118fa},
      }

1160. R. Pappagari, Piotr Żelasko, J. Villalba, Yishay Carmiel, and N. Dehak, “Hierarchical Transformers for Long Document Classification,” in Automatic Speech Recognition & Understanding, 2019.
      [BibTeX] [Link]

      @inproceedings{204852089,
      title = {Hierarchical Transformers for Long Document Classification},
      author = {{R. Pappagari} and {Piotr Żelasko} and {J. Villalba} and {Yishay Carmiel} and {N. Dehak}},
      year = 2019,
      month = {10},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/46b3ba0f3cb8340bc94f26e0fdf6dc4e38f68948},
      }

1161. Ruizhi Li, Gregory Sell, Xiaofei Wang, Shinji Watanabe, and H. Hermansky, “A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{204838317,
      title = {A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition},
      author = {{Ruizhi Li} and {Gregory Sell} and {Xiaofei Wang} and {Shinji Watanabe} and {H. Hermansky}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/de5057c1da9391269e926d4661d4558072db9f18},
      }

1162. Prithviraj Dhar, Ankan Bansal, C. Castillo, Joshua Gleason, P. Phillips, and R. Chellappa, “How are attributes expressed in face DCNNs?,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2019.
      [BibTeX] [Link]

      @inproceedings{204509539,
      title = {How are attributes expressed in face DCNNs?},
      author = {{Prithviraj Dhar} and {Ankan Bansal} and {C. Castillo} and {Joshua Gleason} and {P. Phillips} and {R. Chellappa}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/73587f97500203b94a9f312b0b86891f62326679},
      }

1163. Vishwanath A. Sindagi, R. Yasarla, and Vishal M. Patel, “Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method,” in IEEE International Conference on Computer Vision, 2019.
      [BibTeX] [Link]

      @inproceedings{204956017,
      title = {Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method},
      author = {{Vishwanath A. Sindagi} and {R. Yasarla} and {Vishal M. Patel}},
      year = 2019,
      month = {10},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/eba021cb47869f0b7d5b94b3ab3c3b1ec702f071},
      }

1164. Alycen Wiacek, Eduardo A. Gonzalez, N. Dehak, and M. L. Lediju Bell, “CohereNet: A deep learning approach to coherence-based beamforming,” in IUS, 2019.
      [BibTeX] [Link]

      @inproceedings{209320431,
      title = {CohereNet: A deep learning approach to coherence-based beamforming},
      author = {{Alycen Wiacek} and {Eduardo A. Gonzalez} and {N. Dehak} and {M. L. Lediju Bell}},
      year = 2019,
      month = {10},
      booktitle = {IUS},
      url = {https://www.semanticscholar.org/paper/02eac7f9a573c7b2852235733bf8d1920ce788ee},
      }

1165. Reham Badawy, Farhan Hameed, Lauren Bataille, Max A. Little, Kasper Claes, S. Saria, J. Cedarbaum, D. Stephenson, J. Neville, W. Maetzler, A. Espay, B. Bloem, T. Simuni, and D. Karlin, “Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.,” in Digital Biomarkers, 2019.
      [BibTeX] [Link]

      @inproceedings{208116583,
      title = {Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.},
      author = {{Reham Badawy} and {Farhan Hameed} and {Lauren Bataille} and {Max A. Little} and {Kasper Claes} and {S. Saria} and {J. Cedarbaum} and {D. Stephenson} and {J. Neville} and {W. Maetzler} and {A. Espay} and {B. Bloem} and {T. Simuni} and {D. Karlin}},
      year = 2019,
      month = {10},
      booktitle = {Digital Biomarkers},
      url = {https://www.semanticscholar.org/paper/74f408cb7953dadadc1461162a79f9c38506b79b},
      }

1166. Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, and A. Yuille, “Localizing Occluders with Compositional Convolutional Networks,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019.
      [BibTeX] [Link]

      @inproceedings{207781755,
      title = {Localizing Occluders with Compositional Convolutional Networks},
      author = {{Adam Kortylewski} and {Qing Liu} and {Huiyu Wang} and {Zhishuai Zhang} and {A. Yuille}},
      year = 2019,
      month = {10},
      booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
      url = {https://www.semanticscholar.org/paper/213db9136dfe6532146bbbc1aac3f1383eb21d0c},
      }

1167. M. Post, S. Ding, M. Martindale, and W. Wu, “An Exploration of Placeholding in Neural Machine Translation,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 182–192.
      [BibTeX] [Link]

      @inproceedings{post-etal-2019-exploration,
      title = "An Exploration of Placeholding in Neural Machine Translation",
      author = "Post, Matt and
      Ding, Shuoyang and
      Martindale, Marianna and
      Wu, Winston",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6618",
      pages = "182--192",
      }

1168. P. Koehn, F. Guzmán, V. Chaudhary, and J. Pino, “Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions,” in Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), Florence, Italy, 2019, p. 54–72. doi:10.18653/v1/W19-5404
      [BibTeX] [Abstract] [Link]
      

      Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2{\%} and 10{\%} of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.

      @inproceedings{koehn-etal-2019-findings,
      title = "Findings of the {WMT} 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions",
      author = "Koehn, Philipp and
      Guzm{\'a}n, Francisco and
      Chaudhary, Vishrav and
      Pino, Juan",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5404",
      doi = "10.18653/v1/W19-5404",
      pages = "54--72",
      abstract = "Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2{\%} and 10{\%} of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.",
      }

1169. T. Lippincott, P. Shapiro, K. Duh, and P. McNamee, “JHU System Description for the MADAR Arabic Dialect Identification Shared Task,” in Proceedings of the Fourth Arabic Natural Language Processing Workshop, Florence, Italy, 2019, p. 264–268. doi:10.18653/v1/W19-4634
      [BibTeX] [Abstract] [Link]
      

      Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.

      @inproceedings{lippincott-etal-2019-jhu,
      title = "{JHU} System Description for the {MADAR} {A}rabic Dialect Identification Shared Task",
      author = "Lippincott, Tom and
      Shapiro, Pamela and
      Duh, Kevin and
      McNamee, Paul",
      editor = "El-Hajj, Wassim and
      Belguith, Lamia Hadrich and
      Bougares, Fethi and
      Magdy, Walid and
      Zitouni, Imed and
      Tomeh, Nadi and
      El-Haj, Mahmoud and
      Zaghouani, Wajdi",
      booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-4634",
      doi = "10.18653/v1/W19-4634",
      pages = "264--268",
      abstract = "Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.",
      }

1170. M. Martindale, M. Carpuat, K. Duh, and P. McNamee, “Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 233–243.
      [BibTeX] [Link]

      @inproceedings{martindale-etal-2019-identifying,
      title = "Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation",
      author = "Martindale, Marianna and
      Carpuat, Marine and
      Duh, Kevin and
      McNamee, Paul",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6623",
      pages = "233--243",
      }

1171. L. Barrault, O. Bojar, M. R. Costa-jussà, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, P. Koehn, S. Malmasi, C. Monz, M. Müller, S. Pal, M. Post, and M. Zampieri, “Findings of the 2019 Conference on Machine Translation (WMT19),” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Florence, Italy, 2019, p. 1–61. doi:10.18653/v1/W19-5301
      [BibTeX] [Abstract] [Link]
      

      This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

      @inproceedings{barrault-etal-2019-findings,
      title = "Findings of the 2019 Conference on Machine Translation ({WMT}19)",
      author = {Barrault, Lo{\"\i}c and
      Bojar, Ond{\v{r}}ej and
      Costa-juss{\`a}, Marta R. and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Koehn, Philipp and
      Malmasi, Shervin and
      Monz, Christof and
      M{\"u}ller, Mathias and
      Pal, Santanu and
      Post, Matt and
      Zampieri, Marcos},
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5301",
      doi = "10.18653/v1/W19-5301",
      pages = "1--61",
      abstract = "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.",
      }

1172. M. Yarmohammadi, X. Ma, S. Hisamoto, M. Rahman, Y. Wang, H. Xu, D. Povey, P. Koehn, and K. Duh, “Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 12–20.
      [BibTeX] [Link]

      @inproceedings{yarmohammadi-etal-2019-robust,
      title = "Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings",
      author = "Yarmohammadi, Mahsa and
      Ma, Xutai and
      Hisamoto, Sorami and
      Rahman, Muhammad and
      Wang, Yiming and
      Xu, Hainan and
      Povey, Daniel and
      Koehn, Philipp and
      Duh, Kevin",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6602",
      pages = "12--20",
      }

1173. S. Ding, H. Xu, and P. Koehn, “Saliency-driven Word Alignment Interpretation for Neural Machine Translation,” in Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers), Florence, Italy, 2019, p. 1–12. doi:10.18653/v1/W19-5201
      [BibTeX] [Abstract] [Link]
      

      Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.

      @inproceedings{ding-etal-2019-saliency,
      title = "Saliency-driven Word Alignment Interpretation for Neural Machine Translation",
      author = "Ding, Shuoyang and
      Xu, Hainan and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5201",
      doi = "10.18653/v1/W19-5201",
      pages = "1--12",
      abstract = "Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.",
      }

1174. K. Marchisio, Y. K. Lal, and P. Koehn, “Johns Hopkins University Submission for WMT News Translation Task,” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Florence, Italy, 2019, p. 287–293. doi:10.18653/v1/W19-5329
      [BibTeX] [Abstract] [Link]
      

      We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques {–} sampling, filtering, iterative backtranslation, and continued training {–} previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.

      @inproceedings{marchisio-etal-2019-johns,
      title = "{J}ohns {H}opkins {U}niversity Submission for {WMT} News Translation Task",
      author = "Marchisio, Kelly and
      Lal, Yash Kumar and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5329",
      doi = "10.18653/v1/W19-5329",
      pages = "287--293",
      abstract = "We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques {--} sampling, filtering, iterative backtranslation, and continued training {--} previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.",
      }

1175. M. Post and K. Duh, “JHU 2019 Robustness Task System Description,” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Florence, Italy, 2019, p. 552–558. doi:10.18653/v1/W19-5366
      [BibTeX] [Abstract] [Link]
      

      We describe the JHU submissions to the French{–}English, Japanese{–}English, and English{–}Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR→EN) and a handful of hyperparameters settings (JA↔EN). As expected, our systems performed reasonably.

      @inproceedings{post-duh-2019-jhu,
      title = "{JHU} 2019 Robustness Task System Description",
      author = "Post, Matt and
      Duh, Kevin",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5366",
      doi = "10.18653/v1/W19-5366",
      pages = "552--558",
      abstract = "We describe the JHU submissions to the French{--}English, Japanese{--}English, and English{--}Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR→EN) and a handful of hyperparameters settings (JA↔EN). As expected, our systems performed reasonably.",
      }

1176. A. Renduchintala, P. Koehn, and J. Eisner, “Simple Construction of Mixed-Language Texts for Vocabulary Learning,” in Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, Florence, Italy, 2019, p. 369–379. doi:10.18653/v1/W19-4439
      [BibTeX] [Abstract] [Link]
      

      We present a machine foreign-language teacher that takes documents written in a student{‘}s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk {“}students{”} were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.

      @inproceedings{renduchintala-etal-2019-simple,
      title = "Simple Construction of Mixed-Language Texts for Vocabulary Learning",
      author = "Renduchintala, Adithya and
      Koehn, Philipp and
      Eisner, Jason",
      editor = "Yannakoudakis, Helen and
      Kochmar, Ekaterina and
      Leacock, Claudia and
      Madnani, Nitin and
      Pil{\'a}n, Ildik{\'o} and
      Zesch, Torsten",
      booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-4439",
      doi = "10.18653/v1/W19-4439",
      pages = "369--379",
      abstract = "We present a machine foreign-language teacher that takes documents written in a student{'}s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk {``}students{''} were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.",
      }

1177. V. Chaudhary, Y. Tang, F. Guzmán, H. Schwenk, and P. Koehn, “Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings,” in Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), Florence, Italy, 2019, p. 261–266. doi:10.18653/v1/W19-5435
      [BibTeX] [Abstract] [Link]
      

      In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.

      @inproceedings{chaudhary-etal-2019-low,
      title = "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
      author = "Chaudhary, Vishrav and
      Tang, Yuqing and
      Guzm{\'a}n, Francisco and
      Schwenk, Holger and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5435",
      doi = "10.18653/v1/W19-5435",
      pages = "261--266",
      abstract = "In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.",
      }

1178. X. Li, P. Michel, A. Anastasopoulos, Y. Belinkov, N. Durrani, O. Firat, P. Koehn, G. Neubig, J. Pino, and H. Sajjad, “Findings of the First Shared Task on Machine Translation Robustness,” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Florence, Italy, 2019, p. 91–102. doi:10.18653/v1/W19-5303
      [BibTeX] [Abstract] [Link]
      

      We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models{‘} robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson{‘}s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.

      @inproceedings{li-etal-2019-findings,
      title = "Findings of the First Shared Task on Machine Translation Robustness",
      author = "Li, Xian and
      Michel, Paul and
      Anastasopoulos, Antonios and
      Belinkov, Yonatan and
      Durrani, Nadir and
      Firat, Orhan and
      Koehn, Philipp and
      Neubig, Graham and
      Pino, Juan and
      Sajjad, Hassan",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Martins, Andr{\'e} and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-5303",
      doi = "10.18653/v1/W19-5303",
      pages = "91--102",
      abstract = "We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models{'} robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson{'}s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.",
      }

1179. S. Ding, A. Renduchintala, and K. Duh, “A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 204–213.
      [BibTeX] [Link]

      @inproceedings{ding-etal-2019-call,
      title = "A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation",
      author = "Ding, Shuoyang and
      Renduchintala, Adithya and
      Duh, Kevin",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6620",
      pages = "204--213",
      }

1180. K. Marchisio, J. Guo, C. Lai, and P. Koehn, “Controlling the Reading Level of Machine Translation Output,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 193–203.
      [BibTeX] [Link]

      @inproceedings{marchisio-etal-2019-controlling,
      title = "Controlling the Reading Level of Machine Translation Output",
      author = "Marchisio, Kelly and
      Guo, Jialiang and
      Lai, Cheng-I and
      Koehn, Philipp",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6619",
      pages = "193--203",
      }

1181. A. Renduchintala, P. Shapiro, K. Duh, and P. Koehn, “Character-Aware Decoder for Translation into Morphologically Rich Languages,” in Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland, 2019, p. 244–255.
      [BibTeX] [Link]

      @inproceedings{renduchintala-etal-2019-character,
      title = "Character-Aware Decoder for Translation into Morphologically Rich Languages",
      author = "Renduchintala, Adithya and
      Shapiro, Pamela and
      Duh, Kevin and
      Koehn, Philipp",
      editor = "Forcada, Mikel and
      Way, Andy and
      Haddow, Barry and
      Sennrich, Rico",
      booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
      month = aug,
      year = "2019",
      address = "Dublin, Ireland",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/W19-6624",
      pages = "244--255",
      }

1182. A. Benton, H. Khayrallah, B. Gujral, D. A. Reisinger, S. Zhang, and R. Arora, “Deep Generalized Canonical Correlation Analysis,” in Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), Florence, Italy, 2019, p. 1–6. doi:10.18653/v1/W19-4301
      [BibTeX] [Abstract] [Link]
      

      We present Deep Generalized Canonical Correlation Analysis (DGCCA) {–} a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic {&} articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.

      @inproceedings{benton-etal-2019-deep,
      title = "Deep Generalized Canonical Correlation Analysis",
      author = "Benton, Adrian and
      Khayrallah, Huda and
      Gujral, Biman and
      Reisinger, Dee Ann and
      Zhang, Sheng and
      Arora, Raman",
      editor = "Augenstein, Isabelle and
      Gella, Spandana and
      Ruder, Sebastian and
      Kann, Katharina and
      Can, Burcu and
      Welbl, Johannes and
      Conneau, Alexis and
      Ren, Xiang and
      Rei, Marek",
      booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
      month = aug,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-4301",
      doi = "10.18653/v1/W19-4301",
      pages = "1--6",
      abstract = "We present Deep Generalized Canonical Correlation Analysis (DGCCA) {--} a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic {\&} articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.",
      }

1183. A. Renduchintala, P. Koehn, and Jason Eisner, “Simple Construction of Mixed-Language Texts for Vocabulary Learning,” in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), Florence, 2019, p. 369–379. doi:10.18653/v1/W19-4439
      [BibTeX] [Link]

      @InProceedings{renduchintala-et-al-2019-bea,
      aclid = "W19-4439",
      doi = "10.18653/v1/W19-4439",
      author = "Adithya Renduchintala and Philipp Koehn and Jason
      Eisner",
      title = "Simple Construction of Mixed-Language Texts for
      Vocabulary Learning",
      booktitle = "Proceedings of the 14th Workshop on Innovative Use of
      NLP for Building Educational Applications (BEA)",
      pages = "369--379",
      year = "2019",
      month = aug,
      address = "Florence",
      URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2019-bea",
      }

1184. Z. Li, T. Chen, and B. Van Durme, “Learning to Rank for Plausible Plausibility,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 4818–4823. doi:10.18653/v1/P19-1475
      [BibTeX] [Abstract] [Link]
      

      Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.

      @inproceedings{li-etal-2019-learning,
      title = "Learning to Rank for Plausible Plausibility",
      author = "Li, Zhongyang and
      Chen, Tongfei and
      Van Durme, Benjamin",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1475",
      doi = "10.18653/v1/P19-1475",
      pages = "4818--4823",
      abstract = "Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.",
      }

1185. S. Zhang, X. Ma, K. Duh, and B. Van Durme, “AMR Parsing as Sequence-to-Graph Transduction,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 80–94. doi:10.18653/v1/P19-1009
      [BibTeX] [Abstract] [Link]
      

      We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3{\%} on LDC2017T10) and AMR 1.0 (70.2{\%} on LDC2014T12).

      @inproceedings{zhang-etal-2019-amr,
      title = "{AMR} Parsing as Sequence-to-Graph Transduction",
      author = "Zhang, Sheng and
      Ma, Xutai and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1009",
      doi = "10.18653/v1/P19-1009",
      pages = "80--94",
      abstract = "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3{\%} on LDC2017T10) and AMR 1.0 (70.2{\%} on LDC2014T12).",
      }

1186. G. Nicolai and D. Yarowsky, “Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 1765–1774. doi:10.18653/v1/P19-1172
      [BibTeX] [Abstract] [Link]
      

      A large percentage of computational tools are concentrated in a very small subset of the planet{‘}s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.

      @inproceedings{nicolai-yarowsky-2019-learning,
      title = "Learning Morphosyntactic Analyzers from the {B}ible via Iterative Annotation Projection across 26 Languages",
      author = "Nicolai, Garrett and
      Yarowsky, David",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1172",
      doi = "10.18653/v1/P19-1172",
      pages = "1765--1774",
      abstract = "A large percentage of computational tools are concentrated in a very small subset of the planet{'}s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.",
      }

1187. Y. Belinkov, A. Poliak, S. Shieber, B. Van Durme, and A. Rush, “Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 877–891. doi:10.18653/v1/P19-1084
      [BibTeX] [Abstract] [Link]
      

      Natural Language Inference (NLI) datasets often contain hypothesis-only biases{–-}artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.

      @inproceedings{belinkov-etal-2019-dont,
      title = "Don{'}t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference",
      author = "Belinkov, Yonatan and
      Poliak, Adam and
      Shieber, Stuart and
      Van Durme, Benjamin and
      Rush, Alexander",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1084",
      doi = "10.18653/v1/P19-1084",
      pages = "877--891",
      abstract = "Natural Language Inference (NLI) datasets often contain hypothesis-only biases{---}artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.",
      }

1188. Y. K. Lal, V. Kumar, M. Dhar, M. Shrivastava, and P. Koehn, “De-Mixing Sentiment from Code-Mixed Text,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, Italy, 2019, p. 371–377. doi:10.18653/v1/P19-2052
      [BibTeX] [Abstract] [Link]
      

      Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today{‘}s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs – the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results – 83.54{\%} accuracy and 0.827 F1 score – on a benchmark dataset.

      @inproceedings{lal-etal-2019-de,
      title = "De-Mixing Sentiment from Code-Mixed Text",
      author = "Lal, Yash Kumar and
      Kumar, Vaibhav and
      Dhar, Mrinal and
      Shrivastava, Manish and
      Koehn, Philipp",
      editor = "Alva-Manchego, Fernando and
      Choi, Eunsol and
      Khashabi, Daniel",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-2052",
      doi = "10.18653/v1/P19-2052",
      pages = "371--377",
      abstract = "Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today{'}s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54{\%} accuracy and 0.827 F1 score - on a benchmark dataset.",
      }

1189. S. Vashishtha, B. Van Durme, and A. S. White, “Fine-Grained Temporal Relation Extraction,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 2906–2919. doi:10.18653/v1/P19-1280
      [BibTeX] [Abstract] [Link]
      

      We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.

      @inproceedings{vashishtha-etal-2019-fine,
      title = "Fine-Grained Temporal Relation Extraction",
      author = "Vashishtha, Siddharth and
      Van Durme, Benjamin and
      White, Aaron Steven",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1280",
      doi = "10.18653/v1/P19-1280",
      pages = "2906--2919",
      abstract = "We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.",
      }

1190. A. Wang, J. Hula, P. Xia, R. Pappagari, T. R. McCoy, R. Patel, N. Kim, I. Tenney, Y. Huang, K. Yu, S. Jin, B. Chen, B. Van Durme, E. Grave, E. Pavlick, and S. R. Bowman, “Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, p. 4465–4476. doi:10.18653/v1/P19-1439
      [BibTeX] [Abstract] [Link]
      

      Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo{‘}s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.

      @inproceedings{wang-etal-2019-tell,
      title = "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
      author = "Wang, Alex and
      Hula, Jan and
      Xia, Patrick and
      Pappagari, Raghavendra and
      McCoy, R. Thomas and
      Patel, Roma and
      Kim, Najoung and
      Tenney, Ian and
      Huang, Yinghui and
      Yu, Katherin and
      Jin, Shuning and
      Chen, Berlin and
      Van Durme, Benjamin and
      Grave, Edouard and
      Pavlick, Ellie and
      Bowman, Samuel R.",
      editor = "Korhonen, Anna and
      Traum, David and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2019",
      address = "Florence, Italy",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P19-1439",
      doi = "10.18653/v1/P19-1439",
      pages = "4465--4476",
      abstract = "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo{'}s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.",
      }

1191. S. J. Mielke, R. Cotterell, K. Gorman, B. Roark, and J. Eisner, “What Kind of Language Is Hard to Language-Model?,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, 2019, p. 4975–4989. doi:10.18653/v1/P19-1491
      [BibTeX] [Link]

      @InProceedings{mielke-et-al-2019,
      aclid = "P19-1491",
      doi = "10.18653/v1/P19-1491",
      author = "Sabrina J. Mielke and Ryan Cotterell and Kyle Gorman
      and Brian Roark and Jason Eisner",
      title = "What Kind of Language Is Hard to Language-Model?",
      booktitle = "Proceedings of the 57th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "4975--4989",
      year = "2019",
      month = jul,
      address = "Florence",
      URL = "http://cs.jhu.edu/~jason/papers/#mielke-et-al-2019",
      }

1192. E. J. Hu, H. Khayrallah, R. Culkin, P. Xia, T. Chen, M. Post, and B. Van Durme, “Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, 2019, p. 839–850. doi:10.18653/v1/N19-1090
      [BibTeX] [Abstract] [Link]
      

      Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.

      @inproceedings{hu-etal-2019-improved,
      title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting",
      author = "Hu, J. Edward and
      Khayrallah, Huda and
      Culkin, Ryan and
      Xia, Patrick and
      Chen, Tongfei and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Burstein, Jill and
      Doran, Christy and
      Solorio, Thamar",
      booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N19-1090",
      doi = "10.18653/v1/N19-1090",
      pages = "839--850",
      abstract = "Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.",
      }

1193. S. Ding and P. Koehn, “Parallelizable Stack Long Short-Term Memory,” in Proceedings of the Third Workshop on Structured Prediction for NLP, Minneapolis, Minnesota, 2019, p. 1–6. doi:10.18653/v1/W19-1501
      [BibTeX] [Abstract] [Link]
      

      Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.

      @inproceedings{ding-koehn-2019-parallelizable,
      title = "Parallelizable Stack Long Short-Term Memory",
      author = "Ding, Shuoyang and
      Koehn, Philipp",
      editor = "Martins, Andre and
      Vlachos, Andreas and
      Kozareva, Zornitsa and
      Ravi, Sujith and
      Lampouras, Gerasimos and
      Niculae, Vlad and
      Kreutzer, Julia",
      booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-1501",
      doi = "10.18653/v1/W19-1501",
      pages = "1--6",
      abstract = "Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.",
      }

1194. B. Thompson, J. Gwinnup, H. Khayrallah, K. Duh, and P. Koehn, “Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, 2019, p. 2062–2068. doi:10.18653/v1/N19-1209
      [BibTeX] [Abstract] [Link]
      

      Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC){–-}a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable.

      @inproceedings{thompson-etal-2019-overcoming,
      title = "Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation",
      author = "Thompson, Brian and
      Gwinnup, Jeremy and
      Khayrallah, Huda and
      Duh, Kevin and
      Koehn, Philipp",
      editor = "Burstein, Jill and
      Doran, Christy and
      Solorio, Thamar",
      booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N19-1209",
      doi = "10.18653/v1/N19-1209",
      pages = "2062--2068",
      abstract = "Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC){---}a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable.",
      }

1195. P. Shapiro and K. Duh, “Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation,” in Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, Ann Arbor, Michigan, 2019, p. 214–222. doi:10.18653/v1/W19-1424
      [BibTeX] [Abstract] [Link]
      

      When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.

      @inproceedings{shapiro-duh-2019-comparing,
      title = "Comparing Pipelined and Integrated Approaches to Dialectal {A}rabic Neural Machine Translation",
      author = "Shapiro, Pamela and
      Duh, Kevin",
      editor = {Zampieri, Marcos and
      Nakov, Preslav and
      Malmasi, Shervin and
      Ljube{\v{s}}i{\'c}, Nikola and
      Tiedemann, J{\"o}rg and
      Ali, Ahmed},
      booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
      month = jun,
      year = "2019",
      address = "Ann Arbor, Michigan",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-1424",
      doi = "10.18653/v1/W19-1424",
      pages = "214--222",
      abstract = "When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.",
      }

1196. S. Amir, M. Dredze, and J. W. Ayers, “Mental Health Surveillance over Social Media with Digital Cohorts,” in Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, Minneapolis, Minnesota, 2019, p. 114–120. doi:10.18653/v1/W19-3013
      [BibTeX] [Abstract] [Link]
      

      The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.

      @inproceedings{amir-etal-2019-mental,
      title = "Mental Health Surveillance over Social Media with Digital Cohorts",
      author = "Amir, Silvio and
      Dredze, Mark and
      Ayers, John W.",
      editor = "Niederhoffer, Kate and
      Hollingshead, Kristy and
      Resnik, Philip and
      Resnik, Rebecca and
      Loveys, Kate",
      booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-3013",
      doi = "10.18653/v1/W19-3013",
      pages = "114--120",
      abstract = "The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.",
      }

1197. Y. Belinkov, A. Poliak, S. Shieber, B. Van Durme, and A. Rush, “On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference,” in Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), Minneapolis, Minnesota, 2019, p. 256–262. doi:10.18653/v1/S19-1028
      [BibTeX] [Abstract] [Link]
      

      Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.

      @inproceedings{belinkov-etal-2019-adversarial,
      title = "On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference",
      author = "Belinkov, Yonatan and
      Poliak, Adam and
      Shieber, Stuart and
      Van Durme, Benjamin and
      Rush, Alexander",
      editor = "Mihalcea, Rada and
      Shutova, Ekaterina and
      Ku, Lun-Wei and
      Evang, Kilian and
      Poria, Soujanya",
      booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S19-1028",
      doi = "10.18653/v1/S19-1028",
      pages = "256--262",
      abstract = "Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.",
      }

1198. T. Lippincott, “Graph convolutional networks for exploring authorship hypotheses,” in Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Minneapolis, USA, 2019, p. 76–81. doi:10.18653/v1/W19-2510
      [BibTeX] [Abstract] [Link]
      

      This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.

      @inproceedings{lippincott-2019-graph,
      title = "Graph convolutional networks for exploring authorship hypotheses",
      author = "Lippincott, Tom",
      editor = "Alex, Beatrice and
      Degaetano-Ortlieb, Stefania and
      Kazantseva, Anna and
      Reiter, Nils and
      Szpakowicz, Stan",
      booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
      month = jun,
      year = "2019",
      address = "Minneapolis, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W19-2510",
      doi = "10.18653/v1/W19-2510",
      pages = "76--81",
      abstract = "This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.",
      }

1199. N. Kim, R. Patel, A. Poliak, P. Xia, A. Wang, T. McCoy, I. Tenney, A. Ross, T. Linzen, B. Van Durme, S. R. Bowman, and E. Pavlick, “Probing What Different NLP Tasks Teach Machines about Function Word Comprehension,” in Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), Minneapolis, Minnesota, 2019, p. 235–249. doi:10.18653/v1/S19-1026
      [BibTeX] [Abstract] [Link]
      

      We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG{–-}our most syntactic objective{–-}performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.

      @inproceedings{kim-etal-2019-probing,
      title = "Probing What Different {NLP} Tasks Teach Machines about Function Word Comprehension",
      author = "Kim, Najoung and
      Patel, Roma and
      Poliak, Adam and
      Xia, Patrick and
      Wang, Alex and
      McCoy, Tom and
      Tenney, Ian and
      Ross, Alexis and
      Linzen, Tal and
      Van Durme, Benjamin and
      Bowman, Samuel R. and
      Pavlick, Ellie",
      editor = "Mihalcea, Rada and
      Shutova, Ekaterina and
      Ku, Lun-Wei and
      Evang, Kilian and
      Poria, Soujanya",
      booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S19-1026",
      doi = "10.18653/v1/S19-1026",
      pages = "235--249",
      abstract = "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG{---}our most syntactic objective{---}performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.",
      }

1200. X. Zhang, P. Shapiro, G. Kumar, P. McNamee, M. Carpuat, and K. Duh, “Curriculum Learning for Domain Adaptation in Neural Machine Translation,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, 2019, p. 1903–1915. doi:10.18653/v1/N19-1189
      [BibTeX] [Abstract] [Link]
      

      We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.

      @inproceedings{zhang-etal-2019-curriculum,
      title = "Curriculum Learning for Domain Adaptation in Neural Machine Translation",
      author = "Zhang, Xuan and
      Shapiro, Pamela and
      Kumar, Gaurav and
      McNamee, Paul and
      Carpuat, Marine and
      Duh, Kevin",
      editor = "Burstein, Jill and
      Doran, Christy and
      Solorio, Thamar",
      booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N19-1189",
      doi = "10.18653/v1/N19-1189",
      pages = "1903--1915",
      abstract = "We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.",
      }

1201. O. Adams, M. Wiesner, S. Watanabe, and D. Yarowsky, “Massively Multilingual Adversarial Speech Recognition,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, 2019, p. 96–108. doi:10.18653/v1/N19-1009
      [BibTeX] [Abstract] [Link]
      

      We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.

      @inproceedings{adams-etal-2019-massively,
      title = "Massively Multilingual Adversarial Speech Recognition",
      author = "Adams, Oliver and
      Wiesner, Matthew and
      Watanabe, Shinji and
      Yarowsky, David",
      editor = "Burstein, Jill and
      Doran, Christy and
      Solorio, Thamar",
      booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
      month = jun,
      year = "2019",
      address = "Minneapolis, Minnesota",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N19-1009",
      doi = "10.18653/v1/N19-1009",
      pages = "96--108",
      abstract = "We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.",
      }

1202. H. Mei, G. Qin, and J. Eisner, “Imputing Missing Events in Continuous-Time Event Streams,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, 2019.
      [BibTeX] [Link]

      @InProceedings{mei-et-al-2019,
      author = "Hongyuan Mei and Guanghui Qin and Jason Eisner",
      title = "Imputing Missing Events in Continuous-Time Event
      Streams",
      booktitle = "Proceedings of the 36th International Conference on
      Machine Learning",
      year = "2019",
      month = jun,
      address = "Long Beach, California",
      URL = "http://cs.jhu.edu/~jason/papers/#mei-et-al-2019",
      }

1203. C. Lin, H. Zhu, M. Gormley, and J. Eisner, “Neural Finite-State Transducers: Beyond Rational Relations,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, 2019, p. 272–283. doi:10.18653/v1/N19-1024
      [BibTeX] [Link]

      @InProceedings{lin-et-al-2019,
      aclid = "N19-1024",
      doi = "10.18653/v1/N19-1024",
      author = "Chu-Cheng Lin and Hao Zhu and Matthew Gormley and
      Jason Eisner",
      title = "Neural Finite-State Transducers: Beyond Rational
      Relations",
      booktitle = "Proceedings of the 2019 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "272--283",
      year = "2019",
      month = jun,
      address = "Minneapolis",
      URL = "http://cs.jhu.edu/~jason/papers/#lin-et-al-2019",
      }

1204. E. Vylomova, R. Cotterell, T. Baldwin, T. Cohn, and J. Eisner, “Contextualization of Morphological Inflection,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, 2019, p. 2018–2024. doi:10.18653/v1/N19-1203
      [BibTeX] [Link]

      @InProceedings{vylomova-et-al-2019,
      aclid = "N19-1203",
      doi = "10.18653/v1/N19-1203",
      author = "Ekaterina Vylomova and Ryan Cotterell and Tim Baldwin
      and Trevor Cohn and Jason Eisner",
      title = "Contextualization of Morphological Inflection",
      booktitle = "Proceedings of the 2019 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "2018--2024",
      year = "2019",
      month = jun,
      address = "Minneapolis",
      URL = "http://cs.jhu.edu/~jason/papers/#vylomova-et-al-2019",
      }

1205. H. Inaguma, S. Kiyono, N. E. Y. Soplin, J. Suzuki, K. Duh, and S. Watanabe, “ESPnet How2 Speech Translation System for IWSLT 2019: Pre-training, Knowledge Distillation, and Going Deeper,” in Proceedings of the 16th International Conference on Spoken Language Translation, Hong Kong, 2019.
      [BibTeX] [Abstract] [Link]
      

      This paper describes the ESPnet submissions to the How2 Speech Translation task at IWSLT2019. In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST). We first compare RNN-based models and Transformer, and then confirm Transformer models significantly and consistently outperform RNN models in all tasks and corpora. Next, we investigate pre-training of E2E-ST models with the ASR and MT tasks. On top of the pre-training, we further explore knowledge distillation from the NMT model and the deeper speech encoder, and confirm drastic improvements over the baseline model. All of our codes are publicly available in ESPnet.

      @inproceedings{inaguma-etal-2019-espnet,
      title = "{ESP}net How2 Speech Translation System for {IWSLT} 2019: Pre-training, Knowledge Distillation, and Going Deeper",
      author = "Inaguma, Hirofumi and
      Kiyono, Shun and
      Soplin, Nelson Enrique Yalta and
      Suzuki, Jun and
      Duh, Kevin and
      Watanabe, Shinji",
      editor = {Niehues, Jan and
      Cattoni, Rolando and
      St{\"u}ker, Sebastian and
      Negri, Matteo and
      Turchi, Marco and
      Ha, Thanh-Le and
      Salesky, Elizabeth and
      Sanabria, Ramon and
      Barrault, Loic and
      Specia, Lucia and
      Federico, Marcello},
      booktitle = "Proceedings of the 16th International Conference on Spoken Language Translation",
      month = nov # " 2-3",
      year = "2019",
      address = "Hong Kong",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2019.iwslt-1.4",
      abstract = "This paper describes the ESPnet submissions to the How2 Speech Translation task at IWSLT2019. In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST). We first compare RNN-based models and Transformer, and then confirm Transformer models significantly and consistently outperform RNN models in all tasks and corpora. Next, we investigate pre-training of E2E-ST models with the ASR and MT tasks. On top of the pre-training, we further explore knowledge distillation from the NMT model and the deeper speech encoder, and confirm drastic improvements over the baseline model. All of our codes are publicly available in ESPnet.",
      }

1206. R. Yasarla, Federico Perazzi, and Vishal M. Patel, “Deblurring Face Images Using Uncertainty Guided Multi-Stream Semantic Networks,” in IEEE Transactions on Image Processing, 2019.
      [BibTeX] [Link]

      @inproceedings{198986118,
      title = {Deblurring Face Images Using Uncertainty Guided Multi-Stream Semantic Networks},
      author = {{R. Yasarla} and {Federico Perazzi} and {Vishal M. Patel}},
      year = 2019,
      month = {7},
      booktitle = {IEEE Transactions on Image Processing},
      url = {https://www.semanticscholar.org/paper/718bf7785ee8d9eac6e459fb84c2756f4d013778},
      }

1207. Yuyin Zhou, D. Dreizin, Yingwei Li, Zhishuai Zhang, Yan Wang, and A. Yuille, “Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures,” in MLMI@MICCAI, 2019.
      [BibTeX] [Link]

      @inproceedings{195345687,
      title = {Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures},
      author = {{Yuyin Zhou} and {D. Dreizin} and {Yingwei Li} and {Zhishuai Zhang} and {Yan Wang} and {A. Yuille}},
      year = 2019,
      month = {6},
      booktitle = {MLMI@MICCAI},
      url = {https://www.semanticscholar.org/paper/1dcbb96e92357dd5a4315f770ac92954744239e5},
      }

1208. Gregory Hager, Ann W. Drobnis, Fei Fang, R. Ghani, A. Greenwald, Terah Lyons, D. Parkes, J. Schultz, S. Saria, Stephen F. Smith, and Milind Tambe, “Artificial Intelligence for Social Good,” in arXiv.org, 2019.
      [BibTeX] [Link]

      @inproceedings{58006991,
      title = {Artificial Intelligence for Social Good},
      author = {{Gregory Hager} and {Ann W. Drobnis} and {Fei Fang} and {R. Ghani} and {A. Greenwald} and {Terah Lyons} and {D. Parkes} and {J. Schultz} and {S. Saria} and {Stephen F. Smith} and {Milind Tambe}},
      year = 2019,
      month = {1},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c26fc9a1ccc05d8e1a025a8866a14f29ef595b8c},
      }

1209. Poojan Oza and Vishal M. Patel, “One-Class Convolutional Neural Network,” in IEEE Signal Processing Letters, 2019.
      [BibTeX] [Link]

      @inproceedings{57753677,
      title = {One-Class Convolutional Neural Network},
      author = {{Poojan Oza} and {Vishal M. Patel}},
      year = 2019,
      month = {1},
      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/00695a31a80221c7125e49885a4767896ec2c4f7},
      }

1210. Samik Sadhu and H. Hermansky, “Modulation Vectors as Robust Feature Representation for ASR in Domain Mismatched Conditions,” in Interspeech, 2019.
      [BibTeX] [Link]

      @inproceedings{203193656,
      title = {Modulation Vectors as Robust Feature Representation for ASR in Domain Mismatched Conditions},
      author = {{Samik Sadhu} and {H. Hermansky}},
      year = 2019,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/325021c80755bf709ede748845bf9529957ee1ff},
      }

1211. Yunmo Chen, Seth Ebner, Tongfei Chen, Patrick Xia, Elias Stengel-Eskin, Tzu-Ray Su, J. E. Hu, Nils Holzenberger, Ryan Culkin, Craig Harman, Max Thomas, Thomas Lippincott, A. White, Kyle Rawlins, and Benjamin Van Durme, “NIST TAC SM-KBP 2019 System Description: JHU/UR Framework,” in Text Analysis Conference, 2019.
      [BibTeX] [Link]

      @inproceedings{221461628,
      title = {NIST TAC SM-KBP 2019 System Description: JHU/UR Framework},
      author = {{Yunmo Chen} and {Seth Ebner} and {Tongfei Chen} and {Patrick Xia} and {Elias Stengel-Eskin} and {Tzu-Ray Su} and {J. E. Hu} and {Nils Holzenberger} and {Ryan Culkin} and {Craig Harman} and {Max Thomas} and {Thomas Lippincott} and {A. White} and {Kyle Rawlins} and {Benjamin Van Durme}},
      year = 2019,
      booktitle = {Text Analysis Conference},
      url = {https://www.semanticscholar.org/paper/86304bbf271cbd48a6801a55e17837316a8250ec},
      }

1212. Nikita Ivkin, D. Rothchild, Enayat Ullah, V. Braverman, I. Stoica, and R. Arora, “Communication-efficient distributed SGD with Sketching,” in Neural Information Processing Systems, 2019.
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      @inproceedings{75135063,
      title = {Communication-efficient distributed SGD with Sketching},
      author = {{Nikita Ivkin} and {D. Rothchild} and {Enayat Ullah} and {V. Braverman} and {I. Stoica} and {R. Arora}},
      year = 2019,
      month = {3},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/73b72bf922fd1f5b97fd5b7aa32ce748cad168ac},
      }

1213. Vishwanath A. Sindagi and Vishal M. Patel, “Inverse Attention Guided Deep Crowd Counting Network,” in Advanced Video and Signal Based Surveillance, 2019.
      [BibTeX] [Link]

      @inproceedings{195776150,
      title = {Inverse Attention Guided Deep Crowd Counting Network},
      author = {{Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2019,
      month = {7},
      booktitle = {Advanced Video and Signal Based Surveillance},
      url = {https://www.semanticscholar.org/paper/6ae585d4ca4581a5ee218a8d55c22a14d6dfe901},
      }

1214. Faisal Mahmood, Wenhao Xu, N. Durr, Jeremiah W. Johnson, and A. Yuille, “Structured Prediction using cGANs with Fusion Discriminator,” in DGS@ICLR, 2019.
      [BibTeX] [Link]

      @inproceedings{68046158,
      title = {Structured Prediction using cGANs with Fusion Discriminator},
      author = {{Faisal Mahmood} and {Wenhao Xu} and {N. Durr} and {Jeremiah W. Johnson} and {A. Yuille}},
      year = 2019,
      month = {3},
      booktitle = {DGS@ICLR},
      url = {https://www.semanticscholar.org/paper/f8041091abc5951a177e1385ed157476148f1db2},
      }

1215. Yuyin Zhou, Yingwei Li, Zhishuai Zhang, Yan Wang, Angtian Wang, E. Fishman, A. Yuille, and Seyoun Park, “Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019.
      [BibTeX] [Link]

      @inproceedings{202541232,
      title = {Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation},
      author = {{Yuyin Zhou} and {Yingwei Li} and {Zhishuai Zhang} and {Yan Wang} and {Angtian Wang} and {E. Fishman} and {A. Yuille} and {Seyoun Park}},
      year = 2019,
      month = {9},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/0d6824b16966c7d1ef1be8c823b452ad509444b2},
      }

1216. Mousmita Sarma, Pegah Ghahremani, Daniel Povey, N. Goel, K. K. Sarma, and N. Dehak, “Improving Emotion Identification Using Phone Posteriors in Raw Speech Waveform Based DNN,” in Interspeech, 2019.
      [BibTeX] [Link]

      @inproceedings{202724212,
      title = {Improving Emotion Identification Using Phone Posteriors in Raw Speech Waveform Based DNN},
      author = {{Mousmita Sarma} and {Pegah Ghahremani} and {Daniel Povey} and {N. Goel} and {K. K. Sarma} and {N. Dehak}},
      year = 2019,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/d8d6ce03b270fbd5e31f9a8fff7af5b39ad5902e},
      }

1217. Qing Liu, Lingxi Xie, Huiyu Wang, and A. Yuille, “Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval,” in IEEE International Conference on Computer Vision, 2019.
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      @inproceedings{102350931,
      title = {Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval},
      author = {{Qing Liu} and {Lingxi Xie} and {Huiyu Wang} and {A. Yuille}},
      year = 2019,
      month = {4},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/d974d0bdba8974e5bafb5211efbb8303c7fdc540},
      }

1218. Amit Kumar and R. Chellappa, “Landmark Detection in Low Resolution Faces with Semi-Supervised Learning,” in arXiv.org, 2019.
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      @inproceedings{199001178,
      title = {Landmark Detection in Low Resolution Faces with Semi-Supervised Learning},
      author = {{Amit Kumar} and {R. Chellappa}},
      year = 2019,
      month = {7},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/f0928c32a9b483dc0a0e6b34fa356ba525622edb},
      }

1219. Poorya Mianjy and R. Arora, “On Dropout and Nuclear Norm Regularization,” in International Conference on Machine Learning, 2019.
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      @inproceedings{167217988,
      title = {On Dropout and Nuclear Norm Regularization},
      author = {{Poorya Mianjy} and {R. Arora}},
      year = 2019,
      month = {5},
      booktitle = {International Conference on Machine Learning},
      url = {https://www.semanticscholar.org/paper/c98c286aae6407b03b0ee2d1adb131ad554ae225},
      }

1220. Poojan Oza and Vishal M. Patel, “C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition,” in Computer Vision and Pattern Recognition, 2019.
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      @inproceedings{91184609,
      title = {C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition},
      author = {{Poojan Oza} and {Vishal M. Patel}},
      year = 2019,
      month = {4},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/967d532a66dab7edcb818b0f9dc59fe8da7dc171},
      }

1221. Jorge Andrés Gómez García, L. Moro-Velázquez, and Juan Ignacio Godino-Llorente, “On the design of automatic voice condition analysis systems. Part II: Review of speaker recognition techniques and study on the effects of different variability factors,” in Biomedical Signal Processing and Control, 2019.
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      @inproceedings{56487758,
      title = {On the design of automatic voice condition analysis systems. Part II: Review of speaker recognition techniques and study on the effects of different variability factors},
      author = {{Jorge Andrés Gómez García} and {L. Moro-Velázquez} and {Juan Ignacio Godino-Llorente}},
      year = 2019,
      month = {2},
      booktitle = {Biomedical Signal Processing and Control},
      url = {https://www.semanticscholar.org/paper/e561c015fc549944f673e55a2080f2f345e08c95},
      }

1222. Ruizhi Li, Gregory Sell, and H. Hermansky, “Performance Monitoring for End-to-End Speech Recognition,” in Interspeech, 2019.
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      @inproceedings{131773802,
      title = {Performance Monitoring for End-to-End Speech Recognition},
      author = {{Ruizhi Li} and {Gregory Sell} and {H. Hermansky}},
      year = 2019,
      month = {4},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/035af6e1dd6e138243f5d75b6a4cfad7a80ecd6f},
      }

1223. Prithviraj Dhar, C. Castillo, and R. Chellappa, “On Measuring the Iconicity of a Face,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2019.
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      @inproceedings{67876984,
      title = {On Measuring the Iconicity of a Face},
      author = {{Prithviraj Dhar} and {C. Castillo} and {R. Chellappa}},
      year = 2019,
      month = {1},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/a5f80b45ba1339f68798d7d1591d690ee249a60e},
      }

1224. P. S. Nidadavolu, J. Villalba, and N. Dehak, “Cycle-GANs for Domain Adaptation of Acoustic Features for Speaker Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
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      @inproceedings{145960082,
      title = {Cycle-GANs for Domain Adaptation of Acoustic Features for Speaker Recognition},
      author = {{P. S. Nidadavolu} and {J. Villalba} and {N. Dehak}},
      year = 2019,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/20719e4f29ed1ffc06b4281f2446798e1571ebd7},
      }

1225. R. Arora, T. V. Marinov, and M. Mohri, “Bandits with Feedback Graphs and Switching Costs,” in Neural Information Processing Systems, 2019.
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      @inproceedings{198967971,
      title = {Bandits with Feedback Graphs and Switching Costs},
      author = {{R. Arora} and {T. V. Marinov} and {M. Mohri}},
      year = 2019,
      month = {7},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/5146f2b387efea0bd36ef2c38e01293eb29fa811},
      }

1226. Yiming Wang, David Snyder, Hainan Xu, Vimal Manohar, P. S. Nidadavolu, Daniel Povey, and S. Khudanpur, “The JHU ASR System for VOiCES from a Distance Challenge 2019,” in Interspeech, 2019.
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      @inproceedings{199523672,
      title = {The JHU ASR System for VOiCES from a Distance Challenge 2019},
      author = {{Yiming Wang} and {David Snyder} and {Hainan Xu} and {Vimal Manohar} and {P. S. Nidadavolu} and {Daniel Povey} and {S. Khudanpur}},
      year = 2019,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/7597acccb162251283a03be367130a37e25b1c8d},
      }

1227. Xing Di, B. Riggan, Shuowen Hu, Nathan J. Short, and Vishal M. Patel, “Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis,” in International Conference on Biometrics, 2019.
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      @inproceedings{118685884,
      title = {Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis},
      author = {{Xing Di} and {B. Riggan} and {Shuowen Hu} and {Nathan J. Short} and {Vishal M. Patel}},
      year = 2019,
      month = {4},
      booktitle = {International Conference on Biometrics},
      url = {https://www.semanticscholar.org/paper/d017644fbbff3953095d0da64cdb9e9bc40770c6},
      }

1228. S. Wongvibulsin, S. Martin, S. Saria, S. Zeger, and S. Murphy, “An Individualized, Data-Driven Digital Approach for Precision Behavior Change,” in American Journal of Lifestyle Medicine, 2019.
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      @inproceedings{150153512,
      title = {An Individualized, Data-Driven Digital Approach for Precision Behavior Change},
      author = {{S. Wongvibulsin} and {S. Martin} and {S. Saria} and {S. Zeger} and {S. Murphy}},
      year = 2019,
      month = {4},
      booktitle = {American Journal of Lifestyle Medicine},
      url = {https://www.semanticscholar.org/paper/68b46b0d90900114a90020e3ee47bcdc207164ea},
      }

1229. Qihang Yu, Yingda Xia, Lingxi Xie, E. Fishman, and A. Yuille, “Thickened 2D Networks for Efficient 3D Medical Image Segmentation.,” in arXiv: Computer Vision and Pattern Recognition, 2019.
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      @inproceedings{210145596,
      title = {Thickened 2D Networks for Efficient 3D Medical Image Segmentation.},
      author = {{Qihang Yu} and {Yingda Xia} and {Lingxi Xie} and {E. Fishman} and {A. Yuille}},
      year = 2019,
      month = {4},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/590900d4df128b72547b930875a365dd9da2e394},
      }

1230. Sangwook Park, D. Han, and Mounya Elhilali, “A Study of a Cross-Language Perception Based on Cortical Analysis Using Biomimetic STRFs,” in Interspeech, 2019.
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      @inproceedings{202743401,
      title = {A Study of a Cross-Language Perception Based on Cortical Analysis Using Biomimetic STRFs},
      author = {{Sangwook Park} and {D. Han} and {Mounya Elhilali}},
      year = 2019,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/90e41f373a7b203267fa3f34896c07ce7a58af0d},
      }

1231. D. Dreizin, Yuyin Zhou, Yixiao Zhang, N. Tirada, and A. Yuille, “Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT,” in Journal of digital imaging, 2019.
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      @inproceedings{174812418,
      title = {Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT},
      author = {{D. Dreizin} and {Yuyin Zhou} and {Yixiao Zhang} and {N. Tirada} and {A. Yuille}},
      year = 2019,
      month = {6},
      booktitle = {Journal of digital imaging},
      url = {https://www.semanticscholar.org/paper/4db18c067e062fb9ed0a58081655b0255a93f668},
      }

1232. Shih-Chii Liu, J. Harris, Mounya Elhilali, and M. Slaney, “Editorial: Bio-inspired Audio Processing, Models and Systems,” in Frontiers in Neuroscience, 2019.
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      @inproceedings{202559412,
      title = {Editorial: Bio-inspired Audio Processing, Models and Systems},
      author = {{Shih-Chii Liu} and {J. Harris} and {Mounya Elhilali} and {M. Slaney}},
      year = 2019,
      month = {9},
      booktitle = {Frontiers in Neuroscience},
      url = {https://www.semanticscholar.org/paper/d89b3d1f270d921e057a549b733f07605c4b6feb},
      }

1233. Peter F. Schulam and S. Saria, “Can You Trust This Prediction? Auditing Pointwise Reliability After Learning,” in International Conference on Artificial Intelligence and Statistics, 2019.
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      @inproceedings{67855357,
      title = {Can You Trust This Prediction? Auditing Pointwise Reliability After Learning},
      author = {{Peter F. Schulam} and {S. Saria}},
      year = 2019,
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1258. Ahmed Z. Alsinan, Vishal M. Patel, and I. Hacihaliloglu, “Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN,” in International Journal of Computer Assisted Radiology and Surgery, 2019.
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1259. Boyu Lu, Jun-Cheng Chen, and R. Chellappa, “Unsupervised Domain-Specific Deblurring via Disentangled Representations,” in Computer Vision and Pattern Recognition, 2019.
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1260. Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, A. Yuille, and Li Fei-Fei, “Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation,” in Computer Vision and Pattern Recognition, 2019.
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1271. Chun-Chieh Chang, Ashish Arora, Leibny Paola García-Perera, David Etter, Daniel Povey, and S. Khudanpur, “Optical Character Recognition with Chinese and Korean Character Decomposition,” in 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019.
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1272. Jorge Andrés Gómez García, L. Moro-Velázquez, J. Mendes-Laureano, G. Castellanos-Domínguez, and Juan Ignacio Godino-Llorente, “Emulating the perceptual capabilities of a human evaluator to map the GRB scale for the assessment of voice disorders,” in Engineering applications of artificial intelligence, 2019.
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1273. Jonathan D. Jones, Gregory Hager, and S. Khudanpur, “Toward Computer Vision Systems That Understand Real-World Assembly Processes,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2019.
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1274. J. Villalba, D. Garcia-Romero, Nanxin Chen, Gregory Sell, Jonas Borgstrom, A. McCree, David Snyder, Saurabh Kataria, Leibny Paola García-Perera, Fred Richardson, P. Torres-Carrasquillo, and N. Dehak, “The JHU-MIT System Description for NIST SRE19 AV.” 2019.
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1276. Qingfu Wan, Weichao Qiu, and A. Yuille, “Patch-based 3D Human Pose Refinement,” in CVPR Workshops, 2019.
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1277. Yuyin Zhou, Qihang Yu, Yan Wang, Lingxi Xie, Wei Shen, E. Fishman, and A. Yuille, “2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans,” in Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, 2019.
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1278. Chen Change Loy, Xiaoming Liu, Tae-Kyun Kim, F. D. L. Torre, and R. Chellappa, “Editorial: Special Issue on Deep Learning for Face Analysis,” in International Journal of Computer Vision, 2019.
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1279. Goncalo Dias Pais, Srikumar Ramalingam, V. Govindu, J. Nascimento, R. Chellappa, and Pedro Miraldo, “3DRegNet: A Deep Neural Network for 3D Point Registration,” in Computer Vision and Pattern Recognition, 2019.
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1281. E. González-Sosa, R. Vera-Rodríguez, Julian Fierrez, F. Alonso-Fernandez, and Vishal M. Patel, “Exploring Body Texture From mmW Images for Person Recognition,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2019.
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1282. Ilya Kavalerov, Weilin Li, W. Czaja, and R. Chellappa, “Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images,” in arXiv.org, 2019.
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1285. Christian Cosgrove and A. Yuille, “Adversarial Examples for Edge Detection: They Exist, and They Transfer,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2019.
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1286. David Snyder, J. Villalba, Nanxin Chen, Daniel Povey, Gregory Sell, N. Dehak, and S. Khudanpur, “The JHU Speaker Recognition System for the VOiCES 2019 Challenge,” in Interspeech, 2019.
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1288. Pengcheng Xu, Prudhvi K. Gurram, Gene T. Whipps, and R. Chellappa, “Wasserstein Distance Based Domain Adaptation for Object Detection,” in arXiv.org, 2019.
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1289. Huda Khayrallah, Rebecca Knowles, Kevin Duh, and Matt Post, “An Interactive Teaching Tool for Introducing Novices to Machine Translation,” in Technical Symposium on Computer Science Education, 2019.
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1290. Wei-An Lin, Haofu Liao, Cheng Peng, Xiaohang Sun, Jingdan Zhang, Jiebo Luo, R. Chellappa, and S. Zhou, “DuDoNet: Dual Domain Network for CT Metal Artifact Reduction,” in Computer Vision and Pattern Recognition, 2019.
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1291. L. Chu, Seyoun Park, S. Kawamoto, D. Fouladi, S. Shayesteh, E. Zinreich, J. Graves, K. Horton, R. Hruban, A. Yuille, K. Kinzler, B. Vogelstein, and E. Fishman, “Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.,” in AJR. American journal of roentgenology, 2019.
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1292. Francisco Guzmán, Peng-Jen Chen, Myle Ott, J. Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato, “Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English,” in arXiv.org, 2019.
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1293. Gaspar Tognetti, Jonah P. Sengupta, P. Pouliquen, and A. Andreou, “Characterization of a pseudo-DRAM Crossbar Computational Memory Array in 55nm CMOS : (Invited Paper),” in Annual Conference on Information Sciences and Systems, 2019.
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1294. J. E. Hu, Rachel Rudinger, Matt Post, and Benjamin Van Durme, “ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation,” in AAAI Conference on Artificial Intelligence, 2019.
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1295. Yuyin Zhou, Zhe Li, S. Bai, Chong Wang, Xinlei Chen, Mei Han, E. Fishman, and A. Yuille, “Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation,” in IEEE International Conference on Computer Vision, 2019.
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1296. S. Saria and Adarsh Subbaswamy, “Tutorial: Safe and Reliable Machine Learning,” in arXiv.org, 2019.
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1297. Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, and A. Yuille, “Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2019.
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1298. Mounya Elhilali, “Chapter 12 Modulation Representations for Speech and Music.” 2019.
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1299. Fengze Liu, Yuyin Zhou, E. Fishman, and A. Yuille, “FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans,” in MLMI@MICCAI, 2019.
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1300. B. Althouse, D. Weinberger, S. Scarpino, V. Pitzer, J. Ayers, E. Wenger, I. C. Fung, Mark Dredze, and Hao Hu, “Google searches accurately forecast RSV hospitalizations,” in bioRxiv, 2019.
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1301. Runtao Liu, Chenxi Liu, Yutong Bai, and A. Yuille, “CLEVR-Ref+: Diagnosing Visual Reasoning With Referring Expressions,” in Computer Vision and Pattern Recognition, 2019.
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1304. Nanxin Chen, J. Villalba, and N. Dehak, “Tied Mixture of Factor Analyzers Layer to Combine Frame Level Representations in Neural Speaker Embeddings,” in Interspeech, 2019.
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1305. Jingxiao Zheng, Ruichi Yu, Jun-Cheng Chen, Boyu Lu, C. Castillo, and R. Chellappa, “Supplementary Material of Modeling the Uncertainty of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition.” 2019.
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1323. Mohammed Senoussaoui, P. Cardinal, N. Dehak, and Alessandro Lameiras Koerich, “Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods,” in arXiv.org, 2019.
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1346. Jinyi Yang, Lucas Ondel, Vimal Manohar, and H. Hermansky, “Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019.
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1348. Seyoun Park, L. Chu, E. Fishman, A. Yuille, B. Vogelstein, K. Kinzler, K. Horton, R. Hruban, E. Zinreich, D. Fouladi, S. Shayesteh, J. Graves, and S. Kawamoto, “Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation.,” in Diagnostic and Interventional Imaging, 2019.
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1349. Arthita Ghosh, Max Ehrlich, L. Davis, and R. Chellappa, “Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer,” in IEEE International Geoscience and Remote Sensing Symposium, 2019.
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1350. Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, and A. Yuille, “TDMPNet: Prototype Network with Recurrent Top-Down Modulation for Robust Object Classification Under Partial Occlusion,” in ECCV Workshops, 2019.
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1351. Jingxiao Zheng, Ruichi Yu, Jun-Cheng Chen, Boyu Lu, C. Castillo, and R. Chellappa, “Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition,” in IEEE International Conference on Computer Vision, 2019.
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1352. Adarsh Subbaswamy, Bryant Chen, and S. Saria, “A unifying causal framework for analyzing dataset shift-stable learning algorithms,” in Journal of Causal Inference, 2019.
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1353. Peter F. Schulam and S. Saria, “Auditing Pointwise Reliability Subsequent to Training,” in arXiv.org, 2019.
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1354. Yutong Bai, Qing Liu, Lingxi Xie, Yan Zheng, Weichao Qiu, and A. Yuille, “Real Image Domain Real Image Domain Training Testing Virtual Image Domain 3 D Model with Aggregated Annotations.” 2019.
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1355. Fengze Liu, Yingda Xia, D. Yang, A. Yuille, and Daguang Xu, “An Alarm System for Segmentation Algorithm Based on Shape Model,” in IEEE International Conference on Computer Vision, 2019.
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1356. L. Chu, Seyoun Park, S. Kawamoto, Yan Wang, Yuyin Zhou, W. Shen, Zhuotun Zhu, Yingda Xia, Lingxi Xie, Fengze Liu, Qihang Yu, D. Fouladi, S. Shayesteh, E. Zinreich, J. Graves, K. Horton, A. Yuille, R. Hruban, K. Kinzler, B. Vogelstein, and E. Fishman, “Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience.,” in Journal of the American College of Radiology, 2019.
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1357. Desh Raj, David Snyder, Daniel Povey, and S. Khudanpur, “Probing the Information Encoded in X-Vectors,” in Automatic Speech Recognition & Understanding, 2019.
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1358. Chenxu Luo and A. Yuille, “Grouped Spatial-Temporal Aggregation for Efficient Action Recognition,” in IEEE International Conference on Computer Vision, 2019.
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1359. Rajeev Ranjan, Vishal M. Patel, and R. Chellappa, “HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
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1360. Tao Chen, Mark Dredze, J. Weiner, and Hadi Kharrazi, “Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records,” in J. Am. Medical Informatics Assoc., 2019.
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1361. Kate D. Fischl, Adam B. Cellon, T. Stewart, T. Horiuchi, and A. Andreou, “Socio-Emotional Robot with Distributed Multi-Platform Neuromorphic Processing : (Invited Presentation),” in Annual Conference on Information Sciences and Systems, 2019.
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1362. Vishwanath A. Sindagi and Vishal M. Patel, “Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting,” in IEEE International Conference on Computer Vision, 2019.
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1363. A. Nobles, Mark Dredze, and J. Ayers, ““Repeal and replace”: increased demand for intrauterine devices following the 2016 presidential election,” in Contraception, 2019.
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1364. D. Garcia-Romero, David Snyder, Shinji Watanabe, Gregory Sell, A. McCree, Daniel Povey, and S. Khudanpur, “Speaker Recognition Benchmark Using the CHiME-5 Corpus,” in Interspeech, 2019.
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1365. Lifeng Huang, Chengying Gao, Yuyin Zhou, Cihang Xie, A. Yuille, C. Zou, and Ning Liu, “Universal Physical Camouflage Attacks on Object Detectors,” in Computer Vision and Pattern Recognition, 2019.
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1366. M. Naphade, Zheng Tang, Ming-Ching Chang, D. Anastasiu, Anuj Sharma, R. Chellappa, Shuo Wang, Pranamesh Chakraborty, Tingting Huang, Jenq-Neng Hwang, and Siwei Lyu, “The 2019 AI City Challenge,” in CVPR Workshops, 2019.
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1367. N. Higgins, DF Little, BD Yerkes, KM Nave, A. Kuruvilla-Mathew, Mounya Elhilali, and JS Snyder, “Neural correlates of perceptual switching while listening to bistable auditory streaming stimuli,” in NeuroImage, 2019.
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1368. Xiaolei Huang, Michael C. Smith, Amelia M. Jamison, David A. Broniatowski, Mark Dredze, S. Quinn, Justin Cai, and Michael J. Paul, “Can online self-reports assist in real-time identification of influenza vaccination uptake? A cross-sectional study of influenza vaccine-related tweets in the USA, 2013–2017,” in BMJ Open, 2019.
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1369. M. Kaufman, D. Dey, C. Crainiceanu, and Mark Dredze, “#MeToo and Google Inquiries Into Sexual Violence: A Hashtag Campaign Can Sustain Information Seeking,” in Journal of Interpersonal Violence, 2019.
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1370. Zhuotun Zhu, Chenxi Liu, Dong Yang, A. Yuille, and Daguang Xu, “V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation,” in International Conference on 3D Vision, 2019.
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1371. Elllington West, Ian McLane, Dan McLane, Dimitra Emmanouilidou, Mounya Elhilali, J. West, A. Ward, I. Busch-Vishniac, Jaishree McLane, and Brandon Dottin-Haley, “Introducing Feelix, a digital stethoscope incorporating active noise control and automatic detection of lung sound abnormalities,” in Journal of the Acoustical Society of America, 2019.
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1372. P. Galiatsatos, A. Follin, N. Uradu, F. Alghanim, Y. Daniel, S. Saria, Jennifer Townsend, Carol Sylvester, Arjun Chanmugam, and Edward S. Chen, “The Association Between Neighborhood Socioeconomic Disadvantage and Readmissions for Patients Hospitalized with Sepsis,” in C94. THE IMPACT OF SOCIAL DETERMINANTS IN PULMONARY AND CRITICAL CARE, 2019.
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1373. Iiris Sundin, Peter F. Schulam, E. Siivola, Aki Vehtari, S. Saria, and Samuel Kaski, “Active Learning for Decision-Making from Imbalanced Observational Data,” in International Conference on Machine Learning, 2019.
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1375. M. Adrian, J. Coifman, M. Pullmann, J. Blossom, C. Chandler, Glen A. Coppersmith, P. Thompson, and Aaron R. Lyon, “Implementation Determinants and Outcomes of a Technology-Enabled Service Targeting Suicide Risk in High Schools: Mixed Methods Study (Preprint).” 2019.
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1376. J. Villalba, Nanxin Chen, David Snyder, D. Garcia-Romero, A. McCree, Gregory Sell, Jonas Borgstrom, Fred Richardson, Suwon Shon, François Grondin, Réda Dehak, Leibny Paola García-Perera, P. Torres-Carrasquillo, and N. Dehak, “The JHU-MIT System Description for NIST SRE18.” 2019.
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1377. L. Moro-Velázquez, Jorge Andrés Gómez García, Juan Ignacio Godino-Llorente, J. Villalba, J. Rusz, S. Shattuck-Hufnagel, and N. Dehak, “A forced gaussians based methodology for the differential evaluation of Parkinson’s Disease by means of speech processing,” in Biomedical Signal Processing and Control, 2019.
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1378. D. Garcia-Romero, David Snyder, Gregory Sell, A. McCree, Daniel Povey, and S. Khudanpur, “x-Vector DNN Refinement with Full-Length Recordings for Speaker Recognition,” in Interspeech, 2019.
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1379. Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, and A. Yuille, “Compositional Convolutional Networks For Robust Object Classification under Occlusion,” in arXiv.org, 2019.
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1380. Hongjing Lu, Qing Liu, Nicholas Ichien, A. Yuille, and K. Holyoak, “Seeing the Meaning: Vision Meets Semantics in Solving Pictorial Analogy Problems,” in Annual Meeting of the Cognitive Science Society, 2019.
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1381. Lifeng Huang, Chengying Gao, Yuyin Zhou, C. Zou, Cihang Xie, A. Yuille, and Ning Liu, “Training set Constraint I + Pattern RPN Attack RPN C & R Attack Classification & Regression NMS Print on Garment Camera Setting in Attack Scene Transform.” 2019.
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1382. Francis Ferraro, Max Thomas, Matthew R. Gormley, Travis Wolfe, Craig Harman, and Benjamin Van Durme, “Data associated with the publication “Concretely Annotated Corpora”.” 2019.
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1383. Ashish Arora, Chun-Chieh Chang, Babak Rekabdar, Daniel Povey, David Etter, Desh Raj, Hossein Hadian, J. Trmal, Leibny Paola García-Perera, Shinji Watanabe, Vimal Manohar, Yiwen Shao, and S. Khudanpur, “Using ASR Methods for OCR,” in IEEE International Conference on Document Analysis and Recognition, 2019.
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1384. Jiamin Xie, Leibny Paola García-Perera, Daniel Povey, and S. Khudanpur, “Multi-PLDA Diarization on Children’s Speech,” in Interspeech, 2019.
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1385. Cheng Peng, Wei-An Lin, R. Chellappa, and S. K. Zhou, “Towards multi-sequence MR image recovery from undersampled k-space data,” in International Conference on Medical Imaging with Deep Learning, 2019.
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1386. Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, and R. Chellappa, “A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification,” in IEEE International Conference on Computer Vision, 2019.
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1387. Vishwanath A. Sindagi and Vishal M. Patel, “DAFE-FD: Density Aware Feature Enrichment for Face Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2019.
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1388. Tae Soo Kim, Michael Peven, Weichao Qiu, A. Yuille, and Gregory Hager, “Synthesizing Attributes with Unreal Engine for Fine-grained Activity Analysis,” in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2019.
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1389. Angel Mario Castro Martinez, Lukas Gerlach, G. P. Vayá, H. Hermansky, Jasper Ooster, and B. Meyer, “DNN-based performance measures for predicting error rates in automatic speech recognition and optimizing hearing aid parameters,” in Speech Communication, 2019.
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1390. R. Yasarla and Vishal M. Patel, “Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining,” in Computer Vision and Pattern Recognition, 2019.
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1391. J. Villalba, Nanxin Chen, David Snyder, D. Garcia-Romero, A. McCree, Gregory Sell, Jonas Borgstrom, Fred Richardson, Suwon Shon, François Grondin, Réda Dehak, Leibny Paola García-Perera, Daniel Povey, P. Torres-Carrasquillo, S. Khudanpur, and N. Dehak, “State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18,” in Interspeech, 2019.
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1392. S. J. Mielke and J. Eisner, “Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, 2019, p. 6843–6850. doi:10.1609/aaai.v33i01.33016843
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      pages = "6843--6850",
      year = "2019",
      month = jan,
      address = "Honolulu",
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1393. Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, A. Yuille, and Mohammad Rastegari, “ELASTIC: Improving CNNs With Dynamic Scaling Policies,” in Computer Vision and Pattern Recognition, 2018.
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1394. Siyuan Qiao, Zhe L. Lin, Jianming Zhang, and A. Yuille, “Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization,” in Computer Vision and Pattern Recognition, 2018.
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1395. Yiming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, and A. Yuille, “Towards Accurate Task Accomplishment with Low-Cost Robotic Arms.” 2018.
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1396. Mohammed E. Fathy, A. Alavi, and R. Chellappa, “Nonlinear Subspace Feature Enhancement for Image Set Classification,” in Asian Conference on Computer Vision, 2018.
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1397. Benjamin Skerritt-Davis and Mounya Elhilali, “A Model for Statistical Regularity Extraction from Dynamic Sounds,” in Acta Acustica united with Acustica, 2018.
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1398. Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, and A. Yuille, “Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning,” in Computer Vision and Pattern Recognition, 2018.
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1399. Yingwei Li, S. Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, and A. Yuille, “Learning Transferable Adversarial Examples via Ghost Networks,” in AAAI Conference on Artificial Intelligence, 2018.
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1400. Adarsh Subbaswamy, Peter F. Schulam, and S. Saria, “Learning Predictive Models That Transport,” in arXiv.org, 2018.
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1401. Vimal Manohar, Pegah Ghahremani, Daniel Povey, and S. Khudanpur, “A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models,” in Spoken Language Technology Workshop, 2018.
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1402. S. Saria, A. Butte, and A. Sheikh, “Better medicine through machine learning: What’s real, and what’s artificial?,” in PLoS Medicine, 2018.
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1403. Yiming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, and A. Yuille, “CRAVES: Controlling Robotic Arm With a Vision-Based Economic System,” in Computer Vision and Pattern Recognition, 2018.
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1404. Mahdi Abavisani, Hamid Reza Vaezi Joze, and Vishal M. Patel, “Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training,” in Computer Vision and Pattern Recognition, 2018.
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1405. He Zhang, B. Riggan, Shuowen Hu, Nathan J. Short, and Vishal M. Patel, “Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks,” in International Journal of Computer Vision, 2018.
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1406. Adarsh Subbaswamy, Peter F. Schulam, and S. Saria, “Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport,” in International Conference on Artificial Intelligence and Statistics, 2018.
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1407. Yansheng Li, Yongjun Zhang, Xin Huang, and A. Yuille, “Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images,” in Isprs Journal of Photogrammetry and Remote Sensing, 2018.
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1408. J. D. Arias-Londoño, Jorge Andrés Gómez García, L. Moro-Velázquez, and Juan Ignacio Godino-Llorente, “ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge,” in 2018 IEEE International Conference on Big Data (Big Data), 2018.
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1409. Matthew Wiesner, Adithya Renduchintala, Shinji Watanabe, Chunxi Liu, N. Dehak, and S. Khudanpur, “Pretraining by Backtranslation for End-to-End ASR in Low-Resource Settings,” in Interspeech, 2018.
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      author = {{Matthew Wiesner} and {Adithya Renduchintala} and {Shinji Watanabe} and {Chunxi Liu} and {N. Dehak} and {S. Khudanpur}},
      year = 2018,
      month = {12},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/bd8922f8cc8284553dc9e6db529af309298451fe},
      }

1410. Tianwei Ni, Lingxi Xie, Huangjie Zheng, E. Fishman, and A. Yuille, “Elastic Boundary Projection for 3D Medical Image Segmentation,” in Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{54443973,
      title = {Elastic Boundary Projection for 3D Medical Image Segmentation},
      author = {{Tianwei Ni} and {Lingxi Xie} and {Huangjie Zheng} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/afa1b2e96cea3bedf6777fc698e372e79022a116},
      }

1411. Chenglin Yang, Lingxi Xie, Chi Su, and A. Yuille, “Snapshot Distillation: Teacher-Student Optimization in One Generation,” in Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{54436113,
      title = {Snapshot Distillation: Teacher-Student Optimization in One Generation},
      author = {{Chenglin Yang} and {Lingxi Xie} and {Chi Su} and {A. Yuille}},
      year = 2018,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/a167d8a4ee261540c2b709dde2d94572c6ea3fc8},
      }

1412. Y. Balaji, S. Sankaranarayanan, and R. Chellappa, “MetaReg: Towards Domain Generalization using Meta-Regularization,” in Neural Information Processing Systems, 2018.
      [BibTeX] [Link]

      @inproceedings{53979606,
      title = {MetaReg: Towards Domain Generalization using Meta-Regularization},
      author = {{Y. Balaji} and {S. Sankaranarayanan} and {R. Chellappa}},
      year = 2018,
      month = {12},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/3dd8bf5cca76b1690a2642b73b509fb3a27e4f36},
      }

1413. Cihang Xie, Yuxin Wu, L. Maaten, A. Yuille, and Kaiming He, “Feature Denoising for Improving Adversarial Robustness,” in Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{54462665,
      title = {Feature Denoising for Improving Adversarial Robustness},
      author = {{Cihang Xie} and {Yuxin Wu} and {L. Maaten} and {A. Yuille} and {Kaiming He}},
      year = 2018,
      month = {12},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/41071dbbbcbb27af3fec70de045f19c28535f5b7},
      }

1414. N. Dehak, “Evaluation of Neurological Diseases by Means of Speech Processing and Multimodal Analysis,” in IEEE Signal Processing in Medicine and Biology Symposium, 2018.
      [BibTeX] [Link]

      @inproceedings{58669977,
      title = {Evaluation of Neurological Diseases by Means of Speech Processing and Multimodal Analysis},
      author = {{N. Dehak}},
      year = 2018,
      month = {12},
      booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
      url = {https://www.semanticscholar.org/paper/c59513c624e1f54bd78809efb8ccd5bea7dce50f},
      }

1415. Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, A. Yuille, and Mohammad Rastegari, “ELASTIC: Improving CNNs with Instance Specific Scaling Policies,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{55687967,
      title = {ELASTIC: Improving CNNs with Instance Specific Scaling Policies},
      author = {{Huiyu Wang} and {Aniruddha Kembhavi} and {Ali Farhadi} and {A. Yuille} and {Mohammad Rastegari}},
      year = 2018,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/5fe7add7bb041eb52c9983fbdd792bfad1af9992},
      }

1416. Hossein Hadian, Daniel Povey, H. Sameti, J. Trmal, and S. Khudanpur, “Improving LF-MMI Using Unconstrained Supervisions for ASR,” in Spoken Language Technology Workshop, 2018.
      [BibTeX] [Link]

      @inproceedings{61808960,
      title = {Improving LF-MMI Using Unconstrained Supervisions for ASR},
      author = {{Hossein Hadian} and {Daniel Povey} and {H. Sameti} and {J. Trmal} and {S. Khudanpur}},
      year = 2018,
      month = {12},
      booktitle = {Spoken Language Technology Workshop},
      url = {https://www.semanticscholar.org/paper/3f1431686216c96e0e812d830bf3328a6814fa73},
      }

1417. Jingxiao Zheng, Rajeev Ranjan, Ching-Hui Chen, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “An Automatic System for Unconstrained Video-Based Face Recognition,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2018.
      [BibTeX] [Link]

      @inproceedings{54549797,
      title = {An Automatic System for Unconstrained Video-Based Face Recognition},
      author = {{Jingxiao Zheng} and {Rajeev Ranjan} and {Ching-Hui Chen} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {12},
      booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
      url = {https://www.semanticscholar.org/paper/da3f1fd1362426540d66c9f993469f50dacddc99},
      }

1418. Amit Kumar, A. Alavi, and R. Chellappa, “KEPLER: Simultaneous estimation of keypoints and 3D pose of unconstrained faces in a unified framework by learning efficient H-CNN regressors,” in Image and Vision Computing, 2018.
      [BibTeX] [Link]

      @inproceedings{53115077,
      title = {KEPLER: Simultaneous estimation of keypoints and 3D pose of unconstrained faces in a unified framework by learning efficient H-CNN regressors},
      author = {{Amit Kumar} and {A. Alavi} and {R. Chellappa}},
      year = 2018,
      month = {11},
      booktitle = {Image and Vision Computing},
      url = {https://www.semanticscholar.org/paper/e8d98b76d82065abfcf20194918a737b7e5e4c4b},
      }

1419. Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, and A. Yuille, “Progressive Recurrent Learning for Visual Recognition,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{54032226,
      title = {Progressive Recurrent Learning for Visual Recognition},
      author = {{Xutong Ren} and {Lingxi Xie} and {Chen Wei} and {Siyuan Qiao} and {Chi Su} and {Jiaying Liu} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/39223c8e64221062cd992e7e89e6db858014eac1},
      }

1420. Maneet Singh, Richa Singh, Mayank Vatsa, N. Ratha, and R. Chellappa, “Recognizing Disguised Faces in the Wild,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2018.
      [BibTeX] [Link]

      @inproceedings{53728928,
      title = {Recognizing Disguised Faces in the Wild},
      author = {{Maneet Singh} and {Richa Singh} and {Mayank Vatsa} and {N. Ratha} and {R. Chellappa}},
      year = 2018,
      month = {11},
      booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
      url = {https://www.semanticscholar.org/paper/47b14a600e6728fb964b3cc964433480560142fa},
      }

1421. Huangjie Zheng, Lingxi Xie, Tianwei Ni, Ya Zhang, Yanfeng Wang, Qi Tian, E. Fishman, and A. Yuille, “Phase Collaborative Network for Multi-Phase Medical Imaging Segmentation,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{54067407,
      title = {Phase Collaborative Network for Multi-Phase Medical Imaging Segmentation},
      author = {{Huangjie Zheng} and {Lingxi Xie} and {Tianwei Ni} and {Ya Zhang} and {Yanfeng Wang} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/b40770062d22188a2dc21b6e2f96f8078ff3d8ed},
      }

1422. A. D. McCarthy, M. Silfverberg, R. Cotterell, M. Hulden, and D. Yarowsky, “Marrying Universal Dependencies and Universal Morphology,” in Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), Brussels, Belgium, 2018, p. 91–101. doi:10.18653/v1/W18-6011
      [BibTeX] [Abstract] [Link]
      

      The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages{–-}UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project{‘}s annotations could be used to validate the other{‘}s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13{\%} recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.

      @inproceedings{mccarthy-etal-2018-marrying,
      title = "Marrying {U}niversal {D}ependencies and {U}niversal {M}orphology",
      author = "McCarthy, Arya D. and
      Silfverberg, Miikka and
      Cotterell, Ryan and
      Hulden, Mans and
      Yarowsky, David",
      editor = "de Marneffe, Marie-Catherine and
      Lynn, Teresa and
      Schuster, Sebastian",
      booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
      month = nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6011",
      doi = "10.18653/v1/W18-6011",
      pages = "91--101",
      abstract = "The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages{---}UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project{'}s annotations could be used to validate the other{'}s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13{\%} recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.",
      }

1423. Matthew Maciejewski, Gregory Sell, Leibny Paola García-Perera, Shinji Watanabe, and S. Khudanpur, “Building Corpora for Single-Channel Speech Separation Across Multiple Domains,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{53228380,
      title = {Building Corpora for Single-Channel Speech Separation Across Multiple Domains},
      author = {{Matthew Maciejewski} and {Gregory Sell} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {S. Khudanpur}},
      year = 2018,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c5141ed9ed785a6a1df61b36883e6dfa19a59ff7},
      }

1424. Nils Holzenberger, Shruti Palaskar, P. Madhyastha, Florian Metze, and R. Arora, “Learning from Multiview Correlations in Open-domain Videos,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{53717388,
      title = {Learning from Multiview Correlations in Open-domain Videos},
      author = {{Nils Holzenberger} and {Shruti Palaskar} and {P. Madhyastha} and {Florian Metze} and {R. Arora}},
      year = 2018,
      month = {11},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a01ae256dfe7bd10734fec8a66549fb7ea876a05},
      }

1425. Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, and R. Chellappa, “Learning Without Memorizing,” in Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{53776855,
      title = {Learning Without Memorizing},
      author = {{Prithviraj Dhar} and {Rajat Vikram Singh} and {Kuan-Chuan Peng} and {Ziyan Wu} and {R. Chellappa}},
      year = 2018,
      month = {11},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/162a4c6f964880ec90b40fefa6d4d99d3ad321ec},
      }

1426. Xiaofei Wang, Ruizhi Li, Sri Harish Reddy Mallidi, Takaaki Hori, Shinji Watanabe, and H. Hermansky, “Stream Attention-based Multi-array End-to-end Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{53288567,
      title = {Stream Attention-based Multi-array End-to-end Speech Recognition},
      author = {{Xiaofei Wang} and {Ruizhi Li} and {Sri Harish Reddy Mallidi} and {Takaaki Hori} and {Shinji Watanabe} and {H. Hermansky}},
      year = 2018,
      month = {11},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/bf50833a46839d3932663b472d6145418f9d0bd6},
      }

1427. A. Benton and M. Dredze, “Using Author Embeddings to Improve Tweet Stance Classification,” in Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, Brussels, Belgium, 2018, p. 184–194. doi:10.18653/v1/W18-6124
      [BibTeX] [Abstract] [Link]
      

      Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification {–} where a tweet is categorized according to a viewpoint it espouses {–} the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pretraining method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited.

      @inproceedings{benton-dredze-2018-using,
      title = "Using Author Embeddings to Improve Tweet Stance Classification",
      author = "Benton, Adrian and
      Dredze, Mark",
      editor = "Xu, Wei and
      Ritter, Alan and
      Baldwin, Tim and
      Rahimi, Afshin",
      booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
      month = nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6124",
      doi = "10.18653/v1/W18-6124",
      pages = "184--194",
      abstract = "Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification {--} where a tweet is categorized according to a viewpoint it espouses {--} the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pretraining method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited.",
      }

1428. Ruizhi Li, Xiaofei Wang, Sri Harish Reddy Mallidi, Takaaki Hori, Shinji Watanabe, and H. Hermansky, “Multi-encoder multi-resolution framework for end-to-end speech recognition,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{53288160,
      title = {Multi-encoder multi-resolution framework for end-to-end speech recognition},
      author = {{Ruizhi Li} and {Xiaofei Wang} and {Sri Harish Reddy Mallidi} and {Takaaki Hori} and {Shinji Watanabe} and {H. Hermansky}},
      year = 2018,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/f17e182fcb7fbbff2257824174ed6f7df512a42b},
      }

1429. Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, and R. Chellappa, “Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
      [BibTeX] [Link]

      @inproceedings{53865412,
      title = {Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection},
      author = {{Hongyu Xu} and {Xutao Lv} and {Xiaoyu Wang} and {Zhou Ren} and {R. Chellappa}},
      year = 2018,
      month = {11},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/5232d6cba44a7bb67e8627ce4c2f4f93dce31e47},
      }

1430. Sandeep Reddy Kothinti, Keisuke Imoto, D. Chakrabarty, Gregory Sell, Shinji Watanabe, and Mounya Elhilali, “Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{53250287,
      title = {Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection},
      author = {{Sandeep Reddy Kothinti} and {Keisuke Imoto} and {D. Chakrabarty} and {Gregory Sell} and {Shinji Watanabe} and {Mounya Elhilali}},
      year = 2018,
      month = {11},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/e5efd7e2087e58c5a8860398dfcf143aa9dc865e},
      }

1431. Z. Wood-Doughty, N. Andrews, and M. Dredze, “Convolutions Are All You Need (For Classifying Character Sequences),” in Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, Brussels, Belgium, 2018, p. 208–213. doi:10.18653/v1/W18-6127
      [BibTeX] [Abstract] [Link]
      

      While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.

      @inproceedings{wood-doughty-etal-2018-convolutions,
      title = "Convolutions Are All You Need (For Classifying Character Sequences)",
      author = "Wood-Doughty, Zach and
      Andrews, Nicholas and
      Dredze, Mark",
      editor = "Xu, Wei and
      Ritter, Alan and
      Baldwin, Tim and
      Rahimi, Afshin",
      booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
      month = nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6127",
      doi = "10.18653/v1/W18-6127",
      pages = "208--213",
      abstract = "While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.",
      }

1432. Chenxu Luo, Xiao Chu, and A. Yuille, “OriNet: A Fully Convolutional Network for 3D Human Pose Estimation,” in British Machine Vision Conference, 2018.
      [BibTeX] [Link]

      @inproceedings{52292103,
      title = {OriNet: A Fully Convolutional Network for 3D Human Pose Estimation},
      author = {{Chenxu Luo} and {Xiao Chu} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {British Machine Vision Conference},
      url = {https://www.semanticscholar.org/paper/ad7889a2525c345d701b8b57e441afe8ac3370ad},
      }

1433. Yingda Xia, Fengze Liu, D. Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, A. Yuille, and H. Roth, “3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
      [BibTeX] [Link]

      @inproceedings{54223408,
      title = {3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training},
      author = {{Yingda Xia} and {Fengze Liu} and {D. Yang} and {Jinzheng Cai} and {Lequan Yu} and {Zhuotun Zhu} and {Daguang Xu} and {A. Yuille} and {H. Roth}},
      year = 2018,
      month = {11},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/d7000a609c9aa59c1fd893cdfa6f51cd9cd22354},
      }

1434. S. Saria, “Individualized sepsis treatment using reinforcement learning,” in Nature Network Boston, 2018.
      [BibTeX] [Link]

      @inproceedings{53226992,
      title = {Individualized sepsis treatment using reinforcement learning},
      author = {{S. Saria}},
      year = 2018,
      month = {11},
      booktitle = {Nature Network Boston},
      url = {https://www.semanticscholar.org/paper/556929f1e8097e090f121a52b9ef42461d2273f3},
      }

1435. Xuan Zhang, Manish Kumar, Huda Khayrallah, Kenton Murray, Jeremy Gwinnup, Marianna J. Martindale, Paul McNamee, Kevin Duh, and Marine Carpuat, “An Empirical Exploration of Curriculum Learning for Neural Machine Translation,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{53295888,
      title = {An Empirical Exploration of Curriculum Learning for Neural Machine Translation},
      author = {{Xuan Zhang} and {Manish Kumar} and {Huda Khayrallah} and {Kenton Murray} and {Jeremy Gwinnup} and {Marianna J. Martindale} and {Paul McNamee} and {Kevin Duh} and {Marine Carpuat}},
      year = 2018,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/b43ffb0d4f8d1c66632b78ad74d92ab1218a6976},
      }

1436. Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, E. Fishman, and A. Yuille, “Generalized Coarse-to-Fine Visual Recognition with Progressive Training,” in arXiv: Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{119310436,
      title = {Generalized Coarse-to-Fine Visual Recognition with Progressive Training},
      author = {{Xutong Ren} and {Lingxi Xie} and {Chen Wei} and {Siyuan Qiao} and {Chi Su} and {Jiaying Liu} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/4edd6898ba70c9a8a2099b0244c78de69ed9e52a},
      }

1437. Jaejin Cho, Shinji Watanabe, Takaaki Hori, M. Baskar, H. Inaguma, J. Villalba, and N. Dehak, “Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{53245942,
      title = {Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition},
      author = {{Jaejin Cho} and {Shinji Watanabe} and {Takaaki Hori} and {M. Baskar} and {H. Inaguma} and {J. Villalba} and {N. Dehak}},
      year = 2018,
      month = {11},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/8f963beca679cb1129df0a944c6de4b126e20fd5},
      }

1438. J. Ayers, Mark Dredze, E. Leas, Theodore L. Caputi, Jon-Patrick Allem, and Joanna E. Cohen, “Next generation media monitoring: Global coverage of electronic nicotine delivery systems (electronic cigarettes) on Bing, Google and Twitter, 2013-2018,” in PLoS ONE, 2018.
      [BibTeX] [Link]

      @inproceedings{53268904,
      title = {Next generation media monitoring: Global coverage of electronic nicotine delivery systems (electronic cigarettes) on Bing, Google and Twitter, 2013-2018},
      author = {{J. Ayers} and {Mark Dredze} and {E. Leas} and {Theodore L. Caputi} and {Jon-Patrick Allem} and {Joanna E. Cohen}},
      year = 2018,
      month = {11},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/637ed794b41f2a50f8b2efce4ef02b3b12c8c057},
      }

1439. Hossein Hadian, H. Sameti, Daniel Povey, and S. Khudanpur, “Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{51977097,
      title = {Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR},
      author = {{Hossein Hadian} and {H. Sameti} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      month = {11},
      booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
      url = {https://www.semanticscholar.org/paper/6aa83f912110c63f0da5dc8a8464c9dc2c589076},
      }

1440. Huangjie Zheng, Lingxi Xie, Tianwei Ni, Ya Zhang, Yanfeng Wang, Qi Tian, E. Fishman, and A. Yuille, “Phase Collaborative Network for Two-Phase Medical Image Segmentation,” in arXiv: Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{202566009,
      title = {Phase Collaborative Network for Two-Phase Medical Image Segmentation},
      author = {{Huangjie Zheng} and {Lingxi Xie} and {Tianwei Ni} and {Ya Zhang} and {Yanfeng Wang} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/c7794dd53f20e7b15d156bd92cda6d740314003e},
      }

1441. Zili Huang, Leibny Paola García-Perera, J. Villalba, Daniel Povey, and N. Dehak, “JHU Diarization System Description,” in IberSPEECH Conference, 2018.
      [BibTeX] [Link]

      @inproceedings{54177614,
      title = {JHU Diarization System Description},
      author = {{Zili Huang} and {Leibny Paola García-Perera} and {J. Villalba} and {Daniel Povey} and {N. Dehak}},
      year = 2018,
      month = {11},
      booktitle = {IberSPEECH Conference},
      url = {https://www.semanticscholar.org/paper/23a109da0c4ce0314f6f016da679a4e1fd6960ef},
      }

1442. Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo Wang, and A. Yuille, “Robust Face Detection via Learning Small Faces on Hard Images,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
      [BibTeX] [Link]

      @inproceedings{53863913,
      title = {Robust Face Detection via Learning Small Faces on Hard Images},
      author = {{Zhishuai Zhang} and {Wei Shen} and {Siyuan Qiao} and {Yan Wang} and {Bo Wang} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/25a784f7f8c94c42821ee078587fc38dffcd00a4},
      }

1443. Yutong Bai, Qing Liu, Lingxi Xie, Yan Zheng, Weichao Qiu, and A. Yuille, “Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data,” in IEEE International Conference on Computer Vision, 2018.
      [BibTeX] [Link]

      @inproceedings{53961017,
      title = {Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data},
      author = {{Yutong Bai} and {Qing Liu} and {Lingxi Xie} and {Yan Zheng} and {Weichao Qiu} and {A. Yuille}},
      year = 2018,
      month = {11},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/87bed8d35625a75d8ba1953bb6bb71a3625020c0},
      }

1444. Joshua Gleason, Rajeev Ranjan, S. Schwarcz, C. Castillo, Jun-Cheng Chen, and R. Chellappa, “A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
      [BibTeX] [Link]

      @inproceedings{53717843,
      title = {A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos},
      author = {{Joshua Gleason} and {Rajeev Ranjan} and {S. Schwarcz} and {C. Castillo} and {Jun-Cheng Chen} and {R. Chellappa}},
      year = 2018,
      month = {11},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/1a2e40b8ef509ed099bb7e77862ed5ddca52c3a2},
      }

1445. T. Lippincott, “Portable, layer-wise task performance monitoring for NLP models,” in Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Brussels, Belgium, 2018, p. 350–352. doi:10.18653/v1/W18-5445
      [BibTeX] [Abstract] [Link]
      

      There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns.

      @inproceedings{lippincott-2018-portable,
      title = "Portable, layer-wise task performance monitoring for {NLP} models",
      author = "Lippincott, Tom",
      editor = "Linzen, Tal and
      Chrupa{\l}a, Grzegorz and
      Alishahi, Afra",
      booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
      month = nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-5445",
      doi = "10.18653/v1/W18-5445",
      pages = "350--352",
      abstract = "There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns.",
      }

1446. R. Arora, M. Dinitz, T. V. Marinov, and M. Mohri, “Policy Regret in Repeated Games,” in Neural Information Processing Systems, 2018.
      [BibTeX] [Link]

      @inproceedings{53285373,
      title = {Policy Regret in Repeated Games},
      author = {{R. Arora} and {M. Dinitz} and {T. V. Marinov} and {M. Mohri}},
      year = 2018,
      month = {11},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/c1482f2409af234da7d9771ddac4e88b45ec8e86},
      }

1447. D. Wang and J. Eisner, “Synthetic Data Made to Order: The Case of Parsing,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, 2018, p. 1325–1337. doi:10.18653/v1/D18-1163
      [BibTeX] [Link]

      @InProceedings{wang-eisner-2018-emnlp,
      aclid = "D18-1163",
      doi = "10.18653/v1/D18-1163",
      author = "Dingquan Wang and Jason Eisner",
      title = "Synthetic Data Made to Order: The Case of Parsing",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "1325--1337",
      year = "2018",
      month = nov,
      address = "Brussels",
      URL = "http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-emnlp",
      }

1448. Cheng-I Lai, A. Abad, Korin Richmond, J. Yamagishi, N. Dehak, and Simon King, “Attentive Filtering Networks for Audio Replay Attack Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{53109997,
      title = {Attentive Filtering Networks for Audio Replay Attack Detection},
      author = {{Cheng-I Lai} and {A. Abad} and {Korin Richmond} and {J. Yamagishi} and {N. Dehak} and {Simon King}},
      year = 2018,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/f9a8ffe3778f4962de63d1153d5041722a7eba81},
      }

1449. Pushpendre Rastogi, Adam Poliak, V. Lyzinski, and Benjamin Van Durme, “Neural variational entity set expansion for automatically populated knowledge graphs,” in Information Retrieval Journal, 2018.
      [BibTeX] [Link]

      @inproceedings{49652556,
      title = {Neural variational entity set expansion for automatically populated knowledge graphs},
      author = {{Pushpendre Rastogi} and {Adam Poliak} and {V. Lyzinski} and {Benjamin Van Durme}},
      year = 2018,
      month = {10},
      booktitle = {Information Retrieval Journal},
      url = {https://www.semanticscholar.org/paper/ba1bd9b465c26441555f73b9a2a4026dcfb11683},
      }

1450. Christos Sapsanis, Nathaniel Welsh, Michael Pozin, Guillaume Garreau, Gaspar Tognetti, Hani Bakhshaee, P. Pouliquen, R. Mittal, W. R. Thompson, and A. Andreou, “StethoVest: A simultaneous multichannel wearable system for cardiac acoustic mapping,” in Biomedical Circuits and Systems Conference, 2018.
      [BibTeX] [Link]

      @inproceedings{56717729,
      title = {StethoVest: A simultaneous multichannel wearable system for cardiac acoustic mapping},
      author = {{Christos Sapsanis} and {Nathaniel Welsh} and {Michael Pozin} and {Guillaume Garreau} and {Gaspar Tognetti} and {Hani Bakhshaee} and {P. Pouliquen} and {R. Mittal} and {W. R. Thompson} and {A. Andreou}},
      year = 2018,
      month = {10},
      booktitle = {Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/f0f1d49a014881e74de58f328db3a54aae6863b9},
      }

1451. Jun-Cheng Chen, Wei-An Lin, Jingxiao Zheng, and R. Chellappa, “A Real-Time Multi-Task Single Shot Face Detector,” in International Conference on Information Photonics, 2018.
      [BibTeX] [Link]

      @inproceedings{52191412,
      title = {A Real-Time Multi-Task Single Shot Face Detector},
      author = {{Jun-Cheng Chen} and {Wei-An Lin} and {Jingxiao Zheng} and {R. Chellappa}},
      year = 2018,
      month = {10},
      booktitle = {International Conference on Information Photonics},
      url = {https://www.semanticscholar.org/paper/6043070c2f2f592601e90d2c71dc6fafca48056b},
      }

1452. Kate D. Fischl, A. Andreou, T. Stewart, and Kaitlin L. Fair, “Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System,” in Biomedical Circuits and Systems Conference, 2018.
      [BibTeX] [Link]

      @inproceedings{56717992,
      title = {Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System},
      author = {{Kate D. Fischl} and {A. Andreou} and {T. Stewart} and {Kaitlin L. Fair}},
      year = 2018,
      month = {10},
      booktitle = {Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/55ce934130d3bd91f2144fb6efbb239c44822216},
      }

1453. K.P. Subbalakshmi, A. Galstyan, R. Chellappa, and Charles Clancy, “Sensemaking Research Roadmap.” 2018.
      [BibTeX] [Link]

      @inproceedings{69464873,
      title = {Sensemaking Research Roadmap},
      author = {{K.P. Subbalakshmi} and {A. Galstyan} and {R. Chellappa} and {Charles Clancy}},
      year = 2018,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d93c6b12d5b2131dd196c790abc1135c9b6ffcab},
      }

1454. Chenxu Luo, Zhenheng Yang, Peng Wang, Y. Wang, W. Xu, R. Nevatia, and A. Yuille, “Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
      [BibTeX] [Link]

      @inproceedings{52987932,
      title = {Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding},
      author = {{Chenxu Luo} and {Zhenheng Yang} and {Peng Wang} and {Y. Wang} and {W. Xu} and {R. Nevatia} and {A. Yuille}},
      year = 2018,
      month = {10},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/ba5c19a02696029566b9e01f5f55e908d25763bd},
      }

1455. M. Post, “A Call for Clarity in Reporting BLEU Scores,” in Proceedings of the Third Conference on Machine Translation: Research Papers, Brussels, Belgium, 2018, p. 186–191. doi:10.18653/v1/W18-6319
      [BibTeX] [Abstract] [Link]
      

      The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to {“}the{”} BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.

      @inproceedings{post-2018-call,
      title = "A Call for Clarity in Reporting {BLEU} Scores",
      author = "Post, Matt",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Specia, Lucia and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
      month = oct,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6319",
      doi = "10.18653/v1/W18-6319",
      pages = "186--191",
      abstract = "The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to {``}the{''} BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.",
      }

1456. B. Thompson, H. Khayrallah, A. Anastasopoulos, A. D. McCarthy, K. Duh, R. Marvin, P. McNamee, J. Gwinnup, T. Anderson, and P. Koehn, “Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation,” in Proceedings of the Third Conference on Machine Translation: Research Papers, Brussels, Belgium, 2018, p. 124–132. doi:10.18653/v1/W18-6313
      [BibTeX] [Abstract] [Link]
      

      To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component{‘}s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.

      @inproceedings{thompson-etal-2018-freezing,
      title = "Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation",
      author = "Thompson, Brian and
      Khayrallah, Huda and
      Anastasopoulos, Antonios and
      McCarthy, Arya D. and
      Duh, Kevin and
      Marvin, Rebecca and
      McNamee, Paul and
      Gwinnup, Jeremy and
      Anderson, Tim and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Specia, Lucia and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
      month = oct,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6313",
      doi = "10.18653/v1/W18-6313",
      pages = "124--132",
      abstract = "To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component{'}s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.",
      }

1457. Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme, “ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{53116244,
      title = {ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension},
      author = {{Sheng Zhang} and {Xiaodong Liu} and {Jingjing Liu} and {Jianfeng Gao} and {Kevin Duh} and {Benjamin Van Durme}},
      year = 2018,
      month = {10},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/a5b66ee341cb990f7f70a124b5fab3316d3b7e27},
      }

1458. Daniel R. Mendat, A. Cassidy, Guido Zarrella, and A. Andreou, “Word2vec Word Similarities on IBM’s TrueNorth Neurosynaptic System,” in Biomedical Circuits and Systems Conference, 2018.
      [BibTeX] [Link]

      @inproceedings{56717891,
      title = {Word2vec Word Similarities on IBM's TrueNorth Neurosynaptic System},
      author = {{Daniel R. Mendat} and {A. Cassidy} and {Guido Zarrella} and {A. Andreou}},
      year = 2018,
      month = {10},
      booktitle = {Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/f185dbbf55b2229aecf82f486550a2f9d71be1d4},
      }

1459. S. Saria, “Machine Learning Driven Targeted Real-Time Early Warning System Improves Outcomes in Sepsis.” 2018.
      [BibTeX] [Link]

      @inproceedings{69742053,
      title = {Machine Learning Driven Targeted Real-Time Early Warning System Improves Outcomes in Sepsis},
      author = {{S. Saria}},
      year = 2018,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e62fbf12f0fff02cf78b0c5cba5e2de2e50972a9},
      }

1460. H. Khayrallah, H. Xu, and P. Koehn, “The JHU Parallel Corpus Filtering Systems for WMT 2018,” in Proceedings of the Third Conference on Machine Translation: Shared Task Papers, Belgium, Brussels, 2018, p. 896–899. doi:10.18653/v1/W18-6479
      [BibTeX] [Abstract] [Link]
      

      This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.

      @inproceedings{khayrallah-etal-2018-jhu,
      title = "The {JHU} Parallel Corpus Filtering Systems for {WMT} 2018",
      author = "Khayrallah, Huda and
      Xu, Hainan and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Specia, Lucia and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
      month = oct,
      year = "2018",
      address = "Belgium, Brussels",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6479",
      doi = "10.18653/v1/W18-6479",
      pages = "896--899",
      abstract = "This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.",
      }

1461. R. Cotterell, C. Kirov, J. Sylak-Glassman, G. Walther, E. Vylomova, A. D. McCarthy, K. Kann, S. J. Mielke, G. Nicolai, M. Silfverberg, D. Yarowsky, J. Eisner, and M. Hulden, “The CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection,” in Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection, Brussels, 2018, p. 1–27. doi:10.18653/v1/K18-3001
      [BibTeX] [Link]

      @inproceedings{cotterell-etal-2018-conll,
      title = "The {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
      author = "Cotterell, Ryan and
      Kirov, Christo and
      Sylak-Glassman, John and
      Walther, G{\'e}raldine and
      Vylomova, Ekaterina and
      McCarthy, Arya D. and
      Kann, Katharina and
      Mielke, Sabrina J. and
      Nicolai, Garrett and
      Silfverberg, Miikka and
      Yarowsky, David and
      Eisner, Jason and
      Hulden, Mans",
      editor = "Hulden, Mans and
      Cotterell, Ryan",
      booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
      month = oct,
      year = "2018",
      address = "Brussels",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K18-3001",
      doi = "10.18653/v1/K18-3001",
      pages = "1--27",
      }

1462. Peter F. Schulam and S. Saria, “Discretizing Logged Interaction Data Biases Learning for Decision-Making,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{52939610,
      title = {Discretizing Logged Interaction Data Biases Learning for Decision-Making},
      author = {{Peter F. Schulam} and {S. Saria}},
      year = 2018,
      month = {10},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/bd4aa7f9b4941c71978c29dae7345e67ca4318da},
      }

1463. P. Koehn, K. Duh, and B. Thompson, “The JHU Machine Translation Systems for WMT 2018,” in Proceedings of the Third Conference on Machine Translation: Shared Task Papers, Belgium, Brussels, 2018, p. 438–444. doi:10.18653/v1/W18-6417
      [BibTeX] [Abstract] [Link]
      

      We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German{–}English, English{–} German, and Russian{–}English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.

      @inproceedings{koehn-etal-2018-jhu,
      title = "The {JHU} Machine Translation Systems for {WMT} 2018",
      author = "Koehn, Philipp and
      Duh, Kevin and
      Thompson, Brian",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Fishel, Mark and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Post, Matt and
      Specia, Lucia and
      Turchi, Marco and
      Verspoor, Karin",
      booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
      month = oct,
      year = "2018",
      address = "Belgium, Brussels",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-6417",
      doi = "10.18653/v1/W18-6417",
      pages = "438--444",
      abstract = "We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German{--}English, English{--} German, and Russian{--}English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.",
      }

1464. Xing Di, He Zhang, and Vishal M. Patel, “Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis,” in 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018.
      [BibTeX] [Link]

      @inproceedings{57572931,
      title = {Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis},
      author = {{Xing Di} and {He Zhang} and {Vishal M. Patel}},
      year = 2018,
      month = {10},
      booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
      url = {https://www.semanticscholar.org/paper/00c14b348635489fb7622a3982d388d8a2dbf9b9},
      }

1465. Pramuditha Perera and Vishal M. Patel, “Dual-Minimax Probability Machines for One-class Mobile Active Authentication,” in 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018.
      [BibTeX] [Link]

      @inproceedings{133604577,
      title = {Dual-Minimax Probability Machines for One-class Mobile Active Authentication},
      author = {{Pramuditha Perera} and {Vishal M. Patel}},
      year = 2018,
      month = {10},
      booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
      url = {https://www.semanticscholar.org/paper/abef2966d391e9d1b93611f48201004d3581b9ee},
      }

1466. R. Cotterell, C. Kirov, John Sylak-Glassman, G. Walther, Ekaterina Vylomova, A. D. McCarthy, K. Kann, S. Mielke, G. Nicolai, Miikka Silfverberg, D. Yarowsky, J. Eisner, and M. Hulden, “The CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection,” in Proceedings of the CoNLL SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection, Brussels, 2018, p. 1–27. doi:10.18653/v1/K18-3001
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2018-shared,
      aclid = "K18-3001",
      doi = "10.18653/v1/K18-3001",
      author = "Ryan Cotterell and Christo Kirov and John
      Sylak-Glassman and G{\'e}raldine Walther and Ekaterina
      Vylomova and Arya D. McCarthy and Katharina Kann and
      Sabrina Mielke and Garrett Nicolai and Miikka
      Silfverberg and David Yarowsky and Jason Eisner and
      Mans Hulden",
      title = "The {CoNLL}--{SIGMORPHON} 2018 Shared Task: Universal
      Morphological Reinflection",
      booktitle = "Proceedings of the CoNLL SIGMORPHON 2018 Shared Task:
      Universal Morphological Reinflection",
      pages = "1--27",
      year = "2018",
      month = oct,
      address = "Brussels",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-shared",
      }

1467. S. S. R. Kothur, R. Knowles, and P. Koehn, “Document-Level Adaptation for Neural Machine Translation,” in Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, Melbourne, Australia, 2018, p. 64–73. doi:10.18653/v1/W18-2708
      [BibTeX] [Abstract] [Link]
      

      It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator{‘}s corrections within the document itself. We focus on adaptation within a single document {–} appropriate for an interactive translation scenario where a model adapts to a human translator{‘}s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3{\%} novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2{\%} novel word translation accuracy.

      @inproceedings{kothur-etal-2018-document,
      title = "Document-Level Adaptation for Neural Machine Translation",
      author = "Kothur, Sachith Sri Ram and
      Knowles, Rebecca and
      Koehn, Philipp",
      editor = "Birch, Alexandra and
      Finch, Andrew and
      Luong, Thang and
      Neubig, Graham and
      Oda, Yusuke",
      booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-2708",
      doi = "10.18653/v1/W18-2708",
      pages = "64--73",
      abstract = "It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator{'}s corrections within the document itself. We focus on adaptation within a single document {--} appropriate for an interactive translation scenario where a model adapts to a human translator{'}s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3{\%} novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2{\%} novel word translation accuracy.",
      }

1468. K. Sakaguchi and B. Van Durme, “Efficient Online Scalar Annotation with Bounded Support,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, p. 208–218. doi:10.18653/v1/P18-1020
      [BibTeX] [Abstract] [Link]
      

      We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.

      @inproceedings{sakaguchi-van-durme-2018-efficient,
      title = "Efficient Online Scalar Annotation with Bounded Support",
      author = "Sakaguchi, Keisuke and
      Van Durme, Benjamin",
      editor = "Gurevych, Iryna and
      Miyao, Yusuke",
      booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P18-1020",
      doi = "10.18653/v1/P18-1020",
      pages = "208--218",
      abstract = "We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.",
      }

1469. V. C. D. Hoang, P. Koehn, G. Haffari, and T. Cohn, “Iterative Back-Translation for Neural Machine Translation,” in Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, Melbourne, Australia, 2018, p. 18–24. doi:10.18653/v1/W18-2703
      [BibTeX] [Abstract] [Link]
      

      We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.

      @inproceedings{hoang-etal-2018-iterative,
      title = "Iterative Back-Translation for Neural Machine Translation",
      author = "Hoang, Vu Cong Duy and
      Koehn, Philipp and
      Haffari, Gholamreza and
      Cohn, Trevor",
      editor = "Birch, Alexandra and
      Finch, Andrew and
      Luong, Thang and
      Neubig, Graham and
      Oda, Yusuke",
      booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-2703",
      doi = "10.18653/v1/W18-2703",
      pages = "18--24",
      abstract = "We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.",
      }

1470. H. Khayrallah, B. Thompson, K. Duh, and P. Koehn, “Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation,” in Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, Melbourne, Australia, 2018, p. 36–44. doi:10.18653/v1/W18-2705
      [BibTeX] [Abstract] [Link]
      

      Supervised domain adaptation{–-}where a large generic corpus and a smaller in-domain corpus are both available for training{–-}is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model{‘}s output word distribution and that of the out-of-domain model to prevent the model{‘}s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.

      @inproceedings{khayrallah-etal-2018-regularized,
      title = "Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation",
      author = "Khayrallah, Huda and
      Thompson, Brian and
      Duh, Kevin and
      Koehn, Philipp",
      editor = "Birch, Alexandra and
      Finch, Andrew and
      Luong, Thang and
      Neubig, Graham and
      Oda, Yusuke",
      booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-2705",
      doi = "10.18653/v1/W18-2705",
      pages = "36--44",
      abstract = "Supervised domain adaptation{---}where a large generic corpus and a smaller in-domain corpus are both available for training{---}is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model{'}s output word distribution and that of the out-of-domain model to prevent the model{'}s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.",
      }

1471. H. Khayrallah and P. Koehn, “On the Impact of Various Types of Noise on Neural Machine Translation,” in Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, Melbourne, Australia, 2018, p. 74–83. doi:10.18653/v1/W18-2709
      [BibTeX] [Abstract] [Link]
      

      We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.

      @inproceedings{khayrallah-koehn-2018-impact,
      title = "On the Impact of Various Types of Noise on Neural Machine Translation",
      author = "Khayrallah, Huda and
      Koehn, Philipp",
      editor = "Birch, Alexandra and
      Finch, Andrew and
      Luong, Thang and
      Neubig, Graham and
      Oda, Yusuke",
      booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-2709",
      doi = "10.18653/v1/W18-2709",
      pages = "74--83",
      abstract = "We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.",
      }

1472. X. Liu, Y. Shen, K. Duh, and J. Gao, “Stochastic Answer Networks for Machine Reading Comprehension,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, 2018, p. 1694–1704. doi:10.18653/v1/P18-1157
      [BibTeX] [Abstract] [Link]
      

      We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).

      @inproceedings{liu-etal-2018-stochastic,
      title = "Stochastic Answer Networks for Machine Reading Comprehension",
      author = "Liu, Xiaodong and
      Shen, Yelong and
      Duh, Kevin and
      Gao, Jianfeng",
      editor = "Gurevych, Iryna and
      Miyao, Yusuke",
      booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jul,
      year = "2018",
      address = "Melbourne, Australia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P18-1157",
      doi = "10.18653/v1/P18-1157",
      pages = "1694--1704",
      abstract = "We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).",
      }

1473. A. Benton and M. Dredze, “Deep Dirichlet Multinomial Regression,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, 2018, p. 365–374. doi:10.18653/v1/N18-1034
      [BibTeX] [Abstract] [Link]
      

      Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features {–} a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR{‘}s model fit is less sensitive to training parameters than DMR.

      @inproceedings{benton-dredze-2018-deep,
      title = "Deep {D}irichlet Multinomial Regression",
      author = "Benton, Adrian and
      Dredze, Mark",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-1034",
      doi = "10.18653/v1/N18-1034",
      pages = "365--374",
      abstract = "Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features {--} a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR{'}s model fit is less sensitive to training parameters than DMR.",
      }

1474. S. Sasaki, S. Sun, S. Schamoni, K. Duh, and K. Inui, “Cross-Lingual Learning-to-Rank with Shared Representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, Louisiana, 2018, p. 458–463. doi:10.18653/v1/N18-2073
      [BibTeX] [Abstract] [Link]
      

      Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{‘}s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.

      @inproceedings{sasaki-etal-2018-cross,
      title = "Cross-Lingual Learning-to-Rank with Shared Representations",
      author = "Sasaki, Shota and
      Sun, Shuo and
      Schamoni, Shigehiko and
      Duh, Kevin and
      Inui, Kentaro",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-2073",
      doi = "10.18653/v1/N18-2073",
      pages = "458--463",
      abstract = "Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.",
      }

1475. Y. Wang, C. Liu, X. Zeng, and A. Yuille, “Scene Graph Parsing as Dependency Parsing,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, 2018, p. 397–407. doi:10.18653/v1/N18-1037
      [BibTeX] [Abstract] [Link]
      

      In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67{\%} to ground truth graphs on our evaluation set, surpassing best previous approaches by 5{\%}. We further demonstrate the effectiveness of our learned parser on image retrieval applications.

      @inproceedings{wang-etal-2018-scene,
      title = "Scene Graph Parsing as Dependency Parsing",
      author = "Wang, Yu-Siang and
      Liu, Chenxi and
      Zeng, Xiaohui and
      Yuille, Alan",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-1037",
      doi = "10.18653/v1/N18-1037",
      pages = "397--407",
      abstract = "In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67{\%} to ground truth graphs on our evaluation set, surpassing best previous approaches by 5{\%}. We further demonstrate the effectiveness of our learned parser on image retrieval applications.",
      }

1476. P. Shapiro and K. Duh, “Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings,” in Proceedings of the Second Workshop on Subword/Character LEvel Models, New Orleans, 2018, p. 1–11. doi:10.18653/v1/W18-1201
      [BibTeX] [Abstract] [Link]
      

      Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.

      @inproceedings{shapiro-duh-2018-morphological,
      title = "Morphological Word Embeddings for {A}rabic Neural Machine Translation in Low-Resource Settings",
      author = "Shapiro, Pamela and
      Duh, Kevin",
      editor = {Faruqui, Manaal and
      Sch{\"u}tze, Hinrich and
      Trancoso, Isabel and
      Tsvetkov, Yulia and
      Yaghoobzadeh, Yadollah},
      booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
      month = jun,
      year = "2018",
      address = "New Orleans",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-1201",
      doi = "10.18653/v1/W18-1201",
      pages = "1--11",
      abstract = "Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.",
      }

1477. M. Post and D. Vilar, “Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, 2018, p. 1314–1324. doi:10.18653/v1/N18-1119
      [BibTeX] [Abstract] [Link]
      

      The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm{‘}s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.

      @inproceedings{post-vilar-2018-fast,
      title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation",
      author = "Post, Matt and
      Vilar, David",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-1119",
      doi = "10.18653/v1/N18-1119",
      pages = "1314--1324",
      abstract = "The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm{'}s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.",
      }

1478. R. Rudinger, J. Naradowsky, B. Leonard, and B. Van Durme, “Gender Bias in Coreference Resolution,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, Louisiana, 2018, p. 8–14. doi:10.18653/v1/N18-2002
      [BibTeX] [Abstract] [Link]
      

      We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these {“}Winogender schemas,{”} we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

      @inproceedings{rudinger-etal-2018-gender,
      title = "Gender Bias in Coreference Resolution",
      author = "Rudinger, Rachel and
      Naradowsky, Jason and
      Leonard, Brian and
      Van Durme, Benjamin",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-2002",
      doi = "10.18653/v1/N18-2002",
      pages = "8--14",
      abstract = "We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these {``}Winogender schemas,{''} we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.",
      }

1479. Z. Wood-Doughty, N. Andrews, R. Marvin, and M. Dredze, “Predicting Twitter User Demographics from Names Alone,” in Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, New Orleans, Louisiana, USA, 2018, p. 105–111. doi:10.18653/v1/W18-1114
      [BibTeX] [Abstract] [Link]
      

      Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user{‘}s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection

      @inproceedings{wood-doughty-etal-2018-predicting,
      title = "Predicting {T}witter User Demographics from Names Alone",
      author = "Wood-Doughty, Zach and
      Andrews, Nicholas and
      Marvin, Rebecca and
      Dredze, Mark",
      editor = "Nissim, Malvina and
      Patti, Viviana and
      Plank, Barbara and
      Wagner, Claudia",
      booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-1114",
      doi = "10.18653/v1/W18-1114",
      pages = "105--111",
      abstract = "Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user{'}s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection",
      }

1480. H. Mei, S. Zhang, K. Duh, and B. Van Durme, “Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction,” in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, New Orleans, Louisiana, 2018, p. 142–147. doi:10.18653/v1/S18-2017
      [BibTeX] [Abstract] [Link]
      

      Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.

      @inproceedings{mei-etal-2018-halo,
      title = "{H}alo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction",
      author = "Mei, Hongyuan and
      Zhang, Sheng and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Nissim, Malvina and
      Berant, Jonathan and
      Lenci, Alessandro",
      booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S18-2017",
      doi = "10.18653/v1/S18-2017",
      pages = "142--147",
      abstract = "Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.",
      }

1481. R. Rudinger, A. S. White, and B. Van Durme, “Neural Models of Factuality,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, 2018, p. 731–744. doi:10.18653/v1/N18-1067
      [BibTeX] [Abstract] [Link]
      

      We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.

      @inproceedings{rudinger-etal-2018-neural-models,
      title = "Neural Models of Factuality",
      author = "Rudinger, Rachel and
      White, Aaron Steven and
      Van Durme, Benjamin",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-1067",
      doi = "10.18653/v1/N18-1067",
      pages = "731--744",
      abstract = "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.",
      }

1482. T. Lippincott and A. Carrell, “Observational Comparison of Geo-tagged and Randomly-drawn Tweets,” in Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, New Orleans, Louisiana, USA, 2018, p. 50–55. doi:10.18653/v1/W18-1107
      [BibTeX] [Abstract] [Link]
      

      Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information. A particularly attractive property of some tweets is *geo-tagging*, where a user account has opted-in to attaching their current location to each message. Unfortunately (from a researcher{‘}s perspective) only a fraction of Twitter accounts agree to this, and these accounts are likely to have systematic diffences with the general population. This work is an exploratory study of these differences across the full range of Twitter content, and complements previous studies that focus on the English-language subset. Additionally, we compare methods for querying users by self-identified properties, finding that the constrained semantics of the {“}description{”} field provides cleaner, higher-volume results than more complex regular expressions.

      @inproceedings{lippincott-carrell-2018-observational,
      title = "Observational Comparison of Geo-tagged and Randomly-drawn Tweets",
      author = "Lippincott, Tom and
      Carrell, Annabelle",
      editor = "Nissim, Malvina and
      Patti, Viviana and
      Plank, Barbara and
      Wagner, Claudia",
      booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-1107",
      doi = "10.18653/v1/W18-1107",
      pages = "50--55",
      abstract = "Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information. A particularly attractive property of some tweets is *geo-tagging*, where a user account has opted-in to attaching their current location to each message. Unfortunately (from a researcher{'}s perspective) only a fraction of Twitter accounts agree to this, and these accounts are likely to have systematic diffences with the general population. This work is an exploratory study of these differences across the full range of Twitter content, and complements previous studies that focus on the English-language subset. Additionally, we compare methods for querying users by self-identified properties, finding that the constrained semantics of the {``}description{''} field provides cleaner, higher-volume results than more complex regular expressions.",
      }

1483. Z. Wood-Doughty, P. Mahajan, and M. Dredze, “Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter,” in Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, New Orleans, Louisiana, USA, 2018, p. 56–61. doi:10.18653/v1/W18-1108
      [BibTeX] [Abstract] [Link]
      

      Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.

      @inproceedings{wood-doughty-etal-2018-johns,
      title = "{J}ohns {H}opkins or johnny-hopkins: Classifying Individuals versus Organizations on {T}witter",
      author = "Wood-Doughty, Zach and
      Mahajan, Praateek and
      Dredze, Mark",
      editor = "Nissim, Malvina and
      Patti, Viviana and
      Plank, Barbara and
      Wagner, Claudia",
      booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-1108",
      doi = "10.18653/v1/W18-1108",
      pages = "56--61",
      abstract = "Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.",
      }

1484. A. Poliak, J. Naradowsky, A. Haldar, R. Rudinger, and B. Van Durme, “Hypothesis Only Baselines in Natural Language Inference,” in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, New Orleans, Louisiana, 2018, p. 180–191. doi:10.18653/v1/S18-2023
      [BibTeX] [Abstract] [Link]
      

      We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

      @inproceedings{poliak-etal-2018-hypothesis,
      title = "Hypothesis Only Baselines in Natural Language Inference",
      author = "Poliak, Adam and
      Naradowsky, Jason and
      Haldar, Aparajita and
      Rudinger, Rachel and
      Van Durme, Benjamin",
      editor = "Nissim, Malvina and
      Berant, Jonathan and
      Lenci, Alessandro",
      booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S18-2023",
      doi = "10.18653/v1/S18-2023",
      pages = "180--191",
      abstract = "We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.",
      }

1485. S. Zhang, K. Duh, and B. Van Durme, “Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds,” in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, New Orleans, Louisiana, 2018, p. 173–179. doi:10.18653/v1/S18-2022
      [BibTeX] [Abstract] [Link]
      

      Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context {–} both document and sentence level information {–} than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.

      @inproceedings{zhang-etal-2018-fine,
      title = "Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds",
      author = "Zhang, Sheng and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Nissim, Malvina and
      Berant, Jonathan and
      Lenci, Alessandro",
      booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S18-2022",
      doi = "10.18653/v1/S18-2022",
      pages = "173--179",
      abstract = "Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context {--} both document and sentence level information {--} than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.",
      }

1486. K. Loveys, J. Torrez, A. Fine, G. Moriarty, and G. Coppersmith, “Cross-cultural differences in language markers of depression online,” in Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, New Orleans, LA, 2018, p. 78–87. doi:10.18653/v1/W18-0608
      [BibTeX] [Abstract] [Link]
      

      Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.

      @inproceedings{loveys-etal-2018-cross,
      title = "Cross-cultural differences in language markers of depression online",
      author = "Loveys, Kate and
      Torrez, Jonathan and
      Fine, Alex and
      Moriarty, Glen and
      Coppersmith, Glen",
      editor = "Loveys, Kate and
      Niederhoffer, Kate and
      Prud{'}hommeaux, Emily and
      Resnik, Rebecca and
      Resnik, Philip",
      booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
      month = jun,
      year = "2018",
      address = "New Orleans, LA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W18-0608",
      doi = "10.18653/v1/W18-0608",
      pages = "78--87",
      abstract = "Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.",
      }

1487. A. Poliak, Y. Belinkov, J. Glass, and B. Van Durme, “On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, Louisiana, 2018, p. 513–523. doi:10.18653/v1/N18-2082
      [BibTeX] [Abstract] [Link]
      

      We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage

      @inproceedings{poliak-etal-2018-evaluation,
      title = "On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference",
      author = "Poliak, Adam and
      Belinkov, Yonatan and
      Glass, James and
      Van Durme, Benjamin",
      editor = "Walker, Marilyn and
      Ji, Heng and
      Stent, Amanda",
      booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
      month = jun,
      year = "2018",
      address = "New Orleans, Louisiana",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N18-2082",
      doi = "10.18653/v1/N18-2082",
      pages = "513--523",
      abstract = "We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage",
      }

1488. R. Cotterell, C. Kirov, S. J. Mielke, and J. Eisner, “Unsupervised Disambiguation of Syncretism in Inflected Lexicons,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), New Orleans, 2018, p. 548–553. doi:10.18653/v1/N18-2087
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2018-syncretism,
      aclid = "N18-2087",
      doi = "10.18653/v1/N18-2087",
      author = "Ryan Cotterell and Christo Kirov and Sabrina J. Mielke
      and Jason Eisner",
      title = "Unsupervised Disambiguation of Syncretism in Inflected
      Lexicons",
      booktitle = "Proceedings of the 2018 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "548--553",
      year = "2018",
      month = jun,
      address = "New Orleans",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-syncretism",
      }

1489. R. Cotterell, S. J. Mielke, J. Eisner, and B. Roark, “Are All Languages Equally Hard to Language-Model?,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), New Orleans, 2018, p. 536–541. doi:10.18653/v1/N18-2085
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2018-lm,
      aclid = "N18-2085",
      doi = "10.18653/v1/N18-2085",
      author = "Ryan Cotterell and Sabrina J. Mielke and Jason Eisner
      and Brian Roark",
      title = "Are All Languages Equally Hard to Language-Model?",
      booktitle = "Proceedings of the 2018 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "536--541",
      year = "2018",
      month = jun,
      address = "New Orleans",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-lm",
      }

1490. C. Lin and J. Eisner, “Neural Particle Smoothing for Sampling from Conditional Sequence Models,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), New Orleans, 2018, p. 929–941. doi:10.18653/v1/N18-1085
      [BibTeX] [Link]

      @InProceedings{lin-eisner-2018-naacl,
      aclid = "N18-1085",
      doi = "10.18653/v1/N18-1085",
      author = "Chu-Cheng Lin and Jason Eisner",
      title = "Neural Particle Smoothing for Sampling from
      Conditional Sequence Models",
      booktitle = "Proceedings of the 2018 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "929--941",
      year = "2018",
      month = jun,
      address = "New Orleans",
      URL = "http://cs.jhu.edu/~jason/papers/#lin-eisner-2018-naacl",
      }

1491. R. Cotterell and J. Eisner, “A Deep Generative Model of Vowel Formant Typology,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), New Orleans, 2018, p. 37–46. doi:10.18653/v1/N18-1004
      [BibTeX] [Link]

      @InProceedings{cotterell-eisner-2018-naacl,
      aclid = "N18-1004",
      doi = "10.18653/v1/N18-1004",
      author = "Ryan Cotterell and Jason Eisner",
      title = "A Deep Generative Model of Vowel Formant Typology",
      booktitle = "Proceedings of the 2018 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "37--46",
      year = "2018",
      month = jun,
      address = "New Orleans",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2018-naacl",
      }

1492. W. Wu and D. Yarowsky, “Creating Large-Scale Multilingual Cognate Tables,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @inproceedings{wu-yarowsky-2018-creating,
      title = "Creating Large-Scale Multilingual Cognate Tables",
      author = "Wu, Winston and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Cieri, Christopher and
      Declerck, Thierry and
      Goggi, Sara and
      Hasida, Koiti and
      Isahara, Hitoshi and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, H{\'e}l{\`e}ne and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios and
      Tokunaga, Takenobu",
      booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
      month = may,
      year = "2018",
      address = "Miyazaki, Japan",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L18-1538",
      }

1493. W. Wu, N. Vyas, and D. Yarowsky, “Creating a Translation Matrix of the Bible’s Names Across 591 Languages,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @inproceedings{wu-etal-2018-creating,
      title = "Creating a Translation Matrix of the {B}ible{'}s Names Across 591 Languages",
      author = "Wu, Winston and
      Vyas, Nidhi and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Cieri, Christopher and
      Declerck, Thierry and
      Goggi, Sara and
      Hasida, Koiti and
      Isahara, Hitoshi and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, H{\'e}l{\`e}ne and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios and
      Tokunaga, Takenobu",
      booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
      month = may,
      year = "2018",
      address = "Miyazaki, Japan",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L18-1263",
      }

1494. W. Wu and D. Yarowsky, “Massively Translingual Compound Analysis and Translation Discovery,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @inproceedings{wu-yarowsky-2018-massively,
      title = "Massively Translingual Compound Analysis and Translation Discovery",
      author = "Wu, Winston and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Cieri, Christopher and
      Declerck, Thierry and
      Goggi, Sara and
      Hasida, Koiti and
      Isahara, Hitoshi and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, H{\'e}l{\`e}ne and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios and
      Tokunaga, Takenobu",
      booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
      month = may,
      year = "2018",
      address = "Miyazaki, Japan",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L18-1612",
      }

1495. C. Kirov, R. Cotterell, J. Sylak-Glassman, G. Walther, E. Vylomova, P. Xia, M. Faruqui, S. J. Mielke, A. McCarthy, S. Kübler, D. Yarowsky, J. Eisner, and M. Hulden, “UniMorph 2.0: Universal Morphology,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @inproceedings{kirov-etal-2018-unimorph,
      title = "{U}ni{M}orph 2.0: {U}niversal {M}orphology",
      author = {Kirov, Christo and
      Cotterell, Ryan and
      Sylak-Glassman, John and
      Walther, G{\'e}raldine and
      Vylomova, Ekaterina and
      Xia, Patrick and
      Faruqui, Manaal and
      Mielke, Sabrina J. and
      McCarthy, Arya and
      K{\"u}bler, Sandra and
      Yarowsky, David and
      Eisner, Jason and
      Hulden, Mans},
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Cieri, Christopher and
      Declerck, Thierry and
      Goggi, Sara and
      Hasida, Koiti and
      Isahara, Hitoshi and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, H{\'e}l{\`e}ne and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios and
      Tokunaga, Takenobu",
      booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
      month = may,
      year = "2018",
      address = "Miyazaki, Japan",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L18-1293",
      }

1496. X. Ma, K. Li, and P. Koehn, “An Analysis of Source Context Dependency in Neural Machine Translation,” in Proceedings of the 21st Annual Conference of the European Association for Machine Translation, Alicante, Spain, 2018, p. 209–218.
      [BibTeX] [Abstract] [Link]
      

      The encoder-decoder with attention model has become the state of the art for machine translation. However, more investigations are still needed to understand the internal mechanism of this end-to-end model. In this paper, we focus on how neural machine translation (NMT) models consider source information while decoding. We propose a numerical measurement of source context dependency in the NMT models and analyze the behaviors of the NMT decoder with this measurement under several circumstances. Experimental results show that this measurement is an appropriate estimate for source context dependency and consistent over different domains.

      @inproceedings{ma-etal-2018-analysis,
      title = "An Analysis of Source Context Dependency in Neural Machine Translation",
      author = "Ma, Xutai and
      Li, Ke and
      Koehn, Philipp",
      editor = "P{\'e}rez-Ortiz, Juan Antonio and
      S{\'a}nchez-Mart{\'\i}nez, Felipe and
      Espl{\`a}-Gomis, Miquel and
      Popovi{\'c}, Maja and
      Rico, Celia and
      Martins, Andr{\'e} and
      Van den Bogaert, Joachim and
      Forcada, Mikel L.",
      booktitle = "Proceedings of the 21st Annual Conference of the European Association for Machine Translation",
      month = may,
      year = "2018",
      address = "Alicante, Spain",
      url = "https://aclanthology.org/2018.eamt-main.19",
      pages = "209--218",
      abstract = "The encoder-decoder with attention model has become the state of the art for machine translation. However, more investigations are still needed to understand the internal mechanism of this end-to-end model. In this paper, we focus on how neural machine translation (NMT) models consider source information while decoding. We propose a numerical measurement of source context dependency in the NMT models and analyze the behaviors of the NMT decoder with this measurement under several circumstances. Experimental results show that this measurement is an appropriate estimate for source context dependency and consistent over different domains.",
      }

1497. W. Wu and D. Yarowsky, “A Comparative Study of Extremely Low-Resource Transliteration of the World’s Languages,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @inproceedings{wu-yarowsky-2018-comparative,
      title = "A Comparative Study of Extremely Low-Resource Transliteration of the World{'}s Languages",
      author = "Wu, Winston and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Cieri, Christopher and
      Declerck, Thierry and
      Goggi, Sara and
      Hasida, Koiti and
      Isahara, Hitoshi and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, H{\'e}l{\`e}ne and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios and
      Tokunaga, Takenobu",
      booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
      month = may,
      year = "2018",
      address = "Miyazaki, Japan",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L18-1150",
      }

1498. C. Kirov, R. Cotterell, S. -, G. Walther, E. Vylomova, P. Xia, M. Faruqui, S. J. Mielke, A. D. McCarthy, Sandra Kübler, D. Yarowsky, J. Eisner, and M. Hulden, “UniMorph 2.0: Universal Morphology,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.
      [BibTeX] [Link]

      @InProceedings{UNIMORPH-2018,
      author = "Christo Kirov and Ryan Cotterell and John
      Sylak{-}Glassman and G{\'{e}}raldine Walther and
      Ekaterina Vylomova and Patrick Xia and Manaal Faruqui
      and Sabrina J. Mielke and Arya D. McCarthy and Sandra
      K{\"{u}}bler and David Yarowsky and Jason Eisner and
      Mans Hulden",
      title = "{UniMorph} 2.0: Universal Morphology",
      booktitle = "Proceedings of the Eleventh International Conference
      on Language Resources and Evaluation (LREC 2018)",
      year = "2018",
      month = may,
      address = "Miyazaki, Japan",
      URL = "http://cs.jhu.edu/~jason/papers/#UNIMORPH-2018",
      }

1499. S. Shearing, C. Kirov, H. Khayrallah, and D. Yarowsky, “Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract),” in Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Boston, MA, 2018, p. 132–139.
      [BibTeX] [Link]

      @inproceedings{shearing-etal-2018-improving,
      title = "Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)",
      author = "Shearing, Steven and
      Kirov, Christo and
      Khayrallah, Huda and
      Yarowsky, David",
      editor = "Cherry, Colin and
      Neubig, Graham",
      booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
      month = mar,
      year = "2018",
      address = "Boston, MA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/W18-1813",
      pages = "132--139",
      }

1500. F. Hieber, T. Domhan, M. Denkowski, D. Vilar, A. Sokolov, A. Clifton, and M. Post, “The Sockeye Neural Machine Translation Toolkit at AMTA 2018,” in Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Boston, MA, 2018, p. 200–207.
      [BibTeX] [Link]

      @inproceedings{hieber-etal-2018-sockeye,
      title = "The Sockeye Neural Machine Translation Toolkit at {AMTA} 2018",
      author = "Hieber, Felix and
      Domhan, Tobias and
      Denkowski, Michael and
      Vilar, David and
      Sokolov, Artem and
      Clifton, Ann and
      Post, Matt",
      editor = "Cherry, Colin and
      Neubig, Graham",
      booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
      month = mar,
      year = "2018",
      address = "Boston, MA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/W18-1820",
      pages = "200--207",
      }

1501. R. Knowles and P. Koehn, “Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction,” in Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing, Boston, MA, 2018, p. 35–40.
      [BibTeX] [Link]

      @inproceedings{knowles-koehn-2018-lightweight,
      title = "Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction",
      author = "Knowles, Rebecca and
      Koehn, Philipp",
      editor = "Astudillo, Ram{\'o}n and
      Gra{\c{c}}a, Jo{\~a}o and
      Martins, Andr{\'e}",
      booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
      month = mar,
      year = "2018",
      address = "Boston, MA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/W18-2102",
      pages = "35--40",
      }

1502. R. Marvin and P. Koehn, “Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract),” in Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Boston, MA, 2018, p. 125–131.
      [BibTeX] [Link]

      @inproceedings{marvin-koehn-2018-exploring,
      title = "Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)",
      author = "Marvin, Rebecca and
      Koehn, Philipp",
      editor = "Cherry, Colin and
      Neubig, Graham",
      booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
      month = mar,
      year = "2018",
      address = "Boston, MA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/W18-1812",
      pages = "125--131",
      }

1503. R. Knowles, J. Ortega, and P. Koehn, “A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair,” in Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing, Boston, MA, 2018, p. 249–255.
      [BibTeX] [Link]

      @inproceedings{knowles-etal-2018-comparison,
      title = "A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair",
      author = "Knowles, Rebecca and
      Ortega, John and
      Koehn, Philipp",
      editor = "Astudillo, Ram{\'o}n and
      Gra{\c{c}}a, Jo{\~a}o and
      Martins, Andr{\'e}",
      booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
      month = mar,
      year = "2018",
      address = "Boston, MA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/W18-2108",
      pages = "249--255",
      }

1504. J. Eisner and N. W. Filardo, “Treating Machine Learning Algorithms as Declaratively Specified Circuits,” in Proceedings of the Conference on Systems and Machine Learning (SysML), Palo Alto, 2018.
      [BibTeX] [Link]

      @InProceedings{filardo-eisner-2018-sysml,
      author = "Jason Eisner and Nathaniel Wesley Filardo",
      title = "Treating Machine Learning Algorithms as Declaratively
      Specified Circuits",
      booktitle = "Proceedings of the Conference on Systems and Machine
      Learning (SysML)",
      year = "2018",
      month = feb,
      address = "Palo Alto",
      URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2018-sysml",
      }

1505. D. Wang and J. Eisner, “Predicting Fine-Grained Syntactic Typology from Surface Features,” in Proceedings of the Society for Computation in Linguistics (SCiL), Salt Lake City, 2018. doi:10.7275/R5F769RV
      [BibTeX] [Link]

      @InProceedings{wang-eisner-2018-scil,
      doi = "10.7275/R5F769RV",
      author = "Dingquan Wang and Jason Eisner",
      title = "Predicting Fine-Grained Syntactic Typology from
      Surface Features",
      booktitle = "Proceedings of the Society for Computation in
      Linguistics (SCiL)",
      year = "2018",
      month = jan,
      volume = "1",
      address = "Salt Lake City",
      URL = "http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-scil",
      }

1506. R. Cotterell, C. Kirov, M. Hulden, and J. Eisner, “Quantifying the Trade-off Between Two Types of Morphological Complexity,” in Proceedings of the Society for Computation in Linguistics (SCiL), Salt Lake City, 2018, p. 209–210. doi:10.7275/R57P8WK1
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2018-scil,
      doi = "10.7275/R57P8WK1",
      author = "Ryan Cotterell and Christo Kirov and Mans Hulden and
      Jason Eisner",
      title = "Quantifying the Trade-off Between Two Types of
      Morphological Complexity",
      booktitle = "Proceedings of the Society for Computation in
      Linguistics (SCiL)",
      year = "2018",
      month = jan,
      volume = "1",
      pages = "209--210",
      address = "Salt Lake City",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-scil",
      }

1507. Jun-Cheng Chen, Rajeev Ranjan, Vishal M. Patel, C. Castillo, and R. Chellappa, “Unconstrained Face Identification and Verification Using Deep Convolutional Features.” 2018.
      [BibTeX] [Link]

      @inproceedings{86742791,
      title = {Unconstrained Face Identification and Verification Using Deep Convolutional Features},
      author = {{Jun-Cheng Chen} and {Rajeev Ranjan} and {Vishal M. Patel} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {3},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b3da5ca0428dfa0adbaab9f6c37f8ee4e13c5837},
      }

1508. M. Naphade, Ming-Ching Chang, Anuj Sharma, D. Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, R. Chellappa, Jenq-Neng Hwang, and Siwei Lyu, “The 2018 NVIDIA AI City Challenge,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
      [BibTeX] [Link]

      @inproceedings{53377470,
      title = {The 2018 NVIDIA AI City Challenge},
      author = {{M. Naphade} and {Ming-Ching Chang} and {Anuj Sharma} and {D. Anastasiu} and {Vamsi Jagarlamudi} and {Pranamesh Chakraborty} and {Tingting Huang} and {Shuo Wang} and {Ming-Yu Liu} and {R. Chellappa} and {Jenq-Neng Hwang} and {Siwei Lyu}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      url = {https://www.semanticscholar.org/paper/fb488b445a348af720fff18c1912e7c21b3aeda0},
      }

1509. M. Villemur, P. Julián, and A. Andreou, “Energy aware simplicial processor for embedded morphological visual processing in intelligent internet of things,” in Electronics Letters, 2018.
      [BibTeX] [Link]

      @inproceedings{116056180,
      title = {Energy aware simplicial processor for embedded morphological visual processing in intelligent internet of things},
      author = {{M. Villemur} and {P. Julián} and {A. Andreou}},
      year = 2018,
      month = {2},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/fc43348daae5fca797ecfa13623f3acb73f1b405},
      }

1510. Nanxin Chen, J. Villalba, Yishay Carmiel, and N. Dehak, “Measuring Uncertainty in Deep Regression Models: The Case of Age Estimation from Speech,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{52287060,
      title = {Measuring Uncertainty in Deep Regression Models: The Case of Age Estimation from Speech},
      author = {{Nanxin Chen} and {J. Villalba} and {Yishay Carmiel} and {N. Dehak}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/353d401a96939ff6c5836289077663a1f868ab19},
      }

1511. Kevin Duh, M. Funaro, W. DeGouveia, Sonia Bahlani, Dominic Pappas, S. Najjar, I. Tabansky, R. Moldwin, and Joel N.H. Stern, “Crosstalk between the immune system and neural pathways in interstitial cystitis/bladder pain syndrome.,” in Discover medicine, 2018.
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      @inproceedings{49221985,
      title = {Crosstalk between the immune system and neural pathways in interstitial cystitis/bladder pain syndrome.},
      author = {{Kevin Duh} and {M. Funaro} and {W. DeGouveia} and {Sonia Bahlani} and {Dominic Pappas} and {S. Najjar} and {I. Tabansky} and {R. Moldwin} and {Joel N.H. Stern}},
      year = 2018,
      month = {5},
      booktitle = {Discover medicine},
      url = {https://www.semanticscholar.org/paper/02eea1717c357baa1eab58fc79d59860c0f5b002},
      }

1512. Yuyin Zhou, Yan Wang, Peng Tang, S. Bai, Wei Shen, E. Fishman, and A. Yuille, “Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training,” in arXiv: Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{29157054,
      title = {Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training},
      author = {{Yuyin Zhou} and {Yan Wang} and {Peng Tang} and {S. Bai} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {4},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/ef769353501330596f56858042168708b8e257de},
      }

1513. Pouya Samangouei, Maya Kabkab, and R. Chellappa, “Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models,” in International Conference on Learning Representations, 2018.
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      @inproceedings{3458858,
      title = {Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models},
      author = {{Pouya Samangouei} and {Maya Kabkab} and {R. Chellappa}},
      year = 2018,
      month = {2},
      booktitle = {International Conference on Learning Representations},
      url = {https://www.semanticscholar.org/paper/f7bb1636ced9036b3d0edafc7d82ad43164d41a3},
      }

1514. Puyang Wang, Nick. G. Cuccolo, R. Tyagi, I. Hacihaliloglu, and Vishal M. Patel, “Automatic real-time CNN-based neonatal brain ventricles segmentation,” in IEEE International Symposium on Biomedical Imaging, 2018.
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      @inproceedings{44117685,
      title = {Automatic real-time CNN-based neonatal brain ventricles segmentation},
      author = {{Puyang Wang} and {Nick. G. Cuccolo} and {R. Tyagi} and {I. Hacihaliloglu} and {Vishal M. Patel}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Symposium on Biomedical Imaging},
      url = {https://www.semanticscholar.org/paper/4ec2f32591b3918a14079853991c507a1afc77fc},
      }

1515. Yuan Gao, Qi She, Jiayi Ma, Mingbo Zhao, W. Liu, and A. Yuille, “NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction,” in Computer Vision and Pattern Recognition, 2018.
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      @inproceedings{3862519,
      title = {NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction},
      author = {{Yuan Gao} and {Qi She} and {Jiayi Ma} and {Mingbo Zhao} and {W. Liu} and {A. Yuille}},
      year = 2018,
      month = {1},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/8c8d0031b24937d8a8ec7a4c5ab5fda4f4797803},
      }

1516. Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, E. Fishman, and A. Yuille, “Abdominal multi-organ segmentation with organ-attention networks and statistical fusion,” in Medical Image Anal., 2018.
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      @inproceedings{5077252,
      title = {Abdominal multi-organ segmentation with organ-attention networks and statistical fusion},
      author = {{Yan Wang} and {Yuyin Zhou} and {Wei Shen} and {Seyoun Park} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {4},
      booktitle = {Medical Image Anal.},
      url = {https://www.semanticscholar.org/paper/128666e81254858480661e7f9a67461031599ab1},
      }

1517. David Snyder, D. Garcia-Romero, Gregory Sell, A. McCree, Daniel Povey, and S. Khudanpur, “FOR MULTI-SPEAKER CONVERSATIONS USING X-VECTORS.” 2018.
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      @inproceedings{85554700,
      title = {FOR MULTI-SPEAKER CONVERSATIONS USING X-VECTORS},
      author = {{David Snyder} and {D. Garcia-Romero} and {Gregory Sell} and {A. McCree} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b808cfac9c44f27d3716f9280dad4dc2a9bbc8df},
      }

1518. Ankan Bansal, Rajeev Ranjan, C. Castillo, and R. Chellappa, “Deep Features for Recognizing Disguised Faces in the Wild,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
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      @inproceedings{53560998,
      title = {Deep Features for Recognizing Disguised Faces in the Wild},
      author = {{Ankan Bansal} and {Rajeev Ranjan} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      url = {https://www.semanticscholar.org/paper/a50fa5048c61209149de0711b5f1b1806b43da00},
      }

1519. J. Ayers, Theodore L. Caputi, Camille Nebeker, and Mark Dredze, “Don’t quote me: reverse identification of research participants in social media studies,” in npj Digital Medicine, 2018.
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      @inproceedings{51905780,
      title = {Don’t quote me: reverse identification of research participants in social media studies},
      author = {{J. Ayers} and {Theodore L. Caputi} and {Camille Nebeker} and {Mark Dredze}},
      year = 2018,
      month = {8},
      booktitle = {npj Digital Medicine},
      url = {https://www.semanticscholar.org/paper/1ece7c00d2eb6fca5443ff8e15f05a2b8b5985c2},
      }

1520. Chenglin Yang, Lingxi Xie, Siyuan Qiao, and A. Yuille, “Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students,” in AAAI Conference on Artificial Intelligence, 2018.
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      @inproceedings{54986302,
      title = {Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students},
      author = {{Chenglin Yang} and {Lingxi Xie} and {Siyuan Qiao} and {A. Yuille}},
      year = 2018,
      month = {5},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/e2c72b79c2f3ca6b980c540b821323467456ad4a},
      }

1521. Ondrej Bojar, Rajen Chatterjee, C. Federmann, Mark Fishel, Yvette Graham, B. Haddow, Matthias Huck, A. Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, M. Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin M. Verspoor, “Proceedings of the Third Conference on Machine Translation: Shared Task Papers.” 2018.
      [BibTeX] [Link]

      @inproceedings{53224644,
      title = {Proceedings of the Third Conference on Machine Translation: Shared Task Papers},
      author = {{Ondrej Bojar} and {Rajen Chatterjee} and {C. Federmann} and {Mark Fishel} and {Yvette Graham} and {B. Haddow} and {Matthias Huck} and {A. Yepes} and {Philipp Koehn} and {Christof Monz} and {Matteo Negri} and {Aurélie Névéol} and {M. Neves} and {Matt Post} and {Lucia Specia} and {Marco Turchi} and {Karin M. Verspoor}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/32f6756542eafed5906783dcc6567057f95550f4},
      }

1522. Pegah Ghahremani, P. S. Nidadavolu, Nanxin Chen, J. Villalba, Daniel Povey, S. Khudanpur, and N. Dehak, “End-to-end Deep Neural Network Age Estimation,” in Interspeech, 2018.
      [BibTeX] [Link]

      @inproceedings{52192343,
      title = {End-to-end Deep Neural Network Age Estimation},
      author = {{Pegah Ghahremani} and {P. S. Nidadavolu} and {Nanxin Chen} and {J. Villalba} and {Daniel Povey} and {S. Khudanpur} and {N. Dehak}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/c936edddcb803b9eb065b6128c6d0e28d5234db1},
      }

1523. Rashmi Sankepally, Tongfei Chen, Benjamin Van Durme, and Douglas W. Oard, “A Test Collection for Coreferent Mention Retrieval,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018.
      [BibTeX] [Link]

      @inproceedings{44180532,
      title = {A Test Collection for Coreferent Mention Retrieval},
      author = {{Rashmi Sankepally} and {Tongfei Chen} and {Benjamin Van Durme} and {Douglas W. Oard}},
      year = 2018,
      month = {6},
      booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
      url = {https://www.semanticscholar.org/paper/e5b0c46bfb1e48c6da2b12256eeac7c49ad9cd85},
      }

1524. H. Inaguma, X. Zhang, Z. Wang, A. Renduchintala, S. Watanabe, and K. Duh, “The JHU/KyotoU Speech Translation System for IWSLT 2018,” in Proceedings of the 15th International Conference on Spoken Language Translation, Brussels, 2018, p. 153–159.
      [BibTeX] [Abstract] [Link]
      

      This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system{‘}s parameters is important for training on small datasets.

      @inproceedings{inaguma-etal-2018-jhu,
      title = "The {JHU}/{K}yoto{U} Speech Translation System for {IWSLT} 2018",
      author = "Inaguma, Hirofumi and
      Zhang, Xuan and
      Wang, Zhiqi and
      Renduchintala, Adithya and
      Watanabe, Shinji and
      Duh, Kevin",
      editor = "Turchi, Marco and
      Niehues, Jan and
      Frederico, Marcello",
      booktitle = "Proceedings of the 15th International Conference on Spoken Language Translation",
      month = oct # " 29-30",
      year = "2018",
      address = "Brussels",
      publisher = "International Conference on Spoken Language Translation",
      url = "https://aclanthology.org/2018.iwslt-1.23",
      pages = "153--159",
      abstract = "This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system{'}s parameters is important for training on small datasets.",
      }

1525. Theodore L. Caputi, E. Leas, Mark Dredze, and J. Ayers, “Online Sales of Marijuana: An Unrecognized Public Health Dilemma.,” in American Journal of Preventive Medicine, 2018.
      [BibTeX] [Link]

      @inproceedings{4340903,
      title = {Online Sales of Marijuana: An Unrecognized Public Health Dilemma.},
      author = {{Theodore L. Caputi} and {E. Leas} and {Mark Dredze} and {J. Ayers}},
      year = 2018,
      month = {3},
      booktitle = {American Journal of Preventive Medicine},
      url = {https://www.semanticscholar.org/paper/67f7bdd965ed18165acc66bef3b04a6bd4cff28d},
      }

1526. Cathryn S. Cortesa, Jonathan D. Jones, Gregory Hager, S. Khudanpur, B. Landau, and A. Shelton, “Constraints and Development in Children’s Block Construction,” in Annual Meeting of the Cognitive Science Society, 2018.
      [BibTeX] [Link]

      @inproceedings{117728959,
      title = {Constraints and Development in Children's Block Construction},
      author = {{Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {B. Landau} and {A. Shelton}},
      year = 2018,
      booktitle = {Annual Meeting of the Cognitive Science Society},
      url = {https://www.semanticscholar.org/paper/0c1afbd9626b55e21ec44de1de55cb6bd44b744b},
      }

1527. Carolina Lugo-Fagundo, B. Vogelstein, A. Yuille, and E. Fishman, “Deep Learning in Radiology: Now the Real Work Begins.,” in Journal of the American College of Radiology, 2018.
      [BibTeX] [Link]

      @inproceedings{46863624,
      title = {Deep Learning in Radiology: Now the Real Work Begins.},
      author = {{Carolina Lugo-Fagundo} and {B. Vogelstein} and {A. Yuille} and {E. Fishman}},
      year = 2018,
      booktitle = {Journal of the American College of Radiology},
      url = {https://www.semanticscholar.org/paper/1b16160d4e58dac5b7ead14afa8046fc53df01ea},
      }

1528. Poorya Mianjy and R. Arora, “Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization,” in International Conference on Machine Learning, 2018.
      [BibTeX] [Link]

      @inproceedings{64526289,
      title = {Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization},
      author = {{Poorya Mianjy} and {R. Arora}},
      year = 2018,
      month = {7},
      booktitle = {International Conference on Machine Learning},
      url = {https://www.semanticscholar.org/paper/64aff9d0225cf83c46d3dbe20179f0d8a3a5e8d0},
      }

1529. Vijayaditya Peddinti, Yiming Wang, Daniel Povey, and S. Khudanpur, “Low Latency Acoustic Modeling Using Temporal Convolution and LSTMs,” in IEEE Signal Processing Letters, 2018.
      [BibTeX] [Link]

      @inproceedings{3386998,
      title = {Low Latency Acoustic Modeling Using Temporal Convolution and LSTMs},
      author = {{Vijayaditya Peddinti} and {Yiming Wang} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      month = {3},
      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/4c0f4fa6f38f14c66c89528d9d62bc868bdc2d4a},
      }

1530. Matthew Maciejewski, David Snyder, Vimal Manohar, N. Dehak, and S. Khudanpur, “Characterizing Performance of Speaker Diarization Systems on Far-Field Speech Using Standard Methods,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{52291292,
      title = {Characterizing Performance of Speaker Diarization Systems on Far-Field Speech Using Standard Methods},
      author = {{Matthew Maciejewski} and {David Snyder} and {Vimal Manohar} and {N. Dehak} and {S. Khudanpur}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/70217e8f8655923cfe1298c7b10be4fe2c1bab88},
      }

1531. Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, R. Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, and Berlin Chen, “Looking for ELMo’s friends: Sentence-Level Pretraining Beyond Language Modeling,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{57189285,
      title = {Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling},
      author = {{Samuel R. Bowman} and {Ellie Pavlick} and {Edouard Grave} and {Benjamin Van Durme} and {Alex Wang} and {Jan Hula} and {Patrick Xia} and {R. Pappagari} and {R. Thomas McCoy} and {Roma Patel} and {Najoung Kim} and {Ian Tenney} and {Yinghui Huang} and {Katherin Yu} and {Shuning Jin} and {Berlin Chen}},
      year = 2018,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/256623ff025f36d343588bcd0b966c1fd26afcf8},
      }

1532. Chang Liu, F. Sun, Changhu Wang, Feng Wang, and A. Yuille, “WEOTAWEO 2 LSTM WEO 1 Encoding WSS 1 WSS 2 WSSTB Decoding Attention Cell.” 2018.
      [BibTeX] [Link]

      @inproceedings{197640428,
      title = {WEOTAWEO 2 LSTM WEO 1 Encoding WSS 1 WSS 2 WSSTB Decoding Attention Cell},
      author = {{Chang Liu} and {F. Sun} and {Changhu Wang} and {Feng Wang} and {A. Yuille}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/2e5ae83f9f44b606898b1795906de5464ac3482e},
      }

1533. Iskandar Atakhodjaev, B. Bosworth, Brian C. Grubel, M. Kossey, J. Villalba, A. Cooper, N. Dehak, A. Foster, and M. Foster, “Investigation of Deep Learning Attacks on Nonlinear Silicon Photonic PUFs,” in Conference on Lasers and Electro-Optics, 2018.
      [BibTeX] [Link]

      @inproceedings{51977464,
      title = {Investigation of Deep Learning Attacks on Nonlinear Silicon Photonic PUFs},
      author = {{Iskandar Atakhodjaev} and {B. Bosworth} and {Brian C. Grubel} and {M. Kossey} and {J. Villalba} and {A. Cooper} and {N. Dehak} and {A. Foster} and {M. Foster}},
      year = 2018,
      month = {5},
      booktitle = {Conference on Lasers and Electro-Optics},
      url = {https://www.semanticscholar.org/paper/d8a076181efd0f7a88bb272fc40ac804ac2d7c21},
      }

1534. A. S. White, R. Rudinger, K. Rawlins, and B. Van Durme, “Lexicosyntactic Inference in Neural Models,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 4717–4724. doi:10.18653/v1/D18-1501
      [BibTeX] [Abstract] [Link]
      

      We investigate neural models{‘} ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.

      @inproceedings{white-etal-2018-lexicosyntactic,
      title = "Lexicosyntactic Inference in Neural Models",
      author = "White, Aaron Steven and
      Rudinger, Rachel and
      Rawlins, Kyle and
      Van Durme, Benjamin",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1501",
      doi = "10.18653/v1/D18-1501",
      pages = "4717--4724",
      abstract = "We investigate neural models{'} ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.",
      }

1535. Yuki Lama, Tao Chen, Mark Dredze, Amelia M. Jamison, S. Quinn, and David A. Broniatowski, “Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis,” in Journal of Medical Internet Research, 2018.
      [BibTeX] [Link]

      @inproceedings{52275517,
      title = {Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis},
      author = {{Yuki Lama} and {Tao Chen} and {Mark Dredze} and {Amelia M. Jamison} and {S. Quinn} and {David A. Broniatowski}},
      year = 2018,
      month = {9},
      booktitle = {Journal of Medical Internet Research},
      url = {https://www.semanticscholar.org/paper/9089dbdeb2b9bb82195f7f893b3c028425c7f36c},
      }

1536. Tao Chen and Mark Dredze, “Vaccine Images on Twitter: Analysis of What Images are Shared,” in Journal of Medical Internet Research, 2018.
      [BibTeX] [Link]

      @inproceedings{4594103,
      title = {Vaccine Images on Twitter: Analysis of What Images are Shared},
      author = {{Tao Chen} and {Mark Dredze}},
      year = 2018,
      month = {4},
      booktitle = {Journal of Medical Internet Research},
      url = {https://www.semanticscholar.org/paper/0dfaf8504138e212d71eb0cc4940604eccfa7183},
      }

1537. Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. E. Hu, Ellie Pavlick, A. White, and Benjamin Van Durme, “Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{5066983,
      title = {Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations},
      author = {{Adam Poliak} and {Aparajita Haldar} and {Rachel Rudinger} and {J. E. Hu} and {Ellie Pavlick} and {A. White} and {Benjamin Van Durme}},
      year = 2018,
      month = {4},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c40665520563fb872b051bd27d3b43e042869ba4},
      }

1538. Xiaodong Liu, Wei Li, Yuwei Fang, Aerin Kim, Kevin Duh, and Jianfeng Gao, “Stochastic Answer Networks for SQuAD 2.0,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{52824259,
      title = {Stochastic Answer Networks for SQuAD 2.0},
      author = {{Xiaodong Liu} and {Wei Li} and {Yuwei Fang} and {Aerin Kim} and {Kevin Duh} and {Jianfeng Gao}},
      year = 2018,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/d0095adcaa33bc549e273a824c3b66d92897fad8},
      }

1539. Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, J. Trmal, Zhongqiang Huang, S. Khudanpur, and N. Dehak, “The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{195346736,
      title = {The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection},
      author = {{Matthew Wiesner} and {Chunxi Liu} and {Lucas Ondel} and {Craig Harman} and {Vimal Manohar} and {J. Trmal} and {Zhongqiang Huang} and {S. Khudanpur} and {N. Dehak}},
      year = 2018,
      month = {2},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/cf6ac917a3fa0610242b465bc24e32e9297ec706},
      }

1540. Chris Paxton, Yotam Barnoy, Kapil D. Katyal, R. Arora, and Gregory Hager, “Visual Robot Task Planning,” in IEEE International Conference on Robotics and Automation, 2018.
      [BibTeX] [Link]

      @inproceedings{4560151,
      title = {Visual Robot Task Planning},
      author = {{Chris Paxton} and {Yotam Barnoy} and {Kapil D. Katyal} and {R. Arora} and {Gregory Hager}},
      year = 2018,
      month = {3},
      booktitle = {IEEE International Conference on Robotics and Automation},
      url = {https://www.semanticscholar.org/paper/fba7f7b8d606d1ef276a4f6256cdb5acfe37a337},
      }

1541. Yonatan Belinkov, Adam Poliak, Stuart M. Shieber, and Benjamin Van Durme, “Mitigating Bias in Natural Language Inference Using Adversarial Learning.” 2018.
      [BibTeX] [Link]

      @inproceedings{86558012,
      title = {Mitigating Bias in Natural Language Inference Using Adversarial Learning},
      author = {{Yonatan Belinkov} and {Adam Poliak} and {Stuart M. Shieber} and {Benjamin Van Durme}},
      year = 2018,
      month = {9},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a3d2271bd360f9e5c2ac5a24e5e3945329cc1ca8},
      }

1542. R. Rudinger, A. Teichert, R. Culkin, S. Zhang, and B. Van Durme, “Neural-Davidsonian Semantic Proto-role Labeling,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 944–955. doi:10.18653/v1/D18-1114
      [BibTeX] [Abstract] [Link]
      

      We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.

      @inproceedings{rudinger-etal-2018-neural,
      title = "Neural-{D}avidsonian Semantic Proto-role Labeling",
      author = "Rudinger, Rachel and
      Teichert, Adam and
      Culkin, Ryan and
      Zhang, Sheng and
      Van Durme, Benjamin",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1114",
      doi = "10.18653/v1/D18-1114",
      pages = "944--955",
      abstract = "We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.",
      }

1543. Pamela Shapiro and Kevin Duh, “BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{52167331,
      title = {BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis},
      author = {{Pamela Shapiro} and {Kevin Duh}},
      year = 2018,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/1e6d69f8cfd698b8139cde557252753b22c7d712},
      }

1544. Najoung Kim, Kyle Rawlins, Benjamin Van Durme, and P. Smolensky, “Predicting the Argumenthood of English Prepositional Phrases,” in AAAI Conference on Artificial Intelligence, 2018.
      [BibTeX] [Link]

      @inproceedings{69833386,
      title = {Predicting the Argumenthood of English Prepositional Phrases},
      author = {{Najoung Kim} and {Kyle Rawlins} and {Benjamin Van Durme} and {P. Smolensky}},
      year = 2018,
      month = {9},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/4b9bdc6b9a53ea860a8664330f65c49fb40b70a1},
      }

1545. Hainan Xu, Tongfei Chen, Dongji Gao, Yiming Wang, Ke Li, N. Goel, Yishay Carmiel, Daniel Povey, and S. Khudanpur, “A Pruned Rnnlm Lattice-Rescoring Algorithm for Automatic Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{52284302,
      title = {A Pruned Rnnlm Lattice-Rescoring Algorithm for Automatic Speech Recognition},
      author = {{Hainan Xu} and {Tongfei Chen} and {Dongji Gao} and {Yiming Wang} and {Ke Li} and {N. Goel} and {Yishay Carmiel} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/fd8d157e11d840cd89fe2fd7b136f23723cb0e9d},
      }

1546. He Zhang, Vishwanath A. Sindagi, and Vishal M. Patel, “Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
      [BibTeX] [Link]

      @inproceedings{52860200,
      title = {Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network},
      author = {{He Zhang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      url = {https://www.semanticscholar.org/paper/fe7a1ba13abbf391a638d2da18dfbbb7202684cd},
      }

1547. Alexey Kurakin, I. Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, A. Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, and Motoki Abe, “Adversarial Attacks and Defences Competition,” in arXiv.org, 2018.
      [BibTeX] [Link]

      @inproceedings{4551367,
      title = {Adversarial Attacks and Defences Competition},
      author = {{Alexey Kurakin} and {I. Goodfellow} and {Samy Bengio} and {Yinpeng Dong} and {Fangzhou Liao} and {Ming Liang} and {Tianyu Pang} and {Jun Zhu} and {Xiaolin Hu} and {Cihang Xie} and {Jianyu Wang} and {Zhishuai Zhang} and {Zhou Ren} and {A. Yuille} and {Sangxia Huang} and {Yao Zhao} and {Yuzhe Zhao} and {Zhonglin Han} and {Junjiajia Long} and {Yerkebulan Berdibekov} and {Takuya Akiba} and {Seiya Tokui} and {Motoki Abe}},
      year = 2018,
      month = {3},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/ca5642f522cd2cd44948c7e9f337c91e5f26fdcf},
      }

1548. M. Naphade, Ming-Ching Chang, Anuj Sharma, D. Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, R. Chellappa, Jenq-Neng Hwang, and Siwei Lyu, “The NVIDIA AI City Challenge 2018.” 2018.
      [BibTeX] [Link]

      @inproceedings{53982224,
      title = {The NVIDIA AI City Challenge 2018},
      author = {{M. Naphade} and {Ming-Ching Chang} and {Anuj Sharma} and {D. Anastasiu} and {Vamsi Jagarlamudi} and {Pranamesh Chakraborty} and {Tingting Huang} and {Shuo Wang} and {Ming-Yu Liu} and {R. Chellappa} and {Jenq-Neng Hwang} and {Siwei Lyu}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e23c0ab73b8a098d6e3e01200cade2d7603c70e3},
      }

1549. Ahmed M. Ali, N. Dehak, P. Cardinal, Sameer Khurana, S. Yella, James R. Glass, P. Bell, and S. Renals, “C L ] 1 1 A ug 2 01 6 Automatic Dialect Detection in Arabic Broadcast Speech.” 2018.
      [BibTeX] [Link]

      @inproceedings{160029518,
      title = {C L ] 1 1 A ug 2 01 6 Automatic Dialect Detection in Arabic Broadcast Speech},
      author = {{Ahmed M. Ali} and {N. Dehak} and {P. Cardinal} and {Sameer Khurana} and {S. Yella} and {James R. Glass} and {P. Bell} and {S. Renals}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/dc29a72b5f8d06e42d07a46ef582d59c84bbaef3},
      }

1550. Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, E. Fishman, and A. Yuille, “Bridging the Gap Between 2D and 3D Organ Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018.
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      title = {Bridging the Gap Between 2D and 3D Organ Segmentation},
      author = {{Yingda Xia} and {Lingxi Xie} and {Fengze Liu} and {Zhuotun Zhu} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {4},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/8388d1ef48d17b77d2c6784ab4836f97d1144a67},
      }

1551. Daniel Povey, Gaofeng Cheng, Yiming Wang, Ke Li, Hainan Xu, M. Yarmohammadi, and S. Khudanpur, “Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks,” in Interspeech, 2018.
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      title = {Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks},
      author = {{Daniel Povey} and {Gaofeng Cheng} and {Yiming Wang} and {Ke Li} and {Hainan Xu} and {M. Yarmohammadi} and {S. Khudanpur}},
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      }

1552. Ruben Zazo, Phani Sankar Nidadavolu, Nanxin Chen, J. González-Rodríguez, and N. Dehak, “Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks,” in IEEE Access, 2018.
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      author = {{Ruben Zazo} and {Phani Sankar Nidadavolu} and {Nanxin Chen} and {J. González-Rodríguez} and {N. Dehak}},
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1553. R. Knowles and P. Koehn, “Context and Copying in Neural Machine Translation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 3034–3041. doi:10.18653/v1/D18-1339
      [BibTeX] [Abstract] [Link]
      

      Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.

      @inproceedings{knowles-koehn-2018-context,
      title = "Context and Copying in Neural Machine Translation",
      author = "Knowles, Rebecca and
      Koehn, Philipp",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1339",
      doi = "10.18653/v1/D18-1339",
      pages = "3034--3041",
      abstract = "Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.",
      }

1554. Aakarsh Malhotra, Richa Singh, Mayank Vatsa, and Vishal M. Patel, “Person Authentication Using Head Images,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
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      title = {Person Authentication Using Head Images},
      author = {{Aakarsh Malhotra} and {Richa Singh} and {Mayank Vatsa} and {Vishal M. Patel}},
      year = 2018,
      month = {3},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/b9b0b8807a206eee79e69e9abe6c346f9e2eae9b},
      }

1555. Mahdi Abavisani and Vishal M. Patel, “Adversarial domain adaptive subspace clustering,” in International Conference on Identity, Security and Behavior Analysis, 2018.
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      url = {https://www.semanticscholar.org/paper/5262e3cf23e9fef86010bed22e69dd41284657a6},
      }

1556. Nicholas Huang and Mounya Elhilali, “Neural underpinnnings of auditory salience natural soundscapes,” in bioRxiv, 2018.
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      }

1557. Poorya Mianjy, R. Arora, and R. Vidal, “On the Implicit Bias of Dropout,” in International Conference on Machine Learning, 2018.
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      title = {On the Implicit Bias of Dropout},
      author = {{Poorya Mianjy} and {R. Arora} and {R. Vidal}},
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      }

1558. A. Zhan, Srihari Mohan, Christopher G. Tarolli, Ruth B. Schneider, Jamie L. Adams, Saloni Sharma, M. Elson, Kelsey L. Spear, Alistair Glidden, Max A. Little, A. Terzis, E. Dorsey, and S. Saria, “Faculty Opinions recommendation of Using smartphones and machine learning to quantify parkinson disease severity: the mobile parkinson disease score.,” in Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature, 2018.
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1559. L. Moro-Velázquez, Jorge Andrés Gómez García, Juan Ignacio Godino-Llorente, J. Rusz, S. Skodda, F. Grandas, J. Velazquez, J. Orozco-Arroyave, Elmar Nöth, and N. Dehak, “Study of the Automatic Detection of Parkison’s Disease Based on Speaker Recognition Technologies and Allophonic Distillation,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018.
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1560. Shuoyang Ding and Kevin Duh, “How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?,” in arXiv.org, 2018.
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1561. David A. Broniatowski, Amelia M. Jamison, SiHua Qi, Lulwah Alkulaib, Tao Chen, Adrian Benton, S. Quinn, and Mark Dredze, “Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate,” in American Journal of Public Health, 2018.
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1562. Poorya Mianjy and R. Arora, “Stochastic PCA with (cid:96) 2 and (cid:96) 1 Regularization.” 2018.
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1563. Yunhan Zhao, Ye Tian, Wei Shen, and A. Yuille, “Towards Resisting Large Data Variations via Introspective Learning,” in arXiv: Computer Vision and Pattern Recognition, 2018.
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1564. Yunhan Zhao, Ye Tian, Wei Shen, and A. Yuille, “Spatial Transformer Introspective Neural Network,” in arXiv.org, 2018.
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      author = {{Yunhan Zhao} and {Ye Tian} and {Wei Shen} and {A. Yuille}},
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      month = {5},
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      }

1565. A. Yuille and Chenxi Liu, “Deep Nets: What have They Ever Done for Vision?,” in International Journal of Computer Vision, 2018.
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      }

1566. Richard J. Chen, Faisal Mahmood, A. Yuille, and N. Durr, “Rethinking Monocular Depth Estimation with Adversarial Training,” in arXiv.org, 2018.
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1567. M. Hasegawa-Johnson, N. Dehak, and O. Scharenborg, “Position Paper : Indirect Supervision for Dialog Systems in Unwritten Languages.” 2018.
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1568. Xiang Wu, Huaibo Huang, Vishal M. Patel, R. He, and Zhenan Sun, “Disentangled Variational Representation for Heterogeneous Face Recognition,” in AAAI Conference on Artificial Intelligence, 2018.
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1569. Suhas Lohit, Ankan Bansal, Nitesh Shroff, Jaishanker K. Pillai, P. Turaga, and R. Chellappa, “Predicting Dynamical Evolution of Human Activities from a Single Image,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
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      author = {{Suhas Lohit} and {Ankan Bansal} and {Nitesh Shroff} and {Jaishanker K. Pillai} and {P. Turaga} and {R. Chellappa}},
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1570. Zhehuai Chen, Justin Luitjens, Hainan Xu, Yiming Wang, Daniel Povey, and S. Khudanpur, “A GPU-based WFST Decoder with Exact Lattice Generation,” in Interspeech, 2018.
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      }

1571. O. Scharenborg, Patrick Ebel, M. Hasegawa-Johnson, and N. Dehak, “Building an ASR System for Mboshi Using A Cross-Language Definition of Acoustic Units Approach,” in Workshop on Spoken Language Technologies for Under-resourced Languages, 2018.
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      title = {Building an ASR System for Mboshi Using A Cross-Language Definition of Acoustic Units Approach},
      author = {{O. Scharenborg} and {Patrick Ebel} and {M. Hasegawa-Johnson} and {N. Dehak}},
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1572. R. Arora, V. Braverman, and Jalaj Upadhyay, “Differentially Private Robust Low-Rank Approximation,” in Neural Information Processing Systems, 2018.
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      }

1573. Puyang Wang, Vishal M. Patel, and I. Hacihaliloglu, “Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018.
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1574. Amit Kumar and R. Chellappa, “Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
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1575. Arthita Ghosh, Max Ehrlich, Sohil Shah, L. Davis, and R. Chellappa, “Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
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1576. Li Liu, Jie Chen, P. Fieguth, Guoying Zhao, R. Chellappa, and M. Pietikäinen, “A Survey of Recent Advances in Texture Representation,” in arXiv.org, 2018.
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1577. Hainan Xu, Ke Li, Yiming Wang, Jian Wang, Shiyin Kang, Xie Chen, Daniel Povey, and S. Khudanpur, “Neural Network Language Modeling with Letter-Based Features and Importance Sampling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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1578. Yuan Gao and A. Yuille, “: Estimation of 3D Category-Specific Object Structure: Symmetry, Manhattan and/or Multiple Images.” 2018.
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1579. Pouya Samangouei, Mahyar Najibi, L. Davis, and R. Chellappa, “Face-MagNet: Magnifying Feature Maps to Detect Small Faces,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
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1580. Glen A. Coppersmith, Ryan Leary, P. Crutchley, and Alex B. Fine, “Natural Language Processing of Social Media as Screening for Suicide Risk,” in Biomedical Informatics Insights, 2018.
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1581. Shuoyang Ding, Huda Khayrallah, Philipp Koehn, Matt Post, Manish Kumar, and Kevin Duh, “Machine Translation Systems for WMT 2017.” 2018.
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1582. Maya Kabkab, Pouya Samangouei, and R. Chellappa, “Task-Aware Compressed Sensing with Generative Adversarial Networks,” in AAAI Conference on Artificial Intelligence, 2018.
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1583. J. Choi, K. M. Irick, Justin Hardin, Weichao Qiu, A. Yuille, Jack Sampson, and N. Vijaykrishnan, “Stochastic Functional Verification of DNN Design through Progressive Virtual Dataset Generation,” in International Symposium on Circuits and Systems, 2018.
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1584. Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, J. Trmal, Zhongqiang Huang, N. Dehak, and S. Khudanpur, “Automatic Speech Recognition and Topic Identification from Speech for Almost-Zero-Resource Languages,” in Interspeech, 2018.
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      url = {https://www.semanticscholar.org/paper/6a6fe404d780856926dfaabb43120726bccb02e5},
      }

1585. Pramuditha Perera and Vishal M. Patel, “Learning Deep Features for One-Class Classification,” in IEEE Transactions on Image Processing, 2018.
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      @inproceedings{27289715,
      title = {Learning Deep Features for One-Class Classification},
      author = {{Pramuditha Perera} and {Vishal M. Patel}},
      year = 2018,
      month = {1},
      booktitle = {IEEE Transactions on Image Processing},
      url = {https://www.semanticscholar.org/paper/732c21998e251d64cd58b6a86886ee5907efeaa5},
      }

1586. Peng Tang, Xinggang Wang, S. Bai, Wei Shen, X. Bai, Wenyu Liu, and A. Yuille, “PCL: Proposal Cluster Learning for Weakly Supervised Object Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
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      @inproceedings{49664409,
      title = {PCL: Proposal Cluster Learning for Weakly Supervised Object Detection},
      author = {{Peng Tang} and {Xinggang Wang} and {S. Bai} and {Wei Shen} and {X. Bai} and {Wenyu Liu} and {A. Yuille}},
      year = 2018,
      month = {7},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/2a86bcdfb1d817ddb76ba202319f8267a36c0f62},
      }

1587. Yan Wang, Lingxi Xie, Siyuan Qiao, Ya Zhang, Wenjun Zhang, and A. Yuille, “Multi-Scale Spatially-Asymmetric Recalibration for Image Classification,” in European Conference on Computer Vision, 2018.
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      @inproceedings{4624457,
      title = {Multi-Scale Spatially-Asymmetric Recalibration for Image Classification},
      author = {{Yan Wang} and {Lingxi Xie} and {Siyuan Qiao} and {Ya Zhang} and {Wenjun Zhang} and {A. Yuille}},
      year = 2018,
      month = {4},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/d4a06ba45f59844edeb9e94864c353291cb50d83},
      }

1588. Daniel Povey, Hossein Hadian, Pegah Ghahremani, Ke Li, and S. Khudanpur, “A Time-Restricted Self-Attention Layer for ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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      @inproceedings{46974195,
      title = {A Time-Restricted Self-Attention Layer for ASR},
      author = {{Daniel Povey} and {Hossein Hadian} and {Pegah Ghahremani} and {Ke Li} and {S. Khudanpur}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a6e4beb28b345fce7470da122b4e45e2cd0dcd12},
      }

1589. Xiaodong Liu, Kevin Duh, and Jianfeng Gao, “Stochastic Answer Networks for Natural Language Inference,” in arXiv.org, 2018.
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      @inproceedings{5058361,
      title = {Stochastic Answer Networks for Natural Language Inference},
      author = {{Xiaodong Liu} and {Kevin Duh} and {Jianfeng Gao}},
      year = 2018,
      month = {4},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/6084b58d8b4b0caf3a2a7f3a1bee1cc527927e39},
      }

1590. Puyang Wang and Vishal M. Patel, “Generating high quality visible images from SAR images using CNNs,” in International Radar Conference, 2018.
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      @inproceedings{3591883,
      title = {Generating high quality visible images from SAR images using CNNs},
      author = {{Puyang Wang} and {Vishal M. Patel}},
      year = 2018,
      month = {2},
      booktitle = {International Radar Conference},
      url = {https://www.semanticscholar.org/paper/d24c0596e1badb6cd2de97a6b21a769789a238f8},
      }

1591. Pegah Ghahremani, Hossein Hadian, Hang Lv, Daniel Povey, and S. Khudanpur, “Acoustic Modeling from Frequency Domain Representations of Speech,” in Interspeech, 2018.
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      @inproceedings{52193565,
      title = {Acoustic Modeling from Frequency Domain Representations of Speech},
      author = {{Pegah Ghahremani} and {Hossein Hadian} and {Hang Lv} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/476a781840a3a906cc8fdb045108c4702e089601},
      }

1592. Kayode A. Sanni, Tomas Figliolia, Gaspar Tognetti, P. Pouliquen, and A. Andreou, “A Charge-Based Architecture for Energy-Efficient Vector-Vector Multiplication in 65nm CMOS,” in International Symposium on Circuits and Systems, 2018.
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      @inproceedings{53086238,
      title = {A Charge-Based Architecture for Energy-Efficient Vector-Vector Multiplication in 65nm CMOS},
      author = {{Kayode A. Sanni} and {Tomas Figliolia} and {Gaspar Tognetti} and {P. Pouliquen} and {A. Andreou}},
      year = 2018,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/df50e7680d54dc67dc6bd5b24042800abcabb358},
      }

1593. Amit Kumar, Pirazh Khorramshahi, Wei-An Lin, Prithviraj Dhar, Jun-Cheng Chen, and R. Chellappa, “A Semi-Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
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      @inproceedings{53512560,
      title = {A Semi-Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos},
      author = {{Amit Kumar} and {Pirazh Khorramshahi} and {Wei-An Lin} and {Prithviraj Dhar} and {Jun-Cheng Chen} and {R. Chellappa}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      url = {https://www.semanticscholar.org/paper/e45f68147a64fdadb64cf8103a486d5d0986f9e5},
      }

1594. L. Moro-Velázquez, Jorge Andrés Gómez García, Juan Ignacio Godino-Llorente, J. Villalba, J. Orozco-Arroyave, and N. Dehak, “Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson’s Disease,” in Applied Soft Computing, 2018.
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      @inproceedings{205710736,
      title = {Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease},
      author = {{L. Moro-Velázquez} and {Jorge Andrés Gómez García} and {Juan Ignacio Godino-Llorente} and {J. Villalba} and {J. Orozco-Arroyave} and {N. Dehak}},
      year = 2018,
      booktitle = {Applied Soft Computing},
      url = {https://www.semanticscholar.org/paper/e1ed45247074aafc0fed0b1c7253a3e96785b583},
      }

1595. Hajime Nada, Vishwanath A. Sindagi, He Zhang, and Vishal M. Patel, “Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,” in 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018.
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      @inproceedings{13742055,
      title = {Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results},
      author = {{Hajime Nada} and {Vishwanath A. Sindagi} and {He Zhang} and {Vishal M. Patel}},
      year = 2018,
      month = {4},
      booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
      url = {https://www.semanticscholar.org/paper/686170608fdda879c0e8f613ba7271db5c7458b0},
      }

1596. Gaofeng Cheng, Daniel Povey, Lu Huang, Ji Xu, S. Khudanpur, and Yonghong Yan, “Output-Gate Projected Gated Recurrent Unit for Speech Recognition,” in Interspeech, 2018.
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      @inproceedings{52189232,
      title = {Output-Gate Projected Gated Recurrent Unit for Speech Recognition},
      author = {{Gaofeng Cheng} and {Daniel Povey} and {Lu Huang} and {Ji Xu} and {S. Khudanpur} and {Yonghong Yan}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b199f4815db1110a4c27bec1303f930b8b8da618},
      }

1597. E. González-Sosa, R. Vera-Rodríguez, Julian Fierrez, and Vishal M. Patel, “Person Recognition beyond the Visible Spectrum: Combining Body Shape and Texture from mmW Images,” in International Conference on Biometrics, 2018.
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      @inproceedings{49865314,
      title = {Person Recognition beyond the Visible Spectrum: Combining Body Shape and Texture from mmW Images},
      author = {{E. González-Sosa} and {R. Vera-Rodríguez} and {Julian Fierrez} and {Vishal M. Patel}},
      year = 2018,
      month = {2},
      booktitle = {International Conference on Biometrics},
      url = {https://www.semanticscholar.org/paper/91dec705d119cb3cc40da18f51aafac3c5c191ce},
      }

1598. D. Bates, Axel Heitmueller, Meetali Kakad, and S. Saria, “Why policymakers should care about “big data” in healthcare,” in Health Policy and Technology, 2018.
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      @inproceedings{64729063,
      title = {Why policymakers should care about “big data” in healthcare},
      author = {{D. Bates} and {Axel Heitmueller} and {Meetali Kakad} and {S. Saria}},
      year = 2018,
      month = {6},
      booktitle = {Health Policy and Technology},
      url = {https://www.semanticscholar.org/paper/405f502abf7a8228305791cea3d6b0bd2dcc8bd9},
      }

1599. Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, and A. Yuille, “Deep Co-Training for Semi-Supervised Image Recognition,” in European Conference on Computer Vision, 2018.
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      @inproceedings{3966049,
      title = {Deep Co-Training for Semi-Supervised Image Recognition},
      author = {{Siyuan Qiao} and {Wei Shen} and {Zhishuai Zhang} and {Bo Wang} and {A. Yuille}},
      year = 2018,
      month = {3},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/40c6a2b1cb312f11f8225a733545fdabd436e347},
      }

1600. Y. Balaji, Hamed Hassani, R. Chellappa, and S. Feizi, “Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs,” in International Conference on Machine Learning, 2018.
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      @inproceedings{52947374,
      title = {Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs},
      author = {{Y. Balaji} and {Hamed Hassani} and {R. Chellappa} and {S. Feizi}},
      year = 2018,
      month = {9},
      booktitle = {International Conference on Machine Learning},
      url = {https://www.semanticscholar.org/paper/e8d2ad861e4d107ae2c0d1b7bb053d06022dfe1c},
      }

1601. Xuan Dong, B. Bonev, Weixin Li, Weichao Qiu, Xianjie Chen, and A. Yuille, “Ground-Truth Data Set and Baseline Evaluations for Base-Detail Separation Algorithms at the Part Level,” in IEEE transactions on circuits and systems for video technology (Print), 2018.
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      @inproceedings{3706231,
      title = {Ground-Truth Data Set and Baseline Evaluations for Base-Detail Separation Algorithms at the Part Level},
      author = {{Xuan Dong} and {B. Bonev} and {Weixin Li} and {Weichao Qiu} and {Xianjie Chen} and {A. Yuille}},
      year = 2018,
      month = {3},
      booktitle = {IEEE transactions on circuits and systems for video technology (Print)},
      url = {https://www.semanticscholar.org/paper/704dac63384ab70612b2a7fc6af7783125246ef5},
      }

1602. Ashwin Bellur and Mounya Elhilali, “Sensory Mapping Adaptation Under Multiple Task Scenarios,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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      @inproceedings{52284939,
      title = {Sensory Mapping Adaptation Under Multiple Task Scenarios},
      author = {{Ashwin Bellur} and {Mounya Elhilali}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/cd0f8fe0494eb1f57e7833d11ccd4d5033821d80},
      }

1603. Ankan Bansal, Karan Sikka, Gaurav Sharma, R. Chellappa, and Ajay Divakaran, “Zero-Shot Object Detection,” in European Conference on Computer Vision, 2018.
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      @inproceedings{4799963,
      title = {Zero-Shot Object Detection},
      author = {{Ankan Bansal} and {Karan Sikka} and {Gaurav Sharma} and {R. Chellappa} and {Ajay Divakaran}},
      year = 2018,
      month = {4},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/32bc9334ad0edaec29540320b9f00c9a7aab81f8},
      }

1604. Travis Wolfe, Annabelle Carrell, Mark Dredze, and Benjamin Van Durme, “Summarizing Entities using Distantly Supervised Information Extractors,” in ProfS/KG4IR/Data:Search@SIGIR, 2018.
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      @inproceedings{49586717,
      title = {Summarizing Entities using Distantly Supervised Information Extractors},
      author = {{Travis Wolfe} and {Annabelle Carrell} and {Mark Dredze} and {Benjamin Van Durme}},
      year = 2018,
      booktitle = {ProfS/KG4IR/Data:Search@SIGIR},
      url = {https://www.semanticscholar.org/paper/cab5f70ec2dfe9b33726217babc5ee6a42246a62},
      }

1605. Mahdi Abavisani and Vishal M. Patel, “Multimodal sparse and low-rank subspace clustering,” in Information Fusion, 2018.
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      @inproceedings{22235515,
      title = {Multimodal sparse and low-rank subspace clustering},
      author = {{Mahdi Abavisani} and {Vishal M. Patel}},
      year = 2018,
      booktitle = {Information Fusion},
      url = {https://www.semanticscholar.org/paper/fcd62fbb4031ae078dc6471a7a8bd63966719157},
      }

1606. He Zhang and Vishal M. Patel, “Density-Aware Single Image De-raining Using a Multi-stream Dense Network,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
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      @inproceedings{3406592,
      title = {Density-Aware Single Image De-raining Using a Multi-stream Dense Network},
      author = {{He Zhang} and {Vishal M. Patel}},
      year = 2018,
      month = {2},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/34aaa735409b666b8dc678c23acb600b1d87913b},
      }

1607. Mahdi Abavisani and Vishal M. Patel, “Deep Multimodal Subspace Clustering Networks,” in IEEE Journal on Selected Topics in Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{4939729,
      title = {Deep Multimodal Subspace Clustering Networks},
      author = {{Mahdi Abavisani} and {Vishal M. Patel}},
      year = 2018,
      month = {4},
      booktitle = {IEEE Journal on Selected Topics in Signal Processing},
      url = {https://www.semanticscholar.org/paper/738dcf73061ffdfc69d3101df78d51ec460ea8c1},
      }

1608. Gregory Sell, David Snyder, A. McCree, D. Garcia-Romero, J. Villalba, Matthew Maciejewski, Vimal Manohar, N. Dehak, Daniel Povey, Shinji Watanabe, and S. Khudanpur, “Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge,” in Interspeech, 2018.
      [BibTeX] [Link]

      @inproceedings{52187418,
      title = {Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge},
      author = {{Gregory Sell} and {David Snyder} and {A. McCree} and {D. Garcia-Romero} and {J. Villalba} and {Matthew Maciejewski} and {Vimal Manohar} and {N. Dehak} and {Daniel Povey} and {Shinji Watanabe} and {S. Khudanpur}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/cf6352c789ab51320fa7ca9b1440c685b57fd769},
      }

1609. Vineet Kushwaha, Maneet Singh, Richa Singh, Mayank Vatsa, N. Ratha, and R. Chellappa, “Disguised Faces in the Wild,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
      [BibTeX] [Link]

      @inproceedings{51687895,
      title = {Disguised Faces in the Wild},
      author = {{Vineet Kushwaha} and {Maneet Singh} and {Richa Singh} and {Mayank Vatsa} and {N. Ratha} and {R. Chellappa}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      url = {https://www.semanticscholar.org/paper/284d8ffb2f2d3bc9f793b82f8b7f75f2751b05d7},
      }

1610. H. Chen, Z. Fan, H. Lu, A. Yuille, and S. Rong, “PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 172–181. doi:10.18653/v1/D18-1016
      [BibTeX] [Abstract] [Link]
      

      We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at \url{https://preschool-lab.github.io/PreCo/}.

      @inproceedings{chen-etal-2018-preco,
      title = "{P}re{C}o: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution",
      author = "Chen, Hong and
      Fan, Zhenhua and
      Lu, Hao and
      Yuille, Alan and
      Rong, Shu",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1016",
      doi = "10.18653/v1/D18-1016",
      pages = "172--181",
      abstract = "We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at \url{https://preschool-lab.github.io/PreCo/}.",
      }

1611. Vimal Manohar, Hossein Hadian, Daniel Povey, and S. Khudanpur, “Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
      [BibTeX] [Link]

      @inproceedings{13944519,
      title = {Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI},
      author = {{Vimal Manohar} and {Hossein Hadian} and {Daniel Povey} and {S. Khudanpur}},
      year = 2018,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/33ff2582bff06988d2684eb4de02b3f13ec6a8f6},
      }

1612. Boyu Lu, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2018.
      [BibTeX] [Link]

      @inproceedings{52017806,
      title = {An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification},
      author = {{Boyu Lu} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {8},
      booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
      url = {https://www.semanticscholar.org/paper/8f1abae983acc7123257bece2afd334549dfe94d},
      }

1613. Z. Wood-Doughty, I. Shpitser, and M. Dredze, “Challenges of Using Text Classifiers for Causal Inference,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 4586–4598. doi:10.18653/v1/D18-1488
      [BibTeX] [Abstract] [Link]
      

      Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.

      @inproceedings{wood-doughty-etal-2018-challenges,
      title = "Challenges of Using Text Classifiers for Causal Inference",
      author = "Wood-Doughty, Zach and
      Shpitser, Ilya and
      Dredze, Mark",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1488",
      doi = "10.18653/v1/D18-1488",
      pages = "4586--4598",
      abstract = "Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.",
      }

1614. Wei-An Lin, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “Deep Density Clustering of Unconstrained Faces,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
      [BibTeX] [Link]

      @inproceedings{52832041,
      title = {Deep Density Clustering of Unconstrained Faces},
      author = {{Wei-An Lin} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/b35ff9985aaee9371588330bcef0dfc88d1401d7},
      }

1615. Zach Wood-Doughty, Praateek Mahajan, and Mark Dredze, “Classifying Individuals versus Organizations on Twitter.” 2018.
      [BibTeX] [Link]

      @inproceedings{5033176,
      title = {Classifying Individuals versus Organizations on Twitter},
      author = {{Zach Wood-Doughty} and {Praateek Mahajan} and {Mark Dredze}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fca1ba9e9fdb3f950e35edba6b463bf83cb56f49},
      }

1616. H. Kan, K. Dyagilev, Peter F. Schulam, S. Saria, Hadi Kharrazi, D. Bodycombe, C. Molta, and J. Curtis, “Factors associated with physicians’ prescriptions for rheumatoid arthritis drugs not filled by patients,” in Arthritis Research & Therapy, 2018.
      [BibTeX] [Link]

      @inproceedings{13669616,
      title = {Factors associated with physicians’ prescriptions for rheumatoid arthritis drugs not filled by patients},
      author = {{H. Kan} and {K. Dyagilev} and {Peter F. Schulam} and {S. Saria} and {Hadi Kharrazi} and {D. Bodycombe} and {C. Molta} and {J. Curtis}},
      year = 2018,
      month = {5},
      booktitle = {Arthritis Research & Therapy},
      url = {https://www.semanticscholar.org/paper/35f869eb00f1be7566822c1f8e0f3d0542aa159d},
      }

1617. Enayat Ullah, Poorya Mianjy, T. V. Marinov, and R. Arora, “Streaming Kernel PCA with \tildeO(\sqrtn) Random Features,” in Neural Information Processing Systems, 2018.
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      title = {Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features},
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1618. David Snyder, D. Garcia-Romero, Gregory Sell, Daniel Povey, and S. Khudanpur, “X-Vectors: Robust DNN Embeddings for Speaker Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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1619. Emily M. Hand, C. Castillo, and R. Chellappa, “Doing the Best We Can With What We Have: Multi-Label Balancing With Selective Learning for Attribute Prediction,” in AAAI Conference on Artificial Intelligence, 2018.
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1622. Li Liu, Jie Chen, P. Fieguth, Guoying Zhao, R. Chellappa, and M. Pietikäinen, “From BoW to CNN: Two Decades of Texture Representation for Texture Classification,” in International Journal of Computer Vision, 2018.
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1623. Wenbin Jiang, Min Long, L. Yang, Xiaobai Liu, Hai Jin, A. Yuille, and Ye Chi, “FIPIP: A novel fine-grained parallel partition based intra-frame prediction on heterogeneous many-core systems,” in Future generations computer systems, 2018.
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1624. Thomas S. Murray, Daniel R. Mendat, Kayode A. Sanni, P. Pouliquen, and A. Andreou, “Bio-Inspired Human Action Recognition With a Micro-Doppler Sonar System,” in IEEE Access, 2018.
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1625. Xiaofei Wang, Ruizhi Li, and H. Hermansky, “Stream Attention for Distributed Multi-Microphone Speech Recognition,” in Interspeech, 2018.
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1626. Fred Richardson, P. Torres-Carrasquillo, Jonas Borgstrom, D. Sturim, Youngjune Gwon, J. Villalba, J. Trmal, Nanxin Chen, Réda Dehak, and N. Dehak, “The MIT Lincoln Laboratory / JHU / EPITA-LSE LRE17 System,” in The Speaker and Language Recognition Workshop, 2018.
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1627. Ran Zhao, Yuntian Deng, Mark Dredze, Arun Verma, David S. Rosenberg, and Amanda Stent, “Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning,” in The Florida AI Research Society, 2018.
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1628. X. Lan, Shengping Zhang, P. Yuen, and R. Chellappa, “Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker,” in IEEE Transactions on Image Processing, 2018.
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1629. Rajeev Ranjan, S. Sankaranarayanan, Ankan Bansal, Navaneeth Bodla, Jun-Cheng Chen, Vishal M. Patel, C. Castillo, and R. Chellappa, “Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans,” in IEEE Signal Processing Magazine, 2018.
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1630. Peng Tang, Xinggang Wang, Angtian Wang, Yongluan Yan, Wenyu Liu, Junzhou Huang, and A. Yuille, “Weakly Supervised Region Proposal Network and Object Detection,” in European Conference on Computer Vision, 2018.
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1631. Rajeev Ranjan, Ankan Bansal, Hongyu Xu, S. Sankaranarayanan, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition,” in arXiv.org, 2018.
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1632. Yuyin Zhou, Yan Wang, Peng Tang, Wei Shen, E. Fishman, and A. Yuille, “Semi-supervised multi-organ segmentation via multi-planar co-training,” in arXiv.org, 2018.
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1633. Enayat Ullah, Poorya Mianjy, T. V. Marinov, and R. Arora, “Streaming Kernel PCA with Õ(√n) Random Features,” in Neural Information Processing Systems, 2018.
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1634. Yuan Gao, Qi She, Jiayi Ma, Mingde Zhao, Wei Liu, and A. Yuille, “NDDR-CNN: Layer-wise Feature Fusing in Multi-Task CNN by Neural Discriminative Dimensionality Reduction,” in arXiv.org, 2018.
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1635. Suwon Shon, N. Dehak, D. A. Reynolds, and James R. Glass, “MCE 2018: The 1st Multi-target Speaker Detection and Identification Challenge Evaluation (MCE) Plan, Dataset and Baseline System,” in arXiv.org, 2018.
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1636. Wei-An Lin, Jun-Cheng Chen, Rajeev Ranjan, Ankan Bansal, S. Sankaranarayanan, C. Castillo, and R. Chellappa, “Proximity-Aware Hierarchical Clustering of unconstrained faces,” in Image and Vision Computing, 2018.
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1637. Nam-Gyu Cho, A. Yuille, and Seong-Whan Lee, “A Novel Linelet-Based Representation for Line Segment Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
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1638. P. Frederiksen, J. Villalba, Shinji Watanabe, Z. Tan, and N. Dehak, “Effectiveness of Single-Channel BLSTM Enhancement for Language Identification,” in Interspeech, 2018.
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1639. Y. Balaji, Martin Renqiang Min, Bing Bai, R. Chellappa, and H. Graf, “TFGAN: Improving Conditioning for Text-to-Video Synthesis.” 2018.
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1640. P. S. Nidadavolu, Cheng-I Lai, J. Villalba, and N. Dehak, “Investigation on Bandwidth Extension for Speaker Recognition,” in Interspeech, 2018.
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1641. Chunxi Liu, Matthew Wiesner, Shinji Watanabe, Craig Harman, J. Trmal, N. Dehak, and S. Khudanpur, “Low-Resource Contextual Topic Identification on Speech,” in Spoken Language Technology Workshop, 2018.
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1642. Zhe Wu, Navaneeth Bodla, Bharat Singh, Mahyar Najibi, R. Chellappa, and L. Davis, “Soft Sampling for Robust Object Detection,” in British Machine Vision Conference, 2018.
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1643. Nicholas Huang, M. Slaney, and Mounya Elhilali, “Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals,” in Frontiers in Neuroscience, 2018.
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1644. He Zhang and Vishal M. Patel, “Densely Connected Pyramid Dehazing Network,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
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1645. F. Hieber, Tobias Domhan, Michael J. Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post, “We start by defining the recurrent architecture as implemented in S OCKEYE , following.” 2018.
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1646. Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, J. Trmal, Zhongqiang Huang, N. Dehak, and S. Khudanpur, “Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages,” in arXiv: Computation and Language, 2018.
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1647. Hossein Hadian, H. Sameti, Daniel Povey, and S. Khudanpur, “End-to-end Speech Recognition Using Lattice-free MMI,” in Interspeech, 2018.
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1648. Ondrej Bojar, Rajen Chatterjee, C. Federmann, Mark Fishel, Yvette Graham, B. Haddow, Matthias Huck, A. Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin M. Verspoor, “Proceedings of the Third Conference on Machine Translation: Research Papers.” 2018.
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1649. Chris Callison-Burch and Benjamin Van Durme, “Large-Scale Paraphrasing for Natural Language Understanding.” 2018.
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1650. Michelle Yuan, Benjamin Van Durme, and Jordan L. Ying, “Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages,” in Neural Information Processing Systems, 2018.
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1651. Lucas Ondel, Pierre Godard, L. Besacier, Elin Larsen, M. Hasegawa-Johnson, O. Scharenborg, Emmanuel Dupoux, L. Burget, François Yvon, and S. Khudanpur, “Bayesian Models for Unit Discovery on a Very Low Resource Language,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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1652. A. Hammond, Michael J. Paul, J. Hobelmann, Animesh Koratana, Mark Dredze, and M. Chisolm, “Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study,” in JMIR Mental Health, 2018.
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      }

1653. Nanxin Chen, J. Villalba, and N. Dehak, “An Investigation of Non-linear i-vectors for Speaker Verification,” in Interspeech, 2018.
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      title = {An Investigation of Non-linear i-vectors for Speaker Verification},
      author = {{Nanxin Chen} and {J. Villalba} and {N. Dehak}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/813f21bcc64ccf2a4744e8eb14b65298bbd769dc},
      }

1654. Jesús Antonio Villalba López, N. Brümmer, and N. Dehak, “End-to-End versus Embedding Neural Networks for Language Recognition in Mismatched Conditions,” in The Speaker and Language Recognition Workshop, 2018.
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      month = {6},
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1655. O. Scharenborg, Sebastian Tiesmeyer, M. Hasegawa-Johnson, and N. Dehak, “Visualizing Phoneme Category Adaptation in Deep Neural Networks,” in Interspeech, 2018.
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1656. Pramuditha Perera and Vishal M. Patel, “Efficient and Low Latency Detection of Intruders in Mobile Active Authentication,” in IEEE Transactions on Information Forensics and Security, 2018.
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1657. S. Zhang, X. Ma, R. Rudinger, K. Duh, and B. Van Durme, “Cross-lingual Decompositional Semantic Parsing,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, p. 1664–1675. doi:10.18653/v1/D18-1194
      [BibTeX] [Abstract] [Link]
      

      We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.

      @inproceedings{zhang-etal-2018-cross,
      title = "Cross-lingual Decompositional Semantic Parsing",
      author = "Zhang, Sheng and
      Ma, Xutai and
      Rudinger, Rachel and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Riloff, Ellen and
      Chiang, David and
      Hockenmaier, Julia and
      Tsujii, Jun{'}ichi",
      booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
      month = oct # "-" # nov,
      year = "2018",
      address = "Brussels, Belgium",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D18-1194",
      doi = "10.18653/v1/D18-1194",
      pages = "1664--1675",
      abstract = "We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.",
      }

1658. Adithya Renduchintala, Pamela Shapiro, Kevin Duh, and Philipp Koehn, “Character-Aware Decoder for Neural Machine Translation,” in arXiv.org, 2018.
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      author = {{Adithya Renduchintala} and {Pamela Shapiro} and {Kevin Duh} and {Philipp Koehn}},
      year = 2018,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/635473bd46ff5a3d3123d7731bb1dd2c2259bf8b},
      }

1659. Dimitra Emmanouilidou, E. McCollum, Daniel E. Park, and Mounya Elhilali, “Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments,” in IEEE Transactions on Biomedical Engineering, 2018.
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1660. Navaneeth Bodla, G. Hua, and R. Chellappa, “Semi-supervised FusedGAN for Conditional Image Generation,” in European Conference on Computer Vision, 2018.
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1661. Mousmita Sarma, Pegah Ghahremani, Daniel Povey, N. Goel, K. K. Sarma, and N. Dehak, “Emotion Identification from Raw Speech Signals Using DNNs,” in Interspeech, 2018.
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1662. Hongyu Xu, Jingjing Zheng, A. Alavi, and R. Chellappa, “Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning,” in arXiv.org, 2018.
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1663. Qi Chen, Weichao Qiu, Yi Zhang, Lingxi Xie, and A. Yuille, “SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data,” in British Machine Vision Conference, 2018.
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1664. Naoyuki Kanda, Rintaro Ikeshita, Shota Horiguchi, Yusuke Fujita, Kenji Nagamatsu, Xiaofei Wang, Vimal Manohar, Nelson Yalta, Matthew Maciejewski, Szu-Jui Chen, A. Subramanian, Ruizhi Li, Zhiqi Wang, Jason Naradowsky, Leibny Paola García-Perera, and Gregory Sell, “The Hitachi/JHU CHiME-5 system: Advances in speech recognition for everyday home environments using multiple microphone arrays,” in 5th International Workshop on Speech Processing in Everyday Environments (CHiME 2018), 2018.
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1665. Iiris Sundin, Peter F. Schulam, E. Siivola, A. Vehtari, S. Saria, and S. Kaski, “Using counterfactual queries to improve models for decision-support.” 2018.
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1666. Wei-An Lin, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “Deep Density Clustering of Unconstrained Faces ( Supplementary Material ).” 2018.
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1667. U. Mahbub, Sayantan Sarkar, and R. Chellappa, “Segment-Based Methods for Facial Attribute Detection from Partial Faces,” in IEEE Transactions on Affective Computing, 2018.
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1668. Adarsh Subbaswamy and S. Saria, “Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms,” in Conference on Uncertainty in Artificial Intelligence, 2018.
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1669. S. Barnes, S. Saria, and S. Levin, “An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes,” in Journal of Healthcare Engineering, 2018.
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1670. U. Mahbub, Jukka Komulainen, Denzil Ferreira, and R. Chellappa, “Continuous Authentication of Smartphones Based on Application Usage,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2018.
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1671. Yuyin Zhou, Yan Wang, Peng Tang, S. Bai, Wei Shen, E. Fishman, and A. Yuille, “Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
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1672. G. Stein-O’Brien, R. Arora, A. Culhane, Alexander V. Favorov, L. Garmire, C. Greene, L. Goff, Yifeng Li, Aloune Ngom, M. Ochs, Yanxun Xu, and E. Fertig, “Enter the Matrix: Factorization Uncovers Knowledge from Omics,” in Trends in Genetics, 2018.
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      author = {{G. Stein-O’Brien} and {R. Arora} and {A. Culhane} and {Alexander V. Favorov} and {L. Garmire} and {C. Greene} and {L. Goff} and {Yifeng Li} and {Aloune Ngom} and {M. Ochs} and {Yanxun Xu} and {E. Fertig}},
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1673. Chenglin Yang, Lingxi Xie, Siyuan Qiao, and A. Yuille, “Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students,” in arXiv.org, 2018.
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      title = {Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students},
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      }

1674. Neville Ryant, Elika Bergelson, Kenneth Ward Church, Alejandrina Cristia, Jun Du, Sriram Ganapathy, S. Khudanpur, Diana Kowalski, Mahesh Krishnamoorthy, Rajat Kulshreshta, M. Liberman, Yu-Ding Lu, Matthew Maciejewski, Florian Metze, Jan Profant, Lei Sun, Yu Tsao, and Zhou Yu, “Enhancement and Analysis of Conversational Speech: JSALT 2017,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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      author = {{Neville Ryant} and {Elika Bergelson} and {Kenneth Ward Church} and {Alejandrina Cristia} and {Jun Du} and {Sriram Ganapathy} and {S. Khudanpur} and {Diana Kowalski} and {Mahesh Krishnamoorthy} and {Rajat Kulshreshta} and {M. Liberman} and {Yu-Ding Lu} and {Matthew Maciejewski} and {Florian Metze} and {Jan Profant} and {Lei Sun} and {Yu Tsao} and {Zhou Yu}},
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1675. Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, E. Fishman, and A. Yuille, “Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018.
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1676. A. O’toole, C. Castillo, Connor J. Parde, Matthew Q. Hill, and R. Chellappa, “Face Space Representations in Deep Convolutional Neural Networks,” in Trends in Cognitive Sciences, 2018.
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1677. Gregory Sell, Kevin Duh, David Snyder, David Etter, and D. Garcia-Romero, “Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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1678. Ning Gao, Mark Dredze, and Douglas W. Oard, “Enhancing Scientific Collaboration Through Knowledge Base Population and Linking for Meetings,” in Hawaii International Conference on System Sciences, 2018.
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1679. Benjamin Skerritt-Davis and Mounya Elhilali, “Detecting change in stochastic sound sequences,” in PLoS Comput. Biol., 2018.
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1680. Shenmin Zhang, Kevin Duh, and Benjamin Van Durme, “Cross-lingual Semantic Parsing,” in arXiv.org, 2018.
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1681. T. V. Marinov, Poorya Mianjy, and R. Arora, “Streaming Principal Component Analysis in Noisy Settings,” in International Conference on Machine Learning, 2018.
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1682. Najoung Kim, Kyle Rawlins, Benjamin Van Durme, and P. Smolensky, “Predicting Argumenthood of English Preposition Phrases,” in arXiv.org, 2018.
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1683. David Snyder, D. Garcia-Romero, A. McCree, Gregory Sell, Daniel Povey, and S. Khudanpur, “Spoken Language Recognition using X-vectors,” in The Speaker and Language Recognition Workshop, 2018.
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1684. Emily M. Hand, C. Castillo, and R. Chellappa, “Predicting Facial Attributes in Video Using Temporal Coherence and Motion-Attention,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
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1685. Ke Li, Hainan Xu, Yiming Wang, Daniel Povey, and S. Khudanpur, “Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition,” in Interspeech, 2018.
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1686. R. Pappagari, J. Villalba, and N. Dehak, “Joint Verification-Identification in end-to-end Multi-Scale CNN Framework for Topic Identification,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018.
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      url = {https://www.semanticscholar.org/paper/298a10dd069404f8846b5f82f46441385e7e4486},
      }

1687. Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo Wang, and A. Yuille, “Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation.” 2018.
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      @inproceedings{29165607,
      title = {Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation},
      author = {{Wei Shen} and {Yilu Guo} and {Yan Wang} and {Kai Zhao} and {Bo Wang} and {A. Yuille}},
      year = 2018,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b},
      }

1688. Zhuotun Zhu, Yingda Xia, Lingxi Xie, E. Fishman, and A. Yuille, “Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018.
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      @inproceedings{49655124,
      title = {Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma},
      author = {{Zhuotun Zhu} and {Yingda Xia} and {Lingxi Xie} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {7},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/2d7b41da8fdf24890e8bbfa762cdffd67ee774e5},
      }

1689. Adarsh Subbaswamy and S. Saria, “Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms,” in arXiv.org, 2018.
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      @inproceedings{51954807,
      title = {Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms},
      author = {{Adarsh Subbaswamy} and {S. Saria}},
      year = 2018,
      month = {8},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/605e16c4cb3cfbfec1f14196f0e263dceeea6ca3},
      }

1690. Fengze Liu, Lingxi Xie, Yingda Xia, E. Fishman, and A. Yuille, “Joint Shape Representation and Classification for Detecting PDAC,” in MLMI@MICCAI, 2018.
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      @inproceedings{13740930,
      title = {Joint Shape Representation and Classification for Detecting PDAC},
      author = {{Fengze Liu} and {Lingxi Xie} and {Yingda Xia} and {E. Fishman} and {A. Yuille}},
      year = 2018,
      month = {4},
      booktitle = {MLMI@MICCAI},
      url = {https://www.semanticscholar.org/paper/dcc190c51eb3c911d0cfd1f907e31cf499adc436},
      }

1691. Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “A Fast and Accurate System for Face Detection, Identification, and Verification,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2018.
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      @inproceedings{52307053,
      title = {A Fast and Accurate System for Face Detection, Identification, and Verification},
      author = {{Rajeev Ranjan} and {Ankan Bansal} and {Jingxiao Zheng} and {Hongyu Xu} and {Joshua Gleason} and {Boyu Lu} and {Anirudh Nanduri} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
      year = 2018,
      month = {9},
      booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
      url = {https://www.semanticscholar.org/paper/8a2deb2b4216f6c065c5e955706ce157d96625a1},
      }

1692. Jaejin Cho, R. Pappagari, Purva Kulkarni, J. Villalba, Yishay Carmiel, and N. Dehak, “Deep Neural Networks for Emotion Recognition Combining Audio and Transcripts,” in Interspeech, 2018.
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      @inproceedings{52188103,
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      author = {{Jaejin Cho} and {R. Pappagari} and {Purva Kulkarni} and {J. Villalba} and {Yishay Carmiel} and {N. Dehak}},
      year = 2018,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/75b2843539dc8567b1502a19b3788adf6a015eb6},
      }

1693. A. Zhan, Srihari Mohan, Christopher G. Tarolli, Ruth B. Schneider, Jamie L. Adams, Saloni Sharma, Molly Elson, Kelsey L. Spear, Alistair Glidden, Max A. Little, A. Terzis, E. R. Dorsey, and S. Saria, “Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score,” in JAMA Neurology, 2018.
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      @inproceedings{263426695,
      title = {Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score},
      author = {{A. Zhan} and {Srihari Mohan} and {Christopher G. Tarolli} and {Ruth B. Schneider} and {Jamie L. Adams} and {Saloni Sharma} and {Molly Elson} and {Kelsey L. Spear} and {Alistair Glidden} and {Max A. Little} and {A. Terzis} and {E. R. Dorsey} and {S. Saria}},
      year = 2018,
      month = {7},
      booktitle = {JAMA Neurology},
      url = {https://www.semanticscholar.org/paper/b8efbba73d30b881ecbf0569decc9c686aa43b8a},
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1694. S. Wongvibulsin, S. Martin, S. Saria, S. Zeger, and S. Murphy, “Delivering Precision Behavior Change Through Digital Medicine.” 2018.
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      @inproceedings{240217022,
      title = {Delivering Precision Behavior Change Through Digital Medicine},
      author = {{S. Wongvibulsin} and {S. Martin} and {S. Saria} and {S. Zeger} and {S. Murphy}},
      year = 2018,
      month = {9},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/be0d988b4f1c48a9c9a7e13f0de0dea516906970},
      }

1695. Piotr Żelasko, Piotr Szymański, Jan Mizgajski, Adrian Szymczak, Yishay Carmiel, and N. Dehak, “Punctuation Prediction Model for Conversational Speech,” in Interspeech, 2018.
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      @inproceedings{49558658,
      title = {Punctuation Prediction Model for Conversational Speech},
      author = {{Piotr Żelasko} and {Piotr Szymański} and {Jan Mizgajski} and {Adrian Szymczak} and {Yishay Carmiel} and {N. Dehak}},
      year = 2018,
      month = {7},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/740f94e0325b67e6ff5efba0f5112c977e21a75a},
      }

1696. Cihang Xie, Zhishuai Zhang, Jianyu Wang, Yuyin Zhou, Zhou Ren, and A. Yuille, “Improving Transferability of Adversarial Examples With Input Diversity,” in Computer Vision and Pattern Recognition, 2018.
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      @inproceedings{3972825,
      title = {Improving Transferability of Adversarial Examples With Input Diversity},
      author = {{Cihang Xie} and {Zhishuai Zhang} and {Jianyu Wang} and {Yuyin Zhou} and {Zhou Ren} and {A. Yuille}},
      year = 2018,
      month = {3},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/f78a911f516625d6b7b76a9a33c1eb14613341c4},
      }

1697. Patrick Xia, Elias Stengel-Eskin, Tongfei Chen, Seth Ebner, Nils Holzenberger, Ryan Culkin, Pushpendre Rastogi, Xutai Ma, and Benjamin Van Durme, “NIST TAC SM-KBP 2018 System Description: JHU/UR Pipeline,” in Text Analysis Conference, 2018.
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      @inproceedings{198234235,
      title = {NIST TAC SM-KBP 2018 System Description: JHU/UR Pipeline},
      author = {{Patrick Xia} and {Elias Stengel-Eskin} and {Tongfei Chen} and {Seth Ebner} and {Nils Holzenberger} and {Ryan Culkin} and {Pushpendre Rastogi} and {Xutai Ma} and {Benjamin Van Durme}},
      year = 2018,
      booktitle = {Text Analysis Conference},
      url = {https://www.semanticscholar.org/paper/41d91688bed379cc78ce181a95b41b11e918ec7d},
      }

1698. Yunhan Zhao, Ye Tian, Charless C. Fowlkes, Wei Shen, and A. Yuille, “Resisting Large Data Variations via Introspective Transformation Network,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018.
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      @inproceedings{214676663,
      title = {Resisting Large Data Variations via Introspective Transformation Network},
      author = {{Yunhan Zhao} and {Ye Tian} and {Charless C. Fowlkes} and {Wei Shen} and {A. Yuille}},
      year = 2018,
      month = {5},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
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      }

1699. P. Phillips, Amy N. Yates, Ying Hu, Carina A. Hahn, E. Noyes, Kelsey Jackson, J. G. Cavazos, G. Jeckeln, Rajeev Ranjan, S. Sankaranarayanan, Jun-Cheng Chen, C. Castillo, R. Chellappa, D. White, and A. O’toole, “Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms,” in Proceedings of the National Academy of Sciences of the United States of America, 2018.
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      @inproceedings{44171361,
      title = {Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms},
      author = {{P. Phillips} and {Amy N. Yates} and {Ying Hu} and {Carina A. Hahn} and {E. Noyes} and {Kelsey Jackson} and {J. G. Cavazos} and {G. Jeckeln} and {Rajeev Ranjan} and {S. Sankaranarayanan} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa} and {D. White} and {A. O’toole}},
      year = 2018,
      month = {5},
      booktitle = {Proceedings of the National Academy of Sciences of the United States of America},
      url = {https://www.semanticscholar.org/paper/8d9e4f3927dc32c685a01fe050707e4793b66e07},
      }

1700. M. Villemur, P. Julián, Tomas Figliolia, and A. Andreou, “Neuromorphic Cellular Neural Network Processor for Intelligent Internet-of-Things,” in International Symposium on Circuits and Systems, 2018.
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      @inproceedings{53086034,
      title = {Neuromorphic Cellular Neural Network Processor for Intelligent Internet-of-Things},
      author = {{M. Villemur} and {P. Julián} and {Tomas Figliolia} and {A. Andreou}},
      year = 2018,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/e1b3d18146cd2bfbc34088a9ec54aec13cb2ad2b},
      }

1701. Zhuotun Zhu, Yingda Xia, Wei Shen, E. Fishman, and A. Yuille, “A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation,” in arXiv.org, 2017.
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      @inproceedings{195347010,
      title = {A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation},
      author = {{Zhuotun Zhu} and {Yingda Xia} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
      year = 2017,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/804f96f4ed40bf2f45849b1eb446c0ffa73a9277},
      }

1702. I. Doxas, A. Andreou, J. Lyon, V. Angelopoulos, San Lu, and P. Pritchett, “PerSEUS: Ultra-Low-Power High Performance Computing for Plasma Simulations.” 2017.
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      @inproceedings{217449336,
      title = {PerSEUS: Ultra-Low-Power High Performance Computing for Plasma Simulations},
      author = {{I. Doxas} and {A. Andreou} and {J. Lyon} and {V. Angelopoulos} and {San Lu} and {P. Pritchett}},
      year = 2017,
      month = {12},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a6ca203e3c71f36055ac7b9680f3b2ffb2e12c63},
      }

1703. Puyang Wang, He Zhang, and Vishal M. Patel, “Generative adversarial network-based restoration of speckled SAR images,” in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017.
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      @inproceedings{3853189,
      title = {Generative adversarial network-based restoration of speckled SAR images},
      author = {{Puyang Wang} and {He Zhang} and {Vishal M. Patel}},
      year = 2017,
      month = {12},
      booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing},
      url = {https://www.semanticscholar.org/paper/02da3136b146d51c233c44345a6927fac72ca95b},
      }

1704. Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, A. Yuille, Jonathan Huang, and K. Murphy, “Progressive Neural Architecture Search,” in European Conference on Computer Vision, 2017.
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      @inproceedings{40430109,
      title = {Progressive Neural Architecture Search},
      author = {{Chenxi Liu} and {Barret Zoph} and {Jonathon Shlens} and {Wei Hua} and {Li-Jia Li} and {Li Fei-Fei} and {A. Yuille} and {Jonathan Huang} and {K. Murphy}},
      year = 2017,
      month = {12},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/5f79398057bf0bbda9ff50067bc1f2950c2a2266},
      }

1705. Vimal Manohar, Daniel Povey, and S. Khudanpur, “JHU Kaldi system for Arabic MGB-3 ASR challenge using diarization, audio-transcript alignment and transfer learning,” in Automatic Speech Recognition & Understanding, 2017.
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      @inproceedings{33423855,
      title = {JHU Kaldi system for Arabic MGB-3 ASR challenge using diarization, audio-transcript alignment and transfer learning},
      author = {{Vimal Manohar} and {Daniel Povey} and {S. Khudanpur}},
      year = 2017,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/6f040f15434ae1d4b67fa6870c63c21cd43f60af},
      }

1706. He Zhang and Vishal M. Patel, “Convolutional Sparse and Low-Rank Coding-Based Image Decomposition,” in IEEE Transactions on Image Processing, 2017.
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      @inproceedings{3987902,
      title = {Convolutional Sparse and Low-Rank Coding-Based Image Decomposition},
      author = {{He Zhang} and {Vishal M. Patel}},
      year = 2017,
      month = {12},
      booktitle = {IEEE Transactions on Image Processing},
      url = {https://www.semanticscholar.org/paper/d10e7da44a71620114f6225265db5e3b305e5e9a},
      }

1707. Zhuotun Zhu, Yingda Xia, Wei-Che Shen, E. Fishman, and A. Yuille, “A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation,” in International Conference on 3D Vision, 2017.
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      @inproceedings{51907178,
      title = {A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation},
      author = {{Zhuotun Zhu} and {Yingda Xia} and {Wei-Che Shen} and {E. Fishman} and {A. Yuille}},
      year = 2017,
      month = {12},
      booktitle = {International Conference on 3D Vision},
      url = {https://www.semanticscholar.org/paper/11f30b7dbdfd99ffcc3338e594bfa67107b99fff},
      }

1708. F. Hieber, Tobias Domhan, Michael J. Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post, “Sockeye: A Toolkit for Neural Machine Translation,” in arXiv.org, 2017.
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      @inproceedings{24218611,
      title = {Sockeye: A Toolkit for Neural Machine Translation},
      author = {{F. Hieber} and {Tobias Domhan} and {Michael J. Denkowski} and {David Vilar} and {Artem Sokolov} and {Ann Clifton} and {Matt Post}},
      year = 2017,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c2f62a32b11f1906f5a3508b22134631cbfaa268},
      }

1709. Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, and R. Chellappa, “Deep Regionlets for Object Detection,” in European Conference on Computer Vision, 2017.
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      @inproceedings{4393782,
      title = {Deep Regionlets for Object Detection},
      author = {{Hongyu Xu} and {Xutao Lv} and {Xiaoyu Wang} and {Zhou Ren} and {R. Chellappa}},
      year = 2017,
      month = {12},
      booktitle = {European Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/f3d87142b80b7a10d33b6eb4d087ef5f2dd89cc9},
      }

1710. Rajeev Ranjan, S. Sankaranarayanan, C. Castillo, and R. Chellappa, “Improving Network Robustness against Adversarial Attacks with Compact Convolution,” in arXiv.org, 2017.
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      @inproceedings{4345097,
      title = {Improving Network Robustness against Adversarial Attacks with Compact Convolution},
      author = {{Rajeev Ranjan} and {S. Sankaranarayanan} and {C. Castillo} and {R. Chellappa}},
      year = 2017,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/a0d809efbab73fa64bf2a82ab94f119f52870ea2},
      }

1711. Xing Di and Vishal M. Patel, “Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs,” in arXiv.org, 2017.
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      @inproceedings{23328600,
      title = {Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs},
      author = {{Xing Di} and {Vishal M. Patel}},
      year = 2017,
      month = {12},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/91e9e19a06614197b5431410cecd29762223e04e},
      }

1712. Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo Wang, and A. Yuille, “Single-Shot Object Detection with Enriched Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{4716232,
      title = {Single-Shot Object Detection with Enriched Semantics},
      author = {{Zhishuai Zhang} and {Siyuan Qiao} and {Cihang Xie} and {Wei Shen} and {Bo Wang} and {A. Yuille}},
      year = 2017,
      month = {12},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/f2666a91f6844d7815f758ec1afbac18caf392f8},
      }

1713. Ning Gao, Gregory Sell, Douglas W. Oard, and Mark Dredze, “Leveraging side information for speaker identification with the Enron conversational telephone speech collection,” in Automatic Speech Recognition & Understanding, 2017.
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      @inproceedings{3235059,
      title = {Leveraging side information for speaker identification with the Enron conversational telephone speech collection},
      author = {{Ning Gao} and {Gregory Sell} and {Douglas W. Oard} and {Mark Dredze}},
      year = 2017,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/54016ad9d5b5a1155e7c22e4c9b947a2837ab7d5},
      }

1714. Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo Wang, and A. Yuille, “Deep Regression Forests for Age Estimation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{9621553,
      title = {Deep Regression Forests for Age Estimation},
      author = {{Wei Shen} and {Yilu Guo} and {Yan Wang} and {Kai Zhao} and {Bo Wang} and {A. Yuille}},
      year = 2017,
      month = {12},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/b17daa0bedfeb0502adf6638b8fe3a64eebb5696},
      }

1715. Pegah Ghahremani, Vimal Manohar, Hossein Hadian, Daniel Povey, and S. Khudanpur, “Investigation of transfer learning for ASR using LF-MMI trained neural networks,” in Automatic Speech Recognition & Understanding, 2017.
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      @inproceedings{9660730,
      title = {Investigation of transfer learning for ASR using LF-MMI trained neural networks},
      author = {{Pegah Ghahremani} and {Vimal Manohar} and {Hossein Hadian} and {Daniel Povey} and {S. Khudanpur}},
      year = 2017,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/b790316a1b4fdd6ad7978e7db85695f5e33c6759},
      }

1716. H. Mei and J. Eisner, “The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process,” in Advances in Neural Information Processing Systems (NeurIPS), Long Beach, CA, 2017, p. 6754–6764.
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      @InProceedings{mei-eisner-2017,
      author = "Hongyuan Mei and Jason Eisner",
      title = "The Neural {H}awkes Process: {A} Neurally
      Self-Modulating Multivariate Point Process",
      booktitle = "Advances in Neural Information Processing Systems
      (NeurIPS)",
      year = "2017",
      month = dec,
      pages = "6754--6764",
      address = "Long Beach, CA",
      note = "First version December 2016 as
      \href{https://arxiv.org/abs/1612.09328v1}{arXiv:1612.09328v1}.",
      URL = "http://cs.jhu.edu/~jason/papers/#mei-eisner-2017",
      }

1717. S. Noar, E. Leas, B. Althouse, Mark Dredze, Dannielle E Kelley, and J. Ayers, “Can a selfie promote public engagement with skin cancer?,” in Preventive Medicine, 2017.
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      @inproceedings{13954919,
      title = {Can a selfie promote public engagement with skin cancer?},
      author = {{S. Noar} and {E. Leas} and {B. Althouse} and {Mark Dredze} and {Dannielle E Kelley} and {J. Ayers}},
      year = 2017,
      month = {11},
      booktitle = {Preventive Medicine},
      url = {https://www.semanticscholar.org/paper/66630b0725f4a0924cef2f000a4ff3b017876769},
      }

1718. Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi-Keung Tang, and A. Yuille, “Adversarial Attacks Beyond the Image Space,” in Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{24142231,
      title = {Adversarial Attacks Beyond the Image Space},
      author = {{Xiaohui Zeng} and {Chenxi Liu} and {Yu-Siang Wang} and {Weichao Qiu} and {Lingxi Xie} and {Yu-Wing Tai} and {Chi-Keung Tang} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/704cffb06e002faf5d8822e5d9f9a2046deafa3a},
      }

1719. Chris Paxton, Kapil D. Katyal, C. Rupprecht, R. Arora, and Gregory Hager, “Learning to Imagine Manipulation Goals for Robot Task Planning,” in arXiv.org, 2017.
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      @inproceedings{25044213,
      title = {Learning to Imagine Manipulation Goals for Robot Task Planning},
      author = {{Chris Paxton} and {Kapil D. Katyal} and {C. Rupprecht} and {R. Arora} and {Gregory Hager}},
      year = 2017,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/778f8258bad0620b996666d883ce261216558ddd},
      }

1720. S. Zhang, K. Duh, and B. Van Durme, “Selective Decoding for Cross-lingual Open Information Extraction,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan, 2017, p. 832–842.
      [BibTeX] [Abstract] [Link]
      

      Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.

      @inproceedings{zhang-etal-2017-selective,
      title = "Selective Decoding for Cross-lingual Open Information Extraction",
      author = "Zhang, Sheng and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-1084",
      pages = "832--842",
      abstract = "Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.",
      }

1721. D. Wang, N. Peng, and K. Duh, “A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan, 2017, p. 383–388.
      [BibTeX] [Abstract] [Link]
      

      We show how to adapt bilingual word embeddings (BWE{‘}s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.

      @inproceedings{wang-etal-2017-multi,
      title = "A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition",
      author = "Wang, Dingquan and
      Peng, Nanyun and
      Duh, Kevin",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-2065",
      pages = "383--388",
      abstract = "We show how to adapt bilingual word embeddings (BWE{'}s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.",
      }

1722. Boyang Deng, Qing Liu, Siyuan Qiao, and A. Yuille, “Few-shot Learning by Exploiting Visual Concepts within CNNs,” in arXiv: Computer Vision and Pattern Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{46821146,
      title = {Few-shot Learning by Exploiting Visual Concepts within CNNs},
      author = {{Boyang Deng} and {Qing Liu} and {Siyuan Qiao} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/2743ecfe5552329202523ac988e052faed34382b},
      }

1723. Boyang Deng, Qing Liu, Siyuan Qiao, and A. Yuille, “Unleashing the Potential of CNNs for Interpretable Few-Shot Learning,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{21360767,
      title = {Unleashing the Potential of CNNs for Interpretable Few-Shot Learning},
      author = {{Boyang Deng} and {Qing Liu} and {Siyuan Qiao} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/a5377cfabdd4fcb6cb6225bd89c362e3d147e665},
      }

1724. B. Van Durme, T. Lippincott, K. Duh, D. Burchfield, A. Poliak, C. Costello, T. Finin, S. Miller, J. Mayfield, P. Koehn, C. Harman, D. Lawrie, C. May, M. Thomas, A. Carrell, J. Chaloux, T. Chen, A. Comerford, M. Dredze, B. Glass, S. Hao, P. Martin, P. Rastogi, R. Sankepally, T. Wolfe, Y. Tran, and T. Zhang, “CADET: Computer Assisted Discovery Extraction and Translation,” in Proceedings of the IJCNLP 2017, System Demonstrations, Tapei, Taiwan, 2017, p. 5–8.
      [BibTeX] [Abstract] [Link]
      

      Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.

      @inproceedings{van-durme-etal-2017-cadet,
      title = "{CADET}: Computer Assisted Discovery Extraction and Translation",
      author = "Van Durme, Benjamin and
      Lippincott, Tom and
      Duh, Kevin and
      Burchfield, Deana and
      Poliak, Adam and
      Costello, Cash and
      Finin, Tim and
      Miller, Scott and
      Mayfield, James and
      Koehn, Philipp and
      Harman, Craig and
      Lawrie, Dawn and
      May, Chandler and
      Thomas, Max and
      Carrell, Annabelle and
      Chaloux, Julianne and
      Chen, Tongfei and
      Comerford, Alex and
      Dredze, Mark and
      Glass, Benjamin and
      Hao, Shudong and
      Martin, Patrick and
      Rastogi, Pushpendre and
      Sankepally, Rashmi and
      Wolfe, Travis and
      Tran, Ying-Ying and
      Zhang, Ted",
      editor = "Park, Seong-Bae and
      Supnithi, Thepchai",
      booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
      month = nov,
      year = "2017",
      address = "Tapei, Taiwan",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/I17-3002",
      pages = "5--8",
      abstract = "Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.",
      }

1725. A. S. White, P. Rastogi, K. Duh, and B. Van Durme, “Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan, 2017, p. 996–1005.
      [BibTeX] [Abstract] [Link]
      

      We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model{‘}s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.

      @inproceedings{white-etal-2017-inference,
      title = "Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework",
      author = "White, Aaron Steven and
      Rastogi, Pushpendre and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-1100",
      pages = "996--1005",
      abstract = "We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model{'}s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.",
      }

1726. Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and A. Yuille, “Mitigating adversarial effects through randomization,” in International Conference on Learning Representations, 2017.
      [BibTeX] [Link]

      @inproceedings{3526769,
      title = {Mitigating adversarial effects through randomization},
      author = {{Cihang Xie} and {Jianyu Wang} and {Zhishuai Zhang} and {Zhou Ren} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {International Conference on Learning Representations},
      url = {https://www.semanticscholar.org/paper/9a089c56eec68df722b2a5a52727143aacdc2532},
      }

1727. Jingjing Zheng, Zhuolin Jiang, and R. Chellappa, “Submodular Attribute Selection for Visual Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
      [BibTeX] [Link]

      @inproceedings{24730594,
      title = {Submodular Attribute Selection for Visual Recognition},
      author = {{Jingjing Zheng} and {Zhuolin Jiang} and {R. Chellappa}},
      year = 2017,
      month = {11},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/58eb9174211d58af76023ce33ee05769de57236c},
      }

1728. Ross B. Girshick, Iasonas Kokkinos, I. Laptev, Jitendra Malik, G. Papandreou, A. Vedaldi, Xiaogang Wang, Shuicheng Yan, and A. Yuille, “Editorial- Deep Learning for Computer Vision,” in Computer Vision and Image Understanding, 2017.
      [BibTeX] [Link]

      @inproceedings{27515766,
      title = {Editorial- Deep Learning for Computer Vision},
      author = {{Ross B. Girshick} and {Iasonas Kokkinos} and {I. Laptev} and {Jitendra Malik} and {G. Papandreou} and {A. Vedaldi} and {Xiaogang Wang} and {Shuicheng Yan} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {Computer Vision and Image Understanding},
      url = {https://www.semanticscholar.org/paper/6b514a6db99bd37ce205e76ddb11f56d76ef3166},
      }

1729. F. Pereira, E. Silva, G. Lafruit, R. Chellappa, and S. Theodoridis, “Plenoptic Imaging: Representation and Processing.” 2017.
      [BibTeX] [Link]

      @inproceedings{63682836,
      title = {Plenoptic Imaging: Representation and Processing},
      author = {{F. Pereira} and {E. Silva} and {G. Lafruit} and {R. Chellappa} and {S. Theodoridis}},
      year = 2017,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/dacfba59e24cb44605a7acb7372a3c5f565ad9dc},
      }

1730. Jianyu Wang, Zhishuai Zhang, Cihang Xie, Yuyin Zhou, Vittal Premachandran, Jun Zhu, Lingxi Xie, and A. Yuille, “Visual Concepts and Compositional Voting,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{13739864,
      title = {Visual Concepts and Compositional Voting},
      author = {{Jianyu Wang} and {Zhishuai Zhang} and {Cihang Xie} and {Yuyin Zhou} and {Vittal Premachandran} and {Jun Zhu} and {Lingxi Xie} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/a4d0338839d72034169f8661abcb2194ca713574},
      }

1731. R. Cotterell and K. Duh, “Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan, 2017, p. 91–96.
      [BibTeX] [Abstract] [Link]
      

      Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world{‘}s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points.

      @inproceedings{cotterell-duh-2017-low,
      title = "Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields",
      author = "Cotterell, Ryan and
      Duh, Kevin",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-2016",
      pages = "91--96",
      abstract = "Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world{'}s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points.",
      }

1732. Y. Shen, X. Liu, K. Duh, and J. Gao, “An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan, 2017, p. 957–966.
      [BibTeX] [Abstract] [Link]
      

      Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. {\%}across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.

      @inproceedings{shen-etal-2017-empirical,
      title = "An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks",
      author = "Shen, Yelong and
      Liu, Xiaodong and
      Duh, Kevin and
      Gao, Jianfeng",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-1096",
      pages = "957--966",
      abstract = "Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. {\%}across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.",
      }

1733. Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo Wang, and A. Yuille, “Gradually Updated Neural Networks for Large-Scale Image Recognition,” in International Conference on Machine Learning, 2017.
      [BibTeX] [Link]

      @inproceedings{44412399,
      title = {Gradually Updated Neural Networks for Large-Scale Image Recognition},
      author = {{Siyuan Qiao} and {Zhishuai Zhang} and {Wei Shen} and {Bo Wang} and {A. Yuille}},
      year = 2017,
      month = {11},
      booktitle = {International Conference on Machine Learning},
      url = {https://www.semanticscholar.org/paper/123f9307da3d718c71af0ffb6f0cce74396e5759},
      }

1734. K. Sakaguchi, M. Post, and B. Van Durme, “Grammatical Error Correction with Neural Reinforcement Learning,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan, 2017, p. 366–372.
      [BibTeX] [Abstract] [Link]
      

      We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.

      @inproceedings{sakaguchi-etal-2017-grammatical,
      title = "Grammatical Error Correction with Neural Reinforcement Learning",
      author = "Sakaguchi, Keisuke and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-2062",
      pages = "366--372",
      abstract = "We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.",
      }

1735. S. Sankaranarayanan, Y. Balaji, Arpit Jain, Ser-Nam Lim, and R. Chellappa, “Unsupervised Domain Adaptation for Semantic Segmentation with GANs,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{11247316,
      title = {Unsupervised Domain Adaptation for Semantic Segmentation with GANs},
      author = {{S. Sankaranarayanan} and {Y. Balaji} and {Arpit Jain} and {Ser-Nam Lim} and {R. Chellappa}},
      year = 2017,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/ccd3dcbccae7d903608530bddf6381db8e723a7d},
      }

1736. Pramuditha Perera, Mahdi Abavisani, and Vishal M. Patel, “In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks,” in International Conference on Pattern Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{656239,
      title = {In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks},
      author = {{Pramuditha Perera} and {Mahdi Abavisani} and {Vishal M. Patel}},
      year = 2017,
      month = {11},
      booktitle = {International Conference on Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/3220ee78ec1499fcd395e5cb212ee62b55bd1856},
      }

1737. S. Sankaranarayanan, Y. Balaji, Arpit Jain, Ser-Nam Lim, and R. Chellappa, “Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{4540721,
      title = {Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation},
      author = {{S. Sankaranarayanan} and {Y. Balaji} and {Arpit Jain} and {Ser-Nam Lim} and {R. Chellappa}},
      year = 2017,
      month = {11},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/dfd72b994765a1979c6872fc8948657885a31752},
      }

1738. Xiaofei Wang, Yonghong Yan, and H. Hermansky, “Stream Attention for far-field multi-microphone ASR,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{34988075,
      title = {Stream Attention for far-field multi-microphone ASR},
      author = {{Xiaofei Wang} and {Yonghong Yan} and {H. Hermansky}},
      year = 2017,
      month = {11},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/e06a85159fb29932ba8a4e99d19ba32b6191b681},
      }

1739. P. Xia and D. Yarowsky, “Deriving Consensus for Multi-Parallel Corpora: an English Bible Study,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan, 2017, p. 448–453.
      [BibTeX] [Abstract] [Link]
      

      What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible{‘}s unique role as alignable bitext for over 800 of the world{‘}s languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself.

      @inproceedings{xia-yarowsky-2017-deriving,
      title = "Deriving Consensus for Multi-Parallel Corpora: an {E}nglish {B}ible Study",
      author = "Xia, Patrick and
      Yarowsky, David",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-2076",
      pages = "448--453",
      abstract = "What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible{'}s unique role as alignable bitext for over 800 of the world{'}s languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself.",
      }

1740. H. Khayrallah, G. Kumar, K. Duh, M. Post, and P. Koehn, “Neural Lattice Search for Domain Adaptation in Machine Translation,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan, 2017, p. 20–25.
      [BibTeX] [Abstract] [Link]
      

      Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.

      @inproceedings{khayrallah-etal-2017-neural,
      title = "Neural Lattice Search for Domain Adaptation in Machine Translation",
      author = "Khayrallah, Huda and
      Kumar, Gaurav and
      Duh, Kevin and
      Post, Matt and
      Koehn, Philipp",
      editor = "Kondrak, Greg and
      Watanabe, Taro",
      booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = nov,
      year = "2017",
      address = "Taipei, Taiwan",
      publisher = "Asian Federation of Natural Language Processing",
      url = "https://aclanthology.org/I17-2004",
      pages = "20--25",
      abstract = "Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.",
      }

1741. G. Stein-O’Brien, R. Arora, A. Culhane, Alexander V. Favorov, C. Greene, L. Goff, Yifeng Li, Aloune Ngom, M. Ochs, Yanun Xu, and E. Fertig, “Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data,” in bioRxiv, 2017.
      [BibTeX] [Link]

      @inproceedings{91732240,
      title = {Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data},
      author = {{G. Stein-O’Brien} and {R. Arora} and {A. Culhane} and {Alexander V. Favorov} and {C. Greene} and {L. Goff} and {Yifeng Li} and {Aloune Ngom} and {M. Ochs} and {Yanun Xu} and {E. Fertig}},
      year = 2017,
      month = {10},
      booktitle = {bioRxiv},
      url = {https://www.semanticscholar.org/paper/1db74eb4555795457185fb75a5b70d17e2047257},
      }

1742. Ning Gao, Mark Dredze, and Douglas W. Oard, “Person entity linking in email with NIL detection,” in J. Assoc. Inf. Sci. Technol., 2017.
      [BibTeX] [Link]

      @inproceedings{29303853,
      title = {Person entity linking in email with NIL detection},
      author = {{Ning Gao} and {Mark Dredze} and {Douglas W. Oard}},
      year = 2017,
      month = {10},
      booktitle = {J. Assoc. Inf. Sci. Technol.},
      url = {https://www.semanticscholar.org/paper/c7660f51186490edba345ca6dc7c987435484a9e},
      }

1743. J. Ayers, B. Althouse, E. Leas, Mark Dredze, and Jon-Patrick Allem, “Internet Searches for Suicide Following the Release of 13 Reasons Why,” in JAMA Internal Medicine, 2017.
      [BibTeX] [Link]

      @inproceedings{3547546,
      title = {Internet Searches for Suicide Following the Release of 13 Reasons Why},
      author = {{J. Ayers} and {B. Althouse} and {E. Leas} and {Mark Dredze} and {Jon-Patrick Allem}},
      year = 2017,
      month = {10},
      booktitle = {JAMA Internal Medicine},
      url = {https://www.semanticscholar.org/paper/4691e85460b30358590c0fa109c543512c27499f},
      }

1744. Lidan Wang, Vishwanath A. Sindagi, and Vishal M. Patel, “High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{3678780,
      title = {High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks},
      author = {{Lidan Wang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2017,
      month = {10},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/71c7191815fd15045a7bfb2ebc21a193d41ab551},
      }

1745. Theodore L. Caputi, E. Leas, Mark Dredze, Joanna E. Cohen, and J. Ayers, “They’re heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products,” in PLoS ONE, 2017.
      [BibTeX] [Link]

      @inproceedings{25120614,
      title = {They’re heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products},
      author = {{Theodore L. Caputi} and {E. Leas} and {Mark Dredze} and {Joanna E. Cohen} and {J. Ayers}},
      year = 2017,
      month = {10},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/58ee4d7d99d2c34585324cc5e01a9eaf6fd8b448},
      }

1746. Xing Di, Vishwanath A. Sindagi, and Vishal M. Patel, “GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks,” in International Conference on Pattern Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{206849289,
      title = {GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks},
      author = {{Xing Di} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2017,
      month = {10},
      booktitle = {International Conference on Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/7ab14d4a08d1a2c5194870c66719d23bee93adbb},
      }

1747. A. Andy, M. Dredze, M. Rwebangira, and C. Callison-Burch, “Constructing an Alias List for Named Entities during an Event,” in Proceedings of the 3rd Workshop on Noisy User-generated Text, Copenhagen, Denmark, 2017, p. 40–44. doi:10.18653/v1/W17-4405
      [BibTeX] [Abstract] [Link]
      

      In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.

      @inproceedings{andy-etal-2017-constructing,
      title = "Constructing an Alias List for Named Entities during an Event",
      author = "Andy, Anietie and
      Dredze, Mark and
      Rwebangira, Mugizi and
      Callison-Burch, Chris",
      editor = "Derczynski, Leon and
      Xu, Wei and
      Ritter, Alan and
      Baldwin, Tim",
      booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-4405",
      doi = "10.18653/v1/W17-4405",
      pages = "40--44",
      abstract = "In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.",
      }

1748. H. Xu and P. Koehn, “Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, p. 2945–2950. doi:10.18653/v1/D17-1319
      [BibTeX] [Abstract] [Link]
      

      We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.

      @inproceedings{xu-koehn-2017-zipporah,
      title = "{Z}ipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora",
      author = "Xu, Hainan and
      Koehn, Philipp",
      editor = "Palmer, Martha and
      Hwa, Rebecca and
      Riedel, Sebastian",
      booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D17-1319",
      doi = "10.18653/v1/D17-1319",
      pages = "2945--2950",
      abstract = "We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.",
      }

1749. O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, S. Huang, M. Huck, P. Koehn, Q. Liu, V. Logacheva, C. Monz, M. Negri, M. Post, R. Rubino, L. Specia, and M. Turchi, “Findings of the 2017 Conference on Machine Translation (WMT17),” in Proceedings of the Second Conference on Machine Translation, Copenhagen, Denmark, 2017, p. 169–214. doi:10.18653/v1/W17-4717
      [BibTeX] [Link]

      @inproceedings{bojar-etal-2017-findings,
      title = "Findings of the 2017 Conference on Machine Translation ({WMT}17)",
      author = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Graham, Yvette and
      Haddow, Barry and
      Huang, Shujian and
      Huck, Matthias and
      Koehn, Philipp and
      Liu, Qun and
      Logacheva, Varvara and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Rubino, Raphael and
      Specia, Lucia and
      Turchi, Marco",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Kreutzer, Julia",
      booktitle = "Proceedings of the Second Conference on Machine Translation",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-4717",
      doi = "10.18653/v1/W17-4717",
      pages = "169--214",
      }

1750. R. Cotterell, E. Vylomova, H. Khayrallah, C. Kirov, and D. Yarowsky, “Paradigm Completion for Derivational Morphology,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, p. 714–720. doi:10.18653/v1/D17-1074
      [BibTeX] [Abstract] [Link]
      

      The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4{\%}. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.

      @inproceedings{cotterell-etal-2017-paradigm,
      title = "Paradigm Completion for Derivational Morphology",
      author = "Cotterell, Ryan and
      Vylomova, Ekaterina and
      Khayrallah, Huda and
      Kirov, Christo and
      Yarowsky, David",
      editor = "Palmer, Martha and
      Hwa, Rebecca and
      Riedel, Sebastian",
      booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D17-1074",
      doi = "10.18653/v1/D17-1074",
      pages = "714--720",
      abstract = "The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4{\%}. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.",
      }

1751. S. Ding, H. Khayrallah, P. Koehn, M. Post, G. Kumar, and K. Duh, “The JHU Machine Translation Systems for WMT 2017,” in Proceedings of the Second Conference on Machine Translation, Copenhagen, Denmark, 2017, p. 276–282. doi:10.18653/v1/W17-4724
      [BibTeX] [Link]

      @inproceedings{ding-etal-2017-jhu,
      title = "The {JHU} Machine Translation Systems for {WMT} 2017",
      author = "Ding, Shuoyang and
      Khayrallah, Huda and
      Koehn, Philipp and
      Post, Matt and
      Kumar, Gaurav and
      Duh, Kevin",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Kreutzer, Julia",
      booktitle = "Proceedings of the Second Conference on Machine Translation",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-4724",
      doi = "10.18653/v1/W17-4724",
      pages = "276--282",
      }

1752. M. N{u{a}}dejde, S. Reddy, R. Sennrich, T. Dwojak, M. Junczys-Dowmunt, P. Koehn, and A. Birch, “Predicting Target Language CCG Supertags Improves Neural Machine Translation,” in Proceedings of the Second Conference on Machine Translation, Copenhagen, Denmark, 2017, p. 68–79. doi:10.18653/v1/W17-4707
      [BibTeX] [Link]

      @inproceedings{nadejde-etal-2017-predicting,
      title = "Predicting Target Language {CCG} Supertags Improves Neural Machine Translation",
      author = "N{\u{a}}dejde, Maria and
      Reddy, Siva and
      Sennrich, Rico and
      Dwojak, Tomasz and
      Junczys-Dowmunt, Marcin and
      Koehn, Philipp and
      Birch, Alexandra",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      Koehn, Philipp and
      Kreutzer, Julia",
      booktitle = "Proceedings of the Second Conference on Machine Translation",
      month = sep,
      year = "2017",
      address = "Copenhagen, Denmark",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-4707",
      doi = "10.18653/v1/W17-4707",
      pages = "68--79",
      }

1753. K. Niederhoffer, J. Schler, P. Crutchley, K. Loveys, and G. Coppersmith, “In your wildest dreams: the language and psychological features of dreams,” in Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality, Vancouver, BC, 2017, p. 13–25. doi:10.18653/v1/W17-3102
      [BibTeX] [Abstract] [Link]
      

      In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.

      @inproceedings{niederhoffer-etal-2017-wildest,
      title = "In your wildest dreams: the language and psychological features of dreams",
      author = "Niederhoffer, Kate and
      Schler, Jonathan and
      Crutchley, Patrick and
      Loveys, Kate and
      Coppersmith, Glen",
      editor = "Hollingshead, Kristy and
      Ireland, Molly E. and
      Loveys, Kate",
      booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
      month = aug,
      year = "2017",
      address = "Vancouver, BC",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-3102",
      doi = "10.18653/v1/W17-3102",
      pages = "13--25",
      abstract = "In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.",
      }

1754. A. Renduchintala, P. Koehn, and J. Eisner, “Knowledge Tracing in Sequential Learning of Inflected Vocabulary,” in Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, 2017, p. 238–247. doi:10.18653/v1/K17-1025
      [BibTeX] [Abstract] [Link]
      

      We present a feature-rich knowledge tracing method that captures a student{‘}s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student{‘}s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.

      @inproceedings{renduchintala-etal-2017-knowledge,
      title = "Knowledge Tracing in Sequential Learning of Inflected Vocabulary",
      author = "Renduchintala, Adithya and
      Koehn, Philipp and
      Eisner, Jason",
      editor = "Levy, Roger and
      Specia, Lucia",
      booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
      month = aug,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K17-1025",
      doi = "10.18653/v1/K17-1025",
      pages = "238--247",
      abstract = "We present a feature-rich knowledge tracing method that captures a student{'}s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student{'}s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.",
      }

1755. K. Loveys, P. Crutchley, E. Wyatt, and G. Coppersmith, “Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language,” in Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality, Vancouver, BC, 2017, p. 85–95. doi:10.18653/v1/W17-3110
      [BibTeX] [Abstract] [Link]
      

      Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.

      @inproceedings{loveys-etal-2017-small,
      title = "Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language",
      author = "Loveys, Kate and
      Crutchley, Patrick and
      Wyatt, Emily and
      Coppersmith, Glen",
      editor = "Hollingshead, Kristy and
      Ireland, Molly E. and
      Loveys, Kate",
      booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
      month = aug,
      year = "2017",
      address = "Vancouver, BC",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-3110",
      doi = "10.18653/v1/W17-3110",
      pages = "85--95",
      abstract = "Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.",
      }

1756. E. Sarioglu Kayi, M. Diab, L. Pauselli, M. Compton, and G. Coppersmith, “Predictive Linguistic Features of Schizophrenia,” in Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Vancouver, Canada, 2017, p. 241–250. doi:10.18653/v1/S17-1028
      [BibTeX] [Abstract] [Link]
      

      Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.

      @inproceedings{sarioglu-kayi-etal-2017-predictive,
      title = "Predictive Linguistic Features of Schizophrenia",
      author = "Sarioglu Kayi, Efsun and
      Diab, Mona and
      Pauselli, Luca and
      Compton, Michael and
      Coppersmith, Glen",
      editor = "Ide, Nancy and
      Herbelot, Aur{\'e}lie and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
      month = aug,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S17-1028",
      doi = "10.18653/v1/S17-1028",
      pages = "241--250",
      abstract = "Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.",
      }

1757. R. Cotterell, C. Kirov, J. Sylak-Glassman, G. Walther, E. Vylomova, P. Xia, M. Faruqui, S. Kübler, D. Yarowsky, J. Eisner, and M. Hulden, “CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages,” in Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, Vancouver, 2017, p. 1–30. doi:10.18653/v1/K17-2001
      [BibTeX] [Link]

      @inproceedings{cotterell-etal-2017-conll,
      title = "{C}o{NLL}-{SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection in 52 Languages",
      author = {Cotterell, Ryan and
      Kirov, Christo and
      Sylak-Glassman, John and
      Walther, G{\'e}raldine and
      Vylomova, Ekaterina and
      Xia, Patrick and
      Faruqui, Manaal and
      K{\"u}bler, Sandra and
      Yarowsky, David and
      Eisner, Jason and
      Hulden, Mans},
      editor = "Hulden, Mans",
      booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
      month = aug,
      year = "2017",
      address = "Vancouver",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K17-2001",
      doi = "10.18653/v1/K17-2001",
      pages = "1--30",
      }

1758. P. Koehn and R. Knowles, “Six Challenges for Neural Machine Translation,” in Proceedings of the First Workshop on Neural Machine Translation, Vancouver, 2017, p. 28–39. doi:10.18653/v1/W17-3204
      [BibTeX] [Abstract] [Link]
      

      We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

      @inproceedings{koehn-knowles-2017-six,
      title = "Six Challenges for Neural Machine Translation",
      author = "Koehn, Philipp and
      Knowles, Rebecca",
      editor = "Luong, Thang and
      Birch, Alexandra and
      Neubig, Graham and
      Finch, Andrew",
      booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
      month = aug,
      year = "2017",
      address = "Vancouver",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-3204",
      doi = "10.18653/v1/W17-3204",
      pages = "28--39",
      abstract = "We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.",
      }

1759. F. Ferraro, A. Poliak, R. Cotterell, and B. Van Durme, “Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles,” in Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Vancouver, Canada, 2017, p. 97–103. doi:10.18653/v1/S17-1011
      [BibTeX] [Abstract] [Link]
      

      We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10{\%} gains over baselines.

      @inproceedings{ferraro-etal-2017-frame,
      title = "Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles",
      author = "Ferraro, Francis and
      Poliak, Adam and
      Cotterell, Ryan and
      Van Durme, Benjamin",
      editor = "Ide, Nancy and
      Herbelot, Aur{\'e}lie and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
      month = aug,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S17-1011",
      doi = "10.18653/v1/S17-1011",
      pages = "97--103",
      abstract = "We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10{\%} gains over baselines.",
      }

1760. Z. Wood-Doughty, M. Smith, D. Broniatowski, and M. Dredze, “How Does Twitter User Behavior Vary Across Demographic Groups?,” in Proceedings of the Second Workshop on NLP and Computational Social Science, Vancouver, Canada, 2017, p. 83–89. doi:10.18653/v1/W17-2912
      [BibTeX] [Abstract] [Link]
      

      Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups{‘} differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter.

      @inproceedings{wood-doughty-etal-2017-twitter,
      title = "How Does {T}witter User Behavior Vary Across Demographic Groups?",
      author = "Wood-Doughty, Zach and
      Smith, Michael and
      Broniatowski, David and
      Dredze, Mark",
      editor = {Hovy, Dirk and
      Volkova, Svitlana and
      Bamman, David and
      Jurgens, David and
      O{'}Connor, Brendan and
      Tsur, Oren and
      Do{\u{g}}ru{\"o}z, A. Seza},
      booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
      month = aug,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-2912",
      doi = "10.18653/v1/W17-2912",
      pages = "83--89",
      abstract = "Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups{'} differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter.",
      }

1761. N. Peng and M. Dredze, “Multi-task Domain Adaptation for Sequence Tagging,” in Proceedings of the 2nd Workshop on Representation Learning for NLP, Vancouver, Canada, 2017, p. 91–100. doi:10.18653/v1/W17-2612
      [BibTeX] [Abstract] [Link]
      

      Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.

      @inproceedings{peng-dredze-2017-multi,
      title = "Multi-task Domain Adaptation for Sequence Tagging",
      author = "Peng, Nanyun and
      Dredze, Mark",
      editor = "Blunsom, Phil and
      Bordes, Antoine and
      Cho, Kyunghyun and
      Cohen, Shay and
      Dyer, Chris and
      Grefenstette, Edward and
      Hermann, Karl Moritz and
      Rimell, Laura and
      Weston, Jason and
      Yih, Scott",
      booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
      month = aug,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-2612",
      doi = "10.18653/v1/W17-2612",
      pages = "91--100",
      abstract = "Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.",
      }

1762. R. Cotterell, C. Kirov, John Sylak-Glassman, G. Walther, Ekaterina Vylomova, P. Xia, M. Faruqui, Sandra Kübler, D. Yarowsky, J. Eisner, and M. Hulden, “CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages,” in Proceedings of the Conference on Natural Language Learning: CoNLL-SIGMORPHON Shared Task System Descriptions, Vancouver, 2017, p. 1–30. doi:10.18653/v1/K17-2001
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2017-shared,
      aclid = "K17-2001",
      doi = "10.18653/v1/K17-2001",
      author = "Ryan Cotterell and Christo Kirov and John
      Sylak-Glassman and G\'{e}raldine Walther and Ekaterina
      Vylomova and Patrick Xia and Manaal Faruqui and Sandra
      K{\"{u}}bler and David Yarowsky and Jason Eisner and
      Mans Hulden",
      title = "{CoNLL}-{SIGMORPHON} 2017 Shared Task: Universal
      Morphological Reinflection in 52 Languages",
      booktitle = "Proceedings of the Conference on Natural Language
      Learning: CoNLL-SIGMORPHON Shared Task System
      Descriptions",
      pages = "1--30",
      year = "2017",
      month = aug,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2017-shared",
      }

1763. A. Renduchintala, P. Koehn, and Jason Eisner, “Knowledge Tracing in Sequential Learning of Inflected Vocabulary,” in Proceedings of the Conference on Natural Language Learning (CoNLL), Vancouver, 2017, p. 238–247. doi:10.18653/v1/K17-1025
      [BibTeX] [Link]

      @InProceedings{renduchintala-et-al-2017-conll,
      aclid = "K17-1025",
      doi = "10.18653/v1/K17-1025",
      author = "Adithya Renduchintala and Philipp Koehn and Jason
      Eisner",
      title = "Knowledge Tracing in Sequential Learning of Inflected
      Vocabulary",
      booktitle = "Proceedings of the Conference on Natural Language
      Learning (CoNLL)",
      pages = "238--247",
      year = "2017",
      month = aug,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2017-conll",
      }

1764. R. Cotterell and J. Eisner, “Probabilistic Typology: Deep Generative Models of Vowel Inventories,” in Proceedings of the Association for Computational Linguistics (ACL), Vancouver, 2017, p. 1182–1192. doi:10.18653/v1/P17-1109
      [BibTeX] [Link]

      @InProceedings{cotterell-eisner-2017-acl,
      aclid = "P17-1109",
      doi = "10.18653/v1/P17-1109",
      author = "Ryan Cotterell and Jason Eisner",
      title = "Probabilistic Typology: Deep Generative Models of
      Vowel Inventories",
      booktitle = "Proceedings of the Association for Computational
      Linguistics (ACL)",
      pages = "1182--1192",
      year = "2017",
      month = aug,
      address = "Vancouver",
      note = "Best Long Paper Award.",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2017-acl",
      }

1765. N. Andrews, M. Dredze, B. Van Durme, and J. Eisner, “Bayesian Modeling of Lexical Resources for Low-Resource Settings,” in Proceedings of the Association for Computational Linguistics (ACL), Vancouver, 2017, p. 1029–1039. doi:10.18653/v1/P17-1095
      [BibTeX] [Link]

      @InProceedings{andrews-et-al-2017,
      aclid = "P17-1095",
      doi = "10.18653/v1/P17-1095",
      author = "Nicholas Andrews and Mark Dredze and Benjamin Van
      Durme and Jason Eisner",
      title = "Bayesian Modeling of Lexical Resources for
      Low-Resource Settings",
      booktitle = "Proceedings of the Association for Computational
      Linguistics (ACL)",
      pages = "1029--1039",
      year = "2017",
      month = aug,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#andrews-et-al-2017",
      }

1766. K. Sakaguchi, M. Post, and B. Van Durme, “Error-repair Dependency Parsing for Ungrammatical Texts,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vancouver, Canada, 2017, p. 189–195. doi:10.18653/v1/P17-2030
      [BibTeX] [Abstract] [Link]
      

      We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.

      @inproceedings{sakaguchi-etal-2017-error,
      title = "Error-repair Dependency Parsing for Ungrammatical Texts",
      author = "Sakaguchi, Keisuke and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Barzilay, Regina and
      Kan, Min-Yen",
      booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jul,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P17-2030",
      doi = "10.18653/v1/P17-2030",
      pages = "189--195",
      abstract = "We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.",
      }

1767. N. Andrews, M. Dredze, B. Van Durme, and J. Eisner, “Bayesian Modeling of Lexical Resources for Low-Resource Settings,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, 2017, p. 1029–1039. doi:10.18653/v1/P17-1095
      [BibTeX] [Abstract] [Link]
      

      Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.

      @inproceedings{andrews-etal-2017-bayesian,
      title = "{B}ayesian Modeling of Lexical Resources for Low-Resource Settings",
      author = "Andrews, Nicholas and
      Dredze, Mark and
      Van Durme, Benjamin and
      Eisner, Jason",
      editor = "Barzilay, Regina and
      Kan, Min-Yen",
      booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jul,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P17-1095",
      doi = "10.18653/v1/P17-1095",
      pages = "1029--1039",
      abstract = "Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.",
      }

1768. T. Wolfe, M. Dredze, and B. Van Durme, “Pocket Knowledge Base Population,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vancouver, Canada, 2017, p. 305–310. doi:10.18653/v1/P17-2048
      [BibTeX] [Abstract] [Link]
      

      Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.

      @inproceedings{wolfe-etal-2017-pocket,
      title = "Pocket Knowledge Base Population",
      author = "Wolfe, Travis and
      Dredze, Mark and
      Van Durme, Benjamin",
      editor = "Barzilay, Regina and
      Kan, Min-Yen",
      booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jul,
      year = "2017",
      address = "Vancouver, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P17-2048",
      doi = "10.18653/v1/P17-2048",
      pages = "305--310",
      abstract = "Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.",
      }

1769. T. Vieira, M. Francis-Landau, N. Filardo, F. Khorasani, and J. Eisner, “Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications,” in Proceedings of the First ACM SIGPLAN Workshop on Machine Learning and Programming Languages (MAPL), Barcelona, 2017, p. 8–17. doi:10.1145/3088525.3088562
      [BibTeX] [Link]

      @InProceedings{vieira-et-al-2017,
      doi = "10.1145/3088525.3088562",
      author = "Tim Vieira and Matthew Francis-Landau and Nathaniel
      Wesley Filardo and Farzad Khorasani and Jason Eisner",
      title = "Dyna: Toward a Self-Optimizing Declarative Language
      for Machine Learning Applications",
      booktitle = "Proceedings of the First ACM SIGPLAN Workshop on
      Machine Learning and Programming Languages (MAPL)",
      pages = "8--17",
      year = "2017",
      month = jun,
      address = "Barcelona",
      publisher = "ACM",
      ISBN = "978-1-4503-5071-6",
      URL = "http://cs.jhu.edu/~jason/papers/#vieira-et-al-2017",
      }

1770. S. Zhang, K. Duh, and B. Van Durme, “MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 64–70.
      [BibTeX] [Abstract] [Link]
      

      Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.

      @inproceedings{zhang-etal-2017-mt,
      title = "{MT}/{IE}: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models",
      author = "Zhang, Sheng and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2011",
      pages = "64--70",
      abstract = "Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.",
      }

1771. A. Benton, G. Coppersmith, and M. Dredze, “Ethical Research Protocols for Social Media Health Research,” in Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, Valencia, Spain, 2017, p. 94–102. doi:10.18653/v1/W17-1612
      [BibTeX] [Abstract] [Link]
      

      Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research.

      @inproceedings{benton-etal-2017-ethical,
      title = "Ethical Research Protocols for Social Media Health Research",
      author = "Benton, Adrian and
      Coppersmith, Glen and
      Dredze, Mark",
      editor = "Hovy, Dirk and
      Spruit, Shannon and
      Mitchell, Margaret and
      Bender, Emily M. and
      Strube, Michael and
      Wallach, Hanna",
      booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-1612",
      doi = "10.18653/v1/W17-1612",
      pages = "94--102",
      abstract = "Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research.",
      }

1772. R. Cotterell, A. Poliak, B. Van Durme, and J. Eisner, “Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 175–181.
      [BibTeX] [Abstract] [Link]
      

      The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.

      @inproceedings{cotterell-etal-2017-explaining,
      title = "Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis",
      author = "Cotterell, Ryan and
      Poliak, Adam and
      Van Durme, Benjamin and
      Eisner, Jason",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2028",
      pages = "175--181",
      abstract = "The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.",
      }

1773. C. Kirov, J. Sylak-Glassman, R. Knowles, R. Cotterell, and M. Post, “A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 112–117.
      [BibTeX] [Abstract] [Link]
      

      A traditional claim in linguistics is that all human languages are equally expressive{–-}able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.

      @inproceedings{kirov-etal-2017-rich,
      title = "A Rich Morphological Tagger for {E}nglish: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax",
      author = "Kirov, Christo and
      Sylak-Glassman, John and
      Knowles, Rebecca and
      Cotterell, Ryan and
      Post, Matt",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2018",
      pages = "112--117",
      abstract = "A traditional claim in linguistics is that all human languages are equally expressive{---}able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.",
      }

1774. A. Poliak, P. Rastogi, P. M. Martin, and B. Van Durme, “Efficient, Compositional, Order-sensitive n-gram Embeddings,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 503–508.
      [BibTeX] [Abstract] [Link]
      

      We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.

      @inproceedings{poliak-etal-2017-efficient,
      title = "Efficient, Compositional, Order-sensitive n-gram Embeddings",
      author = "Poliak, Adam and
      Rastogi, Pushpendre and
      Martin, M. Patrick and
      Van Durme, Benjamin",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2081",
      pages = "503--508",
      abstract = "We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.",
      }

1775. T. Chen and B. Van Durme, “Discriminative Information Retrieval for Question Answering Sentence Selection,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 719–725.
      [BibTeX] [Abstract] [Link]
      

      We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34{\%}-43{\%} improvement in recall for candidate triage for QA.

      @inproceedings{chen-van-durme-2017-discriminative,
      title = "Discriminative Information Retrieval for Question Answering Sentence Selection",
      author = "Chen, Tongfei and
      Van Durme, Benjamin",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2114",
      pages = "719--725",
      abstract = "We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34{\%}-43{\%} improvement in recall for candidate triage for QA.",
      }

1776. A. S. White, K. Rawlins, and B. Van Durme, “The Semantic Proto-Role Linking Model,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, 2017, p. 92–98.
      [BibTeX] [Abstract] [Link]
      

      We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty{‘}s thematic proto-role linking theory.

      @inproceedings{white-etal-2017-semantic,
      title = "The Semantic Proto-Role Linking Model",
      author = "White, Aaron Steven and
      Rawlins, Kyle and
      Van Durme, Benjamin",
      editor = "Lapata, Mirella and
      Blunsom, Phil and
      Koller, Alexander",
      booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E17-2015",
      pages = "92--98",
      abstract = "We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty{'}s thematic proto-role linking theory.",
      }

1777. R. Rudinger, C. May, and B. Van Durme, “Social Bias in Elicited Natural Language Inferences,” in Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, Valencia, Spain, 2017, p. 74–79. doi:10.18653/v1/W17-1609
      [BibTeX] [Abstract] [Link]
      

      We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.

      @inproceedings{rudinger-etal-2017-social,
      title = "Social Bias in Elicited Natural Language Inferences",
      author = "Rudinger, Rachel and
      May, Chandler and
      Van Durme, Benjamin",
      editor = "Hovy, Dirk and
      Spruit, Shannon and
      Mitchell, Margaret and
      Bender, Emily M. and
      Strube, Michael and
      Wallach, Hanna",
      booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
      month = apr,
      year = "2017",
      address = "Valencia, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W17-1609",
      doi = "10.18653/v1/W17-1609",
      pages = "74--79",
      abstract = "We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.",
      }

1778. R. Cotterell, A. Poliak, B. V. Durme, and J. Eisner, “Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis,” in Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics: Human Language Technologies (EACL), Valencia, Spain, 2017, p. 175–181. doi:10.18653/v1/E17-2028
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2017-eacl,
      aclid = "E17-2028",
      doi = "10.18653/v1/E17-2028",
      author = "Ryan Cotterell and Adam Poliak and Benjamin Van Durme
      and Jason Eisner",
      title = "Explaining and Generalizing Skip-Gram through
      Exponential Family Principal Component Analysis",
      booktitle = "Proceedings of the Conference of the European Chapter
      of the Association for Computational Linguistics: Human
      Language Technologies (EACL)",
      pages = "175--181",
      year = "2017",
      month = apr,
      address = "Valencia, Spain",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2017-eacl",
      }

1779. R. Knowles and Matt Post, “Domain Adaptation,” in Encyclopedia of Machine Learning and Data Mining, 2017.
      [BibTeX] [Link]

      @inproceedings{260495067,
      title = {Domain Adaptation},
      author = {{R. Knowles} and {Matt Post}},
      year = 2017,
      booktitle = {Encyclopedia of Machine Learning and Data Mining},
      url = {https://www.semanticscholar.org/paper/920f6573f6acb0e39734bb744c8548b976485812},
      }

1780. Pramuditha Perera, Mahdi Abavisani, and Vishal M. Patel, “Input 2 Encoder Feature Extractor Decoder Output 2 Latent Space Feature Extractor Input 1 Input N Feature Extractor Decoder Output M Decoder Output 1.” 2017.
      [BibTeX] [Link]

      @inproceedings{199386516,
      title = {Input 2 Encoder Feature Extractor Decoder Output 2 Latent Space Feature Extractor Input 1 Input N Feature Extractor Decoder Output M Decoder Output 1},
      author = {{Pramuditha Perera} and {Mahdi Abavisani} and {Vishal M. Patel}},
      year = 2017,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3748d173a071de6351b7399635e61c6a038b03e6},
      }

1781. Sumit Shekhar, Vishal M. Patel, and R. Chellappa, “Synthesis-based Robust Low Resolution Face Recognition,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{16471250,
      title = {Synthesis-based Robust Low Resolution Face Recognition},
      author = {{Sumit Shekhar} and {Vishal M. Patel} and {R. Chellappa}},
      year = 2017,
      month = {7},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/a49df923dde393e4ee84102408100ed9350db53a},
      }

1782. Xing Di and Vishal M. Patel, “Large Margin Multi-Modal Triplet Metric Learning,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{10319901,
      title = {Large Margin Multi-Modal Triplet Metric Learning},
      author = {{Xing Di} and {Vishal M. Patel}},
      year = 2017,
      month = {5},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/ae47339c4a2bff1d29d6d4bb202b56678ffd11d5},
      }

1783. Silvio Amir, Glen A. Coppersmith, Paula Carvalho, Mário J. Silva, and Byron C. Wallace, “Quantifying Mental Health from Social Media with Neural User Embeddings,” in Machine Learning in Health Care, 2017.
      [BibTeX] [Link]

      @inproceedings{3311830,
      title = {Quantifying Mental Health from Social Media with Neural User Embeddings},
      author = {{Silvio Amir} and {Glen A. Coppersmith} and {Paula Carvalho} and {Mário J. Silva} and {Byron C. Wallace}},
      year = 2017,
      month = {4},
      booktitle = {Machine Learning in Health Care},
      url = {https://www.semanticscholar.org/paper/1c4eda4f85559b3c3fcae6ca6ec4a54bff18002e},
      }

1784. Zhishuai Zhang, Cihang Xie, Jianyu Wang, Lingxi Xie, and A. Yuille, “DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{4439025,
      title = {DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion},
      author = {{Zhishuai Zhang} and {Cihang Xie} and {Jianyu Wang} and {Lingxi Xie} and {A. Yuille}},
      year = 2017,
      month = {9},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/adecc9cb7c4e71a401099b26ed5420b8d4f4e90a},
      }

1785. Michael C. Smith, Mark Dredze, S. Quinn, and David A. Broniatowski, “Monitoring Real-time Spatial Public Health Discussions in the Context of Vaccine Hesitancy,” in SMM4H@AMIA, 2017.
      [BibTeX] [Link]

      @inproceedings{21953546,
      title = {Monitoring Real-time Spatial Public Health Discussions in the Context of Vaccine Hesitancy},
      author = {{Michael C. Smith} and {Mark Dredze} and {S. Quinn} and {David A. Broniatowski}},
      year = 2017,
      booktitle = {SMM4H@AMIA},
      url = {https://www.semanticscholar.org/paper/07e5af29b9c2c6de4e4bd4334b68510e0e7826ef},
      }

1786. Andy Ma, Nishi Rawat, A. Reiter, Christine Shrock, A. Zhan, A. Stone, A. Rabiee, S. Griffin, D. Needham, and S. Saria, “Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor,” in Critical Care Medicine, 2017.
      [BibTeX] [Link]

      @inproceedings{28431103,
      title = {Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor},
      author = {{Andy Ma} and {Nishi Rawat} and {A. Reiter} and {Christine Shrock} and {A. Zhan} and {A. Stone} and {A. Rabiee} and {S. Griffin} and {D. Needham} and {S. Saria}},
      year = 2017,
      month = {4},
      booktitle = {Critical Care Medicine},
      url = {https://www.semanticscholar.org/paper/aa2c50398344887acb9775b16d25e46616947541},
      }

1787. Navaneeth Bodla, Bharat Singh, R. Chellappa, and L. Davis, “Soft-NMS — Improving Object Detection with One Line of Code,” in IEEE International Conference on Computer Vision, 2017.
      [BibTeX] [Link]

      @inproceedings{15155826,
      title = {Soft-NMS — Improving Object Detection with One Line of Code},
      author = {{Navaneeth Bodla} and {Bharat Singh} and {R. Chellappa} and {L. Davis}},
      year = 2017,
      month = {4},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/53c0aa8d33d240197caff824a6225fb223c1181c},
      }

1788. Pushpendre Rastogi, V. Lyzinski, and Benjamin Van Durme, “Entity recommendations on a Cold Start Knowledge Graph.” 2017.
      [BibTeX] [Link]

      @inproceedings{85449899,
      title = {Entity recommendations on a Cold Start Knowledge Graph},
      author = {{Pushpendre Rastogi} and {V. Lyzinski} and {Benjamin Van Durme}},
      year = 2017,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/6cb7ab25d87e8b07f18077539ab9497ceb6f9d19},
      }

1789. He Zhang, Vishwanath A. Sindagi, and Vishal M. Patel, “Image De-Raining Using a Conditional Generative Adversarial Network,” in IEEE transactions on circuits and systems for video technology (Print), 2017.
      [BibTeX] [Link]

      @inproceedings{11922819,
      title = {Image De-Raining Using a Conditional Generative Adversarial Network},
      author = {{He Zhang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2017,
      month = {1},
      booktitle = {IEEE transactions on circuits and systems for video technology (Print)},
      url = {https://www.semanticscholar.org/paper/920f0c070701caabf023c600f3e310f1906ca818},
      }

1790. Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, E. Fishman, and A. Yuille, “Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{195347070,
      title = {Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation},
      author = {{Qihang Yu} and {Lingxi Xie} and {Yan Wang} and {Yuyin Zhou} and {E. Fishman} and {A. Yuille}},
      year = 2017,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c8967b94baeddd82db710d6ac450b881dd1b2e9b},
      }

1791. Ashwin Bellur and Mounya Elhilali, “Speech processing using adaptive auditory receptive fields.” 2017.
      [BibTeX] [Link]

      @inproceedings{51997264,
      title = {Speech processing using adaptive auditory receptive fields},
      author = {{Ashwin Bellur} and {Mounya Elhilali}},
      year = 2017,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/6624795c7d391395cc1175b70f7241deca45455c},
      }

1792. Chang Liu, F. Sun, Changhu Wang, Feng Wang, and A. Yuille, “MAT: A Multimodal Attentive Translator for Image Captioning,” in International Joint Conference on Artificial Intelligence, 2017.
      [BibTeX] [Link]

      @inproceedings{14169709,
      title = {MAT: A Multimodal Attentive Translator for Image Captioning},
      author = {{Chang Liu} and {F. Sun} and {Changhu Wang} and {Feng Wang} and {A. Yuille}},
      year = 2017,
      month = {2},
      booktitle = {International Joint Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/2498124e6466ccde28c95477c923e7cd5843f4c0},
      }

1793. Mahyar Najibi, Pouya Samangouei, R. Chellappa, and L. Davis, “SSH: Single Stage Headless Face Detector,” in IEEE International Conference on Computer Vision, 2017.
      [BibTeX] [Link]

      @inproceedings{3197876,
      title = {SSH: Single Stage Headless Face Detector},
      author = {{Mahyar Najibi} and {Pouya Samangouei} and {R. Chellappa} and {L. Davis}},
      year = 2017,
      month = {8},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/a896ddeb0d253739c9aaef7fc1f170a2ba8407d3},
      }

1794. Tom Ko, Vijayaditya Peddinti, Daniel Povey, M. Seltzer, and S. Khudanpur, “A study on data augmentation of reverberant speech for robust speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
      [BibTeX] [Link]

      @inproceedings{23138179,
      title = {A study on data augmentation of reverberant speech for robust speech recognition},
      author = {{Tom Ko} and {Vijayaditya Peddinti} and {Daniel Povey} and {M. Seltzer} and {S. Khudanpur}},
      year = 2017,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/5005a3295dc2c931526438dd6d3f8fae8e34b641},
      }

1795. He Zhang and Vishal M. Patel, “Sparse Representation-Based Open Set Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
      [BibTeX] [Link]

      @inproceedings{10865487,
      title = {Sparse Representation-Based Open Set Recognition},
      author = {{He Zhang} and {Vishal M. Patel}},
      year = 2017,
      month = {5},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/3b1aaac41fc7847dd8a6a66d29d8881f75c91ad5},
      }

1796. Yiming Wang, Vijayaditya Peddinti, Hainan Xu, Xiaohui Zhang, Daniel Povey, and S. Khudanpur, “Backstitch: Counteracting Finite-Sample Bias via Negative Steps,” in Interspeech, 2017.
      [BibTeX] [Link]

      @inproceedings{442108,
      title = {Backstitch: Counteracting Finite-Sample Bias via Negative Steps},
      author = {{Yiming Wang} and {Vijayaditya Peddinti} and {Hainan Xu} and {Xiaohui Zhang} and {Daniel Povey} and {S. Khudanpur}},
      year = 2017,
      month = {8},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/aeec5e369572d5f2cd88e4f5166de439558af933},
      }

1797. Kate D. Fischl, Gaspar Tognetti, Daniel R. Mendat, G. Orchard, J. Rattray, Christos Sapsanis, Laura F. Campbell, Laxaviera Elphage, T. Niebur, Alejandro Pasciaroni, V. Rennoll, Heather Romney, Shamaria Walker, P. Pouliquen, and A. Andreou, “Neuromorphic self-driving robot with retinomorphic vision and spike-based processing/closed-loop control,” in Annual Conference on Information Sciences and Systems, 2017.
      [BibTeX] [Link]

      @inproceedings{27805933,
      title = {Neuromorphic self-driving robot with retinomorphic vision and spike-based processing/closed-loop control},
      author = {{Kate D. Fischl} and {Gaspar Tognetti} and {Daniel R. Mendat} and {G. Orchard} and {J. Rattray} and {Christos Sapsanis} and {Laura F. Campbell} and {Laxaviera Elphage} and {T. Niebur} and {Alejandro Pasciaroni} and {V. Rennoll} and {Heather Romney} and {Shamaria Walker} and {P. Pouliquen} and {A. Andreou}},
      year = 2017,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/4cdaf34f9e01fbb577bdd66296b8afb1b2110f58},
      }

1798. He Zhang and Vishal M. Patel, “Convolutional Sparse and Low-Rank Coding-Based Rain Streak Removal,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017.
      [BibTeX] [Link]

      @inproceedings{25639151,
      title = {Convolutional Sparse and Low-Rank Coding-Based Rain Streak Removal},
      author = {{He Zhang} and {Vishal M. Patel}},
      year = 2017,
      month = {3},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/42bb73d91eb8f0e68ffb34adc9d38b8833d5af20},
      }

1799. Mohamed Al-Badrashiny, Jason Bolton, Arun Tejasvi Chaganty, Kevin Clark, Craig Harman, Lifu Huang, Matthew Lamm, Jinhao Lei, Di Lu, Xiaoman Pan, Ashwin Paranjape, Ellie Pavlick, Haoruo Peng, Peng Qi, Pushpendre Rastogi, A. See, Kai Sun, Max Thomas, Chen-Tse Tsai, Hao Wu, Boliang Zhang, Chris Callison-Burch, Claire Cardie, Heng Ji, Christopher D. Manning, S. Muresan, Owen Rambow, D. Roth, Mark Sammons, and Benjamin Van Durme, “TinkerBell: Cross-lingual Cold-Start Knowledge Base Construction,” in Text Analysis Conference, 2017.
      [BibTeX] [Link]

      @inproceedings{46897703,
      title = {TinkerBell: Cross-lingual Cold-Start Knowledge Base Construction},
      author = {{Mohamed Al-Badrashiny} and {Jason Bolton} and {Arun Tejasvi Chaganty} and {Kevin Clark} and {Craig Harman} and {Lifu Huang} and {Matthew Lamm} and {Jinhao Lei} and {Di Lu} and {Xiaoman Pan} and {Ashwin Paranjape} and {Ellie Pavlick} and {Haoruo Peng} and {Peng Qi} and {Pushpendre Rastogi} and {A. See} and {Kai Sun} and {Max Thomas} and {Chen-Tse Tsai} and {Hao Wu} and {Boliang Zhang} and {Chris Callison-Burch} and {Claire Cardie} and {Heng Ji} and {Christopher D. Manning} and {S. Muresan} and {Owen Rambow} and {D. Roth} and {Mark Sammons} and {Benjamin Van Durme}},
      year = 2017,
      booktitle = {Text Analysis Conference},
      url = {https://www.semanticscholar.org/paper/800a4295003902e59b2d423dffecb02b6b4f99ce},
      }

1800. Vittal Premachandran, Daniel Tarlow, A. Yuille, and Dhruv Batra, “Empirical Minimum Bayes Risk Prediction,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
      [BibTeX] [Link]

      @inproceedings{8377727,
      title = {Empirical Minimum Bayes Risk Prediction},
      author = {{Vittal Premachandran} and {Daniel Tarlow} and {A. Yuille} and {Dhruv Batra}},
      year = 2017,
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/99e6c038864dcce68d4aafc8e2e31273b28180a4},
      }

1801. Frances Yung, Kevin Duh, T. Komura, and Yuji Matsumoto, “A Psycholinguistic Model for the Marking of Discourse Relations,” in Dialogue and Discourse, 2017.
      [BibTeX] [Link]

      @inproceedings{32242456,
      title = {A Psycholinguistic Model for the Marking of Discourse Relations},
      author = {{Frances Yung} and {Kevin Duh} and {T. Komura} and {Yuji Matsumoto}},
      year = 2017,
      month = {1},
      booktitle = {Dialogue and Discourse},
      url = {https://www.semanticscholar.org/paper/9c95830fe00c4119234c5c1861b4145a2685e72b},
      }

1802. Mark Dredze, Zach Wood-Doughty, S. Quinn, and David A. Broniatowski, “Vaccine opponents’ use of Twitter during the 2016 US presidential election: Implications for practice and policy.,” in Vaccine, 2017.
      [BibTeX] [Link]

      @inproceedings{38341631,
      title = {Vaccine opponents' use of Twitter during the 2016 US presidential election: Implications for practice and policy.},
      author = {{Mark Dredze} and {Zach Wood-Doughty} and {S. Quinn} and {David A. Broniatowski}},
      year = 2017,
      month = {8},
      booktitle = {Vaccine},
      url = {https://www.semanticscholar.org/paper/e08d39a6aa0fbe20a20654ca5ee4ef9d130cc0d3},
      }

1803. Sheng Zhang, Kevin Duh, and Benjamin Van Durme, “Extraction with Neural Sequence-to-Sequence Models.” 2017.
      [BibTeX] [Link]

      @inproceedings{35809056,
      title = {Extraction with Neural Sequence-to-Sequence Models},
      author = {{Sheng Zhang} and {Kevin Duh} and {Benjamin Van Durme}},
      year = 2017,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5bd7012eb3d843e0825995a36a127756fdb45b80},
      }

1804. Peter F. Schulam and S. Saria, “What-If Reasoning with Counterfactual Gaussian Processes,” in Neural Information Processing Systems, 2017.
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      @inproceedings{16737818,
      title = {What-If Reasoning with Counterfactual Gaussian Processes},
      author = {{Peter F. Schulam} and {S. Saria}},
      year = 2017,
      month = {3},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/4a8e692ede416d4864e15042696f53300a52089b},
      }

1805. Siyuan Qiao, Chenxi Liu, Wei Shen, and A. Yuille, “Few-Shot Image Recognition by Predicting Parameters from Activations,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{33252535,
      title = {Few-Shot Image Recognition by Predicting Parameters from Activations},
      author = {{Siyuan Qiao} and {Chenxi Liu} and {Wei Shen} and {A. Yuille}},
      year = 2017,
      month = {6},
      booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/3e08a3912ebe494242f6bcd772929cc65307129c},
      }

1806. Hossein Soleimani, Adarsh Subbaswamy, and S. Saria, “Learning Treatment-Response Models from Multivariate Longitudinal Data,” in Conference on Uncertainty in Artificial Intelligence, 2017.
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      @inproceedings{17430590,
      title = {Learning Treatment-Response Models from Multivariate Longitudinal Data},
      author = {{Hossein Soleimani} and {Adarsh Subbaswamy} and {S. Saria}},
      year = 2017,
      month = {4},
      booktitle = {Conference on Uncertainty in Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/73a7144e072356b5c9512bd4a87b22457d33760c},
      }

1807. Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, A. Yuille, and Xiaogang Wang, “Multi-context Attention for Human Pose Estimation,” in Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{15364102,
      title = {Multi-context Attention for Human Pose Estimation},
      author = {{Xiao Chu} and {Wei Yang} and {Wanli Ouyang} and {Cheng Ma} and {A. Yuille} and {Xiaogang Wang}},
      year = 2017,
      month = {2},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/9e671c01163c9ce0ea5699ff81b5173dd03730cf},
      }

1808. E. González-Sosa, R. Vera-Rodríguez, Julian Fierrez, and Vishal M. Patel, “Millimetre wave person recognition: Hand-crafted vs learned features,” in International Conference on Identity, Security and Behavior Analysis, 2017.
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      @inproceedings{3929464,
      title = {Millimetre wave person recognition: Hand-crafted vs learned features},
      author = {{E. González-Sosa} and {R. Vera-Rodríguez} and {Julian Fierrez} and {Vishal M. Patel}},
      year = 2017,
      month = {2},
      booktitle = {International Conference on Identity, Security and Behavior Analysis},
      url = {https://www.semanticscholar.org/paper/35be9df1100320bbe716de78ca0b30e917129d6a},
      }

1809. Zhishuai Zhang, Cihang Xie, Jianyu Wang, Lingxi Xie, and A. Yuille, “DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion,” in arXiv.org, 2017.
      [BibTeX] [Link]

      @inproceedings{195347098,
      title = {DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion},
      author = {{Zhishuai Zhang} and {Cihang Xie} and {Jianyu Wang} and {Lingxi Xie} and {A. Yuille}},
      year = 2017,
      month = {9},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c7780cff11068fecb322a43e459c56267a88aee7},
      }

1810. Pramuditha Perera and Vishal M. Patel, “Towards Multiple User Active Authentication in Mobile Devices,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
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      @inproceedings{26841762,
      title = {Towards Multiple User Active Authentication in Mobile Devices},
      author = {{Pramuditha Perera} and {Vishal M. Patel}},
      year = 2017,
      month = {5},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/a3bcbca1f865849d2a619f609b3667e1d507d5d0},
      }

1811. Mohamed Al-Badrashiny, Jason Bolton, Arun Tejasvi Chaganty, Kevin Clark, Craig Harman, Lifu Huang, Matthew Lamm, Jinhao Lei, Di Lu, Xiaoman Pan, Ashwini Paranjape, Ellie Pavlick, Haoruo Peng, Peng Qi, Pushpendre Rastogi, A. See, Kai Sun, Max Thomas, Chen-Tse Tsai, Hao Wu, Boliang Zhang, Chris Callison-Burch, Claire Cardie, Heng Ji, Christopher Manning, S. Muresan, Owen Rambow, D. Roth, Mark Sammons, and Benjamin Van Durme, “Strategy 3 with CNN 15 Input Word Embedding Linguistic Feature Embedding Left LSTMs Right LSTMs Left LSTMs Character Embedding Word Embedding Right LSTMs.” 2017.
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      @inproceedings{30286186,
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1812. Glen A. Coppersmith, Casey Hilland, O. Frieder, and Ryan Leary, “Scalable mental health analysis in the clinical whitespace via natural language processing,” in 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017.
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1813. Ehsan Jahangiri and A. Yuille, “Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017.
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1815. Kota Hara, Raviteja Vemulapalli, and R. Chellappa, “Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation,” in arXiv.org, 2017.
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1819. Yuyin Zhou, Lingxi Xie, E. Fishman, and A. Yuille, “Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017.
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1820. Christopher Reale, Hyungtae Lee, H. Kwon, and R. Chellappa, “Deep Network Shrinkage Applied to Cross-Spectrum Face Recognition,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
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1822. He Zhang, Vishwanath A. Sindagi, and Vishal M. Patel, “Joint Transmission Map Estimation and Dehazing Using Deep Networks,” in IEEE transactions on circuits and systems for video technology (Print), 2017.
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1823. Nicholas Huang and Mounya Elhilali, “Auditory salience using natural soundscapes,” in Journal of the Acoustical Society of America, 2017.
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1824. Xiaohui Zhang, Vimal Manohar, Daniel Povey, and S. Khudanpur, “Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework,” in Interspeech, 2017.
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1825. B. Meyer, Sri Harish Reddy Mallidi, H. Kayser, and H. Hermansky, “Predicting error rates for unknown data in automatic speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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1826. M. Cernak, Elmar Nöth, Frank Rudzicz, H. Christensen, J. Orozco-Arroyave, R. Arora, T. Bocklet, H. Chinaei, J. Hannink, P. S. Nidadavolu, J. C. Vásquez-Correa, Maria Yancheva, A. Vann, and Nikolai Vogler, “On the impact of non-modal phonation on phonological features,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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1828. Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “Deep Heterogeneous Feature Fusion for Template-Based Face Recognition,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017.
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1829. Ehsan Jahangiri and A. Yuille, “Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections,” in arXiv.org, 2017.
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1835. Xingyu Lin, Hao Wang, Zhihao Li, Yimeng Zhang, A. Yuille, and T. Lee, “Transfer of View-manifold Learning to Similarity Perception of Novel Objects,” in International Conference on Learning Representations, 2017.
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1836. Puyang Wang and Vishal M. Patel, “Extracting Fourier descriptors from compressive measurements,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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1837. J. C. Vásquez-Correa, J. Orozco-Arroyave, R. Arora, Elmar Nöth, N. Dehak, H. Christensen, Frank Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, P. S. Nidadavolu, Maria Yancheva, A. Vann, and Nikolai Vogler, “Multi-view representation learning via gcca for multimodal analysis of Parkinson’s disease,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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1838. X. Lan, P. Yuen, and R. Chellappa, “Robust MIL-Based Feature Template Learning for Object Tracking,” in AAAI Conference on Artificial Intelligence, 2017.
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1839. Ching-Hui Chen, Jun-Cheng Chen, C. Castillo, and R. Chellappa, “Video-Based Face Association and Identification,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
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1840. Hainan Xu, Tongfei Chen, Dongji Gao, Yiming Wang, Ke Li, N. Goel, Yishay Carmiel, Daniel Povey, and S. Khudanpur, “LATTICE-RESCORING ALGORITHM FOR AUTOMATIC SPEECH RECOGNITION.” 2017.
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1841. Wei Shen, Bin Wang, Yuan Jiang, Yan Wang, and A. Yuille, “Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection,” in IEEE International Conference on Computer Vision, 2017.
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1842. Ching-Hui Chen and R. Chellappa, “Face Recognition Using an Outdoor Camera Network.” 2017.
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1845. Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, and A. Yuille, “Detecting Semantic Parts on Partially Occluded Objects,” in British Machine Vision Conference, 2017.
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1846. Emine Merve Kaya and Mounya Elhilali, “Modelling auditory attention,” in Philosophical Transactions of the Royal Society B: Biological Sciences, 2017.
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1848. J. Trmal, Manish Kumar, Vimal Manohar, S. Khudanpur, Matt Post, and Paul McNamee, “Using of heterogeneous corpora for training of an ASR system,” in arXiv.org, 2017.
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1849. Pushpendre Rastogi and Benjamin Van Durme, “Predicting Asymmetric Transitive Relations in Knowledge Bases,” in KG4IR@SIGIR, 2017.
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      @inproceedings{31697546,
      title = {Predicting Asymmetric Transitive Relations in Knowledge Bases},
      author = {{Pushpendre Rastogi} and {Benjamin Van Durme}},
      year = 2017,
      booktitle = {KG4IR@SIGIR},
      url = {https://www.semanticscholar.org/paper/27a5a2da3e41b694870c91c36c430b031d6070ba},
      }

1850. Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Bridger Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, A. Kuncoro, Manish Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, and Pengcheng Yin, “DyNet: The Dynamic Neural Network Toolkit,” in arXiv.org, 2017.
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      @inproceedings{2170930,
      title = {DyNet: The Dynamic Neural Network Toolkit},
      author = {{Graham Neubig} and {Chris Dyer} and {Yoav Goldberg} and {Austin Matthews} and {Bridger Waleed Ammar} and {Antonios Anastasopoulos} and {Miguel Ballesteros} and {David Chiang} and {Daniel Clothiaux} and {Trevor Cohn} and {Kevin Duh} and {Manaal Faruqui} and {Cynthia Gan} and {Dan Garrette} and {Yangfeng Ji} and {Lingpeng Kong} and {A. Kuncoro} and {Manish Kumar} and {Chaitanya Malaviya} and {Paul Michel} and {Yusuke Oda} and {Matthew Richardson} and {Naomi Saphra} and {Swabha Swayamdipta} and {Pengcheng Yin}},
      year = 2017,
      month = {1},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/480d545ac4a4ffff5b1bc291c2de613192e35d91},
      }

1851. Santosh Kesiraju, R. Pappagari, Lucas Ondel, L. Burget, N. Dehak, S. Khudanpur, J. Černocký, and S. Gangashetty, “Topic identification of spoken documents using unsupervised acoustic unit discovery,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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      @inproceedings{27865940,
      title = {Topic identification of spoken documents using unsupervised acoustic unit discovery},
      author = {{Santosh Kesiraju} and {R. Pappagari} and {Lucas Ondel} and {L. Burget} and {N. Dehak} and {S. Khudanpur} and {J. Černocký} and {S. Gangashetty}},
      year = 2017,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/2e8d47fcba60cff5cf5ba9aa535192cccfc37db1},
      }

1852. Kota Hara and R. Chellappa, “Growing Regression Tree Forests by Classification for Continuous Object Pose Estimation,” in International Journal of Computer Vision, 2017.
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      @inproceedings{20825989,
      title = {Growing Regression Tree Forests by Classification for Continuous Object Pose Estimation},
      author = {{Kota Hara} and {R. Chellappa}},
      year = 2017,
      month = {4},
      booktitle = {International Journal of Computer Vision},
      url = {https://www.semanticscholar.org/paper/63f2d43d7407931fa6f5ceb3c901b4d4ec11fbcd},
      }

1853. Lingxi Xie and A. Yuille, “Genetic CNN,” in IEEE International Conference on Computer Vision, 2017.
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      @inproceedings{206770867,
      title = {Genetic CNN},
      author = {{Lingxi Xie} and {A. Yuille}},
      year = 2017,
      month = {3},
      booktitle = {IEEE International Conference on Computer Vision},
      url = {https://www.semanticscholar.org/paper/4f46dba09e075b2e7dfae1ba2a71e8e21b46e88d},
      }

1854. Cathryn S. Cortesa, Jonathan D. Jones, Gregory Hager, S. Khudanpur, A. Shelton, and B. Landau, “Characterizing spatial construction processes: Toward computational tools to understand cognition,” in Annual Meeting of the Cognitive Science Society, 2017.
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      @inproceedings{9495965,
      title = {Characterizing spatial construction processes: Toward computational tools to understand cognition},
      author = {{Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {A. Shelton} and {B. Landau}},
      year = 2017,
      booktitle = {Annual Meeting of the Cognitive Science Society},
      url = {https://www.semanticscholar.org/paper/2c7b2d214ead69910e98995286b8cafa8f35db62},
      }

1855. Ashwin Bellur and Mounya Elhilali, “Feedback-Driven Sensory Mapping Adaptation for Robust Speech Activity Detection,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2017.
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      @inproceedings{15489173,
      title = {Feedback-Driven Sensory Mapping Adaptation for Robust Speech Activity Detection},
      author = {{Ashwin Bellur} and {Mounya Elhilali}},
      year = 2017,
      month = {3},
      booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
      url = {https://www.semanticscholar.org/paper/556f333d2403af6e05e93147da6a1d848b4cb578},
      }

1856. P. Torres-Carrasquillo, Fred Richardson, S. Nercessian, D. Sturim, W. Campbell, Youngjune Gwon, Swaroop Vattam, N. Dehak, Sri Harish Reddy Mallidi, P. S. Nidadavolu, Ruizhi Li, and Réda Dehak, “The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System,” in Interspeech, 2017.
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      @inproceedings{32850737,
      title = {The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System},
      author = {{P. Torres-Carrasquillo} and {Fred Richardson} and {S. Nercessian} and {D. Sturim} and {W. Campbell} and {Youngjune Gwon} and {Swaroop Vattam} and {N. Dehak} and {Sri Harish Reddy Mallidi} and {P. S. Nidadavolu} and {Ruizhi Li} and {Réda Dehak}},
      year = 2017,
      month = {8},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/d6c6f46725f538cf5960d3a4a21eea2e9605f3a8},
      }

1857. Amit Kumar, A. Alavi, and R. Chellappa, “KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
      [BibTeX] [Link]

      @inproceedings{9636241,
      title = {KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors},
      author = {{Amit Kumar} and {A. Alavi} and {R. Chellappa}},
      year = 2017,
      month = {2},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/f7824758800a7b1a386db5bd35f84c81454d017a},
      }

1858. U. Mahbub, Sayantan Sarkar, and R. Chellappa, “Partial Face Detection in the Mobile Domain,” in Image and Vision Computing, 2017.
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      @inproceedings{6077228,
      title = {Partial Face Detection in the Mobile Domain},
      author = {{U. Mahbub} and {Sayantan Sarkar} and {R. Chellappa}},
      year = 2017,
      month = {4},
      booktitle = {Image and Vision Computing},
      url = {https://www.semanticscholar.org/paper/74ba4ab407b90592ffdf884a20e10006d2223015},
      }

1859. Ann Irvine and Mark Dredze, “Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order,” in arXiv.org, 2017.
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      @inproceedings{14792236,
      title = {Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order},
      author = {{Ann Irvine} and {Mark Dredze}},
      year = 2017,
      month = {2},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/1d5028e2babab3e407a55e560f81057a5ebe76f7},
      }

1860. Fangting Xia, Peng Wang, Xianjie Chen, and A. Yuille, “Joint Multi-person Pose Estimation and Semantic Part Segmentation,” in Computer Vision and Pattern Recognition, 2017.
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      @inproceedings{2524528,
      title = {Joint Multi-person Pose Estimation and Semantic Part Segmentation},
      author = {{Fangting Xia} and {Peng Wang} and {Xianjie Chen} and {A. Yuille}},
      year = 2017,
      month = {7},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/fc027fccb19512a439fc17181c34ee1c3aad51b5},
      }

1861. Zhou Ren, Hailin Jin, Zhe L. Lin, Chen Fang, and A. Yuille, “Multiple Instance Visual-Semantic Embedding,” in British Machine Vision Conference, 2017.
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      @inproceedings{52269605,
      title = {Multiple Instance Visual-Semantic Embedding},
      author = {{Zhou Ren} and {Hailin Jin} and {Zhe L. Lin} and {Chen Fang} and {A. Yuille}},
      year = 2017,
      booktitle = {British Machine Vision Conference},
      url = {https://www.semanticscholar.org/paper/6f5fb1966f6048d8094821d25e273ce37080095c},
      }

1862. Vittal Premachandran, B. Bonev, Xiaochen Lian, and A. Yuille, “PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017.
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      @inproceedings{23019075,
      title = {PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector},
      author = {{Vittal Premachandran} and {B. Bonev} and {Xiaochen Lian} and {A. Yuille}},
      year = 2017,
      month = {3},
      booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
      url = {https://www.semanticscholar.org/paper/91bb3680cee8cd37b80e07644f66f9cccf1b1aff},
      }

1863. R. Rudinger, K. Duh, and B. Van Durme, “Skip-Prop: Representing Sentences with One Vector Per Proposition,” in Proceedings of the 12th International Conference on Computational Semantics (IWCS) –- Short papers, 2017.
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      @inproceedings{rudinger-etal-2017-skip,
      title = "Skip-Prop: Representing Sentences with One Vector Per Proposition",
      author = "Rudinger, Rachel and
      Duh, Kevin and
      Van Durme, Benjamin",
      editor = "Gardent, Claire and
      Retor{\'e}, Christian",
      booktitle = "Proceedings of the 12th International Conference on Computational Semantics ({IWCS}) {---} Short papers",
      year = "2017",
      url = "https://aclanthology.org/W17-6936",
      }

1864. Xiaolei Huang, Michael C. Smith, Michael J. Paul, D. Ryzhkov, S. Quinn, David A. Broniatowski, and Mark Dredze, “Examining Patterns of Influenza Vaccination in Social Media,” in AAAI Workshops, 2017.
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      @inproceedings{17885136,
      title = {Examining Patterns of Influenza Vaccination in Social Media},
      author = {{Xiaolei Huang} and {Michael C. Smith} and {Michael J. Paul} and {D. Ryzhkov} and {S. Quinn} and {David A. Broniatowski} and {Mark Dredze}},
      year = 2017,
      booktitle = {AAAI Workshops},
      url = {https://www.semanticscholar.org/paper/81202a14ed728c3d8fa8224f239c526da0c57fba},
      }

1865. Wei-An Lin, Jun-Cheng Chen, and R. Chellappa, “A Proximity-Aware Hierarchical Clustering of Faces,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
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      @inproceedings{1018804,
      title = {A Proximity-Aware Hierarchical Clustering of Faces},
      author = {{Wei-An Lin} and {Jun-Cheng Chen} and {R. Chellappa}},
      year = 2017,
      month = {3},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/b6f758be954d34817d4ebaa22b30c63a4b8ddb35},
      }

1866. H. Qin, T. Shinozaki, and K. Duh, “Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems,” in Proceedings of the 14th International Conference on Spoken Language Translation, Tokyo, Japan, 2017, p. 120–128.
      [BibTeX] [Abstract] [Link]
      

      Neural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In particular, we apply the covariance matrix adaptation-evolution strategy (CMA-ES), and investigate a Pareto-based multi-objective CMA-ES to optimize the translation performance and computational time jointly. Experimental results show that the proposed method automatically finds NMT systems that outperform the initial manual setting.

      @inproceedings{qin-etal-2017-evolution,
      title = "Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems",
      author = "Qin, Hao and
      Shinozaki, Takahiro and
      Duh, Kevin",
      editor = "Sakti, Sakriani and
      Utiyama, Masao",
      booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation",
      month = dec # " 14-15",
      year = "2017",
      address = "Tokyo, Japan",
      publisher = "International Workshop on Spoken Language Translation",
      url = "https://aclanthology.org/2017.iwslt-1.17",
      pages = "120--128",
      abstract = "Neural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In particular, we apply the covariance matrix adaptation-evolution strategy (CMA-ES), and investigate a Pareto-based multi-objective CMA-ES to optimize the translation performance and computational time jointly. Experimental results show that the proposed method automatically finds NMT systems that outperform the initial manual setting.",
      }

1867. Hui Ding, Hao Zhou, S. Zhou, and R. Chellappa, “A Deep Cascade Network for Unaligned Face Attribute Classification,” in AAAI Conference on Artificial Intelligence, 2017.
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      @inproceedings{19231876,
      title = {A Deep Cascade Network for Unaligned Face Attribute Classification},
      author = {{Hui Ding} and {Hao Zhou} and {S. Zhou} and {R. Chellappa}},
      year = 2017,
      month = {9},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/7a16f37ecccca4f9703ce190dc596149b4ccc8d2},
      }

1868. J. Seo, Hani Bakhshaee, Guillaume Garreau, Chi Zhu, A. Andreou, W. R. Thompson, and R. Mittal, “A method for the computational modeling of the physics of heart murmurs,” in Journal of Computational Physics, 2017.
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      @inproceedings{11218912,
      title = {A method for the computational modeling of the physics of heart murmurs},
      author = {{J. Seo} and {Hani Bakhshaee} and {Guillaume Garreau} and {Chi Zhu} and {A. Andreou} and {W. R. Thompson} and {R. Mittal}},
      year = 2017,
      month = {5},
      booktitle = {Journal of Computational Physics},
      url = {https://www.semanticscholar.org/paper/77821c70aa80e73313bbbc3c9896adeca0df1f5d},
      }

1869. Ke Wu, X. Li, Yanif Ahmad, V. Braverman, Randal Burns, Zachary Burwell, M. Dinitz, Mark Dredze, Abhishek Jain, and Philipp Koehn, “DETERMINISTIC CONSTRUCTION OF SYNCHRONIZATION STRING OVER SMALL ALPHABET.” 2017.
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      @inproceedings{54714125,
      title = {DETERMINISTIC CONSTRUCTION OF SYNCHRONIZATION STRING OVER SMALL ALPHABET},
      author = {{Ke Wu} and {X. Li} and {Yanif Ahmad} and {V. Braverman} and {Randal Burns} and {Zachary Burwell} and {M. Dinitz} and {Mark Dredze} and {Abhishek Jain} and {Philipp Koehn}},
      year = 2017,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/72b9c8872c7c5a595a8ee6e41dffd7f91940d1f5},
      }

1870. R. He, B. Lovell, R. Chellappa, Anil K. Jain, and Zhenan Sun, “Editorial: Special issue on ubiquitous biometrics,” in Pattern Recognition, 2017.
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      @inproceedings{57408,
      title = {Editorial: Special issue on ubiquitous biometrics},
      author = {{R. He} and {B. Lovell} and {R. Chellappa} and {Anil K. Jain} and {Zhenan Sun}},
      year = 2017,
      month = {6},
      booktitle = {Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/5aac6f1f916286cc6c4749bf9f4a60fc3089da52},
      }

1871. Kaiqi Huang, T. Tan, S. Maybank, R. Chellappa, and Jake Aggarval, “Guest Editorial Introduction to the Special Issue on Large-Scale Video Analytics for Enhanced Security: Algorithms and Systems,” in IEEE Transactions on Systems, Man & Cybernetics. Systems, 2017.
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      @inproceedings{45442934,
      title = {Guest Editorial Introduction to the Special Issue on Large-Scale Video Analytics for Enhanced Security: Algorithms and Systems},
      author = {{Kaiqi Huang} and {T. Tan} and {S. Maybank} and {R. Chellappa} and {Jake Aggarval}},
      year = 2017,
      month = {4},
      booktitle = {IEEE Transactions on Systems, Man & Cybernetics. Systems},
      url = {https://www.semanticscholar.org/paper/fa36668238e27ccccb000a79ac42fad19f93de68},
      }

1872. Vishwanath A. Sindagi and Vishal M. Patel, “CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting,” in Advanced Video and Signal Based Surveillance, 2017.
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      @inproceedings{3003101,
      title = {CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting},
      author = {{Vishwanath A. Sindagi} and {Vishal M. Patel}},
      year = 2017,
      month = {7},
      booktitle = {Advanced Video and Signal Based Surveillance},
      url = {https://www.semanticscholar.org/paper/9fb7a23910f6464902f1b653025f3aeaa20b90dd},
      }

1873. Ching-Hui Chen, Vishal M. Patel, and R. Chellappa, “Learning from Ambiguously Labeled Face Images,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
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      @inproceedings{2313547,
      title = {Learning from Ambiguously Labeled Face Images},
      author = {{Ching-Hui Chen} and {Vishal M. Patel} and {R. Chellappa}},
      year = 2017,
      month = {2},
      booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      url = {https://www.semanticscholar.org/paper/58fc54590eb587c7227545fe85ec7d479a224fb7},
      }

1874. Michael J. Paul and Mark Dredze, “Social Monitoring for Public Health,” in Synthesis Lectures on Information Concepts Retrieval and Services, 2017.
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      @inproceedings{34317289,
      title = {Social Monitoring for Public Health},
      author = {{Michael J. Paul} and {Mark Dredze}},
      year = 2017,
      month = {8},
      booktitle = {Synthesis Lectures on Information Concepts Retrieval and Services},
      url = {https://www.semanticscholar.org/paper/80d6fb37be35edfa8a0395eb6334424132c6d07e},
      }

1875. J. Snyder and Mounya Elhilali, “Recent advances in exploring the neural underpinnings of auditory scene perception,” in Annals of the New York Academy of Sciences, 2017.
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      @inproceedings{9564893,
      title = {Recent advances in exploring the neural underpinnings of auditory scene perception},
      author = {{J. Snyder} and {Mounya Elhilali}},
      year = 2017,
      month = {5},
      booktitle = {Annals of the New York Academy of Sciences},
      url = {https://www.semanticscholar.org/paper/53285f9bf3206d7aef0a23daff8ae6816d1bcc6f},
      }

1876. R. Arora, T. V. Marinov, Poorya Mianjy, and N. Srebro, “Stochastic Approximation for Canonical Correlation Analysis,” in Neural Information Processing Systems, 2017.
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      @inproceedings{3541591,
      title = {Stochastic Approximation for Canonical Correlation Analysis},
      author = {{R. Arora} and {T. V. Marinov} and {Poorya Mianjy} and {N. Srebro}},
      year = 2017,
      month = {2},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/4ee4f802dac83bb99b98c09e41a8246a7e218826},
      }

1877. J. Villalba, N. Brümmer, and N. Dehak, “Tied Variational Autoencoder Backends for i-Vector Speaker Recognition,” in Interspeech, 2017.
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      @inproceedings{10319016,
      title = {Tied Variational Autoencoder Backends for i-Vector Speaker Recognition},
      author = {{J. Villalba} and {N. Brümmer} and {N. Dehak}},
      year = 2017,
      month = {8},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/4488cba0d06ae06b4b7b99cbb3639731c9eefe32},
      }

1878. Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, and Alexandra Birch, “Syntax-aware Neural Machine Translation Using CCG,” in arXiv.org, 2017.
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      @inproceedings{7037582,
      title = {Syntax-aware Neural Machine Translation Using CCG},
      author = {{Maria Nadejde} and {Siva Reddy} and {Rico Sennrich} and {Tomasz Dwojak} and {Marcin Junczys-Dowmunt} and {Philipp Koehn} and {Alexandra Birch}},
      year = 2017,
      month = {2},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/7f13e66231c96f34f8de2b091e5b5dafb5db5327},
      }

1879. Feng Wang, Xiang Xiang, Chang Liu, T. Tran, A. Reiter, Gregory Hager, H. Quon, Jian Cheng, and A. Yuille, “Regularizing face verification nets for pain intensity regression,” in International Conference on Information Photonics, 2017.
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      @inproceedings{3456350,
      title = {Regularizing face verification nets for pain intensity regression},
      author = {{Feng Wang} and {Xiang Xiang} and {Chang Liu} and {T. Tran} and {A. Reiter} and {Gregory Hager} and {H. Quon} and {Jian Cheng} and {A. Yuille}},
      year = 2017,
      month = {9},
      booktitle = {International Conference on Information Photonics},
      url = {https://www.semanticscholar.org/paper/b0e42d179abfd2d30d4ac543358b805c7c9777ac},
      }

1880. J. Ayers, E. Leas, Jon-Patrick Allem, Adrian Benton, Mark Dredze, B. Althouse, T. Cruz, and J. Unger, “Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015,” in PLoS ONE, 2017.
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      @inproceedings{8487752,
      title = {Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015},
      author = {{J. Ayers} and {E. Leas} and {Jon-Patrick Allem} and {Adrian Benton} and {Mark Dredze} and {B. Althouse} and {T. Cruz} and {J. Unger}},
      year = 2017,
      month = {3},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/5c60a417ae333a4503f0f4642bed9f66d3264ff6},
      }

1881. Pushpendre Rastogi, Adam Poliak, and Benjamin Van Durme, “Training Relation Embeddings under Logical Constraints,” in KG4IR@SIGIR, 2017.
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      @inproceedings{27060217,
      title = {Training Relation Embeddings under Logical Constraints},
      author = {{Pushpendre Rastogi} and {Adam Poliak} and {Benjamin Van Durme}},
      year = 2017,
      booktitle = {KG4IR@SIGIR},
      url = {https://www.semanticscholar.org/paper/234178756b8bf3b2694671583084b22c76c47560},
      }

1882. Chunxi Liu, Jinyi Yang, Ming Sun, Santosh Kesiraju, Alena Rott, Lucas Ondel, Pegah Ghahremani, N. Dehak, L. Burget, and S. Khudanpur, “An empirical evaluation of zero resource acoustic unit discovery,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017.
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      @inproceedings{2342176,
      title = {An empirical evaluation of zero resource acoustic unit discovery},
      author = {{Chunxi Liu} and {Jinyi Yang} and {Ming Sun} and {Santosh Kesiraju} and {Alena Rott} and {Lucas Ondel} and {Pegah Ghahremani} and {N. Dehak} and {L. Burget} and {S. Khudanpur}},
      year = 2017,
      month = {2},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/ada0452efd5b0bc345de1bc66c875d0126c56d2c},
      }

1883. Puyang Wang and Vishal M. Patel, “CS Recovery Algorithm y Compressive Measurements e Edges FDs Fourier Descriptors.” 2017.
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      title = {CS Recovery Algorithm y Compressive Measurements e Edges FDs Fourier Descriptors},
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1885. Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, and Alexandra Birch, “Explorer Predicting Target Language CCG Supertags Improves Neural Machine Translation.” 2017.
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1886. S. Sankaranarayanan, Arpit Jain, R. Chellappa, and Ser-Nam Lim, “Regularizing deep networks using efficient layerwise adversarial training,” in AAAI Conference on Artificial Intelligence, 2017.
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1887. Vishwanath A. Sindagi and Vishal M. Patel, “Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs,” in IEEE International Conference on Computer Vision, 2017.
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1888. Amit Kumar and R. Chellappa, “A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection,” in arXiv.org, 2017.
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1894. S. Sankaranarayanan, Y. Balaji, C. Castillo, and R. Chellappa, “Generate to Adapt: Aligning Domains Using Generative Adversarial Networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
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1899. He Zhang and Vishal M. Patel, “Discriminative Sparse Representations,” in Handbook of Convex Optimization Methods in Imaging Science, 2017.
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1900. Yan Wang, Lingxi Xie, Ya Zhang, Wenjun Zhang, and A. Yuille, “Deep Collaborative Learning for Visual Recognition,” in arXiv.org, 2017.
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1901. N. García, J. Orozco-Arroyave, L. F. D’Haro, N. Dehak, and Elmar Nöth, “Evaluation of the Neurological State of People with Parkinson’s Disease Using i-Vectors,” in Interspeech, 2017.
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1902. Gaofeng Cheng, Vijayaditya Peddinti, Daniel Povey, Vimal Manohar, S. Khudanpur, and Yonghong Yan, “An Exploration of Dropout with LSTMs,” in Interspeech, 2017.
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1903. He Zhang, Vishal M. Patel, and R. Chellappa, “Low-Rank and Joint Sparse Representations for Multi-Modal Recognition,” in IEEE Transactions on Image Processing, 2017.
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1905. He Zhang, Vishal M. Patel, B. Riggan, and Shuowen Hu, “Generative adversarial network-based synthesis of visible faces from polarimetrie thermal faces,” in 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017.
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1906. Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, and Alexandra Birch, “Proceedings of the Conference on Machine Translation (WMT), Volume 1: Research Papers,” in The Association for Computational Linguistics, 2017.
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1907. Allyson Ettinger, Spandana Gella, Matthieu Labeau, Cecilia Ovesdotter Alm, Marine Carpuat, and Mark Dredze, “Proceedings of ACL 2017, Student Research Workshop,” in Annual Meeting of the Association for Computational Linguistics, 2017.
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1908. Kate D. Fischl, Kaitlin L. Fair, Wei-Yu Tsai, Jack Sampson, and A. Andreou, “Path planning on the TrueNorth neurosynaptic system,” in International Symposium on Circuits and Systems, 2017.
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1909. Boyu Lu, Jingxiao Zheng, Jun-Cheng Chen, and R. Chellappa, “Pose-Robust Face Verification by Exploiting Competing Tasks,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017.
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1910. Feng Wang, Xiang Xiang, Chang Liu, T. Tran, A. Reiter, Gregory Hager, H. Quon, Jian Cheng, and A. Yuille, “REGULARIZING FACE NET FOR DISCRETE-VALUED PAIN REGRESSION.” 2017.
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1911. Hossein Hadian, Daniel Povey, H. Sameti, and S. Khudanpur, “Phone Duration Modeling for LVCSR Using Neural Networks,” in Interspeech, 2017.
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1912. Hossein Soleimani, J. Hensman, and S. Saria, “Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
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1913. J. Trmal, Matthew Wiesner, Vijayaditya Peddinti, Xiaohui Zhang, Pegah Ghahremani, Yiming Wang, Vimal Manohar, Hainan Xu, Daniel Povey, and S. Khudanpur, “The Kaldi OpenKWS System: Improving Low Resource Keyword Search,” in Interspeech, 2017.
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1914. Chenxi Liu, Zhe L. Lin, Xiaohui Shen, Jimei Yang, Xin Lu, and A. Yuille, “Recurrent Multimodal Interaction for Referring Image Segmentation,” in IEEE International Conference on Computer Vision, 2017.
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1917. Feng Wang, Xiang Xiang, Chang Liu, T. Tran, A. Reiter, Gregory Hager, H. Quon, Jian Cheng, and A. Yuille, “Transferring Face Verification Nets To Pain and Expression Regression,” in arXiv.org, 2017.
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1918. N. Ahmidi, Lingling Tao, S. Sefati, Yixin Gao, Colin S. Lea, B. B. Haro, L. Zappella, S. Khudanpur, R. Vidal, and Gregory Hager, “A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery,” in IEEE Transactions on Biomedical Engineering, 2017.
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1919. J. Orozco-Arroyave, J. C. Vásquez-Correa, J. Vargas-Bonilla, R. Arora, N. Dehak, P. S. Nidadavolu, H. Christensen, Frank Rudzicz, Maria Yancheva, H. Chinaei, A. Vann, Nikolai Vogler, T. Bocklet, M. Cernak, J. Hannink, and Elmar Nöth, “NeuroSpeech: An open-source software for Parkinson’s speech analysis,” in Digit. Signal Process., 2017.
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1920. Mohammed E. Fathy and R. Chellappa, “Image Set Classification Using Sparse Bayesian Regression,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2017.
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1921. R. Chellappa and Vishal M. Patel, “Sparse and Low-Rank Models for Visual Domain Adaptation.” 2017.
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1922. Sayantan Sarkar, Ankan Bansal, U. Mahbub, and R. Chellappa, “UPSET and ANGRI : Breaking High Performance Image Classifiers,” in arXiv.org, 2017.
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1923. Jie Chen, Vishal M. Patel, Li Liu, Vili Kellokumpu, Guoying Zhao, M. Pietikäinen, and R. Chellappa, “Robust local features for remote face recognition,” in Image and Vision Computing, 2017.
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1924. U. Mahbub, Sayantan Sarkar, and R. Chellappa, “Pooling Facial Segments to Face: The Shallow and Deep Ends,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2017.
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1925. Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, E. Fishman, and A. Yuille, “Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017.
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1926. S. Zhang, R. Rudinger, and B. Van Durme, “An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling,” in Proceedings of the 12th International Conference on Computational Semantics (IWCS) –- Short papers, 2017.
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      editor = "Gardent, Claire and
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1927. Jon-Patrick Allem, E. Leas, Theodore L. Caputi, Mark Dredze, B. Althouse, S. Noar, and J. Ayers, “The Charlie Sheen Effect on Rapid In-home Human Immunodeficiency Virus Test Sales,” in Prevention Science, 2017.
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1928. Pouya Samangouei, Vishal M. Patel, and R. Chellappa, “Facial attributes for active authentication on mobile devices,” in Image and Vision Computing, 2017.
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1929. Peter F. Schulam and S. Saria, “Counterfactual Gaussian Processes for Reliable Decision-making and What-if Reasoning,” in Neural Information Processing Systems, 2017.
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1930. Hossein Soleimani, Adarsh Subbaswamy, and S. Saria, “Learning Treatment-Response Models from Multivariate Longitudinal Data.,” in Conference on Uncertainty in Artificial Intelligence, 2017.
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1931. Leonardo Badino, Luca Franceschi, R. Arora, Michele Donini, and M. Pontil, “A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion,” in Interspeech, 2017.
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1932. E. González-Sosa, R. Vera-Rodríguez, Julian Fierrez, and Vishal M. Patel, “Exploring Body Shape From mmW Images for Person Recognition,” in IEEE Transactions on Information Forensics and Security, 2017.
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1933. N. García, J. C. Vásquez-Correa, J. Orozco-Arroyave, N. Dehak, and Elmar Nöth, “Language Independent Assessment of Motor Impairments of Patients with Parkinson’s Disease Using i-Vectors,” in International Conference on Text, Speech and Dialogue, 2017.
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1934. Ning Gao, Douglas W. Oard, and Mark Dredze, “Support for Interactive Identification of Mentioned Entities in Conversational Speech,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017.
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1935. Lucia Specia, Matt Post, and Michael J. Paul, “Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations,” in Conference on Empirical Methods in Natural Language Processing, 2017.
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1936. Adam Poliak and Benjamin Van Durme, “Generating automatic pseudo-entailments from AMR parses.” 2017.
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1937. Chandler May, Kevin Duh, Benjamin Van Durme, and Ashwin Lall, “Streaming Word Embeddings with the Space-Saving Algorithm,” in arXiv.org, 2017.
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1938. Jingxiao Zheng, Vishal M. Patel, and R. Chellappa, “Recent Developments in Video-Based Face Recognition,” in Handbook of Biometrics for Forensic Science, 2017.
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1939. A. Wong, A. Yuille, and Brian Taylor, “Exploiting Protrusion Cues for Fast and Effective Shape Modeling via Ellipses,” in British Machine Vision Conference, 2017.
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1940. Mounya Elhilali, “Modeling the Cocktail Party Problem.” 2017.
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1941. Fei Cheng, Kevin Duh, and Yuji Matsumoto, “Towards a Consistent Segmentation Level across Multiple Chinese Word Segmentation Corpora.” 2017.
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1942. Chunxi Liu, J. Trmal, Matthew Wiesner, Craig Harman, and S. Khudanpur, “Topic Identification for Speech Without ASR,” in Interspeech, 2017.
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1943. Yan Wang, Lingxi Xie, Chenxi Liu, Ya Zhang, Wenjun Zhang, and A. Yuille, “SORT: Second-Order Response Transform for Visual Recognition,” in IEEE International Conference on Computer Vision, 2017.
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1944. Ankan Bansal, C. Castillo, Rajeev Ranjan, and R. Chellappa, “The Do’s and Don’ts for CNN-Based Face Verification,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017.
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1945. Vishwanath A. Sindagi and Vishal M. Patel, “A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation,” in Pattern Recognition Letters, 2017.
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1946. Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and A. Yuille, “Adversarial Examples for Semantic Segmentation and Object Detection,” in IEEE International Conference on Computer Vision, 2017.
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1947. Heng Zhang, Vishal M. Patel, and R. Chellappa, “Hierarchical Multimodal Metric Learning for Multimodal Classification,” in Computer Vision and Pattern Recognition, 2017.
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1948. Pramuditha Perera and Vishal M. Patel, “Quickest intrusion detection in mobile active user authentication,” in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016.
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1949. Yuyin Zhou, Lingxi Xie, Wei Shen, E. Fishman, and A. Yuille, “Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach,” in arXiv.org, 2016.
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1950. Tomohiro Tanaka, Takafumi Moriya, T. Shinozaki, Shinji Watanabe, Takaaki Hori, and Kevin Duh, “Automated structure discovery and parameter tuning of neural network language model based on evolution strategy,” in Spoken Language Technology Workshop, 2016.
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1951. Xingguo Li, Zhaoran Wang, Junwei Lu, R. Arora, Jarvis D. Haupt, Han Liu, and T. Zhao, “Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization,” in arXiv.org, 2016.
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1952. Jesús Antonio Villalba López, A. Miguel, Alfonso Ortega, and EDUARDO LLEIDA SOLANO, “Bayesian Networks to Model the Variability of Speaker Verification Scores in Adverse Environments,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2016.
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1953. Arthita Ghosh and R. Chellappa, “Deep feature extraction in the DCT domain,” in International Conference on Pattern Recognition, 2016.
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1954. Maya Kabkab, Emily M. Hand, and R. Chellappa, “On the size of Convolutional Neural Networks and generalization performance,” in International Conference on Pattern Recognition, 2016.
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1955. Yi Zhang, Weichao Qiu, Qi Chen, Xiaolin Hu, and A. Yuille, “UnrealStereo: A Synthetic Dataset for Analyzing Stereo Vision,” in arXiv.org, 2016.
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1956. Lee Stearns, U. Oh, Bridget J. Cheng, Leah Findlater, David Ross, R. Chellappa, and Jon E. Froehlich, “Localization of skin features on the hand and wrist from small image patches,” in International Conference on Pattern Recognition, 2016.
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      @inproceedings{1829184,
      title = {Localization of skin features on the hand and wrist from small image patches},
      author = {{Lee Stearns} and {U. Oh} and {Bridget J. Cheng} and {Leah Findlater} and {David Ross} and {R. Chellappa} and {Jon E. Froehlich}},
      year = 2016,
      month = {12},
      booktitle = {International Conference on Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/ac4bc8c956fb8e1ed65f98d5ddeaf42b9bd6d699},
      }

1957. Xingguo Li, Jarvis D. Haupt, Junwei Lu, Zhaoran Wang, R. Arora, Han Liu, and T. Zhao, “Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization,” in IEEE Transactions on Information Theory, 2016.
      [BibTeX] [Link]

      @inproceedings{52065722,
      title = {Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization},
      author = {{Xingguo Li} and {Jarvis D. Haupt} and {Junwei Lu} and {Zhaoran Wang} and {R. Arora} and {Han Liu} and {T. Zhao}},
      year = 2016,
      month = {12},
      booktitle = {IEEE Transactions on Information Theory},
      url = {https://www.semanticscholar.org/paper/cf853068cefee2d78d4dbccc8bca1ea450fc3377},
      }

1958. Maria Nadejde, Alexandra Birch, and Philipp Koehn, “A Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation.” 2016.
      [BibTeX] [Link]

      @inproceedings{249180025,
      title = {A Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation},
      author = {{Maria Nadejde} and {Alexandra Birch} and {Philipp Koehn}},
      year = 2016,
      month = {12},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1c292336122071604970d4f470b1f895a28febfd},
      }

1959. A. Andy, S. Sekine, M. Rwebangira, and M. Dredze, “Name Variation in Community Question Answering Systems,” in Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), Osaka, Japan, 2016, p. 51–60.
      [BibTeX] [Abstract] [Link]
      

      Name Variation in Community Question Answering Systems Abstract Community question answering systems are forums where users can ask and answer questions in various categories. Examples are Yahoo! Answers, Quora, and Stack Overflow. A common challenge with such systems is that a significant percentage of asked questions are left unanswered. In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo! Answers by reusing the answer to the most similar past resolved question to the unanswered question, from the site. Semantically similar questions could be worded differently, thereby making it difficult to find questions that have shared needs. For example, {“}Who is the best player for the Reds?{”} and {“}Who is currently the biggest star at Manchester United?{”} have a shared need but are worded differently; also, {“}Reds{”} and {“}Manchester United{”} are used to refer to the soccer team Manchester United football club. In this research, we focus on question categories that contain a large number of named entities and entity name variations. We show that in these categories, entity linking can be used to identify relevant past resolved questions with shared needs as a given question by disambiguating named entities and matching these questions based on the disambiguated entities, identified entities, and knowledge base information related to these entities. We evaluated our algorithm on a new dataset constructed from Yahoo! Answers. The dataset contains annotated question pairs, (Qgiven, [Qpast, Answer]). We carried out experiments on several question categories and show that an entity-based approach gives good performance when searching for similar questions in entity rich categories.

      @inproceedings{andy-etal-2016-name,
      title = "Name Variation in Community Question Answering Systems",
      author = "Andy, Anietie and
      Sekine, Satoshi and
      Rwebangira, Mugizi and
      Dredze, Mark",
      editor = "Han, Bo and
      Ritter, Alan and
      Derczynski, Leon and
      Xu, Wei and
      Baldwin, Tim",
      booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
      month = dec,
      year = "2016",
      address = "Osaka, Japan",
      publisher = "The COLING 2016 Organizing Committee",
      url = "https://aclanthology.org/W16-3909",
      pages = "51--60",
      abstract = "Name Variation in Community Question Answering Systems Abstract Community question answering systems are forums where users can ask and answer questions in various categories. Examples are Yahoo! Answers, Quora, and Stack Overflow. A common challenge with such systems is that a significant percentage of asked questions are left unanswered. In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo! Answers by reusing the answer to the most similar past resolved question to the unanswered question, from the site. Semantically similar questions could be worded differently, thereby making it difficult to find questions that have shared needs. For example, {``}Who is the best player for the Reds?{''} and {``}Who is currently the biggest star at Manchester United?{''} have a shared need but are worded differently; also, {``}Reds{''} and {``}Manchester United{''} are used to refer to the soccer team Manchester United football club. In this research, we focus on question categories that contain a large number of named entities and entity name variations. We show that in these categories, entity linking can be used to identify relevant past resolved questions with shared needs as a given question by disambiguating named entities and matching these questions based on the disambiguated entities, identified entities, and knowledge base information related to these entities. We evaluated our algorithm on a new dataset constructed from Yahoo! Answers. The dataset contains annotated question pairs, (Qgiven, [Qpast, Answer]). We carried out experiments on several question categories and show that an entity-based approach gives good performance when searching for similar questions in entity rich categories.",
      }

1960. Christopher Reale, N. Nasrabadi, and R. Chellappa, “An analysis of the robustness of deep face recognition networks to noisy training labels,” in IEEE Global Conference on Signal and Information Processing, 2016.
      [BibTeX] [Link]

      @inproceedings{14936914,
      title = {An analysis of the robustness of deep face recognition networks to noisy training labels},
      author = {{Christopher Reale} and {N. Nasrabadi} and {R. Chellappa}},
      year = 2016,
      month = {12},
      booktitle = {IEEE Global Conference on Signal and Information Processing},
      url = {https://www.semanticscholar.org/paper/24e82eaf3257e761d6ca0ffcc2cbca30dfca82e9},
      }

1961. Yi Zhang, Weichao Qiu, Qi Chen, Xiaolin Hu, and A. Yuille, “UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision,” in International Conference on 3D Vision, 2016.
      [BibTeX] [Link]

      @inproceedings{52988559,
      title = {UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision},
      author = {{Yi Zhang} and {Weichao Qiu} and {Qi Chen} and {Xiaolin Hu} and {A. Yuille}},
      year = 2016,
      month = {12},
      booktitle = {International Conference on 3D Vision},
      url = {https://www.semanticscholar.org/paper/06cf0efaf36a3f731b0127f874047758944183d2},
      }

1962. David Snyder, Pegah Ghahremani, Daniel Povey, D. Garcia-Romero, Yishay Carmiel, and S. Khudanpur, “Deep neural network-based speaker embeddings for end-to-end speaker verification,” in Spoken Language Technology Workshop, 2016.
      [BibTeX] [Link]

      @inproceedings{27571108,
      title = {Deep neural network-based speaker embeddings for end-to-end speaker verification},
      author = {{David Snyder} and {Pegah Ghahremani} and {Daniel Povey} and {D. Garcia-Romero} and {Yishay Carmiel} and {S. Khudanpur}},
      year = 2016,
      month = {12},
      booktitle = {Spoken Language Technology Workshop},
      url = {https://www.semanticscholar.org/paper/a8c3907b09d62457c3b1ebce203e2d9e4af0121e},
      }

1963. B. Meyer, Sri Harish Reddy Mallidi, Angel Mario Castro Martinez, G. P. Vayá, H. Kayser, and H. Hermansky, “Performance monitoring for automatic speech recognition in noisy multi-channel environments,” in Spoken Language Technology Workshop, 2016.
      [BibTeX] [Link]

      @inproceedings{20562827,
      title = {Performance monitoring for automatic speech recognition in noisy multi-channel environments},
      author = {{B. Meyer} and {Sri Harish Reddy Mallidi} and {Angel Mario Castro Martinez} and {G. P. Vayá} and {H. Kayser} and {H. Hermansky}},
      year = 2016,
      month = {12},
      booktitle = {Spoken Language Technology Workshop},
      url = {https://www.semanticscholar.org/paper/333c792893ad041b20bf6794f83f3464a4a3c44e},
      }

1964. J. Ayers, E. Leas, Mark Dredze, Jon-Patrick Allem, J. Grabowski, and Linda L. Hill, “Pokémon GO-A New Distraction for Drivers and Pedestrians.,” in JAMA Internal Medicine, 2016.
      [BibTeX] [Link]

      @inproceedings{45297221,
      title = {Pokémon GO-A New Distraction for Drivers and Pedestrians.},
      author = {{J. Ayers} and {E. Leas} and {Mark Dredze} and {Jon-Patrick Allem} and {J. Grabowski} and {Linda L. Hill}},
      year = 2016,
      month = {12},
      booktitle = {JAMA Internal Medicine},
      url = {https://www.semanticscholar.org/paper/2a22e1bd9be80a5390f46b0b522988d6d23ccafc},
      }

1965. Lin F. Yang, R. Arora, V. Braverman, and T. Zhao, “The Physical Systems Behind Optimization Algorithms,” in Neural Information Processing Systems, 2016.
      [BibTeX] [Link]

      @inproceedings{15663225,
      title = {The Physical Systems Behind Optimization Algorithms},
      author = {{Lin F. Yang} and {R. Arora} and {V. Braverman} and {T. Zhao}},
      year = 2016,
      month = {12},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/3fd96fe6f1ea5193536296f291aff00439eb9bbd},
      }

1966. Boyu Lu, Jun-Cheng Chen, and R. Chellappa, “Regularized metric adaptation for unconstrained face verification,” in International Conference on Pattern Recognition, 2016.
      [BibTeX] [Link]

      @inproceedings{662098,
      title = {Regularized metric adaptation for unconstrained face verification},
      author = {{Boyu Lu} and {Jun-Cheng Chen} and {R. Chellappa}},
      year = 2016,
      month = {12},
      booktitle = {International Conference on Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b},
      }

1967. Hongyu Xu, Jingjing Zheng, A. Alavi, and R. Chellappa, “Template regularized sparse coding for face verification,” in International Conference on Pattern Recognition, 2016.
      [BibTeX] [Link]

      @inproceedings{13374811,
      title = {Template regularized sparse coding for face verification},
      author = {{Hongyu Xu} and {Jingjing Zheng} and {A. Alavi} and {R. Chellappa}},
      year = 2016,
      month = {12},
      booktitle = {International Conference on Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/8d3e95c31c93548b8c71dbeee2e9f7180067a888},
      }

1968. Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, E. Fishman, and A. Yuille, “A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016.
      [BibTeX] [Link]

      @inproceedings{11054835,
      title = {A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans},
      author = {{Yuyin Zhou} and {Lingxi Xie} and {Wei Shen} and {Yan Wang} and {E. Fishman} and {A. Yuille}},
      year = 2016,
      month = {12},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/569977bdb3f31d4b7c78ab3834fd34b370330e4e},
      }

1969. Peng Wang, Xiaohui Shen, Bryan C. Russell, Scott D. Cohen, Brian L. Price, and A. Yuille, “SURGE: Surface Regularized Geometry Estimation from a Single Image,” in Neural Information Processing Systems, 2016.
      [BibTeX] [Link]

      @inproceedings{14067023,
      title = {SURGE: Surface Regularized Geometry Estimation from a Single Image},
      author = {{Peng Wang} and {Xiaohui Shen} and {Bryan C. Russell} and {Scott D. Cohen} and {Brian L. Price} and {A. Yuille}},
      year = 2016,
      month = {12},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/e2c1218fcba485dd0e039b972cd35318dbdb3d84},
      }

1970. R. Chellappa, “The changing fortunes of pattern recognition and computer vision,” in Image and Vision Computing, 2016.
      [BibTeX] [Link]

      @inproceedings{12071891,
      title = {The changing fortunes of pattern recognition and computer vision},
      author = {{R. Chellappa}},
      year = 2016,
      month = {11},
      booktitle = {Image and Vision Computing},
      url = {https://www.semanticscholar.org/paper/c9434b58592e3e845262a3785012a042101ff547},
      }

1971. Svitlana Volkova, I. Chetviorkin, Dustin L. Arendt, and Benjamin Van Durme, “Contrasting Public Opinion Dynamics and Emotional Response During Crisis,” in Social Informatics, 2016.
      [BibTeX] [Link]

      @inproceedings{28972826,
      title = {Contrasting Public Opinion Dynamics and Emotional Response During Crisis},
      author = {{Svitlana Volkova} and {I. Chetviorkin} and {Dustin L. Arendt} and {Benjamin Van Durme}},
      year = 2016,
      month = {11},
      booktitle = {Social Informatics},
      url = {https://www.semanticscholar.org/paper/c15962d14b74780272bafea9bb00d65d6e7d3862},
      }

1972. Vittal Premachandran and A. Yuille, “Unsupervised Learning Using Generative Adversarial Training And Clustering.” 2016.
      [BibTeX] [Link]

      @inproceedings{67737867,
      title = {Unsupervised Learning Using Generative Adversarial Training And Clustering},
      author = {{Vittal Premachandran} and {A. Yuille}},
      year = 2016,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c8a5f9670b6d22f718c6815bf47cdaee57f82212},
      }

1973. R. Passonneau, T. Yano, Thomas Lippincott, and Judith L. Klavans, “Functional Semantic Categories for Art History Text – Human Labeling and Preliminary Machine Learning.” 2016.
      [BibTeX] [Link]

      @inproceedings{2818046,
      title = {Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning},
      author = {{R. Passonneau} and {T. Yano} and {Thomas Lippincott} and {Judith L. Klavans}},
      year = 2016,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/7118d3db196389f7ad52ceab022b856f9258ab88},
      }

1974. G. Coppersmith, K. Hollingshead, A. H. Schwartz, M. Ireland, R. Resnik, K. Loveys, A. Foreman, and L. Ingraham, “The Clinical Panel: Leveraging Psychological Expertise During NLP Research,” in Proceedings of the First Workshop on NLP and Computational Social Science, Austin, Texas, 2016, p. 132–137. doi:10.18653/v1/W16-5617
      [BibTeX] [Link]

      @inproceedings{coppersmith-etal-2016-clinical,
      title = "The Clinical Panel: Leveraging Psychological Expertise During {NLP} Research",
      author = "Coppersmith, Glen and
      Hollingshead, Kristy and
      Schwartz, H. Andrew and
      Ireland, Molly and
      Resnik, Rebecca and
      Loveys, Kate and
      Foreman, April and
      Ingraham, Loring",
      editor = {Bamman, David and
      Do{\u{g}}ru{\"o}z, A. Seza and
      Eisenstein, Jacob and
      Hovy, Dirk and
      Jurgens, David and
      O{'}Connor, Brendan and
      Oh, Alice and
      Tsur, Oren and
      Volkova, Svitlana},
      booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
      month = nov,
      year = "2016",
      address = "Austin, Texas",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-5617",
      doi = "10.18653/v1/W16-5617",
      pages = "132--137",
      }

1975. R. Knowles, J. Carroll, and M. Dredze, “Demographer: Extremely Simple Name Demographics,” in Proceedings of the First Workshop on NLP and Computational Social Science, Austin, Texas, 2016, p. 108–113. doi:10.18653/v1/W16-5614
      [BibTeX] [Link]

      @inproceedings{knowles-etal-2016-demographer,
      title = "{D}emographer: Extremely Simple Name Demographics",
      author = "Knowles, Rebecca and
      Carroll, Josh and
      Dredze, Mark",
      editor = {Bamman, David and
      Do{\u{g}}ru{\"o}z, A. Seza and
      Eisenstein, Jacob and
      Hovy, Dirk and
      Jurgens, David and
      O{'}Connor, Brendan and
      Oh, Alice and
      Tsur, Oren and
      Volkova, Svitlana},
      booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
      month = nov,
      year = "2016",
      address = "Austin, Texas",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-5614",
      doi = "10.18653/v1/W16-5614",
      pages = "108--113",
      }

1976. T. Wolfe, M. Dredze, and B. Van Durme, “A Study of Imitation Learning Methods for Semantic Role Labeling,” in Proceedings of the Workshop on Structured Prediction for NLP, Austin, TX, 2016, p. 44–53. doi:10.18653/v1/W16-5905
      [BibTeX] [Link]

      @inproceedings{wolfe-etal-2016-study,
      title = "A Study of Imitation Learning Methods for Semantic Role Labeling",
      author = "Wolfe, Travis and
      Dredze, Mark and
      Van Durme, Benjamin",
      editor = "Chang, Kai-Wei and
      Chang, Ming-Wei and
      Rush, Alexander and
      Srikumar, Vivek",
      booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
      month = nov,
      year = "2016",
      address = "Austin, TX",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-5905",
      doi = "10.18653/v1/W16-5905",
      pages = "44--53",
      }

1977. M. Dredze, N. Andrews, and J. DeYoung, “Twitter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation,” in Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media, Austin, TX, USA, 2016, p. 20–25. doi:10.18653/v1/W16-6204
      [BibTeX] [Link]

      @inproceedings{dredze-etal-2016-twitter,
      title = "{T}witter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation",
      author = "Dredze, Mark and
      Andrews, Nicholas and
      DeYoung, Jay",
      editor = "Ku, Lun-Wei and
      Hsu, Jane Yung-jen and
      Li, Cheng-Te",
      booktitle = "Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media",
      month = nov,
      year = "2016",
      address = "Austin, TX, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-6204",
      doi = "10.18653/v1/W16-6204",
      pages = "20--25",
      }

1978. Junhua Mao, Jiajing Xu, Yushi Jing, and A. Yuille, “Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images,” in Neural Information Processing Systems, 2016.
      [BibTeX] [Link]

      @inproceedings{9461243,
      title = {Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images},
      author = {{Junhua Mao} and {Jiajing Xu} and {Yushi Jing} and {A. Yuille}},
      year = 2016,
      month = {11},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/cc18cb42289fd570a06896b5543b085ebabee57b},
      }

1979. Tomohiro Tanaka, Takafumi Moriya, T. Shinozaki, Shinji Watanabe, Takaaki Hori, and Kevin Duh, “Evolutionary optimization of long short-term memory neural network language model,” in Journal of the Acoustical Society of America, 2016.
      [BibTeX] [Link]

      @inproceedings{125693048,
      title = {Evolutionary optimization of long short-term memory neural network language model},
      author = {{Tomohiro Tanaka} and {Takafumi Moriya} and {T. Shinozaki} and {Shinji Watanabe} and {Takaaki Hori} and {Kevin Duh}},
      year = 2016,
      month = {11},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/faa4468f2ad1c7cedaf04bf56ebb20ae4b349952},
      }

1980. A. S. White, D. Reisinger, K. Sakaguchi, T. Vieira, S. Zhang, R. Rudinger, K. Rawlins, and B. Van Durme, “Universal Decompositional Semantics on Universal Dependencies,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 2016, p. 1713–1723. doi:10.18653/v1/D16-1177
      [BibTeX] [Link]

      @inproceedings{white-etal-2016-universal,
      title = "Universal Decompositional Semantics on {U}niversal {D}ependencies",
      author = "White, Aaron Steven and
      Reisinger, Drew and
      Sakaguchi, Keisuke and
      Vieira, Tim and
      Zhang, Sheng and
      Rudinger, Rachel and
      Rawlins, Kyle and
      Van Durme, Benjamin",
      editor = "Su, Jian and
      Duh, Kevin and
      Carreras, Xavier",
      booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
      month = nov,
      year = "2016",
      address = "Austin, Texas",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D16-1177",
      doi = "10.18653/v1/D16-1177",
      pages = "1713--1723",
      }

1981. R. Arora, A. Basu, Poorya Mianjy, and Anirbit Mukherjee, “Understanding Deep Neural Networks with Rectified Linear Units,” in Electron. Colloquium Comput. Complex., 2016.
      [BibTeX] [Link]

      @inproceedings{3482308,
      title = {Understanding Deep Neural Networks with Rectified Linear Units},
      author = {{R. Arora} and {A. Basu} and {Poorya Mianjy} and {Anirbit Mukherjee}},
      year = 2016,
      month = {11},
      booktitle = {Electron. Colloquium Comput. Complex.},
      url = {https://www.semanticscholar.org/paper/9375729d21a344a5ccccd5f53556ddf90b957cd9},
      }

1982. Rajeev Ranjan, S. Sankaranarayanan, C. Castillo, and R. Chellappa, “An All-In-One Convolutional Neural Network for Face Analysis,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2016.
      [BibTeX] [Link]

      @inproceedings{5896299,
      title = {An All-In-One Convolutional Neural Network for Face Analysis},
      author = {{Rajeev Ranjan} and {S. Sankaranarayanan} and {C. Castillo} and {R. Chellappa}},
      year = 2016,
      month = {11},
      booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
      url = {https://www.semanticscholar.org/paper/93420d9212dd15b3ef37f566e4d57e76bb2fab2f},
      }

1983. I. Viñals, Jesús Antonio Villalba López, Alfonso Ortega, A. Miguel, and EDUARDO LLEIDA SOLANO, “Bottleneck Based Front-End for Diarization Systems,” in IberSPEECH Conference, 2016.
      [BibTeX] [Link]

      @inproceedings{2926504,
      title = {Bottleneck Based Front-End for Diarization Systems},
      author = {{I. Viñals} and {Jesús Antonio Villalba López} and {Alfonso Ortega} and {A. Miguel} and {EDUARDO LLEIDA SOLANO}},
      year = 2016,
      month = {11},
      booktitle = {IberSPEECH Conference},
      url = {https://www.semanticscholar.org/paper/75f27636050d9a2e0a4cf3ea1856a3d7c31e83f5},
      }

1984. T. Lippincott and B. Van Durme, “Fluency detection on communication networks,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 2016, p. 1025–1029. doi:10.18653/v1/D16-1107
      [BibTeX] [Link]

      @inproceedings{lippincott-van-durme-2016-fluency,
      title = "Fluency detection on communication networks",
      author = "Lippincott, Tom and
      Van Durme, Benjamin",
      editor = "Su, Jian and
      Duh, Kevin and
      Carreras, Xavier",
      booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
      month = nov,
      year = "2016",
      address = "Austin, Texas",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D16-1107",
      doi = "10.18653/v1/D16-1107",
      pages = "1025--1029",
      }

1985. Xiantong Zhen, Ling Shao, S. Maybank, and R. Chellappa, “Handcrafted vs. learned representations for human action recognition,” in Image and Vision Computing, 2016.
      [BibTeX] [Link]

      @inproceedings{4314532,
      title = {Handcrafted vs. learned representations for human action recognition},
      author = {{Xiantong Zhen} and {Ling Shao} and {S. Maybank} and {R. Chellappa}},
      year = 2016,
      month = {11},
      booktitle = {Image and Vision Computing},
      url = {https://www.semanticscholar.org/paper/d5d55ad2848d908d3b237860327f3a2a19b53b75},
      }

1986. Raviteja Vemulapalli, Felipe Arrate, and R. Chellappa, “R3DG features: Relative 3D geometry-based skeletal representations for human action recognition,” in Computer Vision and Image Understanding, 2016.
      [BibTeX] [Link]

      @inproceedings{11823644,
      title = {R3DG features: Relative 3D geometry-based skeletal representations for human action recognition},
      author = {{Raviteja Vemulapalli} and {Felipe Arrate} and {R. Chellappa}},
      year = 2016,
      month = {11},
      booktitle = {Computer Vision and Image Understanding},
      url = {https://www.semanticscholar.org/paper/da1cc72354f70a187d46664c2318c58d8183c379},
      }

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      author = {{Zhuotun Zhu} and {Lingxi Xie} and {A. Yuille}},
      year = 2016,
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      year = 2016,
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      @InProceedings{francislandau-et-al-2016,
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      author = "Matthew Francis-Landau and Bing Xue and Jason Eisner
      and Vivek Sarkar",
      title = "Fine-grained parallelism in probabilistic parsing with
      {H}abanero {J}ava",
      booktitle = "Proceedings of the Sixth Workshop on Irregular
      Applications: Architectures and Algorithms (IA$^3$)",
      pages = "78--81",
      year = "2016",
      month = nov,
      address = "Salt Lake City",
      publisher = "IEEE Press",
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1991. J. Eisner, “Inside-Outside and Forward-Backward Algorithms are Just Backprop,” in Proceedings of the EMNLP Workshop on Structured Prediction for NLP, Austin, TX, 2016, p. 1–17. doi:10.18653/v1/W16-5901
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      @InProceedings{eisner-2016,
      aclid = "W16-5901",
      doi = "10.18653/v1/W16-5901",
      author = "Jason Eisner",
      title = "Inside-Outside and Forward-Backward Algorithms are
      Just Backprop",
      booktitle = "Proceedings of the EMNLP Workshop on Structured
      Prediction for NLP",
      pages = "1--17",
      year = "2016",
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1992. T. Vieira, R. Cotterell, and J. Eisner, “Speed-Accuracy Tradeoffs in Tagging with Variable-Order CRFs and Structured Sparsity,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, 2016, p. 1973–1978. doi:10.18653/v1/D16-1206
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      @InProceedings{vieira-cotterell-eisner-2016,
      aclid = "D16-1206",
      doi = "10.18653/v1/D16-1206",
      author = "Tim Vieira and Ryan Cotterell and Jason Eisner",
      title = "Speed-Accuracy Tradeoffs in Tagging with
      Variable-Order {CRF}s and Structured Sparsity",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "1973--1978",
      year = "2016",
      month = nov,
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      URL = "http://cs.jhu.edu/~jason/papers/#vieira-cotterell-eisner-2016",
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      author = {{S. Saria} and {Daniel P. Robinson}},
      year = 2016,
      month = {10},
      booktitle = {},
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1994. Tongfei Chen and Benjamin Van Durme, “Discriminative Information Retrieval for Knowledge Discovery,” in arXiv.org, 2016.
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      month = {10},
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      author = {{Dimitra Emmanouilidou} and {Mounya Elhilali}},
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1997. Adrian Benton, Braden Hancock, Glen A. Coppersmith, J. Ayers, and Mark Dredze, “After Sandy Hook Elementary: A Year in the Gun Control Debate on Twitter,” in arXiv.org, 2016.
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2000. X. Wu, K. Duh, and Y. Matsumoto, “A Generalized Framework for Hierarchical Word Sequence Language Model,” in Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers, Seoul, South Korea, 2016, p. 69–75.
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      @inproceedings{wu-etal-2016-generalized,
      title = "A Generalized Framework for Hierarchical Word Sequence Language Model",
      author = "Wu, Xiaoyi and
      Duh, Kevin and
      Matsumoto, Yuji",
      booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
      month = oct,
      year = "2016",
      address = "Seoul, South Korea",
      url = "https://aclanthology.org/Y16-2004",
      pages = "69--75",
      }

2001. A. White, D. Reisinger, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme, “Computational linking theory,” in arXiv.org, 2016.
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      @inproceedings{16247870,
      title = {Computational linking theory},
      author = {{A. White} and {D. Reisinger} and {Rachel Rudinger} and {Kyle Rawlins} and {Benjamin Van Durme}},
      year = 2016,
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      }

2002. R. Cotterell, C. Kirov, J. Sylak-Glassman, D. Yarowsky, J. Eisner, and M. Hulden, “The SIGMORPHON 2016 Shared Task–-Morphological Reinflection,” in Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Berlin, Germany, 2016, p. 10–22. doi:10.18653/v1/W16-2002
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      @inproceedings{cotterell-etal-2016-sigmorphon,
      title = "The {SIGMORPHON} 2016 Shared {T}ask{---}{M}orphological Reinflection",
      author = "Cotterell, Ryan and
      Kirov, Christo and
      Sylak-Glassman, John and
      Yarowsky, David and
      Eisner, Jason and
      Hulden, Mans",
      editor = "Elsner, Micha and
      Kuebler, Sandra",
      booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-2002",
      doi = "10.18653/v1/W16-2002",
      pages = "10--22",
      }

2003. F. Yung, K. Duh, T. Komura, and Y. Matsumoto, “Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany, 2016, p. 531–536. doi:10.18653/v1/P16-2086
      [BibTeX] [Link]

      @inproceedings{yung-etal-2016-modelling-interpretation,
      title = "Modelling the Interpretation of Discourse Connectives by {B}ayesian Pragmatics",
      author = "Yung, Frances and
      Duh, Kevin and
      Komura, Taku and
      Matsumoto, Yuji",
      editor = "Erk, Katrin and
      Smith, Noah A.",
      booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-2086",
      doi = "10.18653/v1/P16-2086",
      pages = "531--536",
      }

2004. O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow, M. Huck, A. Jimeno Yepes, P. Koehn, V. Logacheva, C. Monz, M. Negri, A. Névéol, M. Neves, M. Popel, M. Post, R. Rubino, C. Scarton, L. Specia, M. Turchi, K. Verspoor, and M. Zampieri, “Findings of the 2016 Conference on Machine Translation,” in Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers, Berlin, Germany, 2016, p. 131–198. doi:10.18653/v1/W16-2301
      [BibTeX] [Link]

      @inproceedings{bojar-etal-2016-findings,
      title = "Findings of the 2016 Conference on Machine Translation",
      author = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Graham, Yvette and
      Haddow, Barry and
      Huck, Matthias and
      Jimeno Yepes, Antonio and
      Koehn, Philipp and
      Logacheva, Varvara and
      Monz, Christof and
      Negri, Matteo and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Popel, Martin and
      Post, Matt and
      Rubino, Raphael and
      Scarton, Carolina and
      Specia, Lucia and
      Turchi, Marco and
      Verspoor, Karin and
      Zampieri, Marcos",
      editor = {Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Guillou, Liane and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Pecina, Pavel and
      Popel, Martin and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Specia, Lucia and
      Verspoor, Karin and
      Tiedemann, J{\"o}rg and
      Turchi, Marco},
      booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-2301",
      doi = "10.18653/v1/W16-2301",
      pages = "131--198",
      }

2005. N. Peng and M. Dredze, “Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany, 2016, p. 149–155. doi:10.18653/v1/P16-2025
      [BibTeX] [Link]

      @inproceedings{peng-dredze-2016-improving,
      title = "Improving Named Entity Recognition for {C}hinese Social Media with Word Segmentation Representation Learning",
      author = "Peng, Nanyun and
      Dredze, Mark",
      editor = "Erk, Katrin and
      Smith, Noah A.",
      booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-2025",
      doi = "10.18653/v1/P16-2025",
      pages = "149--155",
      }

2006. A. Benton, R. Arora, and M. Dredze, “Learning Multiview Embeddings of Twitter Users,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany, 2016, p. 14–19. doi:10.18653/v1/P16-2003
      [BibTeX] [Link]

      @inproceedings{benton-etal-2016-learning,
      title = "Learning Multiview Embeddings of {T}witter Users",
      author = "Benton, Adrian and
      Arora, Raman and
      Dredze, Mark",
      editor = "Erk, Katrin and
      Smith, Noah A.",
      booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-2003",
      doi = "10.18653/v1/P16-2003",
      pages = "14--19",
      }

2007. F. Yung, K. Duh, T. Komura, and Y. Matsumoto, “Modelling the Usage of Discourse Connectives as Rational Speech Acts,” in Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, 2016, p. 302–313. doi:10.18653/v1/K16-1030
      [BibTeX] [Link]

      @inproceedings{yung-etal-2016-modelling,
      title = "Modelling the Usage of Discourse Connectives as Rational Speech Acts",
      author = "Yung, Frances and
      Duh, Kevin and
      Komura, Taku and
      Matsumoto, Yuji",
      editor = "Riezler, Stefan and
      Goldberg, Yoav",
      booktitle = "Proceedings of the 20th {SIGNLL} Conference on Computational Natural Language Learning",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K16-1030",
      doi = "10.18653/v1/K16-1030",
      pages = "302--313",
      }

2008. M. N{u{a}}dejde, A. Birch, and P. Koehn, “Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation,” in Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers, Berlin, Germany, 2016, p. 32–42. doi:10.18653/v1/W16-2204
      [BibTeX] [Link]

      @inproceedings{nadejde-etal-2016-modeling,
      title = "Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation",
      author = "N{\u{a}}dejde, Maria and
      Birch, Alexandra and
      Koehn, Philipp",
      editor = {Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Guillou, Liane and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Pecina, Pavel and
      Popel, Martin and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Specia, Lucia and
      Verspoor, Karin and
      Tiedemann, J{\"o}rg and
      Turchi, Marco},
      booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-2204",
      doi = "10.18653/v1/W16-2204",
      pages = "32--42",
      }

2009. A. Renduchintala, R. Knowles, P. Koehn, and J. Eisner, “Creating Interactive Macaronic Interfaces for Language Learning,” in Proceedings of ACL-2016 System Demonstrations, Berlin, Germany, 2016, p. 133–138. doi:10.18653/v1/P16-4023
      [BibTeX] [Link]

      @inproceedings{renduchintala-etal-2016-creating,
      title = "Creating Interactive Macaronic Interfaces for Language Learning",
      author = "Renduchintala, Adithya and
      Knowles, Rebecca and
      Koehn, Philipp and
      Eisner, Jason",
      editor = "Pradhan, Sameer and
      Apidianaki, Marianna",
      booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-4023",
      doi = "10.18653/v1/P16-4023",
      pages = "133--138",
      }

2010. A. Renduchintala, R. Knowles, P. Koehn, and J. Eisner, “User Modeling in Language Learning with Macaronic Texts,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, 2016, p. 1859–1869. doi:10.18653/v1/P16-1175
      [BibTeX] [Link]

      @inproceedings{renduchintala-etal-2016-user,
      title = "User Modeling in Language Learning with Macaronic Texts",
      author = "Renduchintala, Adithya and
      Knowles, Rebecca and
      Koehn, Philipp and
      Eisner, Jason",
      editor = "Erk, Katrin and
      Smith, Noah A.",
      booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-1175",
      doi = "10.18653/v1/P16-1175",
      pages = "1859--1869",
      }

2011. S. Ding, K. Duh, H. Khayrallah, P. Koehn, and M. Post, “The JHU Machine Translation Systems for WMT 2016,” in Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers, Berlin, Germany, 2016, p. 272–280. doi:10.18653/v1/W16-2310
      [BibTeX] [Link]

      @inproceedings{ding-etal-2016-jhu,
      title = "The {JHU} Machine Translation Systems for {WMT} 2016",
      author = "Ding, Shuoyang and
      Duh, Kevin and
      Khayrallah, Huda and
      Koehn, Philipp and
      Post, Matt",
      editor = {Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Guillou, Liane and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Pecina, Pavel and
      Popel, Martin and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Specia, Lucia and
      Verspoor, Karin and
      Tiedemann, J{\"o}rg and
      Turchi, Marco},
      booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-2310",
      doi = "10.18653/v1/W16-2310",
      pages = "272--280",
      }

2012. P. Koehn, “Computer Aided Translation,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, Berlin, Germany, 2016.
      [BibTeX] [Abstract] [Link]
      

      Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a “Home Edition” to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.

      @inproceedings{koehn-2016-computer,
      title = "Computer Aided Translation",
      author = "Koehn, Philipp",
      editor = "Birch, Alexandra and
      Zuidema, Willem",
      booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P16-5003",
      abstract = "Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a ``Home Edition'' to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.",
      }

2013. R. Knowles, A. Renduchintala, P. Koehn, and J. Eisner, “Analyzing Learner Understanding of Novel L2 Vocabulary,” in Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, 2016, p. 126–135. doi:10.18653/v1/K16-1013
      [BibTeX] [Link]

      @inproceedings{knowles-etal-2016-analyzing,
      title = "Analyzing Learner Understanding of Novel {L}2 Vocabulary",
      author = "Knowles, Rebecca and
      Renduchintala, Adithya and
      Koehn, Philipp and
      Eisner, Jason",
      editor = "Riezler, Stefan and
      Goldberg, Yoav",
      booktitle = "Proceedings of the 20th {SIGNLL} Conference on Computational Natural Language Learning",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/K16-1013",
      doi = "10.18653/v1/K16-1013",
      pages = "126--135",
      }

2014. C. Buck and P. Koehn, “Quick and Reliable Document Alignment via TF/IDF-weighted Cosine Distance,” in Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers, Berlin, Germany, 2016, p. 672–678. doi:10.18653/v1/W16-2365
      [BibTeX] [Link]

      @inproceedings{buck-koehn-2016-quick,
      title = "Quick and Reliable Document Alignment via {TF}/{IDF}-weighted Cosine Distance",
      author = "Buck, Christian and
      Koehn, Philipp",
      editor = {Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Chatterjee, Rajen and
      Federmann, Christian and
      Guillou, Liane and
      Haddow, Barry and
      Huck, Matthias and
      Yepes, Antonio Jimeno and
      N{\'e}v{\'e}ol, Aur{\'e}lie and
      Neves, Mariana and
      Pecina, Pavel and
      Popel, Martin and
      Koehn, Philipp and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Specia, Lucia and
      Verspoor, Karin and
      Tiedemann, J{\"o}rg and
      Turchi, Marco},
      booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
      month = aug,
      year = "2016",
      address = "Berlin, Germany",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-2365",
      doi = "10.18653/v1/W16-2365",
      pages = "672--678",
      }

2015. R. Cotterell, C. Kirov, John Sylak-Glassman, D. Yarowsky, J. Eisner, and M. Hulden, “The SIGMORPHON 2016 Shared Task–-Morphological Reinflection,” in Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Berlin, 2016, p. 10–22. doi:10.18653/v1/W16-2002
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2016-shared,
      aclid = "W16-2002",
      doi = "10.18653/v1/W16-2002",
      author = "Ryan Cotterell and Christo Kirov and John
      Sylak-Glassman and David Yarowsky and Jason Eisner and
      Mans Hulden",
      title = "The {SIGMORPHON 2016} Shared Task---Morphological
      Reinflection",
      booktitle = "Proceedings of the 14th SIGMORPHON Workshop on
      Computational Research in Phonetics, Phonology, and
      Morphology",
      pages = "10--22",
      year = "2016",
      month = aug,
      address = "Berlin",
      note = "Supplementary material (4 pages) also available.",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2016-shared",
      }

2016. R. Knowles, A. Renduchintala, Philipp Koehn, and J. Eisner, “Analyzing Learner Understanding of Novel L2 Vocabulary,” in Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL), Berlin, 2016, p. 126–135. doi:10.18653/v1/K16-1013
      [BibTeX] [Link]

      @InProceedings{knowles-et-al-2016,
      aclid = "K16-1013",
      doi = "10.18653/v1/K16-1013",
      author = "Rebecca Knowles and Adithya Renduchintala and Philipp
      Koehn and Jason Eisner",
      title = "Analyzing Learner Understanding of Novel {L2}
      Vocabulary",
      booktitle = "Proceedings of the 20th SIGNLL Conference on
      Computational Natural Language Learning (CoNLL)",
      pages = "126--135",
      year = "2016",
      month = aug,
      address = "Berlin",
      URL = "http://cs.jhu.edu/~jason/papers/#knowles-et-al-2016",
      }

2017. A. Renduchintala, R. Knowles, Philipp Koehn, and J. Eisner, “Creating Interactive Macaronic Interfaces for Language Learning,” in Proceedings of ACL-2016 System Demonstrations, Berlin, 2016, p. 133–138. doi:10.18653/v1/P16-4023
      [BibTeX] [Link]

      @InProceedings{renduchintala-et-al-2016-acl-macui,
      aclid = "P16-4023",
      doi = "10.18653/v1/P16-4023",
      author = "Adithya Renduchintala and Rebecca Knowles and Philipp
      Koehn and Jason Eisner",
      title = "Creating Interactive Macaronic Interfaces for Language
      Learning",
      booktitle = "Proceedings of ACL-2016 System Demonstrations",
      pages = "133--138",
      year = "2016",
      month = aug,
      address = "Berlin",
      URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2016-acl-macui",
      }

2018. A. Renduchintala, R. Knowles, Philipp Koehn, and J. Eisner, “User Modeling in Language Learning with Macaronic Texts,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, 2016, p. 1859–1869. doi:10.18653/v1/P16-1175
      [BibTeX] [Link]

      @InProceedings{renduchintala-et-al-2016-acl-macmodel,
      aclid = "P16-1175",
      doi = "10.18653/v1/P16-1175",
      author = "Adithya Renduchintala and Rebecca Knowles and Philipp
      Koehn and Jason Eisner",
      title = "User Modeling in Language Learning with Macaronic
      Texts",
      booktitle = "Proceedings of the 54th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "1859--1869",
      year = "2016",
      month = aug,
      address = "Berlin",
      URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2016-acl-macmodel",
      }

2019. R. Cotterell, H. Schütze, and Jason Eisner, “Morphological Smoothing and Extrapolation of Word Embeddings,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, 2016, p. 1651–1660. doi:10.18653/v1/P16-1156
      [BibTeX] [Link]

      @InProceedings{cotterell-et-al-2016-acl,
      aclid = "P16-1156",
      doi = "10.18653/v1/P16-1156",
      author = "Ryan Cotterell and Hinrich Sch{\"{u}}tze and Jason
      Eisner",
      title = "Morphological Smoothing and Extrapolation of Word
      Embeddings",
      booktitle = "Proceedings of the 54th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "1651--1660",
      year = "2016",
      month = aug,
      address = "Berlin",
      note = "Supplementary material (4 pages) also available.",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2016-acl",
      }

2020. N. W. Filardo and J. Eisner, “Rigid Tree Automata With Isolation,” in Proceedings of the Fourth International Workshop on Trends in Tree Automata and Tree Transducers (TTATT), Seoul, 2016.
      [BibTeX] [Link]

      @InProceedings{filardo-eisner-2016-ttatt,
      author = "Nathaniel Wesley Filardo and Jason Eisner",
      title = "Rigid Tree Automata With Isolation",
      booktitle = "Proceedings of the Fourth International Workshop on
      Trends in Tree Automata and Tree Transducers (TTATT)",
      year = "2016",
      month = aug,
      address = "Seoul",
      note = "7 pages",
      URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2016-ttatt",
      }

2021. M. Dredze, M. Osborne, and P. Kambadur, “Geolocation for Twitter: Timing Matters,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, 2016, p. 1064–1069. doi:10.18653/v1/N16-1122
      [BibTeX] [Link]

      @inproceedings{dredze-etal-2016-geolocation,
      title = "Geolocation for {T}witter: Timing Matters",
      author = "Dredze, Mark and
      Osborne, Miles and
      Kambadur, Prabhanjan",
      editor = "Knight, Kevin and
      Nenkova, Ani and
      Rambow, Owen",
      booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2016",
      address = "San Diego, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N16-1122",
      doi = "10.18653/v1/N16-1122",
      pages = "1064--1069",
      }

2022. G. Coppersmith, K. Ngo, R. Leary, and A. Wood, “Exploratory Analysis of Social Media Prior to a Suicide Attempt,” in Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, 2016, p. 106–117. doi:10.18653/v1/W16-0311
      [BibTeX] [Link]

      @inproceedings{coppersmith-etal-2016-exploratory,
      title = "Exploratory Analysis of Social Media Prior to a Suicide Attempt",
      author = "Coppersmith, Glen and
      Ngo, Kim and
      Leary, Ryan and
      Wood, Anthony",
      editor = "Hollingshead, Kristy and
      Ungar, Lyle",
      booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
      month = jun,
      year = "2016",
      address = "San Diego, CA, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-0311",
      doi = "10.18653/v1/W16-0311",
      pages = "106--117",
      }

2023. C. Napoles, C. Callison-Burch, and M. Post, “Sentential Paraphrasing as Black-Box Machine Translation,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, California, 2016, p. 62–66. doi:10.18653/v1/N16-3013
      [BibTeX] [Link]

      @inproceedings{napoles-etal-2016-sentential,
      title = "Sentential Paraphrasing as Black-Box Machine Translation",
      author = "Napoles, Courtney and
      Callison-Burch, Chris and
      Post, Matt",
      editor = "DeNero, John and
      Finlayson, Mark and
      Reddy, Sravana",
      booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
      month = jun,
      year = "2016",
      address = "San Diego, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N16-3013",
      doi = "10.18653/v1/N16-3013",
      pages = "62--66",
      }

2024. N. Gao, M. Dredze, and D. Oard, “Knowledge Base Population for Organization Mentions in Email,” in Proceedings of the 5th Workshop on Automated Knowledge Base Construction, San Diego, CA, 2016, p. 24–28. doi:10.18653/v1/W16-1305
      [BibTeX] [Link]

      @inproceedings{gao-etal-2016-knowledge,
      title = "Knowledge Base Population for Organization Mentions in Email",
      author = "Gao, Ning and
      Dredze, Mark and
      Oard, Douglas",
      editor = "Pujara, Jay and
      Rocktaschel, Tim and
      Chen, Danqi and
      Singh, Sameer",
      booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
      month = jun,
      year = "2016",
      address = "San Diego, CA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W16-1305",
      doi = "10.18653/v1/W16-1305",
      pages = "24--28",
      }

2025. M. Yu, M. Dredze, R. Arora, and M. R. Gormley, “Embedding Lexical Features via Low-Rank Tensors,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, 2016, p. 1019–1029. doi:10.18653/v1/N16-1117
      [BibTeX] [Link]

      @inproceedings{yu-etal-2016-embedding,
      title = "Embedding Lexical Features via Low-Rank Tensors",
      author = "Yu, Mo and
      Dredze, Mark and
      Arora, Raman and
      Gormley, Matthew R.",
      editor = "Knight, Kevin and
      Nenkova, Ani and
      Rambow, Owen",
      booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2016",
      address = "San Diego, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N16-1117",
      doi = "10.18653/v1/N16-1117",
      pages = "1019--1029",
      }

2026. P. Rastogi, R. Cotterell, and Jason Eisner, “Weighting Finite-State Transductions With Neural Context,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), San Diego, 2016, p. 623–633. doi:10.18653/v1/N16-1076
      [BibTeX] [Link]

      @InProceedings{rastogi-cotterell-eisner-2016,
      aclid = "N16-1076",
      doi = "10.18653/v1/N16-1076",
      author = "Pushpendre Rastogi and Ryan Cotterell and Jason
      Eisner",
      title = "Weighting Finite-State Transductions With Neural
      Context",
      booktitle = "Proceedings of the 2016 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "623--633",
      note = "11 pages. Supplementary material (1 page) also
      available",
      year = "2016",
      month = jun,
      address = "San Diego",
      URL = "http://cs.jhu.edu/~jason/papers/#rastogi-cotterell-eisner-2016",
      }

2027. E. Chodroff, M. Maciejewski, J. Trmal, S. Khudanpur, and J. Godfrey, “New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia, 2016, p. 1323–1327.
      [BibTeX] [Abstract] [Link]
      

      The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.

      @inproceedings{chodroff-etal-2016-new,
      title = "New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification",
      author = "Chodroff, Eleanor and
      Maciejewski, Matthew and
      Trmal, Jan and
      Khudanpur, Sanjeev and
      Godfrey, John",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Declerck, Thierry and
      Goggi, Sara and
      Grobelnik, Marko and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, Helene and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios",
      booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
      month = may,
      year = "2016",
      address = "Portoro{\v{z}}, Slovenia",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L16-1210",
      pages = "1323--1327",
      abstract = "The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.",
      }

2028. J. Sylak-Glassman, C. Kirov, and D. Yarowsky, “Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia, 2016, p. 3116–3120.
      [BibTeX] [Abstract] [Link]
      

      Structured, complete inflectional paradigm data exists for very few of the world{‘}s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.

      @inproceedings{sylak-glassman-etal-2016-remote,
      title = "Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages",
      author = "Sylak-Glassman, John and
      Kirov, Christo and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Declerck, Thierry and
      Goggi, Sara and
      Grobelnik, Marko and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, Helene and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios",
      booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
      month = may,
      year = "2016",
      address = "Portoro{\v{z}}, Slovenia",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L16-1497",
      pages = "3116--3120",
      abstract = "Structured, complete inflectional paradigm data exists for very few of the world{'}s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.",
      }

2029. C. Kirov, J. Sylak-Glassman, R. Que, and D. Yarowsky, “Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), Portorož, Slovenia, 2016, p. 3121–3126.
      [BibTeX] [Abstract] [Link]
      

      Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.

      @inproceedings{kirov-etal-2016-large,
      title = "Very-large Scale Parsing and Normalization of {W}iktionary Morphological Paradigms",
      author = "Kirov, Christo and
      Sylak-Glassman, John and
      Que, Roger and
      Yarowsky, David",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Declerck, Thierry and
      Goggi, Sara and
      Grobelnik, Marko and
      Maegaard, Bente and
      Mariani, Joseph and
      Mazo, Helene and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios",
      booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
      month = may,
      year = "2016",
      address = "Portoro{\v{z}}, Slovenia",
      publisher = "European Language Resources Association (ELRA)",
      url = "https://aclanthology.org/L16-1498",
      pages = "3121--3126",
      abstract = "Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.",
      }

2030. L. F. Yang, R. Arora, V. Braverman, and T. Zhao, “A Brief Review of Popular Optimization Algorithms.” 2016.
      [BibTeX] [Link]

      @inproceedings{260498753,
      title = {A Brief Review of Popular Optimization Algorithms},
      author = {{L. F. Yang} and {R. Arora} and {V. Braverman} and {T. Zhao}},
      year = 2016,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/639c9de668028da9ce10c013c5fbba6a77f1f33c},
      }

2031. Nazre Batool and R. Chellappa, “Modeling of Facial Wrinkles for Applications in Computer Vision.” 2016.
      [BibTeX] [Link]

      @inproceedings{148519249,
      title = {Modeling of Facial Wrinkles for Applications in Computer Vision},
      author = {{Nazre Batool} and {R. Chellappa}},
      year = 2016,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d75c22c18a45ab00eefd6b61522715954de3c015},
      }

2032. Patrice Boucher, P. Dufour, P. Plusquellec, N. Dehak, P. Dumouchel, and P. Cardinal, “PHYSIOSTRESS: A multimodal corpus of data on acute stress and physiological activation.” 2016.
      [BibTeX] [Link]

      @inproceedings{150655590,
      title = {PHYSIOSTRESS: A multimodal corpus of data on acute stress and physiological activation},
      author = {{Patrice Boucher} and {P. Dufour} and {P. Plusquellec} and {N. Dehak} and {P. Dumouchel} and {P. Cardinal}},
      year = 2016,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c5c225999b6d2ad316fe7693b398e290407a43c3},
      }

2033. Chandler May, Ryan Cotterell, and Benjamin Van Durme, “An Analysis of Lemmatization on Topic Models of Morphologically Rich Language,” in arXiv: Computation and Language, 2016.
      [BibTeX] [Link]

      @inproceedings{150374178,
      title = {An Analysis of Lemmatization on Topic Models of Morphologically Rich Language},
      author = {{Chandler May} and {Ryan Cotterell} and {Benjamin Van Durme}},
      year = 2016,
      month = {8},
      booktitle = {arXiv: Computation and Language},
      url = {https://www.semanticscholar.org/paper/964fffb68ecd534d99909656e2c67069bf91168a},
      }

2034. R. Chellappa, A. Heyden, D. Laurendeau, M. Felsberg, and M. Borga, “Special issue on ICPR 2014 awarded papers,” in Pattern Recognition Letters, 2016.
      [BibTeX] [Link]

      @inproceedings{207329778,
      title = {Special issue on ICPR 2014 awarded papers},
      author = {{R. Chellappa} and {A. Heyden} and {D. Laurendeau} and {M. Felsberg} and {M. Borga}},
      year = 2016,
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2079. Vishal M. Patel, R. Chellappa, Deepak Chandra, and Brandon Barbello, “Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges,” in IEEE Signal Processing Magazine, 2016.
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2080. Mohammed Senoussaoui, P. Cardinal, N. Dehak, and Alessandro Lameiras Koerich, “Native Language Detection Using the I-Vector Framework,” in Interspeech, 2016.
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2081. Roozbeh Mottaghi, S. Fidler, A. Yuille, R. Urtasun, and Devi Parikh, “Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
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2082. Yanbo Xu, Yanxun Xu, and S. Saria, “A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves,” in arXiv: Learning, 2016.
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2083. Emily M. Hand and R. Chellappa, “Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification,” in arXiv.org, 2016.
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2084. Hongyu Xu, Jingjing Zheng, A. Alavi, and R. Chellappa, “Learning a structured dictionary for video-based face recognition,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2016.
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2085. S. Zafeiriou, Guoying Zhao, M. Pietikäinen, R. Chellappa, I. Kotsia, and J. Cohn, “Editorial of special issue on spontaneous facial behaviour analysis,” in Computer Vision and Image Understanding, 2016.
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2086. Pouya Samangouei and R. Chellappa, “Convolutional neural networks for attribute-based active authentication on mobile devices,” in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016.
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2087. S. Sankaranarayanan, A. Alavi, C. Castillo, and R. Chellappa, “Triplet probabilistic embedding for face verification and clustering,” in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016.
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2088. Xing Di and Vishal M. Patel, “Deep Tattoo Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
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2089. Vijayaditya Peddinti, Vimal Manohar, Yiming Wang, Daniel Povey, and S. Khudanpur, “Far-Field ASR Without Parallel Data,” in Interspeech, 2016.
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2090. Yuan Gao, Jiayi Ma, and A. Yuille, “Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples,” in IEEE Transactions on Image Processing, 2016.
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2091. Nanyun Peng and Mark Dredze, “Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media,” in arXiv.org, 2016.
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2093. Kristin N. Hageman, Z. Kalayjian, Francisco Tejada, Bryce Chiang, Mehdi A. Rahman, G. Fridman, C. Dai, P. Pouliquen, Julio Georgiou, C. C. Santina, and A. Andreou, “A CMOS Neural Interface for a Multichannel Vestibular Prosthesis,” in IEEE Transactions on Biomedical Circuits and Systems, 2016.
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2094. J. Ayers, B. Althouse, Jon-Patrick Allem, E. Leas, Mark Dredze, and Rebecca S. Williams, “Revisiting the Rise of Electronic Nicotine Delivery Systems Using Search Query Surveillance.,” in American Journal of Preventive Medicine, 2016.
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2095. Richard O. Chen, Grace Gao, San-He Wu, Yu-Hsiang Ou, Peter F. Schulam, L. Casciola-Rosen, F. Wigley, L. Hummers, and S. Saria, “A Novel Computational Approach for Scalable Biomarker Discovery in Autoimmune Diseases.” 2016.
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2096. U. Pal, Cheng-lin Liu, R. Chellappa, K. Kise, Liang Wang, Paolo Remagnino, H. Byun, R. Parameswaran, and Chee Seng Chan, “Welcome message from the chair.” 2016.
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2097. Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu, “A Representation Learning Framework for Multi-Source Transfer Parsing,” in AAAI Conference on Artificial Intelligence, 2016.
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2098. Ruizhi Li, Sri Harish Reddy Mallidi, L. Burget, Oldrich Plchot, and N. Dehak, “Exploiting Hidden-Layer Responses of Deep Neural Networks for Language Recognition,” in Interspeech, 2016.
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2100. B. Gujral, H. Khayrallah, and P. Koehn, “Translation of Unknown Words in Low Resource Languages,” in Conferences of the Association for Machine Translation in the Americas: MT Researchers’ Track, Austin, TX, USA, 2016, p. 163–176.
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2101. Lingxi Xie, Liang Zheng, Jingdong Wang, A. Yuille, and Qi Tian, “InterActive: Inter-Layer Activeness Propagation,” in Computer Vision and Pattern Recognition, 2016.
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2102. Guoguo Chen, Daniel Povey, and S. Khudanpur, “Acoustic data-driven pronunciation lexicon generation for logographic languages,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016.
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2103. Mark Dredze, David A. Broniatowski, Michael C. Smith, and Karen Hilyard, “Understanding Vaccine Refusal: Why We Need Social Media Now.,” in American Journal of Preventive Medicine, 2016.
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2104. Lingxi Xie, Qi Tian, John Flynn, Jingdong Wang, and A. Yuille, “Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons,” in European Conference on Computer Vision, 2016.
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      }

2105. R. Arora, Poorya Mianjy, and T. V. Marinov, “Stochastic Optimization for Multiview Representation Learning using Partial Least Squares,” in International Conference on Machine Learning, 2016.
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2106. Sri Harish Reddy Mallidi and H. Hermansky, “Novel neural network based fusion for multistream ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016.
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      author = {{Sri Harish Reddy Mallidi} and {H. Hermansky}},
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2107. Kaustav Nandy, R. Chellappa, Amit Kumar, and S. Lockett, “Segmentation of Nuclei From 3D Microscopy Images of Tissue via Graphcut Optimization,” in IEEE Journal on Selected Topics in Signal Processing, 2016.
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      year = 2016,
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2108. Daniel R. Mendat, S. Chin, S. Furber, and A. Andreou, “Neuromorphic sampling on the SpiNNaker and parallella chip multiprocessors,” in 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS), 2016.
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2109. E. Leas, B. Althouse, Mark Dredze, Nick Obradovich, J. Fowler, S. Noar, Jon-Patrick Allem, and J. Ayers, “Big Data Sensors of Organic Advocacy: The Case of Leonardo DiCaprio and Climate Change,” in PLoS ONE, 2016.
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2110. Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahremani, Vimal Manohar, Xingyu Na, Yiming Wang, and S. Khudanpur, “Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI,” in Interspeech, 2016.
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2111. Mark Dredze, David A. Broniatowski, and Karen Hilyard, “Zika vaccine misconceptions: A social media analysis.,” in Vaccine, 2016.
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2112. Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu, “A Distributed Representation-Based Framework for Cross-Lingual Transfer Parsing,” in Journal of Artificial Intelligence Research, 2016.
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      }

2113. Hui Ding, S. Zhou, and R. Chellappa, “FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2016.
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      author = {{Hui Ding} and {S. Zhou} and {R. Chellappa}},
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2114. Yuan Gao and A. Yuille, “Symmetric Non-rigid Structure from Motion for Category-Specific Object Structure Estimation,” in European Conference on Computer Vision, 2016.
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2115. Xiaochen Lian, Qing Zhou, Nicolas Christou, and A. Yuille, “Electronic Theses and Dissertations Title Mining Spatial and Spatio-Temporal ROIs for Action Recognition Permalink.” 2016.
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2116. Ming-hui Du and R. Chellappa, “Face Association for Videos Using Conditional Random Fields and Max-Margin Markov Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
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      author = {{Ming-hui Du} and {R. Chellappa}},
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2117. Yanbo Xu, Yanxun Xu, and S. Saria, “A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves,” in arXiv.org, 2016.
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2118. Z. Tan, N. Dehak, J. Larsen, and Zhanyu Ma, “Proceedings of the First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE 2016).” 2016.
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      title = {Proceedings of the First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE 2016)},
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      }

2119. J. Ayers, J. L. Westmaas, E. Leas, Adrian Benton, Yunqi Chen, Mark Dredze, and B. Althouse, “Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout,” in JMIR Public Health and Surveillance, 2016.
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      title = {Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout},
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      }

2120. H. Mallidi and H. Hermansky, “NOVEL NEURAL NETWORK BASED FUSION FOR MULTISTREAM ASR Sri.” 2016.
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      title = {NOVEL NEURAL NETWORK BASED FUSION FOR MULTISTREAM ASR Sri},
      author = {{H. Mallidi} and {H. Hermansky}},
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2121. H. Aziz, E. Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, R. Meir, S. Saria, Gerardo I. Simari, Lirong Xia, and W. Yeoh, “AI’s 10 to Watch,” in IEEE Intelligent Systems, 2016.
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      author = {{H. Aziz} and {E. Bareinboim} and {Yejin Choi} and {Daniel J. Hsu} and {Shivaram Kalyanakrishnan} and {R. Meir} and {S. Saria} and {Gerardo I. Simari} and {Lirong Xia} and {W. Yeoh}},
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2122. R. Knowles and P. Koehn, “Neural Interactive Translation Prediction,” in Conferences of the Association for Machine Translation in the Americas: MT Researchers’ Track, Austin, TX, USA, 2016, p. 107–120.
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      We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6{\%} vs. 43.3{\%}) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment.

      @inproceedings{knowles-koehn-2016-neural,
      title = "Neural Interactive Translation Prediction",
      author = "Knowles, Rebecca and
      Koehn, Philipp",
      editor = "Green, Spence and
      Schwartz, Lane",
      booktitle = "Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track",
      month = oct # " 28 - " # nov # " 1",
      year = "2016",
      address = "Austin, TX, USA",
      publisher = "The Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2016.amta-researchers.9",
      pages = "107--120",
      abstract = "We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6{\%} vs. 43.3{\%}) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment.",
      }

2123. L. Davis, R. Chellappa, Derek Hoiem, A. Gupta, M. Hebert, E. Aminoff, HyunSoo Park, D. Forsyth, Jianbo Shi, and M. Tarr, “Rich Representations with Exposed Semantics for Deep Visual Reasoning.” 2016.
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      @inproceedings{195947975,
      title = {Rich Representations with Exposed Semantics for Deep Visual Reasoning},
      author = {{L. Davis} and {R. Chellappa} and {Derek Hoiem} and {A. Gupta} and {M. Hebert} and {E. Aminoff} and {HyunSoo Park} and {D. Forsyth} and {Jianbo Shi} and {M. Tarr}},
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      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a4b74383c3946aabba06641bd531bcb54301eac7},
      }

2124. Jingjing Zheng, Zhuolin Jiang, and R. Chellappa, “Cross-View Action Recognition via Transferable Dictionary Learning.,” in IEEE Transactions on Image Processing, 2016.
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      author = {{Jingjing Zheng} and {Zhuolin Jiang} and {R. Chellappa}},
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      }

2125. Supreeth Achar, H. Ackermann, Antonio Agudo, N. Ahuja, Zeynep Akata, Alahari Karteek, P. Aljabar, Y. Aloimonos, Jose Alvarez, Alexander Andreopoulos, Bjoern Andres, A. Angelova, Stanislaw Antol, Relja Arandjelović, P. Arbelaez, Devansh Arpit, S. Asif, Freddie Åström, L. Astola, V. Athitsos, Mathieu Aubry, S. Avidan, Arunava Banerjee, J. Barreto, Jorge Batista, Dhruv Batra, Maximilian Baust, Vasileios Belagiannis, Samy Bengio, Fabian Benitez-Quiroz, S. Biasotti, Hakan Bilen, Guillaume-Alexandre Bilodeau, Federica Bogo, Lubomir D. Bourdev, Y-Lan Boureau, Edmond Boyer, Steve Branson, Ernesto Brau, José Henrique Brito, G. Brostow, Michael Brown, Z. Bylinskii, Martin Byröd, Octavia Camps, Liangliang Cao, Guillaume Caron, João Carreira, D. Casas, Luka Cehovin, Hakan Cevikalp, Antonin Chambolle, Jason Chang, G. Charpiat, B. Chaudhuri, R. Chellappa, Jie Chen, A. Cherian, Guilhem Chéron, Cheng-Chieh Chiang, J. Chiverton, Minsu Cho, Wongun Choi, Yejin Choi, Wenchang Chu, Yu Chuang, R. G. Cinbis, T. Cootes, J. Corso, Andrew Cotter, Aaron C. Courville, David J. Crandall, Rozenn Dahyot, Yuchao Dai, Jifeng Dai, and M. Daoudi, “Appreciation to IJCV Reviewers of 2016,” in International Journal of Computer Vision, 2016.
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      @inproceedings{52847658,
      title = {Appreciation to IJCV Reviewers of 2016},
      author = {{Supreeth Achar} and {H. Ackermann} and {Antonio Agudo} and {N. Ahuja} and {Zeynep Akata} and {Alahari Karteek} and {P. Aljabar} and {Y. Aloimonos} and {Jose Alvarez} and {Alexander Andreopoulos} and {Bjoern Andres} and {A. Angelova} and {Stanislaw Antol} and {Relja Arandjelović} and {P. Arbelaez} and {Devansh Arpit} and {S. Asif} and {Freddie Åström} and {L. Astola} and {V. Athitsos} and {Mathieu Aubry} and {S. Avidan} and {Arunava Banerjee} and {J. Barreto} and {Jorge Batista} and {Dhruv Batra} and {Maximilian Baust} and {Vasileios Belagiannis} and {Samy Bengio} and {Fabian Benitez-Quiroz} and {S. Biasotti} and {Hakan Bilen} and {Guillaume-Alexandre Bilodeau} and {Federica Bogo} and {Lubomir D. Bourdev} and {Y-Lan Boureau} and {Edmond Boyer} and {Steve Branson} and {Ernesto Brau} and {José Henrique Brito} and {G. Brostow} and {Michael Brown} and {Z. Bylinskii} and {Martin Byröd} and {Octavia Camps} and {Liangliang Cao} and {Guillaume Caron} and {João Carreira} and {D. Casas} and {Luka Cehovin} and {Hakan Cevikalp} and {Antonin Chambolle} and {Jason Chang} and {G. Charpiat} and {B. Chaudhuri} and {R. Chellappa} and {Jie Chen} and {A. Cherian} and {Guilhem Chéron} and {Cheng-Chieh Chiang} and {J. Chiverton} and {Minsu Cho} and {Wongun Choi} and {Yejin Choi} and {Wenchang Chu} and {Yu Chuang} and {R. G. Cinbis} and {T. Cootes} and {J. Corso} and {Andrew Cotter} and {Aaron C. Courville} and {David J. Crandall} and {Rozenn Dahyot} and {Yuchao Dai} and {Jifeng Dai} and {M. Daoudi}},
      year = 2016,
      booktitle = {International Journal of Computer Vision},
      url = {https://www.semanticscholar.org/paper/6477881b6bae80423b69a8d8a24b7f4b1f1f068a},
      }

2126. Francis Ferraro and Benjamin Van Durme, “A Unified Bayesian Model of Scripts, Frames and Language,” in AAAI Conference on Artificial Intelligence, 2016.
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      title = {A Unified Bayesian Model of Scripts, Frames and Language},
      author = {{Francis Ferraro} and {Benjamin Van Durme}},
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      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/ae7221f4731570b8ebe763000f981c7e5b1664a8},
      }

2127. Yixin Gao, S. Vedula, Gyusung I. Lee, Mija R. Lee, S. Khudanpur, and Gregory Hager, “Query-by-example surgical activity detection,” in International Journal of Computer Assisted Radiology and Surgery, 2016.
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      title = {Query-by-example surgical activity detection},
      author = {{Yixin Gao} and {S. Vedula} and {Gyusung I. Lee} and {Mija R. Lee} and {S. Khudanpur} and {Gregory Hager}},
      year = 2016,
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      }

2128. S. Vedula, Anand Malpani, Lingling Tao, George Chen, Yixin Gao, P. Poddar, N. Ahmidi, Christopher Paxton, R. Vidal, S. Khudanpur, Gregory Hager, and C. C. Chen, “Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task,” in PLoS ONE, 2016.
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      title = {Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task},
      author = {{S. Vedula} and {Anand Malpani} and {Lingling Tao} and {George Chen} and {Yixin Gao} and {P. Poddar} and {N. Ahmidi} and {Christopher Paxton} and {R. Vidal} and {S. Khudanpur} and {Gregory Hager} and {C. C. Chen}},
      year = 2016,
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      }

2129. Ondrej Bojar, C. Federmann, B. Haddow, Philipp Koehn, Matt Post, and Lucia Specia, “Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools and Data Sets to an Integrated Ecosystem”.” 2016.
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      author = {{Ondrej Bojar} and {C. Federmann} and {B. Haddow} and {Philipp Koehn} and {Matt Post} and {Lucia Specia}},
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      }

2130. U. Mahbub, Vishal M. Patel, Deepak Chandra, Brandon Barbello, and R. Chellappa, “Partial face detection for continuous authentication,” in International Conference on Information Photonics, 2016.
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      title = {Partial face detection for continuous authentication},
      author = {{U. Mahbub} and {Vishal M. Patel} and {Deepak Chandra} and {Brandon Barbello} and {R. Chellappa}},
      year = 2016,
      month = {3},
      booktitle = {International Conference on Information Photonics},
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      }

2131. D. Reynolds, Elizabeth C Godoy, Fred Richardson, E. Singer, D. Sturim, P. Torres-Carrasquillo, N. Dehak, and Stephen Shum, “MITLL 2015 Language Recognition Evaluation System Description.” 2016.
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      title = {MITLL 2015 Language Recognition Evaluation System Description},
      author = {{D. Reynolds} and {Elizabeth C Godoy} and {Fred Richardson} and {E. Singer} and {D. Sturim} and {P. Torres-Carrasquillo} and {N. Dehak} and {Stephen Shum}},
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      }

2132. Xingguo Li, T. Zhao, R. Arora, Han Liu, and Mingyi Hong, “An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization,” in International Conference on Artificial Intelligence and Statistics, 2016.
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      title = {An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization},
      author = {{Xingguo Li} and {T. Zhao} and {R. Arora} and {Han Liu} and {Mingyi Hong}},
      year = 2016,
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      }

2133. A. Alavi, Vishal M. Patel, and R. Chellappa, “Optimized Kernel-based Projection Space of Riemannian Manifolds,” in arXiv: Computer Vision and Pattern Recognition, 2016.
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      @inproceedings{16223054,
      title = {Optimized Kernel-based Projection Space of Riemannian Manifolds},
      author = {{A. Alavi} and {Vishal M. Patel} and {R. Chellappa}},
      year = 2016,
      month = {2},
      booktitle = {arXiv: Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/a05da0eae221ce50f6d742ea8611fbf084e1a557},
      }

2134. Chun-yu Wang, Yizhou Wang, and A. Yuille, “Mining 3D Key-Pose-Motifs for Action Recognition,” in Computer Vision and Pattern Recognition, 2016.
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      @inproceedings{14987613,
      title = {Mining 3D Key-Pose-Motifs for Action Recognition},
      author = {{Chun-yu Wang} and {Yizhou Wang} and {A. Yuille}},
      year = 2016,
      month = {6},
      booktitle = {Computer Vision and Pattern Recognition},
      url = {https://www.semanticscholar.org/paper/a2379e503a5e66c79b29a3732413a6759f105a38},
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2135. Jun-Cheng Chen, Amit Kumar, Rajeev Ranjan, Vishal M. Patel, A. Alavi, and R. Chellappa, “A cascaded convolutional neural network for age estimation of unconstrained faces,” in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016.
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2136. D. Chakrabarty and Mounya Elhilali, “Abnormal sound event detection using temporal trajectories mixtures,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016.
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2137. Yixin Gao, S. Vedula, Gyusung I. Lee, Mija R. Lee, S. Khudanpur, and Gregory Hager, “Unsupervised surgical data alignment with application to automatic activity annotation,” in IEEE International Conference on Robotics and Automation, 2016.
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2138. Pegah Ghahremani, Vimal Manohar, Daniel Povey, and S. Khudanpur, “Acoustic Modelling from the Signal Domain Using CNNs,” in Interspeech, 2016.
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2139. Xingguo Li, T. Zhao, R. Arora, Han Liu, and Jarvis D. Haupt, “Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning,” in International Conference on Machine Learning, 2016.
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2140. Jiayi Ma, Ji Zhao, and A. Yuille, “Non-Rigid Point Set Registration by Preserving Global and Local Structures,” in IEEE Transactions on Image Processing, 2016.
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2141. A. Zhan, Max A. Little, Denzil A. Harris, S. Abiola, E. Dorsey, S. Saria, and A. Terzis, “High Frequency Remote Monitoring of Parkinson’s Disease via Smartphone: Platform Overview and Medication Response Detection,” in arXiv.org, 2016.
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2142. Weichao Qiu and A. Yuille, “UnrealCV: Connecting Computer Vision to Unreal Engine,” in ECCV Workshops, 2016.
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2143. Christopher Reale, N. Nasrabadi, H. Kwon, and R. Chellappa, “Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
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2144. He Zhang and Vishal M. Patel, “Convolutional Sparse Coding-based Image Decomposition,” in British Machine Vision Conference, 2016.
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2145. R. Chellappa, Jun-Cheng Chen, Rajeev Ranjan, S. Sankaranarayanan, Amit Kumar, Vishal M. Patel, and C. Castillo, “Towards the design of an end-to-end automated system for image and video-based recognition,” in Information Theory and Applications Workshop, 2016.
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2146. Yanbo Xu, Yanxun Xu, and S. Saria, “A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series,” in Machine Learning in Health Care, 2016.
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2147. Tetsuji Ogawa, Sri Harish Reddy Mallidi, Emmanuel Dupoux, Jordan Cohen, Naomi H Feldman, and H. Hermansky, “A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation,” in International Conference on Pattern Recognition, 2016.
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2148. C. Morimoto and R. Chellappa, “Fast Image Stabilization and Mosaicking.” 2016.
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2149. Jimeng Sun, J. Rehg, S. Saria, Hua Xu, and E. Xing, “Advanced Machine Learning for Healthcare,” in Summit on Clinical Research Informatics, 2016.
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2150. M. Biggerstaff, David Alper, Mark Dredze, S. Fox, I. C. Fung, K. Hickmann, B. Lewis, Roni Rosenfeld, J. Shaman, Ming-Hsiang Tsou, P. Velardi, Alessandro Vespignani, and L. Finelli, “Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge,” in BMC Infectious Diseases, 2016.
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2151. Jingjing Zheng, Zhuolin Jiang, and R. Chellappa, “Cross-View Action Recognition via Transferable Dictionary Learning,” in IEEE Transactions on Image Processing, 2016.
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2152. A. Andreou, Tomas Figliolia, Kayode A. Sanni, Thomas S. Murray, Gaspar Tognetti, Daniel R. Mendat, J. Molin, M. Villemur, P. Pouliquen, P. Julián, R. Etienne-Cummings, and I. Doxas, “Bio-inspired system architecture for energy efficient, BIGDATA computing with application to wide area motion imagery,” in 2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS), 2016.
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2153. Jun-Cheng Chen, Jingxiao Zheng, Vishal M. Patel, and R. Chellappa, “Fisher vector encoded deep convolutional features for unconstrained face verification,” in International Conference on Information Photonics, 2016.
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2154. Xingguo Li, R. Arora, Han Liu, Jarvis D. Haupt, and Tuo Zhao, “Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction,” in arXiv: Learning, 2016.
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2155. Deyi Xiong, Kevin Duh, Eneko Agirre, Nora Aranberri, and Houfeng Wang, “Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation (SedMT 2016).” 2016.
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2156. S. Sankaranarayanan, A. Alavi, and R. Chellappa, “Triplet Similarity Embedding for Face Verification,” in arXiv.org, 2016.
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2157. Jian Su, Kevin Duh, and X. Carreras, “Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing,” in Conference on Empirical Methods in Natural Language Processing, 2016.
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2158. Animesh Koratana, Mark Dredze, M. Chisolm, Matthew W. Johnson, and Michael J. Paul, “Studying Anonymous Health Issues and Substance Use on College Campuses with Yik Yak,” in AAAI Workshop: WWW and Population Health Intelligence, 2016.
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2159. Nanyun Peng and Mark Dredze, “Multi-task Multi-domain Representation Learning for Sequence Tagging,” in arXiv.org, 2016.
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2160. M. Choudhury, Emre Kıcıman, Mark Dredze, Glen A. Coppersmith, and Mrinal Kumar, “Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media,” in International Conference on Human Factors in Computing Systems, 2016.
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2161. Stephen Shum, David F. Harwath, N. Dehak, and James R. Glass, “On the Use of Acoustic Unit Discovery for Language Recognition,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2016.
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2162. Ondrej Bojar, C. Federmann, B. Haddow, Philipp Koehn, Matt Post, and Lucia Specia, “Ten Years of WMT Evaluation Campaigns: Lessons Learnt.” 2016.
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2163. J. Ayers, B. Althouse, Mark Dredze, E. Leas, and S. Noar, “News and Internet Searches About Human Immunodeficiency Virus After Charlie Sheen’s Disclosure.,” in JAMA Internal Medicine, 2016.
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2164. Jingxiao Zheng, Jun-Cheng Chen, Navaneeth Bodla, Vishal M. Patel, and R. Chellappa, “VLAD encoded Deep Convolutional features for unconstrained face verification,” in International Conference on Pattern Recognition, 2016.
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2165. Chunyu Wang, John Flynn, Yizhou Wang, and A. Yuille, “Recognizing Actions in 3D Using Action-Snippets and Activated Simplices,” in AAAI Conference on Artificial Intelligence, 2016.
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2166. Adrian Benton, Michael J. Paul, Braden Hancock, and Mark Dredze, “Collective Supervision of Topic Models for Predicting Surveys with Social Media,” in AAAI Conference on Artificial Intelligence, 2016.
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2167. M. N{u{a}}dejde, A. Birch, and P. Koehn, “A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation,” in Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C, 2016.
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      String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5{\%}. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model{‘}s accuracy by 1.5{\%} over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7{\%}.

      @inproceedings{nadejde-etal-2016-neural,
      title = "A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation",
      author = "N{\u{a}}dejde, Maria and
      Birch, Alexandra and
      Koehn, Philipp",
      editor = {Cettolo, Mauro and
      Niehues, Jan and
      St{\"u}ker, Sebastian and
      Bentivogli, Luisa and
      Cattoni, Rolando and
      Federico, Marcello},
      booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
      month = dec # " 8-9",
      year = "2016",
      address = "Seattle, Washington D.C",
      publisher = "International Workshop on Spoken Language Translation",
      url = "https://aclanthology.org/2016.iwslt-1.11",
      abstract = "String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5{\%}. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model{'}s accuracy by 1.5{\%} over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7{\%}.",
      }

2168. Neeraja Nagarajan, B. Smart, A. Nastasi, Z. J. Effendi, S. Murali, Z. Berger, Eric B. Schneider, Mark Dredze, and J. Canner, “An analysis of twitter conversations on global surgical care,” in Annals of global health, 2016.
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2169. Mahdi Abavisani and Vishal M. Patel, “Domain Adaptive Subspace Clustering,” in British Machine Vision Conference, 2016.
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2170. Sri Harish Reddy Mallidi and H. Hermansky, “A Framework for Practical Multistream ASR,” in Interspeech, 2016.
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2171. R. Amarasingham, A. Audet, D. Bates, I. Glenn Cohen, Martin Entwistle, G. Escobar, V. Liu, L. Etheredge, B. Lo, L. Ohno-Machado, S. Ram, S. Saria, L. Schilling, Anand Shahi, W. Stewart, E. Steyerberg, and Bin Xie, “Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges,” in eGEMs, 2016.
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2172. Peter F. Schulam and S. Saria, “Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses,” in Journal of machine learning research, 2016.
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2173. S. Vedula, Anand Malpani, N. Ahmidi, S. Khudanpur, Gregory Hager, and C. C. Chen, “Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment.,” in Journal of Surgical Education, 2016.
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2174. Wenbin Jiang, Bin Luo, Hai Jin, A. Yuille, and Jinsheng Xiao, “A Novel Parallelized Feature Extraction in Grouped Scale Space Based on Graphic Processing Units,” in Journal of Internet Technology, 2016.
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2175. Rebecca Knowles, Adithya Renduchintala, Philipp Koehn, and Jason Eisner, “Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL),” in The Association for Computational Linguistics, 2016.
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2176. Constantin Spille, H. Kayser, H. Hermansky, and B. Meyer, “Assessing Speech Quality in Speech-Aware Hearing Aids Based on Phoneme Posteriorgrams,” in Interspeech, 2016.
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2177. A. Alavi, Vishal M. Patel, and R. Chellappa, “Distance Preserving Projection Space of Symmetric Positive Definite Manifolds,” in arXiv.org, 2016.
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2178. Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, X. Bai, and A. Yuille, “DeepSkeleton: Learning Multi-Task Scale-Associated Deep Side Outputs for Object Skeleton Extraction in Natural Images,” in IEEE Transactions on Image Processing, 2016.
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2179. Svitlana Volkova, Yoram Bachrach, and Benjamin Van Durme, “Mining User Interests to Predict Perceived Psycho-Demographic Traits on Twitter,” in International Conference on Big Data Computing Service and Applications, 2016.
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2180. Nitesh Shroff, Rushil Anirudh, and R. Chellappa, “Summarization and Search Over Geometric Spaces.” 2016.
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2181. Xingguo Li, Haoming Jiang, Jarvis D. Haupt, R. Arora, Han Liu, Mingyi Hong, and T. Zhao, “On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don’t Worry About its Nonsmooth Loss Function,” in Conference on Uncertainty in Artificial Intelligence, 2016.
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2182. Chunxi Liu, A. Jansen, and S. Khudanpur, “Context-dependent point process models for keyword search and detection-based ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016.
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2183. Liang-Chieh Chen, G. Papandreou, Iasonas Kokkinos, K. Murphy, and A. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
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2184. Yuan Gao and A. Yuille, “Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images,” in Computer Vision and Pattern Recognition, 2016.
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2185. Mark Dredze, P. Kambadur, Gary Kazantsev, Gideon Mann, and M. Osborne, “How Twitter is Changing the Nature of Financial News Discovery,” in DSMM@SIGMOD, 2016.
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2186. Jingjing Zheng, Zhuolin Jiang, and R. Chellappa, “Cross-view Action Recognition via Transferable Dictionary Learning.,” in IEEE Transactions on Image Processing, 2016.
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2187. Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, and R. Chellappa, “Gaussian Conditional Random Field Network for Semantic Segmentation,” in Computer Vision and Pattern Recognition, 2016.
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2188. Chunxi Liu, P. Jyothi, Hao Tang, Vimal Manohar, Rose Sloan, Tyler Kekona, M. Hasegawa-Johnson, and S. Khudanpur, “Adapting ASR for under-resourced languages using mismatched transcriptions,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016.
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2189. R. Knowles, Mark Dredze, K. Evans, E. Lasser, Tom M. Richards, J. Weiner, and Hadi Kharrazi, “High Risk Pregnancy Prediction from Clinical Text.” 2016.
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2190. Pushpendre Rastogi and Benjamin Van Durme, “A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations,” in arXiv.org, 2016.
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2191. D. A. Shaw and R. Chellappa, “Domain Adaptation Using the Grassmann Manifold.” 2016.
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2192. A. Andreou, Andrew A. Dykman, Kate D. Fischl, Guillaume Garreau, Daniel R. Mendat, G. Orchard, A. Cassidy, P. Merolla, J. Arthur, Rodrigo Alvarez-Icaza, Bryan L. Jackson, and D. Modha, “Real-time sensory information processing using the TrueNorth Neurosynaptic System,” in International Symposium on Circuits and Systems, 2016.
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2193. Michael A. Carlin and Mounya Elhilali, “A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2015.
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2194. Vijayaditya Peddinti, Guoguo Chen, Vimal Manohar, Tom Ko, Daniel Povey, and S. Khudanpur, “JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS,” in Automatic Speech Recognition & Understanding, 2015.
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2195. Takafumi Moriya, Tomohiro Tanaka, T. Shinozaki, Shinji Watanabe, and Kevin Duh, “Automation of system building for state-of-the-art large vocabulary speech recognition using evolution strategy,” in Automatic Speech Recognition & Understanding, 2015.
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2196. Sri Harish Reddy Mallidi, Tetsuji Ogawa, and H. Hermansky, “Uncertainty estimation of DNN classifiers,” in Automatic Speech Recognition & Understanding, 2015.
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2197. Roger Hsiao, Jeff Z. Ma, William Hartmann, M. Karafiát, F. Grézl, L. Burget, Igor Szöke, J. Černocký, Shinji Watanabe, Zhuo Chen, Sri Harish Reddy Mallidi, H. Hermansky, Stavros Tsakalidis, and R. Schwartz, “Robust speech recognition in unknown reverberant and noisy conditions,” in Automatic Speech Recognition & Understanding, 2015.
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2198. Hani Bakhshaee, J. Seo, Chi Zhu, Nathaniel Welsh, Guillaume Garreau, Gaspar Tognetti, A. Andreou, and R. Mittal, “Fluid Dynamics of the Generation and Transmission of Heart Sounds: (1) A Cardiothoracic Phantom Based Study of Aortic Stenosis Murmurs,” in Bulletin of the American Physical Society, 2015.
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2199. D. Chakrabarty and Mounya Elhilali, “Exploring the role of temporal dynamics in acoustic scene classification,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2015.
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2201. Xiaoyi Wu, Yuji Matsumoto, Kevin Duh, and Hiroyuki Shindo, “An Improved Hierarchical Word Sequence Language Model Using Word Association,” in International Conference on Statistical Language and Speech Processing, 2015.
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2202. Xiaodong Liu, Fei Cheng, Kevin Duh, and Yuji Matsumoto, “A Hybrid Ranking Approach to Chinese Spelling Check,” in ACM Trans. Asian Low Resour. Lang. Inf. Process., 2015.
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2203. Kavita A. Ganesan, Brian J. Stankiewicz, David Yarowsky, A. Rafferty, Michael Nossal, and Anthony R. Davis, “Identification de sections codables dans des documents médicaux.” 2015.
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2204. R. Brinkgreve and Matt Post, “Geotechnical Ultimate Limit State Design Using Finite Elements.” 2015.
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2205. Matt Post, Yuan Cao, and Manish Kumar, “Joshua 6: A phrase-based and hierarchical statistical machine translation system,” in Prague Bulletin of Mathematical Linguistics, 2015.
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2206. Yu Zhang, Guoguo Chen, Dong Yu, K. Yao, S. Khudanpur, and James R. Glass, “Highway long short-term memory RNNS for distant speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2015.
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      @inproceedings{1502626,
      title = {Highway long short-term memory RNNS for distant speech recognition},
      author = {{Yu Zhang} and {Guoguo Chen} and {Dong Yu} and {K. Yao} and {S. Khudanpur} and {James R. Glass}},
      year = 2015,
      month = {10},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/9cee45ef1212ebbc7d468f9b1d7df24f5005e64d},
      }

2207. O. Bojar, R. Chatterjee, C. Federmann, B. Haddow, M. Huck, C. Hokamp, P. Koehn, V. Logacheva, C. Monz, M. Negri, M. Post, C. Scarton, L. Specia, and M. Turchi, “Findings of the 2015 Workshop on Statistical Machine Translation,” in Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, 2015, p. 1–46. doi:10.18653/v1/W15-3001
      [BibTeX] [Link]

      @inproceedings{bojar-etal-2015-findings,
      title = "Findings of the 2015 Workshop on Statistical Machine Translation",
      author = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajen and
      Federmann, Christian and
      Haddow, Barry and
      Huck, Matthias and
      Hokamp, Chris and
      Koehn, Philipp and
      Logacheva, Varvara and
      Monz, Christof and
      Negri, Matteo and
      Post, Matt and
      Scarton, Carolina and
      Specia, Lucia and
      Turchi, Marco",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajan and
      Federmann, Christian and
      Haddow, Barry and
      Hokamp, Chris and
      Huck, Matthias and
      Logacheva, Varvara and
      Pecina, Pavel",
      booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-3001",
      doi = "10.18653/v1/W15-3001",
      pages = "1--46",
      }

2208. N. Peng and M. Dredze, “Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, p. 548–554. doi:10.18653/v1/D15-1064
      [BibTeX] [Link]

      @inproceedings{peng-dredze-2015-named,
      title = "Named Entity Recognition for {C}hinese Social Media with Jointly Trained Embeddings",
      author = "Peng, Nanyun and
      Dredze, Mark",
      editor = "M{\`a}rquez, Llu{\'\i}s and
      Callison-Burch, Chris and
      Su, Jian",
      booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D15-1064",
      doi = "10.18653/v1/D15-1064",
      pages = "548--554",
      }

2209. R. Rudinger, P. Rastogi, F. Ferraro, and B. Van Durme, “Script Induction as Language Modeling,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, p. 1681–1686. doi:10.18653/v1/D15-1195
      [BibTeX] [Link]

      @inproceedings{rudinger-etal-2015-script,
      title = "Script Induction as Language Modeling",
      author = "Rudinger, Rachel and
      Rastogi, Pushpendre and
      Ferraro, Francis and
      Van Durme, Benjamin",
      editor = "M{\`a}rquez, Llu{\'\i}s and
      Callison-Burch, Chris and
      Su, Jian",
      booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D15-1195",
      doi = "10.18653/v1/D15-1195",
      pages = "1681--1686",
      }

2210. M. R. Gormley, M. Yu, and M. Dredze, “Improved Relation Extraction with Feature-Rich Compositional Embedding Models,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, p. 1774–1784. doi:10.18653/v1/D15-1205
      [BibTeX] [Link]

      @inproceedings{gormley-etal-2015-improved,
      title = "Improved Relation Extraction with Feature-Rich Compositional Embedding Models",
      author = "Gormley, Matthew R. and
      Yu, Mo and
      Dredze, Mark",
      editor = "M{\`a}rquez, Llu{\'\i}s and
      Callison-Burch, Chris and
      Su, Jian",
      booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D15-1205",
      doi = "10.18653/v1/D15-1205",
      pages = "1774--1784",
      }

2211. F. Yung, K. Duh, and Y. Matsumoto, “Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation,” in Proceedings of the Second Workshop on Discourse in Machine Translation, Lisbon, Portugal, 2015, p. 142–152. doi:10.18653/v1/W15-2519
      [BibTeX] [Link]

      @inproceedings{yung-etal-2015-crosslingual,
      title = "Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation",
      author = "Yung, Frances and
      Duh, Kevin and
      Matsumoto, Yuji",
      editor = "Webber, Bonnie and
      Carpuat, Marine and
      Popescu-Belis, Andrei and
      Hardmeier, Christian",
      booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-2519",
      doi = "10.18653/v1/W15-2519",
      pages = "142--152",
      }

2212. G. Kumar, G. Blackwood, J. Trmal, D. Povey, and S. Khudanpur, “A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, p. 1902–1907. doi:10.18653/v1/D15-1218
      [BibTeX] [Link]

      @inproceedings{kumar-etal-2015-coarse,
      title = "A Coarse-Grained Model for Optimal Coupling of {ASR} and {SMT} Systems for Speech Translation",
      author = "Kumar, Gaurav and
      Blackwood, Graeme and
      Trmal, Jan and
      Povey, Daniel and
      Khudanpur, Sanjeev",
      editor = "M{\`a}rquez, Llu{\'\i}s and
      Callison-Burch, Chris and
      Su, Jian",
      booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D15-1218",
      doi = "10.18653/v1/D15-1218",
      pages = "1902--1907",
      }

2213. B. Haddow, M. Huck, A. Birch, N. Bogoychev, and P. Koehn, “The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015,” in Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, 2015, p. 126–133. doi:10.18653/v1/W15-3013
      [BibTeX] [Link]

      @inproceedings{haddow-etal-2015-edinburgh,
      title = "The {E}dinburgh/{JHU} Phrase-based Machine Translation Systems for {WMT} 2015",
      author = "Haddow, Barry and
      Huck, Matthias and
      Birch, Alexandra and
      Bogoychev, Nikolay and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajan and
      Federmann, Christian and
      Haddow, Barry and
      Hokamp, Chris and
      Huck, Matthias and
      Logacheva, Varvara and
      Pecina, Pavel",
      booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-3013",
      doi = "10.18653/v1/W15-3013",
      pages = "126--133",
      }

2214. P. Williams, R. Sennrich, M. Nadejde, M. Huck, and P. Koehn, “Edinburgh’s Syntax-Based Systems at WMT 2015,” in Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, 2015, p. 199–209. doi:10.18653/v1/W15-3024
      [BibTeX] [Link]

      @inproceedings{williams-etal-2015-edinburghs,
      title = "{E}dinburgh{'}s Syntax-Based Systems at {WMT} 2015",
      author = "Williams, Philip and
      Sennrich, Rico and
      Nadejde, Maria and
      Huck, Matthias and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Chatterjee, Rajan and
      Federmann, Christian and
      Haddow, Barry and
      Hokamp, Chris and
      Huck, Matthias and
      Logacheva, Varvara and
      Pecina, Pavel",
      booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
      month = sep,
      year = "2015",
      address = "Lisbon, Portugal",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-3024",
      doi = "10.18653/v1/W15-3024",
      pages = "199--209",
      }

2215. N. Peng, R. Cotterell, and J. Eisner, “Dual Decomposition Inference for Graphical Models over Strings,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Lisbon, 2015, p. 917–927. doi:10.18653/v1/D15-1108
      [BibTeX] [Link]

      @InProceedings{peng-cotterell-eisner-2015,
      aclid = "D15-1108",
      doi = "10.18653/v1/D15-1108",
      author = "Nanyun Peng and Ryan Cotterell and Jason Eisner",
      title = "Dual Decomposition Inference for Graphical Models over
      Strings",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "917--927",
      year = "2015",
      month = sep,
      address = "Lisbon",
      URL = "http://cs.jhu.edu/~jason/papers/#peng-cotterell-eisner-2015",
      }

2216. F. Yung, K. Duh, and Y. Matsumoto, “Sequential Annotation and Chunking of Chinese Discourse Structure,” in Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, Beijing, China, 2015, p. 1–6. doi:10.18653/v1/W15-3101
      [BibTeX] [Link]

      @inproceedings{yung-etal-2015-sequential,
      title = "Sequential Annotation and Chunking of {C}hinese Discourse Structure",
      author = "Yung, Frances and
      Duh, Kevin and
      Matsumoto, Yuji",
      editor = "Yu, Liang-Chih and
      Sui, Zhifang and
      Zhang, Yue and
      Ng, Vincent",
      booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-3101",
      doi = "10.18653/v1/W15-3101",
      pages = "1--6",
      }

2217. E. Pavlick, P. Rastogi, J. Ganitkevitch, B. Van Durme, and C. Callison-Burch, “PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 425–430. doi:10.3115/v1/P15-2070
      [BibTeX] [Link]

      @inproceedings{pavlick-etal-2015-ppdb,
      title = "{PPDB} 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification",
      author = "Pavlick, Ellie and
      Rastogi, Pushpendre and
      Ganitkevitch, Juri and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2070",
      doi = "10.3115/v1/P15-2070",
      pages = "425--430",
      }

2218. E. Pavlick, J. Bos, M. Nissim, C. Beller, B. Van Durme, and C. Callison-Burch, “Adding Semantics to Data-Driven Paraphrasing,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, p. 1512–1522. doi:10.3115/v1/P15-1146
      [BibTeX] [Link]

      @inproceedings{pavlick-etal-2015-adding,
      title = "Adding Semantics to Data-Driven Paraphrasing",
      author = "Pavlick, Ellie and
      Bos, Johan and
      Nissim, Malvina and
      Beller, Charley and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-1146",
      doi = "10.3115/v1/P15-1146",
      pages = "1512--1522",
      }

2219. J. Guo, W. Che, D. Yarowsky, H. Wang, and T. Liu, “Cross-lingual Dependency Parsing Based on Distributed Representations,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, p. 1234–1244. doi:10.3115/v1/P15-1119
      [BibTeX] [Link]

      @inproceedings{guo-etal-2015-cross,
      title = "Cross-lingual Dependency Parsing Based on Distributed Representations",
      author = "Guo, Jiang and
      Che, Wanxiang and
      Yarowsky, David and
      Wang, Haifeng and
      Liu, Ting",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-1119",
      doi = "10.3115/v1/P15-1119",
      pages = "1234--1244",
      }

2220. E. Pavlick, T. Wolfe, P. Rastogi, C. Callison-Burch, M. Dredze, and B. Van Durme, “FrameNet+: Fast Paraphrastic Tripling of FrameNet,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 408–413. doi:10.3115/v1/P15-2067
      [BibTeX] [Link]

      @inproceedings{pavlick-etal-2015-framenet,
      title = "{F}rame{N}et+: Fast Paraphrastic Tripling of {F}rame{N}et",
      author = "Pavlick, Ellie and
      Wolfe, Travis and
      Rastogi, Pushpendre and
      Callison-Burch, Chris and
      Dredze, Mark and
      Van Durme, Benjamin",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2067",
      doi = "10.3115/v1/P15-2067",
      pages = "408--413",
      }

2221. J. Sylak-Glassman, C. Kirov, D. Yarowsky, and R. Que, “A Language-Independent Feature Schema for Inflectional Morphology,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 674–680. doi:10.3115/v1/P15-2111
      [BibTeX] [Link]

      @inproceedings{sylak-glassman-etal-2015-language,
      title = "A Language-Independent Feature Schema for Inflectional Morphology",
      author = "Sylak-Glassman, John and
      Kirov, Christo and
      Yarowsky, David and
      Que, Roger",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2111",
      doi = "10.3115/v1/P15-2111",
      pages = "674--680",
      }

2222. N. Peng, M. Yu, and M. Dredze, “An Empirical Study of Chinese Name Matching and Applications,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 377–383. doi:10.3115/v1/P15-2062
      [BibTeX] [Link]

      @inproceedings{peng-etal-2015-empirical,
      title = "An Empirical Study of {C}hinese Name Matching and Applications",
      author = "Peng, Nanyun and
      Yu, Mo and
      Dredze, Mark",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2062",
      doi = "10.3115/v1/P15-2062",
      pages = "377--383",
      }

2223. H. Ouchi, H. Shindo, K. Duh, and Y. Matsumoto, “Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, 2015, p. 961–970. doi:10.3115/v1/P15-1093
      [BibTeX] [Link]

      @inproceedings{ouchi-etal-2015-joint,
      title = "Joint Case Argument Identification for {J}apanese Predicate Argument Structure Analysis",
      author = "Ouchi, Hiroki and
      Shindo, Hiroyuki and
      Duh, Kevin and
      Matsumoto, Yuji",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-1093",
      doi = "10.3115/v1/P15-1093",
      pages = "961--970",
      }

2224. C. Napoles, K. Sakaguchi, M. Post, and J. Tetreault, “Ground Truth for Grammatical Error Correction Metrics,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 588–593. doi:10.3115/v1/P15-2097
      [BibTeX] [Link]

      @inproceedings{napoles-etal-2015-ground,
      title = "Ground Truth for Grammatical Error Correction Metrics",
      author = "Napoles, Courtney and
      Sakaguchi, Keisuke and
      Post, Matt and
      Tetreault, Joel",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2097",
      doi = "10.3115/v1/P15-2097",
      pages = "588--593",
      }

2225. F. Cheng, K. Duh, and Y. Matsumoto, “Synthetic Word Parsing Improves Chinese Word Segmentation,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 262–267. doi:10.3115/v1/P15-2043
      [BibTeX] [Link]

      @inproceedings{cheng-etal-2015-synthetic,
      title = "Synthetic Word Parsing Improves {C}hinese Word Segmentation",
      author = "Cheng, Fei and
      Duh, Kevin and
      Matsumoto, Yuji",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2043",
      doi = "10.3115/v1/P15-2043",
      pages = "262--267",
      }

2226. E. Pavlick, J. Ganitkevitch, T. P. Chan, X. Yao, B. Van Durme, and C. Callison-Burch, “Domain-Specific Paraphrase Extraction,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China, 2015, p. 57–62. doi:10.3115/v1/P15-2010
      [BibTeX] [Link]

      @inproceedings{pavlick-etal-2015-domain,
      title = "Domain-Specific Paraphrase Extraction",
      author = "Pavlick, Ellie and
      Ganitkevitch, Juri and
      Chan, Tsz Ping and
      Yao, Xuchen and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Zong, Chengqing and
      Strube, Michael",
      booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
      month = jul,
      year = "2015",
      address = "Beijing, China",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P15-2010",
      doi = "10.3115/v1/P15-2010",
      pages = "57--62",
      }

2227. R. Rudinger, V. Demberg, A. Modi, B. Van Durme, and M. Pinkal, “Learning to predict script events from domain-specific text,” in Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, Denver, Colorado, 2015, p. 205–210. doi:10.18653/v1/S15-1024
      [BibTeX] [Link]

      @inproceedings{rudinger-etal-2015-learning,
      title = "Learning to predict script events from domain-specific text",
      author = "Rudinger, Rachel and
      Demberg, Vera and
      Modi, Ashutosh and
      Van Durme, Benjamin and
      Pinkal, Manfred",
      editor = "Palmer, Martha and
      Boleda, Gemma and
      Rosso, Paolo",
      booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
      month = jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S15-1024",
      doi = "10.18653/v1/S15-1024",
      pages = "205--210",
      }

2228. N. Peng, F. Ferraro, M. Yu, N. Andrews, J. DeYoung, M. Thomas, M. R. Gormley, T. Wolfe, C. Harman, B. Van Durme, and M. Dredze, “A Concrete Chinese NLP Pipeline,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Denver, Colorado, 2015, p. 86–90. doi:10.3115/v1/N15-3018
      [BibTeX] [Link]

      @inproceedings{peng-etal-2015-concrete,
      title = "A Concrete {C}hinese {NLP} Pipeline",
      author = "Peng, Nanyun and
      Ferraro, Francis and
      Yu, Mo and
      Andrews, Nicholas and
      DeYoung, Jay and
      Thomas, Max and
      Gormley, Matthew R. and
      Wolfe, Travis and
      Harman, Craig and
      Van Durme, Benjamin and
      Dredze, Mark",
      editor = "Gerber, Matt and
      Havasi, Catherine and
      Lacatusu, Finley",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
      month = jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-3018",
      doi = "10.3115/v1/N15-3018",
      pages = "86--90",
      }

2229. R. Cotterell and J. Eisner, “Penalized Expectation Propagation for Graphical Models Over Strings,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Denver, 2015, p. 932–942. doi:10.3115/v1/N15-1094
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      @InProceedings{cotterell-eisner-2015,
      aclid = "N15-1094",
      doi = "10.3115/v1/N15-1094",
      author = "Ryan Cotterell and Jason Eisner",
      title = "Penalized Expectation Propagation for Graphical Models
      Over Strings",
      booktitle = "Proceedings of the 2015 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "932--942",
      note = "Supplementary material (11 pages) also available",
      year = "2015",
      month = jun,
      address = "Denver",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2015",
      }

2230. K. Yao, Trevor Cohn, Katerina Vylomova, Kevin Duh, and Chris Dyer, “Depth-Gated Recurrent Neural Networks.” 2015.
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      author = {{K. Yao} and {Trevor Cohn} and {Katerina Vylomova} and {Kevin Duh} and {Chris Dyer}},
      year = 2015,
      month = {8},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b777a55505ee2ffb4f8f9ada916e4e4a5f13a4ed},
      }

2231. Xiaohui Zhang, Daniel Povey, and S. Khudanpur, “A diversity-penalizing ensemble training method for deep learning,” in Interspeech, 2015.
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      title = {A diversity-penalizing ensemble training method for deep learning},
      author = {{Xiaohui Zhang} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/aad6fa33ad5da8808d527969414f7928a41ae6b1},
      }

2232. A. Andreou, T. Abraham, W. R. Thompson, J. Seo, and R. Mittal, “Mapping the cardiac acousteome: An overview of technologies, tools and methods,” in Annual Conference on Information Sciences and Systems, 2015.
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      title = {Mapping the cardiac acousteome: An overview of technologies, tools and methods},
      author = {{A. Andreou} and {T. Abraham} and {W. R. Thompson} and {J. Seo} and {R. Mittal}},
      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/cd4f57bde7af3a2c33f3f676ae607ee82d61651b},
      }

2233. Dimitra Emmanouilidou, E. McCollum, Daniel E. Park, and Mounya Elhilali, “Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries,” in IEEE Transactions on Biomedical Engineering, 2015.
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      @inproceedings{1490966,
      title = {Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries},
      author = {{Dimitra Emmanouilidou} and {E. McCollum} and {Daniel E. Park} and {Mounya Elhilali}},
      year = 2015,
      month = {4},
      booktitle = {IEEE Transactions on Biomedical Engineering},
      url = {https://www.semanticscholar.org/paper/99813f2bf125258d2d3e56175886a6e010bcab97},
      }

2234. Thomas S. Murray, Daniel R. Mendat, P. Pouliquen, and A. Andreou, “The Johns Hopkins University multimodal dataset for human action recognition,” in Defense + Security Symposium, 2015.
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      year = 2015,
      month = {5},
      booktitle = {Defense + Security Symposium},
      url = {https://www.semanticscholar.org/paper/fa944a7ffa9e081e7cbd2ccfeea5421adcb6fbe2},
      }

2235. P. Williams, Rico Sennrich, Maria Nadejde, Matthias Huck, and Philipp Koehn, “Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015,” in The Association for Computational Linguistics, 2015.
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      @inproceedings{208015049,
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      author = {{P. Williams} and {Rico Sennrich} and {Maria Nadejde} and {Matthias Huck} and {Philipp Koehn}},
      year = 2015,
      month = {9},
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/573cb0fe7491e98ba413afe21cc423cb10d66363},
      }

2236. Kayode A. Sanni, Guillaume Garreau, J. Molin, and A. Andreou, “FPGA implementation of a Deep Belief Network architecture for character recognition using stochastic computation,” in Annual Conference on Information Sciences and Systems, 2015.
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      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/0b963df7a367edb59b83cdc60a02d18252fa9179},
      }

2237. X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y. Wang, “Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 912–921. doi:10.3115/v1/N15-1092
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      @inproceedings{liu-etal-2015-representation,
      title = "Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval",
      author = "Liu, Xiaodong and
      Gao, Jianfeng and
      He, Xiaodong and
      Deng, Li and
      Duh, Kevin and
      Wang, Ye-yi",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1092",
      doi = "10.3115/v1/N15-1092",
      pages = "912--921",
      }

2238. A. Benton and M. Dredze, “Entity Linking for Spoken Language,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 225–230. doi:10.3115/v1/N15-1024
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      @inproceedings{benton-dredze-2015-entity,
      title = "Entity Linking for Spoken Language",
      author = "Benton, Adrian and
      Dredze, Mark",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1024",
      doi = "10.3115/v1/N15-1024",
      pages = "225--230",
      }

2239. Vijayaditya Peddinti, Guoguo Chen, Daniel Povey, and S. Khudanpur, “Reverberation robust acoustic modeling using i-vectors with time delay neural networks,” in Interspeech, 2015.
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      @inproceedings{12105291,
      title = {Reverberation robust acoustic modeling using i-vectors with time delay neural networks},
      author = {{Vijayaditya Peddinti} and {Guoguo Chen} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/ad51a3816c9c82ba34c24daaa7539ada7a2af19b},
      }

2240. Vimal Manohar, Daniel Povey, and S. Khudanpur, “Semi-supervised maximum mutual information training of deep neural network acoustic models,” in Interspeech, 2015.
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      @inproceedings{9443248,
      title = {Semi-supervised maximum mutual information training of deep neural network acoustic models},
      author = {{Vimal Manohar} and {Daniel Povey} and {S. Khudanpur}},
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      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/83ac318274d1894dc93c3a47c66179d74c591bdf},
      }

2241. Ellie Pavlick, Johan Bos, M. Nissim, Charley Beller, Benjamin Van Durme, and Chris Callison-Burch, “Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015),” in The Association for Computational Linguistics, 2015.
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      @inproceedings{57970994,
      title = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015)},
      author = {{Ellie Pavlick} and {Johan Bos} and {M. Nissim} and {Charley Beller} and {Benjamin Van Durme} and {Chris Callison-Burch}},
      year = 2015,
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/50f5af4ae43c896c599a039dbe9461925e539c7a},
      }

2242. Ashwin Bellur and Mounya Elhilali, “Detection of speech tokens in noise using adaptive spectrotemporal receptive fields,” in Annual Conference on Information Sciences and Systems, 2015.
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      @inproceedings{8565845,
      title = {Detection of speech tokens in noise using adaptive spectrotemporal receptive fields},
      author = {{Ashwin Bellur} and {Mounya Elhilali}},
      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/19de5e254179d52da95985bab4b5c698dd8a2136},
      }

2243. H. Mallidi, Tetsuji Ogawa, and H. Hermansky, “UNCERTAINTY ESTIMATION OF DNN CLASSIFIERS Sri.” 2015.
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      @inproceedings{195814763,
      title = {UNCERTAINTY ESTIMATION OF DNN CLASSIFIERS Sri},
      author = {{H. Mallidi} and {Tetsuji Ogawa} and {H. Hermansky}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fd377b46522cb8d6d8a5f6dd2ea625279b61b3ee},
      }

2244. P. Rastogi, B. Van Durme, and R. Arora, “Multiview LSA: Representation Learning via Generalized CCA,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 556–566. doi:10.3115/v1/N15-1058
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      @inproceedings{rastogi-etal-2015-multiview,
      title = "Multiview {LSA}: Representation Learning via Generalized {CCA}",
      author = "Rastogi, Pushpendre and
      Van Durme, Benjamin and
      Arora, Raman",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1058",
      doi = "10.3115/v1/N15-1058",
      pages = "556--566",
      }

2245. M. Santillana, A. Nguyen, Mark Dredze, Michael J. Paul, E. Nsoesie, and J. Brownstein, “Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance,” in PLoS Comput. Biol., 2015.
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      @inproceedings{24689369,
      title = {Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance},
      author = {{M. Santillana} and {A. Nguyen} and {Mark Dredze} and {Michael J. Paul} and {E. Nsoesie} and {J. Brownstein}},
      year = 2015,
      month = {8},
      booktitle = {PLoS Comput. Biol.},
      url = {https://www.semanticscholar.org/paper/84fed2a181f282fde8251882ea2a3f0b1e65bbe5},
      }

2246. Svitlana Volkova and Benjamin Van Durme, “Online Bayesian Models for Personal Analytics in Social Media,” in AAAI Conference on Artificial Intelligence, 2015.
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      @inproceedings{5742948,
      title = {Online Bayesian Models for Personal Analytics in Social Media},
      author = {{Svitlana Volkova} and {Benjamin Van Durme}},
      year = 2015,
      month = {1},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/6837a302d86ce1a05488a0a033206ff0a5e4b5dd},
      }

2247. Glen A. Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, and Margaret Mitchell, “Shared Task : Depression and PTSD on Twitter.” 2015.
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      @inproceedings{7954526,
      title = {Shared Task : Depression and PTSD on Twitter},
      author = {{Glen A. Coppersmith} and {Mark Dredze} and {Craig Harman} and {Kristy Hollingshead} and {Margaret Mitchell}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b050631bf7cef135805a32b5c09eef4038bd24f7},
      }

2248. Ellie Pavlick, Charley Beller, Benjamin Van Durme, and Chris Callison-Burch, “Adding Semantics to Data-Driven Paraphrasing : Supplementary Material.” 2015.
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      @inproceedings{17083060,
      title = {Adding Semantics to Data-Driven Paraphrasing : Supplementary Material},
      author = {{Ellie Pavlick} and {Charley Beller} and {Benjamin Van Durme} and {Chris Callison-Burch}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c93945626b371b69e4ffc62b8533cc5b5588f28c},
      }

2249. David A. Broniatowski, Michael J. Paul, and Mark Dredze, “Machine learning:Trends, perspectives, and prospects.” 2015.
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      @inproceedings{63987411,
      title = {Machine learning:Trends, perspectives, and prospects},
      author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
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      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a89762ae8574f4b55a1814e72750bfc3be57a70d},
      }

2250. David J. McIver, David A. Broniatowski, Mark Dredze, Michael J. Paul, and A. Dugas, “Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study,” in JMIR Public Health and Surveillance, 2015.
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      @inproceedings{11266353,
      title = {Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study},
      author = {{David J. McIver} and {David A. Broniatowski} and {Mark Dredze} and {Michael J. Paul} and {A. Dugas}},
      year = 2015,
      month = {5},
      booktitle = {JMIR Public Health and Surveillance},
      url = {https://www.semanticscholar.org/paper/f14550978577dd782709130600347f1b3d3db11d},
      }

2251. Michael J. Paul, Mark Dredze, David A. Broniatowski, and N. Generous, “Worldwide Influenza Surveillance through Twitter,” in AAAI Workshop: WWW and Public Health Intelligence, 2015.
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      @inproceedings{17878129,
      title = {Worldwide Influenza Surveillance through Twitter},
      author = {{Michael J. Paul} and {Mark Dredze} and {David A. Broniatowski} and {N. Generous}},
      year = 2015,
      month = {4},
      booktitle = {AAAI Workshop: WWW and Public Health Intelligence},
      url = {https://www.semanticscholar.org/paper/37716db6dd12a67c74fc10d97011a1f59d7369be},
      }

2252. J. Molin, Tomas Figliolia, Kayode A. Sanni, I. Doxas, A. Andreou, and R. Etienne-Cummings, “FPGA emulation of a spike-based, stochastic system for real-time image dewarping,” in Midwest Symposium on Circuits and Systems, 2015.
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      @inproceedings{36076568,
      title = {FPGA emulation of a spike-based, stochastic system for real-time image dewarping},
      author = {{J. Molin} and {Tomas Figliolia} and {Kayode A. Sanni} and {I. Doxas} and {A. Andreou} and {R. Etienne-Cummings}},
      year = 2015,
      month = {8},
      booktitle = {Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/b8cf4bc69d6801dab3e3467c815cbc48b40598e0},
      }

2253. Michael A. Carlin and Mounya Elhilali, “Modeling attention-driven plasticity in auditory cortical receptive fields,” in Frontiers in Computational Neuroscience, 2015.
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      @inproceedings{10680178,
      title = {Modeling attention-driven plasticity in auditory cortical receptive fields},
      author = {{Michael A. Carlin} and {Mounya Elhilali}},
      year = 2015,
      month = {8},
      booktitle = {Frontiers in Computational Neuroscience},
      url = {https://www.semanticscholar.org/paper/04e91e5cd8d49ae5b821096a4a8233d96fbf974e},
      }

2254. Guoguo Chen, Hainan Xu, Minhua Wu, Daniel Povey, and S. Khudanpur, “Pronunciation and silence probability modeling for ASR,” in Interspeech, 2015.
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      @inproceedings{11155786,
      title = {Pronunciation and silence probability modeling for ASR},
      author = {{Guoguo Chen} and {Hainan Xu} and {Minhua Wu} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b903e2a39cfc2d33f1a722af71adc438732cc6bc},
      }

2255. Kayode A. Sanni, Guillaume Garreau, J. Molin, and A. Andreou, “Architecture for Character Recognition Using Stochastic Computation.” 2015.
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      @inproceedings{62093662,
      title = {Architecture for Character Recognition Using Stochastic Computation},
      author = {{Kayode A. Sanni} and {Guillaume Garreau} and {J. Molin} and {A. Andreou}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/f56408962023a53064376969612a2d8c78f2c11a},
      }

2256. Jan Pesán, L. Burget, H. Hermansky, and Karel Veselý, “DNN derived filters for processing of modulation spectrum of speech,” in Interspeech, 2015.
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      @inproceedings{35992761,
      title = {DNN derived filters for processing of modulation spectrum of speech},
      author = {{Jan Pesán} and {L. Burget} and {H. Hermansky} and {Karel Veselý}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/c621cdb3f4d37725e701b32b4f16c414c812c2a8},
      }

2257. Haoyu Wang, E. Hovy, and Mark Dredze, “The Hurricane Sandy Twitter Corpus,” in AAAI Workshop: WWW and Public Health Intelligence, 2015.
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      @inproceedings{20529470,
      title = {The Hurricane Sandy Twitter Corpus},
      author = {{Haoyu Wang} and {E. Hovy} and {Mark Dredze}},
      year = 2015,
      booktitle = {AAAI Workshop: WWW and Public Health Intelligence},
      url = {https://www.semanticscholar.org/paper/bf7f220a2908268a7a1c432a2c2efd81afed6808},
      }

2258. M. Mitchell, K. Hollingshead, and G. Coppersmith, “Quantifying the Language of Schizophrenia in Social Media,” in Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, 2015, p. 11–20. doi:10.3115/v1/W15-1202
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      @inproceedings{mitchell-etal-2015-quantifying,
      title = "Quantifying the Language of Schizophrenia in Social Media",
      author = "Mitchell, Margaret and
      Hollingshead, Kristy and
      Coppersmith, Glen",
      booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
      month = jun # " 5",
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-1202",
      doi = "10.3115/v1/W15-1202",
      pages = "11--20",
      }

2259. Sri Harish Reddy Mallidi, Tetsuji Ogawa, Karel Veselý, P. S. Nidadavolu, and H. Hermansky, “Autoencoder based multi-stream combination for noise robust speech recognition,” in Interspeech, 2015.
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      @inproceedings{39799119,
      title = {Autoencoder based multi-stream combination for noise robust speech recognition},
      author = {{Sri Harish Reddy Mallidi} and {Tetsuji Ogawa} and {Karel Veselý} and {P. S. Nidadavolu} and {H. Hermansky}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/466228a1bca01e507adb217bfc8a014b32d94fc9},
      }

2260. Glen A. Coppersmith, Ryan Leary, and Eric Whyne, “Quantifying Suicidal Ideation via Language Usage on Social Media.” 2015.
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      @inproceedings{54026822,
      title = {Quantifying Suicidal Ideation via Language Usage on Social Media},
      author = {{Glen A. Coppersmith} and {Ryan Leary} and {Eric Whyne}},
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      booktitle = {},
      url = {https://www.semanticscholar.org/paper/faa324883b954543cefdb6a660ee7700d76f1ba4},
      }

2261. Alexandra Birch, Matthias Huck, Nadir Durrani, Nikolay Bogoychev, and Philipp Koehn, “Explorer Edinburgh SLT and MT System Description for the IWSLT 2014 Evaluation.” 2015.
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      @inproceedings{261968231,
      title = {Explorer Edinburgh SLT and MT System Description for the IWSLT 2014 Evaluation},
      author = {{Alexandra Birch} and {Matthias Huck} and {Nadir Durrani} and {Nikolay Bogoychev} and {Philipp Koehn}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/7a95f6ff588c0b7af6499952a24092ab6829d2b1},
      }

2262. Kailash Patil and Mounya Elhilali, “Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases,” in EURASIP Journal on Audio, Speech, and Music Processing, 2015.
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      @inproceedings{8686225,
      title = {Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases},
      author = {{Kailash Patil} and {Mounya Elhilali}},
      year = 2015,
      month = {9},
      booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
      url = {https://www.semanticscholar.org/paper/e1dfc5faeb41e77c4c970020f6eac47f1b2bc569},
      }

2263. H. Hermansky, L. Burget, Jordan Cohen, Emmanuel Dupoux, Naomi H Feldman, J. Godfrey, S. Khudanpur, Matthew Maciejewski, Sri Harish Reddy Mallidi, Anjali Menon, Tetsuji Ogawa, Vijayaditya Peddinti, R. Rose, R. Stern, Matthew Wiesner, and Karel Veselý, “Towards machines that know when they do not know: Summary of work done at 2014 Frederick Jelinek Memorial Workshop,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2015.
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      @inproceedings{374484,
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      author = {{H. Hermansky} and {L. Burget} and {Jordan Cohen} and {Emmanuel Dupoux} and {Naomi H Feldman} and {J. Godfrey} and {S. Khudanpur} and {Matthew Maciejewski} and {Sri Harish Reddy Mallidi} and {Anjali Menon} and {Tetsuji Ogawa} and {Vijayaditya Peddinti} and {R. Rose} and {R. Stern} and {Matthew Wiesner} and {Karel Veselý}},
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      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
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2264. Thomas Lippincott, “Unsupervised approaches to syntactic verb frame acquisition for biomedicine.” 2015.
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      title = {Unsupervised approaches to syntactic verb frame acquisition for biomedicine},
      author = {{Thomas Lippincott}},
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      url = {https://www.semanticscholar.org/paper/40a6a4891ed64a9f3caef028258b18d8fbb181e2},
      }

2265. Deyi Xiong, Kevin Duh, Christian Hardmeier, and Roberto Navigli, “Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015).” 2015.
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      title = {Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)},
      author = {{Deyi Xiong} and {Kevin Duh} and {Christian Hardmeier} and {Roberto Navigli}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/adbe183b8e35779e7abff33ca85461c296155cd0},
      }

2266. D. Chakrabarty and Mounya Elhilali, “Modeling goal-directed attention in tone sequences using a weighted Kalman filter,” in Annual Conference on Information Sciences and Systems, 2015.
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      title = {Modeling goal-directed attention in tone sequences using a weighted Kalman filter},
      author = {{D. Chakrabarty} and {Mounya Elhilali}},
      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/06daafe3fbd7a9cb696dd0a5ce4d68bd4ff1a299},
      }

2267. Mrinal Kumar, Mark Dredze, Glen A. Coppersmith, and M. Choudhury, “Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides,” in ACM Conference on Hypertext & Social Media, 2015.
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      title = {Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides},
      author = {{Mrinal Kumar} and {Mark Dredze} and {Glen A. Coppersmith} and {M. Choudhury}},
      year = 2015,
      month = {8},
      booktitle = {ACM Conference on Hypertext & Social Media},
      url = {https://www.semanticscholar.org/paper/2a0b2f1dc84a35a2d186edaf24fae92d8f1883e9},
      }

2268. K. Strohbehn, R. C. Meitzler, A. Andreou, and R. E. Jenkins, “ANALOG IMAGE PROCESSING WITH SILICON RETINAS.” 2015.
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      title = {ANALOG IMAGE PROCESSING WITH SILICON RETINAS},
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      year = 2015,
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      url = {https://www.semanticscholar.org/paper/c1e0ce486ae3e7a6ef02af3a341d16176ff8ebd6},
      }

2269. T. Wolfe, M. Dredze, and B. Van Durme, “Predicate Argument Alignment using a Global Coherence Model,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 11–20. doi:10.3115/v1/N15-1002
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      @inproceedings{wolfe-etal-2015-predicate,
      title = "Predicate Argument Alignment using a Global Coherence Model",
      author = "Wolfe, Travis and
      Dredze, Mark and
      Van Durme, Benjamin",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1002",
      doi = "10.3115/v1/N15-1002",
      pages = "11--20",
      }

2270. Sebastian Stüker, H. Ney, M. Simpson, Margit Rödder, Volker Steinbiss, A. Tescari, Marcello Federico, and Philipp Koehn, “EU-BRIDGE Final Report.” 2015.
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      @inproceedings{188206416,
      title = {EU-BRIDGE Final Report},
      author = {{Sebastian Stüker} and {H. Ney} and {M. Simpson} and {Margit Rödder} and {Volker Steinbiss} and {A. Tescari} and {Marcello Federico} and {Philipp Koehn}},
      year = 2015,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/6497ea71b3c738c54563bed1666598b64c6ff78c},
      }

2271. Eleanor Chodroff, J. Godfrey, S. Khudanpur, and Colin Wilson, “Structured variability in acoustic realization: a corpus study of voice onset time in American English stops,” in International Congress of Phonetic Sciences, 2015.
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      @inproceedings{19491850,
      title = {Structured variability in acoustic realization: a corpus study of voice onset time in American English stops},
      author = {{Eleanor Chodroff} and {J. Godfrey} and {S. Khudanpur} and {Colin Wilson}},
      year = 2015,
      booktitle = {International Congress of Phonetic Sciences},
      url = {https://www.semanticscholar.org/paper/89d6f2048586e417249b847d76bb6722638e87c5},
      }

2272. M. Villemur, M. D. Federico, P. Julián, A. Andreou, F. Masson, and E. Nebot, “Design of a vanishing point algorithm for custom ASIC,” in Annual Conference on Information Sciences and Systems, 2015.
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      @inproceedings{11812867,
      title = {Design of a vanishing point algorithm for custom ASIC},
      author = {{M. Villemur} and {M. D. Federico} and {P. Julián} and {A. Andreou} and {F. Masson} and {E. Nebot}},
      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/8defdb81dbd9974a83c40ba16729c1fed79817e0},
      }

2273. Xiaodong Liu, Kevin Duh, and Yuji Matsumoto, “Multilingual Topic Models for Bilingual Dictionary Extraction,” in ACM Trans. Asian Low Resour. Lang. Inf. Process., 2015.
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      @inproceedings{6095465,
      title = {Multilingual Topic Models for Bilingual Dictionary Extraction},
      author = {{Xiaodong Liu} and {Kevin Duh} and {Yuji Matsumoto}},
      year = 2015,
      month = {6},
      booktitle = {ACM Trans. Asian Low Resour. Lang. Inf. Process.},
      url = {https://www.semanticscholar.org/paper/acdd7809fe86f300d659792b27733601646fc860},
      }

2274. S. Volkova, B. Van Durme, D. Yarowsky, and Y. Bachrach, “Social Media Predictive Analytics,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Denver, Colorado, 2015, p. 9. doi:10.3115/v1/N15-4005
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      @inproceedings{volkova-etal-2015-social,
      title = "Social Media Predictive Analytics",
      author = "Volkova, Svitlana and
      Van Durme, Benjamin and
      Yarowsky, David and
      Bachrach, Yoram",
      editor = "Liu, Yang and
      Solorio, Thamar",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-4005",
      doi = "10.3115/v1/N15-4005",
      pages = "9",
      }

2275. G. Neubig, P. Arthur, and K. Duh, “Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 293–302. doi:10.3115/v1/N15-1033
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      @inproceedings{neubig-etal-2015-multi,
      title = "Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars",
      author = "Neubig, Graham and
      Arthur, Philip and
      Duh, Kevin",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1033",
      doi = "10.3115/v1/N15-1033",
      pages = "293--302",
      }

2276. Travis Wolfe, Mark Dredze, J. Mayfield, Paul McNamee, Craig Harman, Timothy W. Finin, and Benjamin Van Durme, “Interactive Knowledge Base Population,” in arXiv.org, 2015.
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      @inproceedings{6681632,
      title = {Interactive Knowledge Base Population},
      author = {{Travis Wolfe} and {Mark Dredze} and {J. Mayfield} and {Paul McNamee} and {Craig Harman} and {Timothy W. Finin} and {Benjamin Van Durme}},
      year = 2015,
      month = {5},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/b9d1d9d618c504b669e1d78e9fc6f5efa51e8ca5},
      }

2277. Tomas Figliolia, Thomas S. Murray, and A. Andreou, “Acoustic micro-Doppler signal processing with foveated electronic cochlea,” in Electronics Letters, 2015.
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      title = {Acoustic micro-Doppler signal processing with foveated electronic cochlea},
      author = {{Tomas Figliolia} and {Thomas S. Murray} and {A. Andreou}},
      year = 2015,
      month = {1},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/9e4390846e21f4c4b40b8d2762bec31fd041fda5},
      }

2278. K. Yao, Trevor Cohn, Ekaterina Vylomova, Kevin Duh, and Chris Dyer, “Depth-Gated LSTM,” in arXiv.org, 2015.
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      @inproceedings{10979491,
      title = {Depth-Gated LSTM},
      author = {{K. Yao} and {Trevor Cohn} and {Ekaterina Vylomova} and {Kevin Duh} and {Chris Dyer}},
      year = 2015,
      month = {8},
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      }

2279. Hani Bakhshaee, Guillaume Garreau, Gaspar Tognetti, K. Shoele, R. Carrero, T. Kilmar, Chiang-Jiang Zhu, W. R. Thompson, J. Seo, R. Mittal, and A. Andreou, “Mechanical design, instrumentation and measurements from a hemoacoustic cardiac phantom,” in Annual Conference on Information Sciences and Systems, 2015.
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      @inproceedings{16886349,
      title = {Mechanical design, instrumentation and measurements from a hemoacoustic cardiac phantom},
      author = {{Hani Bakhshaee} and {Guillaume Garreau} and {Gaspar Tognetti} and {K. Shoele} and {R. Carrero} and {T. Kilmar} and {Chiang-Jiang Zhu} and {W. R. Thompson} and {J. Seo} and {R. Mittal} and {A. Andreou}},
      year = 2015,
      month = {3},
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      }

2280. Philipp Koehn, Vicente Alabau, M. Carl, F. Casacuberta, Mercedes García-Martínez, J. González-Rubio, Frank Keller, and Germán Sanchis-Trilles, “Final Public Report.” 2015.
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      @inproceedings{204825228,
      title = {Final Public Report},
      author = {{Philipp Koehn} and {Vicente Alabau} and {M. Carl} and {F. Casacuberta} and {Mercedes García-Martínez} and {J. González-Rubio} and {Frank Keller} and {Germán Sanchis-Trilles}},
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      url = {https://www.semanticscholar.org/paper/cada4d45ac817337a4e7ec64d8325fe6efa33def},
      }

2281. Vijayaditya Peddinti, Daniel Povey, and S. Khudanpur, “A time delay neural network architecture for efficient modeling of long temporal contexts,” in Interspeech, 2015.
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      @inproceedings{8536162,
      title = {A time delay neural network architecture for efficient modeling of long temporal contexts},
      author = {{Vijayaditya Peddinti} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/3a79ac688f2558b2d9693e434f010e041eba0fae},
      }

2282. Keith D. Levin, A. Jansen, and Benjamin Van Durme, “Segmental acoustic indexing for zero resource keyword search,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2015.
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      @inproceedings{2646247,
      title = {Segmental acoustic indexing for zero resource keyword search},
      author = {{Keith D. Levin} and {A. Jansen} and {Benjamin Van Durme}},
      year = 2015,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a11b4da42e6392e8dba76ec6175da856a2e143b4},
      }

2283. Miloš Stanojević, Amir Kamran, Philipp Koehn, and Ondrej Bojar, “Explorer Results of the WMT 15 Metrics Shared Task.” 2015.
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      @inproceedings{145025824,
      title = {Explorer Results of the WMT 15 Metrics Shared Task},
      author = {{Miloš Stanojević} and {Amir Kamran} and {Philipp Koehn} and {Ondrej Bojar}},
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      }

2284. Shiliang Wang, Michael J. Paul, and Mark Dredze, “Social Media as a Sensor of Air Quality and Public Response in China,” in Journal of Medical Internet Research, 2015.
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      @inproceedings{18072917,
      title = {Social Media as a Sensor of Air Quality and Public Response in China},
      author = {{Shiliang Wang} and {Michael J. Paul} and {Mark Dredze}},
      year = 2015,
      month = {3},
      booktitle = {Journal of Medical Internet Research},
      url = {https://www.semanticscholar.org/paper/cc59eba7e3eab4eb95bb264a0db2496e4d19fcf7},
      }

2285. Michael Auli, Adam Lopez, Hieu D. Hoang, and Philipp Koehn, “Explorer A Systematic Analysis of Translation Model Search Spaces.” 2015.
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      @inproceedings{551934,
      title = {Explorer A Systematic Analysis of Translation Model Search Spaces},
      author = {{Michael Auli} and {Adam Lopez} and {Hieu D. Hoang} and {Philipp Koehn}},
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      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d40ba16d683c9dfa5d6f7d6b498a20516f4e2d7b},
      }

2286. Gregory Sell, C. Suied, Mounya Elhilali, and S. Shamma, “Perceptual susceptibility to acoustic manipulations in speaker discrimination.,” in Journal of the Acoustical Society of America, 2015.
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      @inproceedings{5777625,
      title = {Perceptual susceptibility to acoustic manipulations in speaker discrimination.},
      author = {{Gregory Sell} and {C. Suied} and {Mounya Elhilali} and {S. Shamma}},
      year = 2015,
      month = {2},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/1a3a3184e201e603a4a43fd76f856096ef532967},
      }

2287. NeuroData, Gray William, Martin Jg, Coppersmith Gc, Mark Dredze, J. Bogovic, Jerry L Prince, S. Resnick, C. Priebe, and R. J. Vogelstein, “Connectome Classification using statistical graph theory and machine learning.” 2015.
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      @inproceedings{62478138,
      title = {Connectome Classification using statistical graph theory and machine learning},
      author = {{NeuroData} and {Gray William} and {Martin Jg} and {Coppersmith Gc} and {Mark Dredze} and {J. Bogovic} and {Jerry L Prince} and {S. Resnick} and {C. Priebe} and {R. J. Vogelstein}},
      year = 2015,
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      }

2288. John Sylak-Glassman, Christo Kirov, Matt Post, R. Que, and David Yarowsky, “A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of-Speech Tagging,” in International Workshop on Systems and Frameworks for Computational Morphology, 2015.
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      @inproceedings{1151330,
      title = {A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of-Speech Tagging},
      author = {{John Sylak-Glassman} and {Christo Kirov} and {Matt Post} and {R. Que} and {David Yarowsky}},
      year = 2015,
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      }

2289. G. Coppersmith, M. Dredze, C. Harman, and K. Hollingshead, “From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses,” in Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, 2015, p. 1–10. doi:10.3115/v1/W15-1201
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      @inproceedings{coppersmith-etal-2015-adhd,
      title = "From {ADHD} to {SAD}: Analyzing the Language of Mental Health on {T}witter through Self-Reported Diagnoses",
      author = "Coppersmith, Glen and
      Dredze, Mark and
      Harman, Craig and
      Hollingshead, Kristy",
      booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
      month = jun # " 5",
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W15-1201",
      doi = "10.3115/v1/W15-1201",
      pages = "1--10",
      }

2290. Tom Ko, Vijayaditya Peddinti, Daniel Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in Interspeech, 2015.
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      @inproceedings{7360763,
      title = {Audio augmentation for speech recognition},
      author = {{Tom Ko} and {Vijayaditya Peddinti} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/66661a68dbf1d98d794fd025113b103683510303},
      }

2291. Hainan Xu, Guoguo Chen, Daniel Povey, and S. Khudanpur, “Modeling phonetic context with non-random forests for speech recognition,” in Interspeech, 2015.
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      @inproceedings{15549215,
      title = {Modeling phonetic context with non-random forests for speech recognition},
      author = {{Hainan Xu} and {Guoguo Chen} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/78f81f31f0494ffaf0ded28c28b857e0d3bd918c},
      }

2292. Daniel R. Mendat, S. Chin, S. Furber, and A. Andreou, “Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture,” in Annual Conference on Information Sciences and Systems, 2015.
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      @inproceedings{3660684,
      title = {Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture},
      author = {{Daniel R. Mendat} and {S. Chin} and {S. Furber} and {A. Andreou}},
      year = 2015,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/c60e3e0ea9c2ea5db70c96a7fda9966ff415181e},
      }

2293. Pushpendre Rastogi and Benjamin Van Durme, “Sublinear Partition Estimation,” in arXiv.org, 2015.
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      @inproceedings{17858183,
      title = {Sublinear Partition Estimation},
      author = {{Pushpendre Rastogi} and {Benjamin Van Durme}},
      year = 2015,
      month = {8},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/21b4f719993af854d85182d40c13da5f193669dc},
      }

2294. Vassil Panayotov, Guoguo Chen, Daniel Povey, and S. Khudanpur, “Librispeech: An ASR corpus based on public domain audio books,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2015.
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      @inproceedings{2191379,
      title = {Librispeech: An ASR corpus based on public domain audio books},
      author = {{Vassil Panayotov} and {Guoguo Chen} and {Daniel Povey} and {S. Khudanpur}},
      year = 2015,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/34038d9424ce602d7ac917a4e582d977725d4393},
      }

2295. Philipp Koehn, “Natural Language Processing.” 2015.
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      @inproceedings{63162079,
      title = {Natural Language Processing},
      author = {{Philipp Koehn}},
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      url = {https://www.semanticscholar.org/paper/cd3037aaedcfc5708255839ce7f454c03e44b0dc},
      }

2296. M. Yu, M. R. Gormley, and M. Dredze, “Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 2015, p. 1374–1379. doi:10.3115/v1/N15-1155
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      @inproceedings{yu-etal-2015-combining,
      title = "Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction",
      author = "Yu, Mo and
      Gormley, Matthew R. and
      Dredze, Mark",
      editor = "Mihalcea, Rada and
      Chai, Joyce and
      Sarkar, Anoop",
      booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = may # "{--}" # jun,
      year = "2015",
      address = "Denver, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N15-1155",
      doi = "10.3115/v1/N15-1155",
      pages = "1374--1379",
      }

2297. J. Trmal, Guoguo Chen, Daniel Povey, S. Khudanpur, Pegah Ghahremani, Xiaohui Zhang, Vimal Manohar, Chunxi Liu, A. Jansen, D. Klakow, David Yarowsky, and Florian Metze, “A keyword search system using open source software,” in Spoken Language Technology Workshop, 2014.
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      @inproceedings{10224690,
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      author = {{J. Trmal} and {Guoguo Chen} and {Daniel Povey} and {S. Khudanpur} and {Pegah Ghahremani} and {Xiaohui Zhang} and {Vimal Manohar} and {Chunxi Liu} and {A. Jansen} and {D. Klakow} and {David Yarowsky} and {Florian Metze}},
      year = 2014,
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      }

2298. S. Akram, B. Englitz, Mounya Elhilali, J. Simon, and S. Shamma, “Investigating the Neural Correlates of a Streaming Percept in an Informational-Masking Paradigm,” in PLoS ONE, 2014.
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      @inproceedings{6693866,
      title = {Investigating the Neural Correlates of a Streaming Percept in an Informational-Masking Paradigm},
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      year = 2014,
      month = {12},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/0b882ead3509aaae4e3ca4e834cbecac017bc88b},
      }

2299. Lakshmi Krishnan, Mounya Elhilali, and S. Shamma, “Segregating Complex Sound Sources through Temporal Coherence,” in PLoS Comput. Biol., 2014.
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      @inproceedings{2689544,
      title = {Segregating Complex Sound Sources through Temporal Coherence},
      author = {{Lakshmi Krishnan} and {Mounya Elhilali} and {S. Shamma}},
      year = 2014,
      month = {12},
      booktitle = {PLoS Comput. Biol.},
      url = {https://www.semanticscholar.org/paper/9a922f21be098823c0f549bf10644d744e2caffc},
      }

2300. Jonathan Wintrode and S. Khudanpur, “Combining local and broad topic context to improve term detection,” in Spoken Language Technology Workshop, 2014.
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      @inproceedings{1929328,
      title = {Combining local and broad topic context to improve term detection},
      author = {{Jonathan Wintrode} and {S. Khudanpur}},
      year = 2014,
      month = {12},
      booktitle = {Spoken Language Technology Workshop},
      url = {https://www.semanticscholar.org/paper/44b96be6827b92615ff4a5f8c398d5c2d3c1ecf3},
      }

2301. H. He, H. Daumé III, and J. Eisner, “Learning to Search in Branch-and-Bound Algorithms,” in Advances in Neural Information Processing Systems 27 (NeurIPS), Montreal, 2014, p. 3293–3301.
      [BibTeX] [Link]

      @InProceedings{he-daume-eisner-2014,
      author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
      title = "Learning to Search in Branch-and-Bound Algorithms",
      booktitle = "Advances in Neural Information Processing Systems 27
      (NeurIPS)",
      pages = "3293--3301",
      year = "2014",
      month = dec,
      address = "Montreal",
      URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2014",
      }

2302. J. Hong and J. Eisner, “Deriving Multi-Headed Projective Dependency Parses from Link Grammar Parses,” in 13th International Workshop on Treebanks and Linguistic Theories (TLT), Tübingen, 2014.
      [BibTeX] [Link]

      @InProceedings{hong-eisner-2014,
      author = "Juneki Hong and Jason Eisner",
      title = "Deriving Multi-Headed Projective Dependency Parses
      from Link Grammar Parses",
      booktitle = "13th International Workshop on Treebanks and
      Linguistic Theories (TLT)",
      note = "5 pages plus appendices",
      year = "2014",
      month = dec,
      address = "T{\"u}bingen",
      URL = "http://cs.jhu.edu/~jason/papers/#hong-eisner-2014",
      }

2303. Byron C. Wallace, Michael J. Paul, U. Sarkar, T. Trikalinos, and Mark Dredze, “A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews,” in J. Am. Medical Informatics Assoc., 2014.
      [BibTeX] [Link]

      @inproceedings{11927210,
      title = {A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews},
      author = {{Byron C. Wallace} and {Michael J. Paul} and {U. Sarkar} and {T. Trikalinos} and {Mark Dredze}},
      year = 2014,
      month = {11},
      booktitle = {J. Am. Medical Informatics Assoc.},
      url = {https://www.semanticscholar.org/paper/4697d14f9c3ded5182fc8051f62b8ab0c315ac8c},
      }

2304. Joy L. Lee, M. Decamp, Mark Dredze, M. Chisolm, and Z. Berger, “What Are Health-Related Users Tweeting? A Qualitative Content Analysis of Health-Related Users and Their Messages on Twitter,” in Journal of Medical Internet Research, 2014.
      [BibTeX] [Link]

      @inproceedings{20229016,
      title = {What Are Health-Related Users Tweeting? A Qualitative Content Analysis of Health-Related Users and Their Messages on Twitter},
      author = {{Joy L. Lee} and {M. Decamp} and {Mark Dredze} and {M. Chisolm} and {Z. Berger}},
      year = 2014,
      month = {10},
      booktitle = {Journal of Medical Internet Research},
      url = {https://www.semanticscholar.org/paper/78de8021f889aa36ad38f128cbb90cc91c84db0c},
      }

2305. M. Huck, H. Hoang, and P. Koehn, “Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation,” in Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 2014, p. 148–156. doi:10.3115/v1/W14-4018
      [BibTeX] [Link]

      @inproceedings{huck-etal-2014-preference,
      title = "Preference Grammars and Soft Syntactic Constraints for {GHKM} Syntax-based Statistical Machine Translation",
      author = "Huck, Matthias and
      Hoang, Hieu and
      Koehn, Philipp",
      editor = "Wu, Dekai and
      Carpuat, Marine and
      Carreras, Xavier and
      Vecchi, Eva Maria",
      booktitle = "Proceedings of {SSST}-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation",
      month = oct,
      year = "2014",
      address = "Doha, Qatar",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-4018",
      doi = "10.3115/v1/W14-4018",
      pages = "148--156",
      }

2306. Daniel Povey, Xiaohui Zhang, and S. Khudanpur, “Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging,” in International Conference on Learning Representations, 2014.
      [BibTeX] [Link]

      @inproceedings{5464187,
      title = {Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging},
      author = {{Daniel Povey} and {Xiaohui Zhang} and {S. Khudanpur}},
      year = 2014,
      month = {10},
      booktitle = {International Conference on Learning Representations},
      url = {https://www.semanticscholar.org/paper/4030a62e75313110dc4a4c78483f4459dc4526bc},
      }

2307. Michael J. Paul, Mark Dredze, and David A. Broniatowski, “Twitter Improves Influenza Forecasting,” in PLOS Currents, 2014.
      [BibTeX] [Link]

      @inproceedings{4816342,
      title = {Twitter Improves Influenza Forecasting},
      author = {{Michael J. Paul} and {Mark Dredze} and {David A. Broniatowski}},
      year = 2014,
      month = {10},
      booktitle = {PLOS Currents},
      url = {https://www.semanticscholar.org/paper/1a65756a1c9641fdd7dbee5f7b46e72fb40ca772},
      }

2308. M. Di Federico, P. Julián, A. Andreou, and P. Mandolesi, “Fully functional fine-grain vertically integrated 3D focal plane neuromorphic processor,” in IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference, 2014.
      [BibTeX] [Link]

      @inproceedings{40134619,
      title = {Fully functional fine-grain vertically integrated 3D focal plane neuromorphic processor},
      author = {{M. Di Federico} and {P. Julián} and {A. Andreou} and {P. Mandolesi}},
      year = 2014,
      month = {10},
      booktitle = {IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference},
      url = {https://www.semanticscholar.org/paper/ac4535e5ac8a7a622bde497f64249d6f5e24ebdc},
      }

2309. N. Durrani, P. Koehn, H. Schmid, and A. Fraser, “Investigating the Usefulness of Generalized Word Representations in SMT,” in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, 2014, p. 421–432.
      [BibTeX] [Link]

      @inproceedings{durrani-etal-2014-investigating,
      title = "Investigating the Usefulness of Generalized Word Representations in {SMT}",
      author = "Durrani, Nadir and
      Koehn, Philipp and
      Schmid, Helmut and
      Fraser, Alexander",
      editor = "Tsujii, Junichi and
      Hajic, Jan",
      booktitle = "Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
      month = aug,
      year = "2014",
      address = "Dublin, Ireland",
      publisher = "Dublin City University and Association for Computational Linguistics",
      url = "https://aclanthology.org/C14-1041",
      pages = "421--432",
      }

2310. M. Federico, N. Bertoldi, M. Cettolo, M. Negri, M. Turchi, M. Trombetti, A. Cattelan, A. Farina, D. Lupinetti, A. Martines, A. Massidda, H. Schwenk, L. Barrault, F. Blain, P. Koehn, C. Buck, and U. Germann, “The MateCat Tool,” in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations, Dublin, Ireland, 2014, p. 129–132.
      [BibTeX] [Link]

      @inproceedings{federico-etal-2014-matecat,
      title = "The {M}ate{C}at Tool",
      author = {Federico, Marcello and
      Bertoldi, Nicola and
      Cettolo, Mauro and
      Negri, Matteo and
      Turchi, Marco and
      Trombetti, Marco and
      Cattelan, Alessandro and
      Farina, Antonio and
      Lupinetti, Domenico and
      Martines, Andrea and
      Massidda, Alberto and
      Schwenk, Holger and
      Barrault, Lo{\"\i}c and
      Blain, Frederic and
      Koehn, Philipp and
      Buck, Christian and
      Germann, Ulrich},
      editor = "Tounsi, Lamia and
      Rak, Rafal",
      booktitle = "Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: System Demonstrations",
      month = aug,
      year = "2014",
      address = "Dublin, Ireland",
      publisher = "Dublin City University and Association for Computational Linguistics",
      url = "https://aclanthology.org/C14-2028",
      pages = "129--132",
      }

2311. J. Wintrode and S. Khudanpur, “Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 1316–1325. doi:10.3115/v1/P14-1124
      [BibTeX] [Link]

      @inproceedings{wintrode-khudanpur-2014-repeat,
      title = "Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection",
      author = "Wintrode, Jonathan and
      Khudanpur, Sanjeev",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1124",
      doi = "10.3115/v1/P14-1124",
      pages = "1316--1325",
      }

2312. N. Peng, Y. Wang, and M. Dredze, “Learning Polylingual Topic Models from Code-Switched Social Media Documents,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 674–679. doi:10.3115/v1/P14-2110
      [BibTeX] [Link]

      @inproceedings{peng-etal-2014-learning,
      title = "Learning Polylingual Topic Models from Code-Switched Social Media Documents",
      author = "Peng, Nanyun and
      Wang, Yiming and
      Dredze, Mark",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2110",
      doi = "10.3115/v1/P14-2110",
      pages = "674--679",
      }

2313. P. Rastogi and B. Van Durme, “Augmenting FrameNet Via PPDB,” in Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation, Baltimore, Maryland, USA, 2014, p. 1–5. doi:10.3115/v1/W14-2901
      [BibTeX] [Link]

      @inproceedings{rastogi-van-durme-2014-augmenting,
      title = "Augmenting {F}rame{N}et Via {PPDB}",
      author = "Rastogi, Pushpendre and
      Van Durme, Benjamin",
      editor = "Mitamura, Teruko and
      Hovy, Eduard and
      Palmer, Martha",
      booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-2901",
      doi = "10.3115/v1/W14-2901",
      pages = "1--5",
      }

2314. C. Beller, R. Knowles, C. Harman, S. Bergsma, M. Mitchell, and B. Van Durme, “I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 181–186. doi:10.3115/v1/P14-2030
      [BibTeX] [Link]

      @inproceedings{beller-etal-2014-im,
      title = "{I}{'}m a Belieber: Social Roles via Self-identification and Conceptual Attributes",
      author = "Beller, Charley and
      Knowles, Rebecca and
      Harman, Craig and
      Bergsma, Shane and
      Mitchell, Margaret and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2030",
      doi = "10.3115/v1/P14-2030",
      pages = "181--186",
      }

2315. C. May, A. Clemmer, and B. Van Durme, “Particle Filter Rejuvenation and Latent Dirichlet Allocation,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 446–451. doi:10.3115/v1/P14-2073
      [BibTeX] [Link]

      @inproceedings{may-etal-2014-particle,
      title = "Particle Filter Rejuvenation and {L}atent {D}irichlet {A}llocation",
      author = "May, Chandler and
      Clemmer, Alex and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2073",
      doi = "10.3115/v1/P14-2073",
      pages = "446--451",
      }

2316. A. B. Fine, A. F. Frank, F. T. Jaeger, and B. Van Durme, “Biases in Predicting the Human Language Model,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 7–12. doi:10.3115/v1/P14-2002
      [BibTeX] [Link]

      @inproceedings{fine-etal-2014-biases,
      title = "Biases in Predicting the Human Language Model",
      author = "Fine, Alex B. and
      Frank, Austin F. and
      Jaeger, T. Florian and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2002",
      doi = "10.3115/v1/P14-2002",
      pages = "7--12",
      }

2317. N. Andrews, J. Eisner, and M. Dredze, “Robust Entity Clustering via Phylogenetic Inference,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 775–785. doi:10.3115/v1/P14-1073
      [BibTeX] [Link]

      @inproceedings{andrews-etal-2014-robust,
      title = "Robust Entity Clustering via Phylogenetic Inference",
      author = "Andrews, Nicholas and
      Eisner, Jason and
      Dredze, Mark",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1073",
      doi = "10.3115/v1/P14-1073",
      pages = "775--785",
      }

2318. J. Aguilar, C. Beller, P. McNamee, B. Van Durme, S. Strassel, Z. Song, and J. Ellis, “A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards,” in Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation, Baltimore, Maryland, USA, 2014, p. 45–53. doi:10.3115/v1/W14-2907
      [BibTeX] [Link]

      @inproceedings{aguilar-etal-2014-comparison,
      title = "A Comparison of the Events and Relations Across {ACE}, {ERE}, {TAC}-{KBP}, and {F}rame{N}et Annotation Standards",
      author = "Aguilar, Jacqueline and
      Beller, Charley and
      McNamee, Paul and
      Van Durme, Benjamin and
      Strassel, Stephanie and
      Song, Zhiyi and
      Ellis, Joe",
      editor = "Mitamura, Teruko and
      Hovy, Eduard and
      Palmer, Martha",
      booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-2907",
      doi = "10.3115/v1/W14-2907",
      pages = "45--53",
      }

2319. M. R. Gormley, M. Mitchell, B. Van Durme, and M. Dredze, “Low-Resource Semantic Role Labeling,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 1177–1187. doi:10.3115/v1/P14-1111
      [BibTeX] [Link]

      @inproceedings{gormley-etal-2014-low,
      title = "Low-Resource Semantic Role Labeling",
      author = "Gormley, Matthew R. and
      Mitchell, Margaret and
      Van Durme, Benjamin and
      Dredze, Mark",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1111",
      doi = "10.3115/v1/P14-1111",
      pages = "1177--1187",
      }

2320. M. Freitag, S. Peitz, J. Wuebker, H. Ney, M. Huck, R. Sennrich, N. Durrani, M. Nadejde, P. Williams, P. Koehn, T. Herrmann, E. Cho, and A. Waibel, “EU-BRIDGE MT: Combined Machine Translation,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 105–113. doi:10.3115/v1/W14-3310
      [BibTeX] [Link]

      @inproceedings{freitag-etal-2014-eu,
      title = "{EU-BRIDGE} {MT}: Combined Machine Translation",
      author = "Freitag, Markus and
      Peitz, Stephan and
      Wuebker, Joern and
      Ney, Hermann and
      Huck, Matthias and
      Sennrich, Rico and
      Durrani, Nadir and
      Nadejde, Maria and
      Williams, Philip and
      Koehn, Philipp and
      Herrmann, Teresa and
      Cho, Eunah and
      Waibel, Alex",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3310",
      doi = "10.3115/v1/W14-3310",
      pages = "105--113",
      }

2321. M. Osborne, A. Lall, and B. Van Durme, “Exponential Reservoir Sampling for Streaming Language Models,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 687–692. doi:10.3115/v1/P14-2112
      [BibTeX] [Link]

      @inproceedings{osborne-etal-2014-exponential,
      title = "Exponential Reservoir Sampling for Streaming Language Models",
      author = "Osborne, Miles and
      Lall, Ashwin and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2112",
      doi = "10.3115/v1/P14-2112",
      pages = "687--692",
      }

2322. P. Williams, R. Sennrich, M. Nadejde, M. Huck, E. Hasler, and P. Koehn, “Edinburgh’s Syntax-Based Systems at WMT 2014,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 207–214. doi:10.3115/v1/W14-3324
      [BibTeX] [Link]

      @inproceedings{williams-etal-2014-edinburghs,
      title = "{E}dinburgh{'}s Syntax-Based Systems at {WMT} 2014",
      author = "Williams, Philip and
      Sennrich, Rico and
      Nadejde, Maria and
      Huck, Matthias and
      Hasler, Eva and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3324",
      doi = "10.3115/v1/W14-3324",
      pages = "207--214",
      }

2323. P. Koehn, C. Tsoukala, and H. Saint-Amand, “Refinements to Interactive Translation Prediction Based on Search Graphs,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 574–578. doi:10.3115/v1/P14-2094
      [BibTeX] [Link]

      @inproceedings{koehn-etal-2014-refinements,
      title = "Refinements to Interactive Translation Prediction Based on Search Graphs",
      author = "Koehn, Philipp and
      Tsoukala, Chara and
      Saint-Amand, Herve",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2094",
      doi = "10.3115/v1/P14-2094",
      pages = "574--578",
      }

2324. G. Coppersmith, M. Dredze, and C. Harman, “Quantifying Mental Health Signals in Twitter,” in Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Baltimore, Maryland, USA, 2014, p. 51–60. doi:10.3115/v1/W14-3207
      [BibTeX] [Link]

      @inproceedings{coppersmith-etal-2014-quantifying,
      title = "Quantifying Mental Health Signals in {T}witter",
      author = "Coppersmith, Glen and
      Dredze, Mark and
      Harman, Craig",
      editor = "Resnik, Philip and
      Resnik, Rebecca and
      Mitchell, Margaret",
      booktitle = "Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3207",
      doi = "10.3115/v1/W14-3207",
      pages = "51--60",
      }

2325. O. Bojar, C. Buck, C. Federmann, B. Haddow, P. Koehn, J. Leveling, C. Monz, P. Pecina, M. Post, H. Saint-Amand, R. Soricut, L. Specia, and A. Tamchyna, “Findings of the 2014 Workshop on Statistical Machine Translation,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 12–58. doi:10.3115/v1/W14-3302
      [BibTeX] [Link]

      @inproceedings{bojar-etal-2014-findings,
      title = "Findings of the 2014 Workshop on Statistical Machine Translation",
      author = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Leveling, Johannes and
      Monz, Christof and
      Pecina, Pavel and
      Post, Matt and
      Saint-Amand, Herve and
      Soricut, Radu and
      Specia, Lucia and
      Tamchyna, Ale{\v{s}}",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3302",
      doi = "10.3115/v1/W14-3302",
      pages = "12--58",
      }

2326. E. Hasler, B. Haddow, and P. Koehn, “Dynamic Topic Adaptation for SMT using Distributional Profiles,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 445–456. doi:10.3115/v1/W14-3358
      [BibTeX] [Link]

      @inproceedings{hasler-etal-2014-dynamic-topic,
      title = "Dynamic Topic Adaptation for {SMT} using Distributional Profiles",
      author = "Hasler, Eva and
      Haddow, Barry and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3358",
      doi = "10.3115/v1/W14-3358",
      pages = "445--456",
      }

2327. M. Huck, H. Hoang, and P. Koehn, “Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 486–498. doi:10.3115/v1/W14-3362
      [BibTeX] [Link]

      @inproceedings{huck-etal-2014-augmenting,
      title = "Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases",
      author = "Huck, Matthias and
      Hoang, Hieu and
      Koehn, Philipp",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3362",
      doi = "10.3115/v1/W14-3362",
      pages = "486--498",
      }

2328. X. Yao and B. Van Durme, “Information Extraction over Structured Data: Question Answering with Freebase,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 956–966. doi:10.3115/v1/P14-1090
      [BibTeX] [Link]

      @inproceedings{yao-van-durme-2014-information,
      title = "Information Extraction over Structured Data: Question Answering with {F}reebase",
      author = "Yao, Xuchen and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1090",
      doi = "10.3115/v1/P14-1090",
      pages = "956--966",
      }

2329. C. Beller, C. Harman, and B. Van Durme, “Predicting Fine-grained Social Roles with Selectional Preferences,” in Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, Baltimore, MD, USA, 2014, p. 50–55. doi:10.3115/v1/W14-2515
      [BibTeX] [Link]

      @inproceedings{beller-etal-2014-predicting,
      title = "Predicting Fine-grained Social Roles with Selectional Preferences",
      author = "Beller, Charley and
      Harman, Craig and
      Van Durme, Benjamin",
      editor = "Danescu-Niculescu-Mizil, Cristian and
      Eisenstein, Jacob and
      McKeown, Kathleen and
      Smith, Noah A.",
      booktitle = "Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science",
      month = jun,
      year = "2014",
      address = "Baltimore, MD, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-2515",
      doi = "10.3115/v1/W14-2515",
      pages = "50--55",
      }

2330. N. Durrani, B. Haddow, P. Koehn, and K. Heafield, “Edinburgh’s Phrase-based Machine Translation Systems for WMT-14,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 97–104. doi:10.3115/v1/W14-3309
      [BibTeX] [Link]

      @inproceedings{durrani-etal-2014-edinburghs,
      title = "{E}dinburgh{'}s Phrase-based Machine Translation Systems for {WMT}-14",
      author = "Durrani, Nadir and
      Haddow, Barry and
      Koehn, Philipp and
      Heafield, Kenneth",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3309",
      doi = "10.3115/v1/W14-3309",
      pages = "97--104",
      }

2331. Y. Cao and S. Khudanpur, “Online Learning in Tensor Space,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 666–675. doi:10.3115/v1/P14-1063
      [BibTeX] [Link]

      @inproceedings{cao-khudanpur-2014-online,
      title = "Online Learning in Tensor Space",
      author = "Cao, Yuan and
      Khudanpur, Sanjeev",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1063",
      doi = "10.3115/v1/P14-1063",
      pages = "666--675",
      }

2332. X. Yao, J. Berant, and B. Van Durme, “Freebase QA: Information Extraction or Semantic Parsing?,” in Proceedings of the ACL 2014 Workshop on Semantic Parsing, Baltimore, MD, 2014, p. 82–86. doi:10.3115/v1/W14-2416
      [BibTeX] [Link]

      @inproceedings{yao-etal-2014-freebase,
      title = "{F}reebase {QA}: Information Extraction or Semantic Parsing?",
      author = "Yao, Xuchen and
      Berant, Jonathan and
      Van Durme, Benjamin",
      editor = "Artzi, Yoav and
      Kwiatkowski, Tom and
      Berant, Jonathan",
      booktitle = "Proceedings of the {ACL} 2014 Workshop on Semantic Parsing",
      month = jun,
      year = "2014",
      address = "Baltimore, MD",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-2416",
      doi = "10.3115/v1/W14-2416",
      pages = "82--86",
      }

2333. G. Coppersmith and E. Kelly, “Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis,” in Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, Maryland, USA, 2014, p. 22–29. doi:10.3115/v1/W14-3103
      [BibTeX] [Link]

      @inproceedings{coppersmith-kelly-2014-dynamic,
      title = "Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis",
      author = "Coppersmith, Glen and
      Kelly, Erin",
      editor = "Chuang, Jason and
      Green, Spence and
      Hearst, Marti and
      Heer, Jeffrey and
      Koehn, Philipp",
      booktitle = "Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3103",
      doi = "10.3115/v1/W14-3103",
      pages = "22--29",
      }

2334. R. Rudinger and B. Van Durme, “Is the Stanford Dependency Representation Semantic?,” in Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation, Baltimore, Maryland, USA, 2014, p. 54–58. doi:10.3115/v1/W14-2908
      [BibTeX] [Link]

      @inproceedings{rudinger-van-durme-2014-stanford,
      title = "Is the {S}tanford Dependency Representation Semantic?",
      author = "Rudinger, Rachel and
      Van Durme, Benjamin",
      editor = "Mitamura, Teruko and
      Hovy, Eduard and
      Palmer, Martha",
      booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-2908",
      doi = "10.3115/v1/W14-2908",
      pages = "54--58",
      }

2335. M. Yu and M. Dredze, “Improving Lexical Embeddings with Semantic Knowledge,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 2014, p. 545–550. doi:10.3115/v1/P14-2089
      [BibTeX] [Link]

      @inproceedings{yu-dredze-2014-improving,
      title = "Improving Lexical Embeddings with Semantic Knowledge",
      author = "Yu, Mo and
      Dredze, Mark",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-2089",
      doi = "10.3115/v1/P14-2089",
      pages = "545--550",
      }

2336. K. Sakaguchi, M. Post, and B. Van Durme, “Efficient Elicitation of Annotations for Human Evaluation of Machine Translation,” in Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, p. 1–11. doi:10.3115/v1/W14-3301
      [BibTeX] [Link]

      @inproceedings{sakaguchi-etal-2014-efficient,
      title = "Efficient Elicitation of Annotations for Human Evaluation of Machine Translation",
      author = "Sakaguchi, Keisuke and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Specia, Lucia",
      booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-3301",
      doi = "10.3115/v1/W14-3301",
      pages = "1--11",
      }

2337. S. Volkova, G. Coppersmith, and B. Van Durme, “Inferring User Political Preferences from Streaming Communications,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland, 2014, p. 186–196. doi:10.3115/v1/P14-1018
      [BibTeX] [Link]

      @inproceedings{volkova-etal-2014-inferring,
      title = "Inferring User Political Preferences from Streaming Communications",
      author = "Volkova, Svitlana and
      Coppersmith, Glen and
      Van Durme, Benjamin",
      editor = "Toutanova, Kristina and
      Wu, Hua",
      booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jun,
      year = "2014",
      address = "Baltimore, Maryland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P14-1018",
      doi = "10.3115/v1/P14-1018",
      pages = "186--196",
      }

2338. N. Andrews, J. Eisner, and M. Dredze, “Robust Entity Clustering via Phylogenetic Inference,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), Baltimore, 2014, p. 775–785. doi:10.3115/v1/P14-1073
      [BibTeX] [Link]

      @InProceedings{andrews-eisner-dredze-2014,
      aclid = "P14-1073",
      doi = "10.3115/v1/P14-1073",
      author = "Nicholas Andrews and Jason Eisner and Mark Dredze",
      title = "Robust Entity Clustering via Phylogenetic Inference",
      booktitle = "Proceedings of the 52nd Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "775--785",
      year = "2014",
      month = jun,
      address = "Baltimore",
      URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-dredze-2014",
      }

2339. R. Cotterell, N. Peng, and J. Eisner, “Stochastic Contextual Edit Distance and Probabilistic FSTs,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, 2014, p. 625–630. doi:10.3115/v1/P14-2102
      [BibTeX] [Link]

      @InProceedings{cotterell-peng-eisner-2014,
      aclid = "P14-2102",
      doi = "10.3115/v1/P14-2102",
      author = "Ryan Cotterell and Nanyun Peng and Jason Eisner",
      title = "Stochastic Contextual Edit Distance and Probabilistic
      {FST}s",
      booktitle = "Proceedings of the 52nd Annual Meeting of the
      Association for Computational Linguistics (Volume 2:
      Short Papers)",
      pages = "625--630",
      year = "2014",
      month = jun,
      address = "Baltimore",
      URL = "http://cs.jhu.edu/~jason/papers/#cotterell-peng-eisner-2014",
      }

2340. J. Drexler, P. Rastogi, J. Aguilar, B. Van Durme, and M. Post, “A Wikipedia-based Corpus for Contextualized Machine Translation,” in Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, 2014, p. 3593–3596.
      [BibTeX] [Abstract] [Link]
      

      We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.

      @inproceedings{drexler-etal-2014-wikipedia,
      title = "A {W}ikipedia-based Corpus for Contextualized Machine Translation",
      author = "Drexler, Jennifer and
      Rastogi, Pushpendre and
      Aguilar, Jacqueline and
      Van Durme, Benjamin and
      Post, Matt",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Declerck, Thierry and
      Loftsson, Hrafn and
      Maegaard, Bente and
      Mariani, Joseph and
      Moreno, Asuncion and
      Odijk, Jan and
      Piperidis, Stelios",
      booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
      month = may,
      year = "2014",
      address = "Reykjavik, Iceland",
      publisher = "European Language Resources Association (ELRA)",
      url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1217_Paper.pdf",
      pages = "3593--3596",
      abstract = "We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.",
      }

2341. E. Hasler, P. Blunsom, P. Koehn, and B. Haddow, “Dynamic Topic Adaptation for Phrase-based MT,” in Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, 2014, p. 328–337. doi:10.3115/v1/E14-1035
      [BibTeX] [Link]

      @inproceedings{hasler-etal-2014-dynamic,
      title = "Dynamic Topic Adaptation for Phrase-based {MT}",
      author = "Hasler, Eva and
      Blunsom, Phil and
      Koehn, Philipp and
      Haddow, Barry",
      editor = "Wintner, Shuly and
      Goldwater, Sharon and
      Riezler, Stefan",
      booktitle = "Proceedings of the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
      month = apr,
      year = "2014",
      address = "Gothenburg, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E14-1035",
      doi = "10.3115/v1/E14-1035",
      pages = "328--337",
      }

2342. V. Alabau, C. Buck, M. Carl, F. Casacuberta, M. Garc{‘i}a-Mart{‘i}nez, U. Germann, J. González-Rubio, R. Hill, P. Koehn, L. Leiva, B. Mesa-Lao, D. Ortiz-Mart{‘i}nez, H. Saint-Amand, G. Sanchis Trilles, and C. Tsoukala, “CASMACAT: A Computer-assisted Translation Workbench,” in Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, 2014, p. 25–28. doi:10.3115/v1/E14-2007
      [BibTeX] [Link]

      @inproceedings{alabau-etal-2014-casmacat,
      title = "{CASMACAT}: A Computer-assisted Translation Workbench",
      author = "Alabau, Vicent and
      Buck, Christian and
      Carl, Michael and
      Casacuberta, Francisco and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes and
      Germann, Ulrich and
      Gonz{\'a}lez-Rubio, Jes{\'u}s and
      Hill, Robin and
      Koehn, Philipp and
      Leiva, Luis and
      Mesa-Lao, Bartolom{\'e} and
      Ortiz-Mart{\'\i}nez, Daniel and
      Saint-Amand, Herve and
      Sanchis Trilles, Germ{\'a}n and
      Tsoukala, Chara",
      editor = "Wintner, Shuly and
      Tadi{\'c}, Marko and
      Babych, Bogdan",
      booktitle = "Proceedings of the Demonstrations at the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
      month = apr,
      year = "2014",
      address = "Gothenburg, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E14-2007",
      doi = "10.3115/v1/E14-2007",
      pages = "25--28",
      }

2343. N. Durrani, H. Sajjad, H. Hoang, and P. Koehn, “Integrating an Unsupervised Transliteration Model into Statistical Machine Translation,” in Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers, Gothenburg, Sweden, 2014, p. 148–153. doi:10.3115/v1/E14-4029
      [BibTeX] [Link]

      @inproceedings{durrani-etal-2014-integrating,
      title = "Integrating an Unsupervised Transliteration Model into Statistical Machine Translation",
      author = "Durrani, Nadir and
      Sajjad, Hassan and
      Hoang, Hieu and
      Koehn, Philipp",
      editor = "Wintner, Shuly and
      Riezler, Stefan and
      Goldwater, Sharon",
      booktitle = "Proceedings of the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics, volume 2: Short Papers",
      month = apr,
      year = "2014",
      address = "Gothenburg, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E14-4029",
      doi = "10.3115/v1/E14-4029",
      pages = "148--153",
      }

2344. P. Williams and P. Koehn, “Using Feature Structures to Improve Verb Translation in English-to-German Statistical MT,” in Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra), Gothenburg, Sweden, 2014, p. 21–29. doi:10.3115/v1/W14-1005
      [BibTeX] [Link]

      @inproceedings{williams-koehn-2014-using,
      title = "Using Feature Structures to Improve Verb Translation in {E}nglish-to-{G}erman Statistical {MT}",
      author = "Williams, Philip and
      Koehn, Philipp",
      editor = "Banchs, Rafael E. and
      Costa-juss{\`a}, Marta R. and
      Rapp, Reinhard and
      Lambert, Patrik and
      Eberle, Kurt and
      Babych, Bogdan",
      booktitle = "Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation ({H}y{T}ra)",
      month = apr,
      year = "2014",
      address = "Gothenburg, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W14-1005",
      doi = "10.3115/v1/W14-1005",
      pages = "21--29",
      }

2345. Manish Kumar, Matt Post, Daniel Povey, and S. Khudanpur, “Some insights from translating conversational telephone speech,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.
      [BibTeX] [Link]

      @inproceedings{3159287,
      title = {Some insights from translating conversational telephone speech},
      author = {{Manish Kumar} and {Matt Post} and {Daniel Povey} and {S. Khudanpur}},
      year = 2014,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/526992a93d76af65d4208bc91f67deed40ccdff2},
      }

2346. Keith Kintzley, A. Jansen, and H. Hermansky, “Featherweight phonetic keyword search for conversational speech,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.
      [BibTeX] [Link]

      @inproceedings{2361768,
      title = {Featherweight phonetic keyword search for conversational speech},
      author = {{Keith Kintzley} and {A. Jansen} and {H. Hermansky}},
      year = 2014,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/3b50c10c61614ae378703d17dbd9cb283fa38f64},
      }

2347. E. Hasler, B. Haddow, and P. Koehn, “Combining domain and topic adaptation for SMT,” in Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, Vancouver, Canada, 2014, p. 139–151.
      [BibTeX] [Abstract] [Link]
      

      Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.

      @inproceedings{hasler-etal-2014-combining,
      title = "Combining domain and topic adaptation for {SMT}",
      author = "Hasler, Eva and
      Haddow, Barry and
      Koehn, Philipp",
      editor = "Al-Onaizan, Yaser and
      Simard, Michel",
      booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
      month = oct # " 22-26",
      year = "2014",
      address = "Vancouver, Canada",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2014.amta-researchers.11",
      pages = "139--151",
      abstract = "Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.",
      }

2348. H. Hoang, M. Huck, and P. Koehn, “Statistical machine translation with the Moses toolkit,” in Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials, Vancouver, Canada, 2014.
      [BibTeX] [Link]

      @inproceedings{hoang-etal-2014-statistical,
      title = "Statistical machine translation with the {M}oses toolkit",
      author = "Hoang, Hieu and
      Huck, Matthias and
      Koehn, Philipp",
      booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials",
      month = oct # " 22-26",
      year = "2014",
      address = "Vancouver, Canada",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2014.amta-tutorials.5",
      }

2349. David A. Broniatowski, Michael J. Paul, and Mark Dredze, “Twitter: big data opportunities.,” in Science, 2014.
      [BibTeX] [Link]

      @inproceedings{45778614,
      title = {Twitter: big data opportunities.},
      author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
      year = 2014,
      month = {7},
      booktitle = {Science},
      url = {https://www.semanticscholar.org/paper/484589552d3941f25f9e722c4268784aa1b5d465},
      }

2350. Philipp Koehn, “Explorer Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation.” 2014.
      [BibTeX] [Link]

      @inproceedings{67803383,
      title = {Explorer Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation},
      author = {{Philipp Koehn}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/21922bfe55ea4bd5abcc4e274f8dcdeed5b4694b},
      }

2351. F. Casacuberta, Marcello Federico, and Philipp Koehn, “The 11th Conference of the Association for Machine Translation in the Americas Workshop on Interactive and Adaptive Machine Translation.” 2014.
      [BibTeX] [Link]

      @inproceedings{18000624,
      title = {The 11th Conference of the Association for Machine Translation in the Americas Workshop on Interactive and Adaptive Machine Translation},
      author = {{F. Casacuberta} and {Marcello Federico} and {Philipp Koehn}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5b27c09022333453e0901aae6666d39306250eb7},
      }

2352. Matt Post, S. Ravi, and Anand Padmanabhan, “BEFORE THE NATIONAL GREEN TRIBUNAL SOUTHERN ZONE, CHENNAI Appeal No.66 of 2014 (SZ).” 2014.
      [BibTeX] [Link]

      @inproceedings{131772405,
      title = {BEFORE THE NATIONAL GREEN TRIBUNAL SOUTHERN ZONE, CHENNAI Appeal No.66 of 2014 (SZ)},
      author = {{Matt Post} and {S. Ravi} and {Anand Padmanabhan}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a7baa753d96f7d9b15f236fc53a3ed50422bc87e},
      }

2353. Thomas J. Dawidczyk, Josué F Martínez Hardigree, G. Johns, R. Ozgun, Olivia Alley, A. Andreou, N. Marković, and H. Katz, “Visualizing and quantifying charge distributions correlated to threshold voltage shifts in lateral organic transistors.,” in ACS Nano, 2014.
      [BibTeX] [Link]

      @inproceedings{27671912,
      title = {Visualizing and quantifying charge distributions correlated to threshold voltage shifts in lateral organic transistors.},
      author = {{Thomas J. Dawidczyk} and {Josué F Martínez Hardigree} and {G. Johns} and {R. Ozgun} and {Olivia Alley} and {A. Andreou} and {N. Marković} and {H. Katz}},
      year = 2014,
      month = {2},
      booktitle = {ACS Nano},
      url = {https://www.semanticscholar.org/paper/38222721fd352cfa963ad362ab9cf8b0b2af03a6},
      }

2354. S. Volkova and David Yarowsky, “Improving Gender Prediction of Social Media Users via Weighted Annotator Rationales.” 2014.
      [BibTeX] [Link]

      @inproceedings{7103997,
      title = {Improving Gender Prediction of Social Media Users via Weighted Annotator Rationales},
      author = {{S. Volkova} and {David Yarowsky}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b373fcdda89f71eb100499e4ddbbd18468fb2fac},
      }

2355. E. Hasler, Phil Blunsom, Philipp Koehn, and B. Haddow, “Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26-30, 2014, Gothenburg, Sweden.” 2014.
      [BibTeX] [Link]

      @inproceedings{165014213,
      title = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26-30, 2014, Gothenburg, Sweden},
      author = {{E. Hasler} and {Phil Blunsom} and {Philipp Koehn} and {B. Haddow}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c041ae038e4b2dd70af332346bb1b4057c736e9f},
      }

2356. Nagaraj R. Mahajan, N. Mesgarani, and H. Hermansky, “Principal components of auditory spectro-temporal receptive fields,” in Interspeech, 2014.
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      @inproceedings{19894829,
      title = {Principal components of auditory spectro-temporal receptive fields},
      author = {{Nagaraj R. Mahajan} and {N. Mesgarani} and {H. Hermansky}},
      year = 2014,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/022cbdf787bfbc0b59c03912833dc5b5be9eeb02},
      }

2357. Adrian Benton, Jay DeYoung, Adam R. Teichert, Stephen Mayhew, Mark Dredze, Benjamin Van Durme, and Max Thomas, “Faster ( and Better ) Entity Linking with Cascades.” 2014.
      [BibTeX] [Link]

      @inproceedings{15885284,
      title = {Faster ( and Better ) Entity Linking with Cascades},
      author = {{Adrian Benton} and {Jay DeYoung} and {Adam R. Teichert} and {Stephen Mayhew} and {Mark Dredze} and {Benjamin Van Durme} and {Max Thomas}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/04cc3e2947f6183d4ae6959be13544ebd799a8f0},
      }

2358. Germán Sanchis-Trilles, Vicente Alabau, C. Buck, M. Carl, F. Casacuberta, Mercedes García-Martínez, Ulrich Germann, J. González-Rubio, Robin L. Hill, Philipp Koehn, Luis A. Leiva, B. Mesa-Lao, Daniel Ortiz-Martínez, Herve Saint-Amand, Chara Tsoukala, and E. Vidal, “Interactive Translation Prediction vs. Conventional Post-editing in Practice: A Study with the CasMaCat Workbench.” 2014.
      [BibTeX] [Link]

      @inproceedings{4805499,
      title = {Interactive Translation Prediction vs. Conventional Post-editing in Practice: A Study with the CasMaCat Workbench},
      author = {{Germán Sanchis-Trilles} and {Vicente Alabau} and {C. Buck} and {M. Carl} and {F. Casacuberta} and {Mercedes García-Martínez} and {Ulrich Germann} and {J. González-Rubio} and {Robin L. Hill} and {Philipp Koehn} and {Luis A. Leiva} and {B. Mesa-Lao} and {Daniel Ortiz-Martínez} and {Herve Saint-Amand} and {Chara Tsoukala} and {E. Vidal}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c00aed9fc95b763665c40e92e2c0d12bfcdaf553},
      }

2359. Nadir Durrani, Philipp Koehn, Helmut Schmid, and Alexander M. Fraser, “COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin, Ireland.” 2014.
      [BibTeX] [Link]

      @inproceedings{164269994,
      title = {COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin, Ireland},
      author = {{Nadir Durrani} and {Philipp Koehn} and {Helmut Schmid} and {Alexander M. Fraser}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4f5209941e62dac3f6107621dc17edce1be9a2bf},
      }

2360. Shiliang Wang, Michael J. Paul, and Mark Dredze, “Exploring Health Topics in Chinese Social Media: An Analysis of Sina Weibo,” in AAAI Conference on Artificial Intelligence, 2014.
      [BibTeX] [Link]

      @inproceedings{8870697,
      title = {Exploring Health Topics in Chinese Social Media: An Analysis of Sina Weibo},
      author = {{Shiliang Wang} and {Michael J. Paul} and {Mark Dredze}},
      year = 2014,
      month = {6},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/46a0f4c55951a2be5a8f220414ba660e6aba49a3},
      }

2361. M. Freitag, J. Wuebker, S. Peitz, H. Ney, M. Huck, A. Birch, N. Durrani, P. Koehn, M. Mediani, I. Slawik, J. Niehues, E. Cho, A. Waibel, N. Bertoldi, M. Cettolo, and M. Federico, “Combined spoken language translation,” in Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign, Lake Tahoe, California, 2014, p. 57–64.
      [BibTeX] [Abstract] [Link]
      

      EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.

      @inproceedings{freitag-etal-2014-combined,
      title = "Combined spoken language translation",
      author = "Freitag, Markus and
      Wuebker, Joern and
      Peitz, Stephan and
      Ney, Hermann and
      Huck, Matthias and
      Birch, Alexandra and
      Durrani, Nadir and
      Koehn, Philipp and
      Mediani, Mohammed and
      Slawik, Isabel and
      Niehues, Jan and
      Cho, Eunach and
      Waibel, Alex and
      Bertoldi, Nicola and
      Cettolo, Mauro and
      Federico, Marcello",
      editor = {Federico, Marcello and
      St{\"u}ker, Sebastian and
      Yvon, Fran{\c{c}}ois},
      booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign",
      month = dec # " 4-5",
      year = "2014",
      address = "Lake Tahoe, California",
      url = "https://aclanthology.org/2014.iwslt-evaluation.7",
      pages = "57--64",
      abstract = "EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.",
      }

2362. E. Hasler, B. Haddow, and Philipp Koehn, “Proceedings of AMTA 2014.” 2014.
      [BibTeX] [Link]

      @inproceedings{186004143,
      title = {Proceedings of AMTA 2014},
      author = {{E. Hasler} and {B. Haddow} and {Philipp Koehn}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3505ea44ff35f2933ede991edb7d05da4b27237f},
      }

2363. Philipp Koehn, “What is a Better Translation? Reflections on Six Years of Running Evaluation Campaigns..” 2014.
      [BibTeX] [Link]

      @inproceedings{6016891,
      title = {What is a Better Translation? Reflections on Six Years of Running Evaluation Campaigns.},
      author = {{Philipp Koehn}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/444f184a18a69479bcd22814795a34e1536de638},
      }

2364. Glen A. Coppersmith, “Vertex nomination,” in Wiley Interdisciplinary Reviews: Computational Statistics, 2014.
      [BibTeX] [Link]

      @inproceedings{61805046,
      title = {Vertex nomination},
      author = {{Glen A. Coppersmith}},
      year = 2014,
      month = {3},
      booktitle = {Wiley Interdisciplinary Reviews: Computational Statistics},
      url = {https://www.semanticscholar.org/paper/b56d8113b1f5c8a248aac2fc47ef966a9c1bf582},
      }

2365. Alexander M. Fraser, Kevin Knight, Philipp Koehn, Helmut Schmid, and H. Uszkoreit, “Statistical Techniques for Translating to Morphologically Rich Languages (Dagstuhl Seminar 14061),” in Dagstuhl Reports, 2014.
      [BibTeX] [Link]

      @inproceedings{27461148,
      title = {Statistical Techniques for Translating to Morphologically Rich Languages (Dagstuhl Seminar 14061)},
      author = {{Alexander M. Fraser} and {Kevin Knight} and {Philipp Koehn} and {Helmut Schmid} and {H. Uszkoreit}},
      year = 2014,
      booktitle = {Dagstuhl Reports},
      url = {https://www.semanticscholar.org/paper/d3ddd5ece028fb83745773de871900efcffe833c},
      }

2366. Emine Merve Kaya and Mounya Elhilali, “Investigating bottom-up auditory attention,” in Frontiers in Human Neuroscience, 2014.
      [BibTeX] [Link]

      @inproceedings{8322208,
      title = {Investigating bottom-up auditory attention},
      author = {{Emine Merve Kaya} and {Mounya Elhilali}},
      year = 2014,
      month = {5},
      booktitle = {Frontiers in Human Neuroscience},
      url = {https://www.semanticscholar.org/paper/f18f40d243a1570971dd14cdb8b62ecc343e4ffd},
      }

2367. A. Abbasi, D. Adjeroh, Mark Dredze, Michael J. Paul, F. Zahedi, Huimin Zhao, N. Walia, H. Jain, Patrick Sanvanson, R. Shaker, Marco D. Huesch, Richard Beal, W. Zheng, M. Abate, and Arun Ross, “Social Media Analytics for Smart Health,” in IEEE Intelligent Systems, 2014.
      [BibTeX] [Link]

      @inproceedings{206468861,
      title = {Social Media Analytics for Smart Health},
      author = {{A. Abbasi} and {D. Adjeroh} and {Mark Dredze} and {Michael J. Paul} and {F. Zahedi} and {Huimin Zhao} and {N. Walia} and {H. Jain} and {Patrick Sanvanson} and {R. Shaker} and {Marco D. Huesch} and {Richard Beal} and {W. Zheng} and {M. Abate} and {Arun Ross}},
      year = 2014,
      month = {3},
      booktitle = {IEEE Intelligent Systems},
      url = {https://www.semanticscholar.org/paper/49caf06456ca595330040bfba38c22421c50c0d5},
      }

2368. M. Osborne and Mark Dredze, “Facebook, Twitter and Google Plus for Breaking News: Is There a Winner?,” in International Conference on Web and Social Media, 2014.
      [BibTeX] [Link]

      @inproceedings{31566441,
      title = {Facebook, Twitter and Google Plus for Breaking News: Is There a Winner?},
      author = {{M. Osborne} and {Mark Dredze}},
      year = 2014,
      month = {5},
      booktitle = {International Conference on Web and Social Media},
      url = {https://www.semanticscholar.org/paper/6c78a1358f38995462c7358d1679b817edf88b6c},
      }

2369. Philipp Koehn and Ulrich Germann, “Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation,” in The Association for Computational Linguistics, 2014.
      [BibTeX] [Link]

      @inproceedings{195952133,
      title = {Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation},
      author = {{Philipp Koehn} and {Ulrich Germann}},
      year = 2014,
      month = {4},
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/bec1c30940d59fe21f3a69082e12af95ed9b4796},
      }

2370. N. Durrani and P. Koehn, “Improving machine translation via triangulation and transliteration,” in Proceedings of the 17th Annual Conference of the European Association for Machine Translation, Dubrovnik, Croatia, 2014, p. 71–78.
      [BibTeX] [Link]

      @inproceedings{durrani-koehn-2014-improving,
      title = "Improving machine translation via triangulation and transliteration",
      author = "Durrani, Nadir and
      Koehn, Philipp",
      editor = "Cettolo, Mauro and
      Federico, Marcello and
      Specia, Lucia and
      Way, Andy",
      booktitle = "Proceedings of the 17th Annual Conference of the European Association for Machine Translation",
      month = jun # " 16-18",
      year = "2014",
      address = "Dubrovnik, Croatia",
      publisher = "European Association for Machine Translation",
      url = "https://aclanthology.org/2014.eamt-1.17",
      pages = "71--78",
      }

2371. Matt Post and Adam Lopez, “The Machine Translation Leaderboard,” in Prague Bulletin of Mathematical Linguistics, 2014.
      [BibTeX] [Link]

      @inproceedings{44359048,
      title = {The Machine Translation Leaderboard},
      author = {{Matt Post} and {Adam Lopez}},
      year = 2014,
      month = {9},
      booktitle = {Prague Bulletin of Mathematical Linguistics},
      url = {https://www.semanticscholar.org/paper/66fd35e51221e46c2696ccffc06bfcc0e99fee2d},
      }

2372. Yixin Gao, S. Vedula, C. Reiley, N. Ahmidi, Balakrishnan Varadarajan, Henry C. Lin, Lingling Tao, L. Zappella, B. Béjar, D. Yuh, C. C. Chen, R. Vidal, S. Khudanpur, and Gregory Hager, “JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling.” 2014.
      [BibTeX] [Link]

      @inproceedings{16185857,
      title = {JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling},
      author = {{Yixin Gao} and {S. Vedula} and {C. Reiley} and {N. Ahmidi} and {Balakrishnan Varadarajan} and {Henry C. Lin} and {Lingling Tao} and {L. Zappella} and {B. Béjar} and {D. Yuh} and {C. C. Chen} and {R. Vidal} and {S. Khudanpur} and {Gregory Hager}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/efe03a2940e09547bb15035d35e7e07ed59848bf},
      }

2373. Marko Tadić, Philipp Koehn, Johann Roturier, and Andy Way, “Proceedings of the 17th Annual conference of the European Association for Machine Translation, EAMT 2014, Dubrovnik, Croatia, June 16-18, 2014,” in European Association for Machine Translation Conferences/Workshops, 2014.
      [BibTeX] [Link]

      @inproceedings{65075068,
      title = {Proceedings of the 17th Annual conference of the European Association for Machine Translation, EAMT 2014, Dubrovnik, Croatia, June 16-18, 2014},
      author = {{Marko Tadić} and {Philipp Koehn} and {Johann Roturier} and {Andy Way}},
      year = 2014,
      booktitle = {European Association for Machine Translation Conferences/Workshops},
      url = {https://www.semanticscholar.org/paper/92e40989f356f26aea9a4c2c7a86be113493080c},
      }

2374. Chunxi Liu, A. Jansen, Guoguo Chen, Keith Kintzley, J. Trmal, and S. Khudanpur, “Low-resource open vocabulary keyword search using point process models,” in Interspeech, 2014.
      [BibTeX] [Link]

      @inproceedings{16958018,
      title = {Low-resource open vocabulary keyword search using point process models},
      author = {{Chunxi Liu} and {A. Jansen} and {Guoguo Chen} and {Keith Kintzley} and {J. Trmal} and {S. Khudanpur}},
      year = 2014,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/11796a16f69e15c277dd30f5324117c16053c045},
      }

2375. Jonathan Wintrode and S. Khudanpur, “Using Word Repetition to Improve Spoken Term Detection.” 2014.
      [BibTeX] [Link]

      @inproceedings{61355288,
      title = {Using Word Repetition to Improve Spoken Term Detection},
      author = {{Jonathan Wintrode} and {S. Khudanpur}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/ee1adde1aba63ee6e7180580858357e55056a0d2},
      }

2376. L. Shestopalova, Tamás Bohm, A. Bendixen, A. Andreou, Julio Georgiou, Guillaume Garreau, Botond Hajdu, S. Denham, and I. Winkler, “Stimulus: Incongruent/Moving/Joint.” 2014.
      [BibTeX] [Link]

      @inproceedings{146311108,
      title = {Stimulus: Incongruent/Moving/Joint},
      author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
      year = 2014,
      month = {3},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/83cf2445bf234b520b03dd7e47ea9282bc6512aa},
      }

2377. Mark Dredze, Renyuan Cheng, Michael J. Paul, and David A. Broniatowski, “HealthTweets.org: A Platform for Public Health Surveillance Using Twitter,” in AAAI Conference on Artificial Intelligence, 2014.
      [BibTeX] [Link]

      @inproceedings{61657545,
      title = {HealthTweets.org: A Platform for Public Health Surveillance Using Twitter},
      author = {{Mark Dredze} and {Renyuan Cheng} and {Michael J. Paul} and {David A. Broniatowski}},
      year = 2014,
      month = {6},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/3f83f06576ebfbee3978e6ad4a0a4cdc505f1753},
      }

2378. J. Ayers, B. Althouse, Morgan Johnson, Mark Dredze, and Joanna E. Cohen, “What’s the healthiest day?: Circaseptan (weekly) rhythms in healthy considerations.,” in American Journal of Preventive Medicine, 2014.
      [BibTeX] [Link]

      @inproceedings{22399217,
      title = {What's the healthiest day?: Circaseptan (weekly) rhythms in healthy considerations.},
      author = {{J. Ayers} and {B. Althouse} and {Morgan Johnson} and {Mark Dredze} and {Joanna E. Cohen}},
      year = 2014,
      month = {7},
      booktitle = {American Journal of Preventive Medicine},
      url = {https://www.semanticscholar.org/paper/13ae9734f3924e419832b6e474001e62a1efbcd2},
      }

2379. Thomas Schatz, Vijayaditya Peddinti, Xuan-Nga Cao, F. Bach, H. Hermansky, and Emmanuel Dupoux, “Evaluating speech features with the minimal-pair ABX task (II): resistance to noise,” in Interspeech, 2014.
      [BibTeX] [Link]

      @inproceedings{10121150,
      title = {Evaluating speech features with the minimal-pair ABX task (II): resistance to noise},
      author = {{Thomas Schatz} and {Vijayaditya Peddinti} and {Xuan-Nga Cao} and {F. Bach} and {H. Hermansky} and {Emmanuel Dupoux}},
      year = 2014,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/697ce1db9496067a4e7253c058bd83393d366949},
      }

2380. Michael J. Paul and Mark Dredze, “Discovering Health Topics in Social Media Using Topic Models,” in PLoS ONE, 2014.
      [BibTeX] [Link]

      @inproceedings{5868280,
      title = {Discovering Health Topics in Social Media Using Topic Models},
      author = {{Michael J. Paul} and {Mark Dredze}},
      year = 2014,
      month = {8},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/07f07fb7c5993029222ffa21619f226a4feb2e76},
      }

2381. P. Williams, Rico Sennrich, Maria Nadejde, Matthias Huck, E. Hasler, and Philipp Koehn, “Proceedings of the Ninth Workshop on Statistical Machine Translation, WMT@ACL 2014, June 26-27, 2014, Baltimore, Maryland, USA,” in WMT@ACL, 2014.
      [BibTeX] [Link]

      @inproceedings{39497762,
      title = {Proceedings of the Ninth Workshop on Statistical Machine Translation, WMT@ACL 2014, June 26-27, 2014, Baltimore, Maryland, USA},
      author = {{P. Williams} and {Rico Sennrich} and {Maria Nadejde} and {Matthias Huck} and {E. Hasler} and {Philipp Koehn}},
      year = 2014,
      month = {6},
      booktitle = {WMT@ACL},
      url = {https://www.semanticscholar.org/paper/2773f8d158d66d598cfaebe742d322bdc810998e},
      }

2382. Denham, L. I. Winkler, Tamás Bohm, A. Bendixen, A. Andreou, Julio Georgiou, Guillaume Garreau, Botond Hajdu, and L. Susan, “Demonstration #9: Synchrony test (out of phase).” 2014.
      [BibTeX] [Link]

      @inproceedings{186737415,
      title = {Demonstration #9: Synchrony test (out of phase)},
      author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3901b8342371370b47ae994c9729ab3ae104a784},
      }

2383. Francis Ferraro, Max Thomas, Matthew R. Gormley, Travis Wolfe, Craig Harman, and Benjamin Van Durme, “Concretely Annotated Corpora.” 2014.
      [BibTeX] [Link]

      @inproceedings{18170070,
      title = {Concretely Annotated Corpora},
      author = {{Francis Ferraro} and {Max Thomas} and {Matthew R. Gormley} and {Travis Wolfe} and {Craig Harman} and {Benjamin Van Durme}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/928ad9d973a85dfe39bb6c5ecd6633dbf6e2617f},
      }

2384. Philip Williams and Philipp Koehn, “Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra),” in The Association for Computational Linguistics, 2014.
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      @inproceedings{196099345,
      title = {Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)},
      author = {{Philip Williams} and {Philipp Koehn}},
      year = 2014,
      month = {4},
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/d41134d22705effdfc8b98ca0efaacaefec7b852},
      }

2385. Mark Dredze and Michael J. Paul, “Natural Language Processing for Health and Social Media.” 2014.
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      @inproceedings{17398392,
      title = {Natural Language Processing for Health and Social Media},
      author = {{Mark Dredze} and {Michael J. Paul}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a6dd47b4c1945b8e10c4d1e72f1dbb29ed35ab03},
      }

2386. Denham, L. I. Winkler, Tamás Bohm, A. Bendixen, A. Andreou, Julio Georgiou, Guillaume Garreau, Botond Hajdu, and L. Susan, “Demonstration #5: 2 balls standing (8Hz).” 2014.
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      @inproceedings{126421038,
      title = {Demonstration #5: 2 balls standing (8Hz)},
      author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e27ef3733d7881876f91646e14cb4f3d9aadf8fc},
      }

2387. Pegah Ghahremani, B. BabaAli, Daniel Povey, K. Riedhammer, J. Trmal, and S. Khudanpur, “A pitch extraction algorithm tuned for automatic speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.
      [BibTeX] [Link]

      @inproceedings{11688792,
      title = {A pitch extraction algorithm tuned for automatic speech recognition},
      author = {{Pegah Ghahremani} and {B. BabaAli} and {Daniel Povey} and {K. Riedhammer} and {J. Trmal} and {S. Khudanpur}},
      year = 2014,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/080644c599b813b920053a760d744d8abd615250},
      }

2388. Feipeng Li, P. S. Nidadavolu, and H. Hermansky, “A long, deep and wide artificial neural net for robust speech recognition in unknown noise,” in Interspeech, 2014.
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      @inproceedings{3217166,
      title = {A long, deep and wide artificial neural net for robust speech recognition in unknown noise},
      author = {{Feipeng Li} and {P. S. Nidadavolu} and {H. Hermansky}},
      year = 2014,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/08d2dfb9b685501d2ecb01f1bffc85207308f723},
      }

2389. Glen A. Coppersmith, Craig Harman, and Mark Dredze, “Measuring Post Traumatic Stress Disorder in Twitter,” in International Conference on Web and Social Media, 2014.
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      @inproceedings{14612598,
      title = {Measuring Post Traumatic Stress Disorder in Twitter},
      author = {{Glen A. Coppersmith} and {Craig Harman} and {Mark Dredze}},
      year = 2014,
      month = {5},
      booktitle = {International Conference on Web and Social Media},
      url = {https://www.semanticscholar.org/paper/ea24d85e059d7d1dc201bd0380c76caf1f78f1e4},
      }

2390. Xiaohui Zhang, J. Trmal, Daniel Povey, and S. Khudanpur, “Improving deep neural network acoustic models using generalized maxout networks,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.
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      @inproceedings{1129207,
      title = {Improving deep neural network acoustic models using generalized maxout networks},
      author = {{Xiaohui Zhang} and {J. Trmal} and {Daniel Povey} and {S. Khudanpur}},
      year = 2014,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/6182e4b5151aa27ceb75c94543e3f584c991e00f},
      }

2391. L. Shestopalova, Tamás Bohm, A. Bendixen, A. Andreou, J. Georgiou, Guillaume Garreau, Botond Hajdu, S. Denham, and I. Winkler, “Do audio-visual motion cues promote segregation of auditory streams?,” in Frontiers in Neuroscience, 2014.
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      @inproceedings{640769,
      title = {Do audio-visual motion cues promote segregation of auditory streams?},
      author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {J. Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
      year = 2014,
      month = {4},
      booktitle = {Frontiers in Neuroscience},
      url = {https://www.semanticscholar.org/paper/378b134a47091a44dccb8e886b63be78b33f644e},
      }

2392. Denham, L. I. Winkler, Tamás Bohm, A. Bendixen, A. Andreou, Julio Georgiou, Guillaume Garreau, Botond Hajdu, and L. Susan, “Demonstration #1: 1 ball standing (1Hz).” 2014.
      [BibTeX] [Link]

      @inproceedings{188651514,
      title = {Demonstration #1: 1 ball standing (1Hz)},
      author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/95ee2c21760910387899f99c8db5bc64439685b7},
      }

2393. J. Ayers, B. Althouse, and Mark Dredze, “Could behavioral medicine lead the web data revolution?,” in Journal of the American Medical Association (JAMA), 2014.
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      @inproceedings{205055790,
      title = {Could behavioral medicine lead the web data revolution?},
      author = {{J. Ayers} and {B. Althouse} and {Mark Dredze}},
      year = 2014,
      month = {4},
      booktitle = {Journal of the American Medical Association (JAMA)},
      url = {https://www.semanticscholar.org/paper/1f54cf05afa62ce9f959746b86a1dfffa45cf32b},
      }

2394. L. Shestopalova, Tamás Bohm, A. Bendixen, A. Andreou, Julio Georgiou, Guillaume Garreau, Botond Hajdu, S. Denham, and I. Winkler, “Demonstration #6: 2 balls moving (2.67Hz).” 2014.
      [BibTeX] [Link]

      @inproceedings{125906262,
      title = {Demonstration #6: 2 balls moving (2.67Hz)},
      author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/55cd72ac42a28fea8f5e58ce66dc72439d0d88a4},
      }

2395. E. Hasler, B. Haddow, and Philipp Koehn, “Adaptation for SMT using Distributional Profiles.” 2014.
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      @inproceedings{20700802,
      title = {Adaptation for SMT using Distributional Profiles},
      author = {{E. Hasler} and {B. Haddow} and {Philipp Koehn}},
      year = 2014,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0ba14263108d0c3e88cb82de211cb4c566167a6d},
      }

2396. L. E. Ellington, Dimitra Emmanouilidou, Mounya Elhilali, R. Gilman, J. Tielsch, M. A. Chavez, J. Marin-Concha, D. Figueroa, James E. West, and W. Checkley, “Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children,” in Lung, 2014.
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      @inproceedings{7943211,
      title = {Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children},
      author = {{L. E. Ellington} and {Dimitra Emmanouilidou} and {Mounya Elhilali} and {R. Gilman} and {J. Tielsch} and {M. A. Chavez} and {J. Marin-Concha} and {D. Figueroa} and {James E. West} and {W. Checkley}},
      year = 2014,
      month = {6},
      booktitle = {Lung},
      url = {https://www.semanticscholar.org/paper/dcd5e0c02149105bced3c1e44962efcc23d98e6d},
      }

2397. J. Kurtz, K. Wipke, S. Sprik, T. Ramsden, C. Ainscough, G. Saur, and Matt Post, “Early Fuel Cell Market Deployments: ARRA and Combined (IAA, DLA, ARRA); Quarter 3 2012 Composite Data Products.” 2014.
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      @inproceedings{113225783,
      title = {Early Fuel Cell Market Deployments: ARRA and Combined (IAA, DLA, ARRA); Quarter 3 2012 Composite Data Products},
      author = {{J. Kurtz} and {K. Wipke} and {S. Sprik} and {T. Ramsden} and {C. Ainscough} and {G. Saur} and {Matt Post}},
      year = 2014,
      month = {6},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/6cb8e38239f7501492dc03a65e56760b9002e390},
      }

2398. Sriram Ganapathy, Sri Harish Reddy Mallidi, and H. Hermansky, “Robust Feature Extraction Using Modulation Filtering of Autoregressive Models,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2014.
      [BibTeX] [Link]

      @inproceedings{16472807,
      title = {Robust Feature Extraction Using Modulation Filtering of Autoregressive Models},
      author = {{Sriram Ganapathy} and {Sri Harish Reddy Mallidi} and {H. Hermansky}},
      year = 2014,
      month = {8},
      booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
      url = {https://www.semanticscholar.org/paper/852f0eff84ea211a40522f6dd3bf8ec73bc3f0d8},
      }

2399. L. Shuai and Mounya Elhilali, “Task-dependent neural representations of salient events in dynamic auditory scenes,” in Frontiers in Neuroscience, 2014.
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      @inproceedings{2765949,
      title = {Task-dependent neural representations of salient events in dynamic auditory scenes},
      author = {{L. Shuai} and {Mounya Elhilali}},
      year = 2014,
      month = {7},
      booktitle = {Frontiers in Neuroscience},
      url = {https://www.semanticscholar.org/paper/8439c42440eebdea24eddf04b1e6b7abcae7d5bc},
      }

2400. B. Althouse, Jon-Patrick Allem, Matthew A. Childers, Mark Dredze, and J. Ayers, “Population health concerns during the United States’ Great Recession.,” in American Journal of Preventive Medicine, 2014.
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      @inproceedings{20264828,
      title = {Population health concerns during the United States' Great Recession.},
      author = {{B. Althouse} and {Jon-Patrick Allem} and {Matthew A. Childers} and {Mark Dredze} and {J. Ayers}},
      year = 2014,
      month = {2},
      booktitle = {American Journal of Preventive Medicine},
      url = {https://www.semanticscholar.org/paper/1c89fa07ada14df5d5388642d173c8e805f7388f},
      }

2401. G. Kumar, Y. Cao, R. Cotterell, C. Callison-Burch, D. Povey, and S. Khudanpur, “Translations of the Callhome Egyptian Arabic corpus for conversational speech translation,” in Proceedings of the 11th International Workshop on Spoken Language Translation: Papers, Lake Tahoe, California, 2014, p. 244–248.
      [BibTeX] [Abstract] [Link]
      

      Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations.

      @inproceedings{kumar-etal-2014-translations,
      title = "Translations of the Callhome {E}gyptian {A}rabic corpus for conversational speech translation",
      author = "Kumar, Gaurav and
      Cao, Yuan and
      Cotterell, Ryan and
      Callison-Burch, Chris and
      Povey, Daniel and
      Khudanpur, Sanjeev",
      editor = {Federico, Marcello and
      St{\"u}ker, Sebastian and
      Yvon, Fran{\c{c}}ois},
      booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Papers",
      month = dec # " 4-5",
      year = "2014",
      address = "Lake Tahoe, California",
      url = "https://aclanthology.org/2014.iwslt-papers.13",
      pages = "244--248",
      abstract = "Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations.",
      }

2402. Jonathan Wintrode and S. Khudanpur, “Limited resource term detection for effective topic identification of speech,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014.
      [BibTeX] [Link]

      @inproceedings{6872247,
      title = {Limited resource term detection for effective topic identification of speech},
      author = {{Jonathan Wintrode} and {S. Khudanpur}},
      year = 2014,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/d39a86f6a64a143735e6f6e9a6f618cfe6fb06f2},
      }

2403. Guoguo Chen, Oguz Yilmaz, J. Trmal, Daniel Povey, and S. Khudanpur, “Using proxies for OOV keywords in the keyword search task,” in 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013.
      [BibTeX] [Link]

      @inproceedings{17237888,
      title = {Using proxies for OOV keywords in the keyword search task},
      author = {{Guoguo Chen} and {Oguz Yilmaz} and {J. Trmal} and {Daniel Povey} and {S. Khudanpur}},
      year = 2013,
      month = {12},
      booktitle = {2013 IEEE Workshop on Automatic Speech Recognition and Understanding},
      url = {https://www.semanticscholar.org/paper/69952a2b100af2917abd64197d6d0a3d4b6d9c95},
      }

2404. David A. Broniatowski, Michael J. Paul, and Mark Dredze, “National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic,” in PLoS ONE, 2013.
      [BibTeX] [Link]

      @inproceedings{2346247,
      title = {National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic},
      author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
      year = 2013,
      month = {12},
      booktitle = {PLoS ONE},
      url = {https://www.semanticscholar.org/paper/687a6a77fcfe143198c311f734a0d68e00943ceb},
      }

2405. M. Mitchell, J. Aguilar, T. Wilson, and B. Van Durme, “Open Domain Targeted Sentiment,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 2013, p. 1643–1654.
      [BibTeX] [Link]

      @inproceedings{mitchell-etal-2013-open,
      title = "Open Domain Targeted Sentiment",
      author = "Mitchell, Margaret and
      Aguilar, Jacqui and
      Wilson, Theresa and
      Van Durme, Benjamin",
      editor = "Yarowsky, David and
      Baldwin, Timothy and
      Korhonen, Anna and
      Livescu, Karen and
      Bethard, Steven",
      booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2013",
      address = "Seattle, Washington, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D13-1171",
      pages = "1643--1654",
      }

2406. Jonathan Gordon and Benjamin Van Durme, “Reporting bias and knowledge acquisition,” in Conference on Automated Knowledge Base Construction, 2013.
      [BibTeX] [Link]

      @inproceedings{16567195,
      title = {Reporting bias and knowledge acquisition},
      author = {{Jonathan Gordon} and {Benjamin Van Durme}},
      year = 2013,
      month = {10},
      booktitle = {Conference on Automated Knowledge Base Construction},
      url = {https://www.semanticscholar.org/paper/cceb698cbbb828537f2f195fb70b6fdc586d3327},
      }

2407. S. Volkova, T. Wilson, and D. Yarowsky, “Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 2013, p. 1815–1827.
      [BibTeX] [Link]

      @inproceedings{volkova-etal-2013-exploring,
      title = "Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media",
      author = "Volkova, Svitlana and
      Wilson, Theresa and
      Yarowsky, David",
      editor = "Yarowsky, David and
      Baldwin, Timothy and
      Korhonen, Anna and
      Livescu, Karen and
      Bethard, Steven",
      booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2013",
      address = "Seattle, Washington, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D13-1187",
      pages = "1815--1827",
      }

2408. Kailash Patil, D. Pressnitzer, S. Shamma, and Mounya Elhilali, “Correction: Music in Our Ears: The Biological Bases of Musical Timbre Perception,” in PLoS Computational Biology, 2013.
      [BibTeX] [Link]

      @inproceedings{52867967,
      title = {Correction: Music in Our Ears: The Biological Bases of Musical Timbre Perception},
      author = {{Kailash Patil} and {D. Pressnitzer} and {S. Shamma} and {Mounya Elhilali}},
      year = 2013,
      month = {10},
      booktitle = {PLoS Computational Biology},
      url = {https://www.semanticscholar.org/paper/5c4cc9ab4262a01bc892cbe359da7ab8d3915470},
      }

2409. X. Yao, B. Van Durme, C. Callison-Burch, and P. Clark, “Semi-Markov Phrase-Based Monolingual Alignment,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 2013, p. 590–600.
      [BibTeX] [Link]

      @inproceedings{yao-etal-2013-semi,
      title = "Semi-{M}arkov Phrase-Based Monolingual Alignment",
      author = "Yao, Xuchen and
      Van Durme, Benjamin and
      Callison-Burch, Chris and
      Clark, Peter",
      editor = "Yarowsky, David and
      Baldwin, Timothy and
      Korhonen, Anna and
      Livescu, Karen and
      Bethard, Steven",
      booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2013",
      address = "Seattle, Washington, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D13-1056",
      pages = "590--600",
      }

2410. H. He, H. Daumé III, and J. Eisner, “Dynamic Feature Selection for Dependency Parsing,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, 2013, p. 1455–1464.
      [BibTeX] [Link]

      @InProceedings{he-daume-eisner-2013,
      aclid = "D13-1152",
      author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
      title = "Dynamic Feature Selection for Dependency Parsing",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "1455--1464",
      year = "2013",
      month = oct,
      address = "Seattle",
      URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2013",
      }

2411. J. R. Smith, H. Saint-Amand, M. Plamada, P. Koehn, C. Callison-Burch, and A. Lopez, “Dirt Cheap Web-Scale Parallel Text from the Common Crawl,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, p. 1374–1383.
      [BibTeX] [Link]

      @inproceedings{smith-etal-2013-dirt,
      title = "Dirt Cheap Web-Scale Parallel Text from the {C}ommon {C}rawl",
      author = "Smith, Jason R. and
      Saint-Amand, Herve and
      Plamada, Magdalena and
      Koehn, Philipp and
      Callison-Burch, Chris and
      Lopez, Adam",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-1135",
      pages = "1374--1383",
      }

2412. T. Wolfe, B. Van Durme, M. Dredze, N. Andrews, C. Beller, C. Callison-Burch, J. DeYoung, J. Snyder, J. Weese, T. Xu, and X. Yao, “PARMA: A Predicate Argument Aligner,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, Bulgaria, 2013, p. 63–68.
      [BibTeX] [Link]

      @inproceedings{wolfe-etal-2013-parma,
      title = "{PARMA}: A Predicate Argument Aligner",
      author = "Wolfe, Travis and
      Van Durme, Benjamin and
      Dredze, Mark and
      Andrews, Nicholas and
      Beller, Charley and
      Callison-Burch, Chris and
      DeYoung, Jay and
      Snyder, Justin and
      Weese, Jonathan and
      Xu, Tan and
      Yao, Xuchen",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-2012",
      pages = "63--68",
      }

2413. X. Yao, B. Van Durme, and P. Clark, “Automatic Coupling of Answer Extraction and Information Retrieval,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, Bulgaria, 2013, p. 159–165.
      [BibTeX] [Link]

      @inproceedings{yao-etal-2013-automatic,
      title = "Automatic Coupling of Answer Extraction and Information Retrieval",
      author = "Yao, Xuchen and
      Van Durme, Benjamin and
      Clark, Peter",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-2029",
      pages = "159--165",
      }

2414. M. Post, J. Ganitkevitch, L. Orland, J. Weese, Y. Cao, and C. Callison-Burch, “Joshua 5.0: Sparser, Better, Faster, Server,” in Proceedings of the Eighth Workshop on Statistical Machine Translation, Sofia, Bulgaria, 2013, p. 206–212.
      [BibTeX] [Link]

      @inproceedings{post-etal-2013-joshua,
      title = "{J}oshua 5.0: Sparser, Better, Faster, Server",
      author = "Post, Matt and
      Ganitkevitch, Juri and
      Orland, Luke and
      Weese, Jonathan and
      Cao, Yuan and
      Callison-Burch, Chris",
      editor = "Bojar, Ondrej and
      Buck, Christian and
      Callison-Burch, Chris and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Saint-Amand, Herve and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W13-2226",
      pages = "206--212",
      }

2415. A. Irvine and C. Callison-Burch, “Combining Bilingual and Comparable Corpora for Low Resource Machine Translation,” in Proceedings of the Eighth Workshop on Statistical Machine Translation, Sofia, Bulgaria, 2013, p. 262–270.
      [BibTeX] [Link]

      @inproceedings{irvine-callison-burch-2013-combining,
      title = "Combining Bilingual and Comparable Corpora for Low Resource Machine Translation",
      author = "Irvine, Ann and
      Callison-Burch, Chris",
      editor = "Bojar, Ondrej and
      Buck, Christian and
      Callison-Burch, Chris and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Saint-Amand, Herve and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W13-2233",
      pages = "262--270",
      }

2416. S. Volkova, T. Wilson, and D. Yarowsky, “Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual Twitter Streams,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, Bulgaria, 2013, p. 505–510.
      [BibTeX] [Link]

      @inproceedings{volkova-etal-2013-exploring-sentiment,
      title = "Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual {T}witter Streams",
      author = "Volkova, Svitlana and
      Wilson, Theresa and
      Yarowsky, David",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-2090",
      pages = "505--510",
      }

2417. X. Yao, B. Van Durme, C. Callison-Burch, and P. Clark, “A Lightweight and High Performance Monolingual Word Aligner,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, Bulgaria, 2013, p. 702–707.
      [BibTeX] [Link]

      @inproceedings{yao-etal-2013-lightweight,
      title = "A Lightweight and High Performance Monolingual Word Aligner",
      author = "Yao, Xuchen and
      Van Durme, Benjamin and
      Callison-Burch, Chris and
      Clark, Peter",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-2123",
      pages = "702--707",
      }

2418. S. Bergsma and B. Van Durme, “Using Conceptual Class Attributes to Characterize Social Media Users,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, 2013, p. 710–720.
      [BibTeX] [Link]

      @inproceedings{bergsma-van-durme-2013-using,
      title = "Using Conceptual Class Attributes to Characterize Social Media Users",
      author = "Bergsma, Shane and
      Van Durme, Benjamin",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-1070",
      pages = "710--720",
      }

2419. M. Post and S. Bergsma, “Explicit and Implicit Syntactic Features for Text Classification,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Sofia, Bulgaria, 2013, p. 866–872.
      [BibTeX] [Link]

      @inproceedings{post-bergsma-2013-explicit,
      title = "Explicit and Implicit Syntactic Features for Text Classification",
      author = "Post, Matt and
      Bergsma, Shane",
      editor = "Schuetze, Hinrich and
      Fung, Pascale and
      Poesio, Massimo",
      booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P13-2150",
      pages = "866--872",
      }

2420. O. Bojar, C. Buck, C. Callison-Burch, C. Federmann, B. Haddow, P. Koehn, C. Monz, M. Post, R. Soricut, and L. Specia, “Findings of the 2013 Workshop on Statistical Machine Translation,” in Proceedings of the Eighth Workshop on Statistical Machine Translation, Sofia, Bulgaria, 2013, p. 1–44.
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      @inproceedings{bojar-etal-2013-findings,
      title = "Findings of the 2013 {W}orkshop on {S}tatistical {M}achine {T}ranslation",
      author = "Bojar, Ond{\v{r}}ej and
      Buck, Christian and
      Callison-Burch, Chris and
      Federmann, Christian and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      editor = "Bojar, Ondrej and
      Buck, Christian and
      Callison-Burch, Chris and
      Haddow, Barry and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Saint-Amand, Herve and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
      month = aug,
      year = "2013",
      address = "Sofia, Bulgaria",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W13-2201",
      pages = "1--44",
      }

2421. F. Ferraro and J. Eisner, “A Virtual Manipulative for Learning Log-Linear Models,” in Proceedings of the Fourth Workshop on Teaching NLP and CL, Sofia, Bulgaria, 2013, p. 66–76.
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      @InProceedings{ferraro-eisner-2013,
      aclid = "W13-3411",
      author = "Francis Ferraro and Jason Eisner",
      title = "A Virtual Manipulative for Learning Log-Linear
      Models",
      booktitle = "Proceedings of the Fourth Workshop on Teaching NLP and
      CL",
      pages = "66--76",
      year = "2013",
      month = aug,
      address = "Sofia, Bulgaria",
      URL = "http://cs.jhu.edu/~jason/papers/#ferraro-eisner-2013",
      }

2422. P. Littell, L. Levin, J. Eisner, and D. Radev, “Introducing Computational Concepts in a Linguistics Olympiad,” in Proceedings of the Fourth Workshop on Teaching NLP and CL, Sofia, Bulgaria, 2013, p. 18–26.
      [BibTeX] [Link]

      @InProceedings{littell-et-al-2013,
      aclid = "W13-3403",
      author = "Patrick Littell and Lori Levin and Jason Eisner and
      Dragomir Radev",
      title = "Introducing Computational Concepts in a Linguistics
      Olympiad",
      booktitle = "Proceedings of the Fourth Workshop on Teaching NLP and
      CL",
      pages = "18--26",
      year = "2013",
      month = aug,
      address = "Sofia, Bulgaria",
      URL = "http://cs.jhu.edu/~jason/papers/#littell-et-al-2013",
      }

2423. M. Gormley and J. Eisner, “Nonconvex Global Optimization for Latent-Variable Models,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Sofia, Bulgaria, 2013, p. 444–454.
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      @InProceedings{gormley-eisner-2013,
      aclid = "P13-1044",
      author = "Matthew Gormley and Jason Eisner",
      title = "Nonconvex Global Optimization for Latent-Variable
      Models",
      booktitle = "Proceedings of the 51st Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "444--454",
      year = "2013",
      month = aug,
      address = "Sofia, Bulgaria",
      URL = "http://cs.jhu.edu/~jason/papers/#gormley-eisner-2013",
      }

2424. A. Lamb, M. J. Paul, and M. Dredze, “Separating Fact from Fear: Tracking Flu Infections on Twitter,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 789–795.
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      @inproceedings{lamb-etal-2013-separating,
      title = "Separating Fact from Fear: Tracking Flu Infections on {T}witter",
      author = "Lamb, Alex and
      Paul, Michael J. and
      Dredze, Mark",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1097",
      pages = "789--795",
      }

2425. X. Yao, B. Van Durme, C. Callison-Burch, and P. Clark, “Answer Extraction as Sequence Tagging with Tree Edit Distance,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 858–867.
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      @inproceedings{yao-etal-2013-answer,
      title = "Answer Extraction as Sequence Tagging with Tree Edit Distance",
      author = "Yao, Xuchen and
      Van Durme, Benjamin and
      Callison-Burch, Chris and
      Clark, Peter",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1106",
      pages = "858--867",
      }

2426. J. Snyder, R. Knowles, M. Dredze, M. Gormley, and T. Wolfe, “Topic Models and Metadata for Visualizing Text Corpora,” in Proceedings of the 2013 NAACL HLT Demonstration Session, Atlanta, Georgia, 2013, p. 5–9.
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      @inproceedings{snyder-etal-2013-topic,
      title = "Topic Models and Metadata for Visualizing Text Corpora",
      author = "Snyder, Justin and
      Knowles, Rebecca and
      Dredze, Mark and
      Gormley, Matthew and
      Wolfe, Travis",
      editor = "Dyer, Chris and
      Higgins, Derrick",
      booktitle = "Proceedings of the 2013 {NAACL} {HLT} Demonstration Session",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-3002",
      pages = "5--9",
      }

2427. M. Joshi, M. Dredze, W. W. Cohen, and C. P. Rosé, “What’s in a Domain? Multi-Domain Learning for Multi-Attribute Data,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 685–690.
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      @inproceedings{joshi-etal-2013-whats,
      title = "What{'}s in a Domain? Multi-Domain Learning for Multi-Attribute Data",
      author = "Joshi, Mahesh and
      Dredze, Mark and
      Cohen, William W. and
      Ros{\'e}, Carolyn P.",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1080",
      pages = "685--690",
      }

2428. M. J. Paul and M. Dredze, “Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 168–178.
      [BibTeX] [Link]

      @inproceedings{paul-dredze-2013-drug,
      title = "Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models",
      author = "Paul, Michael J. and
      Dredze, Mark",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1017",
      pages = "168--178",
      }

2429. S. Bergsma, M. Dredze, B. Van Durme, T. Wilson, and D. Yarowsky, “Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 1010–1019.
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      @inproceedings{bergsma-etal-2013-broadly,
      title = "Broadly Improving User Classification via Communication-Based Name and Location Clustering on {T}witter",
      author = "Bergsma, Shane and
      Dredze, Mark and
      Van Durme, Benjamin and
      Wilson, Theresa and
      Yarowsky, David",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1121",
      pages = "1010--1019",
      }

2430. J. Ganitkevitch, B. Van Durme, and C. Callison-Burch, “PPDB: The Paraphrase Database,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 758–764.
      [BibTeX] [Link]

      @inproceedings{ganitkevitch-etal-2013-ppdb,
      title = "{PPDB}: The Paraphrase Database",
      author = "Ganitkevitch, Juri and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1092",
      pages = "758--764",
      }

2431. A. Irvine and C. Callison-Burch, “Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, 2013, p. 518–523.
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      @inproceedings{irvine-callison-burch-2013-supervised,
      title = "Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals",
      author = "Irvine, Ann and
      Callison-Burch, Chris",
      editor = "Vanderwende, Lucy and
      Daum{\'e} III, Hal and
      Kirchhoff, Katrin",
      booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-1056",
      pages = "518--523",
      }

2432. M. Palmer, I. Titov, and S. Wu, “Semantic Role Labeling,” in NAACL HLT 2013 Tutorial Abstracts, Atlanta, Georgia, 2013, p. 10–12.
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      @inproceedings{palmer-etal-2013-semantic,
      title = "Semantic Role Labeling",
      author = "Palmer, Martha and
      Titov, Ivan and
      Wu, Shumin",
      editor = "Lin, Jimmy and
      Erk, Katrin",
      booktitle = "NAACL HLT 2013 Tutorial Abstracts",
      month = jun,
      year = "2013",
      address = "Atlanta, Georgia",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N13-4004",
      pages = "10--12",
      }

2433. J. Jiang, T. Moon, H. Daumé III, and J. Eisner, “Prioritized Asynchronous Belief Propagation,” in ICML Workshop on Inferning: Interactions between Inference and Learning, Atlanta, 2013.
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      @InProceedings{jiang-et-al-2013,
      author = "Jiarong Jiang and Taesun Moon and Hal {Daum\'{e} III}
      and Jason Eisner",
      title = "Prioritized Asynchronous Belief Propagation",
      booktitle = "ICML Workshop on Inferning: Interactions between
      Inference and Learning",
      note = "5 pages",
      year = "2013",
      month = jun,
      address = "Atlanta",
      URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2013",
      }

2434. Emine Merve Kaya and Mounya Elhilali, “Abnormality detection in noisy biosignals,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013.
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      @inproceedings{1758169,
      title = {Abnormality detection in noisy biosignals},
      author = {{Emine Merve Kaya} and {Mounya Elhilali}},
      year = 2013,
      month = {7},
      booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
      url = {https://www.semanticscholar.org/paper/28c2e91e1d8f303cc97ee05e4ab438fbebab5a05},
      }

2435. Matt Post, G. Kumar, Adam Lopez, Damianos G. Karakos, Chris Callison-Burch, and S. Khudanpur, “International Workshop on Spoken Language Translation (IWSLT 2013).” 2013.
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      @inproceedings{64631563,
      title = {International Workshop on Spoken Language Translation (IWSLT 2013)},
      author = {{Matt Post} and {G. Kumar} and {Adam Lopez} and {Damianos G. Karakos} and {Chris Callison-Burch} and {S. Khudanpur}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/05e928a860baa780df70e4fbd75c0dc460ebd11d},
      }

2436. Tomas Figliolia, Daniel R. Mendat, A. Russell, Thomas S. Murray, Ernst Nieburyk, R. Etienne-Cummings, and A. Andreou, “Auditory modulation of visual proto-object formation in a hierarchical auditory-visual saliency map,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{20162731,
      title = {Auditory modulation of visual proto-object formation in a hierarchical auditory-visual saliency map},
      author = {{Tomas Figliolia} and {Daniel R. Mendat} and {A. Russell} and {Thomas S. Murray} and {Ernst Nieburyk} and {R. Etienne-Cummings} and {A. Andreou}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/72d30db866851bf56321690cafa2a98d12b3cf40},
      }

2437. Joseph H. Lin, P. Pouliquen, and A. Andreou, “All digital programmable Gaussian pulse generator for ultra-wideband transmitter,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{14694094,
      title = {All digital programmable Gaussian pulse generator for ultra-wideband transmitter},
      author = {{Joseph H. Lin} and {P. Pouliquen} and {A. Andreou}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/afd123b749f1a22016501bb9efbd7b8f86316be7},
      }

2438. Emine Merve Kaya and Mounya Elhilali, “A model of auditory deviance detection,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{7739650,
      title = {A model of auditory deviance detection},
      author = {{Emine Merve Kaya} and {Mounya Elhilali}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/7da35847719e70b946079bbd761a734fc444a2e5},
      }

2439. Jeff Z. Ma, Bing Zhang, S. Matsoukas, Sri Harish Reddy Mallidi, Feipeng Li, and H. Hermansky, “Improvements in language identification on the RATS noisy speech corpus,” in Interspeech, 2013.
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      @inproceedings{37367537,
      title = {Improvements in language identification on the RATS noisy speech corpus},
      author = {{Jeff Z. Ma} and {Bing Zhang} and {S. Matsoukas} and {Sri Harish Reddy Mallidi} and {Feipeng Li} and {H. Hermansky}},
      year = 2013,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/7afff4a3104f7e93fb92649defe1c358862d87d0},
      }

2440. David Yarowsky, Timothy Baldwin, A. Korhonen, Karen Livescu, and Steven Bethard, “Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing,” in Conference on Empirical Methods in Natural Language Processing, 2013.
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      @inproceedings{64853495,
      title = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
      author = {{David Yarowsky} and {Timothy Baldwin} and {A. Korhonen} and {Karen Livescu} and {Steven Bethard}},
      year = 2013,
      booktitle = {Conference on Empirical Methods in Natural Language Processing},
      url = {https://www.semanticscholar.org/paper/edefb0d659077de8254c713cc201fe3cf5e78568},
      }

2441. S. Shamma, Mounya Elhilali, Ling Ma, C. Micheyl, A. Oxenham, D. Pressnitzer, Pingbo Yin, and Yanbo Xu, “Temporal coherence and the streaming of complex sounds.,” in Advances in Experimental Medicine and Biology, 2013.
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      @inproceedings{14717117,
      title = {Temporal coherence and the streaming of complex sounds.},
      author = {{S. Shamma} and {Mounya Elhilali} and {Ling Ma} and {C. Micheyl} and {A. Oxenham} and {D. Pressnitzer} and {Pingbo Yin} and {Yanbo Xu}},
      year = 2013,
      booktitle = {Advances in Experimental Medicine and Biology},
      url = {https://www.semanticscholar.org/paper/68ee363dd0ab8f4909d12e8a9bd7fdf7198ee2a5},
      }

2442. Mark Dredze, Michael J. Paul, S. Bergsma, and Hieu V. Tran, “Carmen: A Twitter Geolocation System with Applications to Public Health.” 2013.
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      @inproceedings{17988849,
      title = {Carmen: A Twitter Geolocation System with Applications to Public Health},
      author = {{Mark Dredze} and {Michael J. Paul} and {S. Bergsma} and {Hieu V. Tran}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9bc46fb12f2c7fae0e9e56e734e6efb9ca07fd98},
      }

2443. R. Ozgun, H. Katz, and A. Andreou, “Organic diode implementations in configurable architectures and temperature sensors,” in 2013 Microsystems for Measurement and Instrumentation: Fulfilling the Promise (MAMNA), 2013.
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      @inproceedings{30083992,
      title = {Organic diode implementations in configurable architectures and temperature sensors},
      author = {{R. Ozgun} and {H. Katz} and {A. Andreou}},
      year = 2013,
      month = {5},
      booktitle = {2013 Microsystems for Measurement and Instrumentation: Fulfilling the Promise (MAMNA)},
      url = {https://www.semanticscholar.org/paper/8c99056b72e9c431450ac04b3629b12495d81da0},
      }

2444. Adam Lopez and Matt Post, “Beyond bitext: Five open problems in machine translation.” 2013.
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      @inproceedings{196114312,
      title = {Beyond bitext: Five open problems in machine translation},
      author = {{Adam Lopez} and {Matt Post}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9f306fe3d1848f8c14c710afd46daa17c2bf8af8},
      }

2445. Thomas S. Murray, P. Pouliquen, and A. Andreou, “Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and.” 2013.
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      @inproceedings{12931494,
      title = {Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and},
      author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
      year = 2013,
      month = {3},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/03c0a941a1364d59e94c550375222e61fcb1cd8d},
      }

2446. Tomas Figliolia and A. Andreou, “Representation of temporal coherence: CHAINS algorithm and FPGA implementation,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{24095543,
      title = {Representation of temporal coherence: CHAINS algorithm and FPGA implementation},
      author = {{Tomas Figliolia} and {A. Andreou}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/083ad5dd283be19f45b1988c7e95f178016d7903},
      }

2447. Thomas Schatz, Vijayaditya Peddinti, F. Bach, A. Jansen, H. Hermansky, and Emmanuel Dupoux, “Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline,” in Interspeech, 2013.
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      @inproceedings{216147164,
      title = {Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline},
      author = {{Thomas Schatz} and {Vijayaditya Peddinti} and {F. Bach} and {A. Jansen} and {H. Hermansky} and {Emmanuel Dupoux}},
      year = 2013,
      month = {8},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/6d683f57a1498676090965141b8962b1e7b0fb4a},
      }

2448. Sri Harish Reddy Mallidi, Sriram Ganapathy, and H. Hermansky, “Robust speaker recognition using spectro-temporal autoregressive models,” in Interspeech, 2013.
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      @inproceedings{14519020,
      title = {Robust speaker recognition using spectro-temporal autoregressive models},
      author = {{Sri Harish Reddy Mallidi} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2013,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/5bdc663d3e65356c5b2b143858d7b0f495647489},
      }

2449. Oldrich Plchot, S. Matsoukas, P. Matejka, N. Dehak, Jeff Z. Ma, Sandro Cumani, O. Glembek, H. Hermansky, Sri Harish Reddy Mallidi, N. Mesgarani, R. Schwartz, Mehdi Soufifar, Z. Tan, Samuel Thomas, Bing Zhang, and Xinhui Zhou, “Developing a speaker identification system for the DARPA RATS project,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{1791325,
      title = {Developing a speaker identification system for the DARPA RATS project},
      author = {{Oldrich Plchot} and {S. Matsoukas} and {P. Matejka} and {N. Dehak} and {Jeff Z. Ma} and {Sandro Cumani} and {O. Glembek} and {H. Hermansky} and {Sri Harish Reddy Mallidi} and {N. Mesgarani} and {R. Schwartz} and {Mehdi Soufifar} and {Z. Tan} and {Samuel Thomas} and {Bing Zhang} and {Xinhui Zhou}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/f00bea11eed756c3ae90dae416945d8708155caf},
      }

2450. Matt Post and D. Gildea, “Bayesian Tree Substitution Grammars as a Usage-based Approach,” in Language and Speech, 2013.
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      title = {Bayesian Tree Substitution Grammars as a Usage-based Approach},
      author = {{Matt Post} and {D. Gildea}},
      year = 2013,
      month = {9},
      booktitle = {Language and Speech},
      url = {https://www.semanticscholar.org/paper/6e1c3999af289c2aeb2dbe0f156fde7dae9cd361},
      }

2451. H. Hermansky, “Long, Deep and Wide Artificial Neural Nets for Dealing with Unexpected Noise in Machine Recognition of Speech,” in International Conference on Text, Speech and Dialogue, 2013.
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      @inproceedings{34957630,
      title = {Long, Deep and Wide Artificial Neural Nets for Dealing with Unexpected Noise in Machine Recognition of Speech},
      author = {{H. Hermansky}},
      year = 2013,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/dfeb0fdb6735d3b23a04d5c315c2f92b4004f7f2},
      }

2452. Francis Ferraro, Benjamin Van Durme, and Yanif Ahmad, “Evaluating Progress in Probabilistic Programming through Topic Models.” 2013.
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      @inproceedings{15678666,
      title = {Evaluating Progress in Probabilistic Programming through Topic Models},
      author = {{Francis Ferraro} and {Benjamin Van Durme} and {Yanif Ahmad}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/584779cfaf2e32b462d604ecd2bba01bd4e7a149},
      }

2453. S. Dura-Bernal, Guillaume Garreau, J. Georgiou, A. Andreou, S. Denham, and T. Wennekers, “Multimodal Integration of Micro-Doppler Sonar and auditory signals for Behavior Classification with convolutional Networks,” in International Journal of Neural Systems, 2013.
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      @inproceedings{91844,
      title = {Multimodal Integration of Micro-Doppler Sonar and auditory signals for Behavior Classification with convolutional Networks},
      author = {{S. Dura-Bernal} and {Guillaume Garreau} and {J. Georgiou} and {A. Andreou} and {S. Denham} and {T. Wennekers}},
      year = 2013,
      month = {8},
      booktitle = {International Journal of Neural Systems},
      url = {https://www.semanticscholar.org/paper/98259ddcfb6e054d516de404f8b0de5d442f1420},
      }

2454. A. Andreou, Thomas S. Murray, and P. Pouliquen, “Signal to symbol converters: Overview, opportunities and challenges,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{16372867,
      title = {Signal to symbol converters: Overview, opportunities and challenges},
      author = {{A. Andreou} and {Thomas S. Murray} and {P. Pouliquen}},
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      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/7a142dddd7c15a3915f8079822c11cdabb3c1dcd},
      }

2455. Damianos G. Karakos, Mark Dredze, and S. Khudanpur, “Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation,” in arXiv.org, 2013.
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      @inproceedings{15808834,
      title = {Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation},
      author = {{Damianos G. Karakos} and {Mark Dredze} and {S. Khudanpur}},
      year = 2013,
      month = {3},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/860a3390dd415290981591a5158ac6dc602d8a5f},
      }

2456. Guoguo Chen, S. Khudanpur, Daniel Povey, J. Trmal, David Yarowsky, and Oguz Yilmaz, “Quantifying the value of pronunciation lexicons for keyword search in lowresource languages,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{2808498,
      title = {Quantifying the value of pronunciation lexicons for keyword search in lowresource languages},
      author = {{Guoguo Chen} and {S. Khudanpur} and {Daniel Povey} and {J. Trmal} and {David Yarowsky} and {Oguz Yilmaz}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/95c439ccffd9ea069cd19a26933c03b2b4aa7197},
      }

2457. D. Rao, Paul McNamee, and Mark Dredze, “Entity Linking: Finding Extracted Entities in a Knowledge Base,” in Multi-source, Multilingual Information Extraction and Summarization, 2013.
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      @inproceedings{6420241,
      title = {Entity Linking: Finding Extracted Entities in a Knowledge Base},
      author = {{D. Rao} and {Paul McNamee} and {Mark Dredze}},
      year = 2013,
      booktitle = {Multi-source, Multilingual Information Extraction and Summarization},
      url = {https://www.semanticscholar.org/paper/35d4af572e687228a8dd2241f85d7a833fcf5e5d},
      }

2458. Mounya Elhilali, “Bayesian inference in auditory scenes,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013.
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      @inproceedings{15718743,
      title = {Bayesian inference in auditory scenes},
      author = {{Mounya Elhilali}},
      year = 2013,
      month = {7},
      booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
      url = {https://www.semanticscholar.org/paper/2b71d33dffbc5eef24fe5f1464b4a5db77c7f235},
      }

2459. Tamás Bohm, L. Shestopalova, A. Bendixen, A. Andreou, J. Georgiou, Guillaume Garreau, P. Pouliquen, A. Cassidy, S. Denham, and I. Winkler, “The role of perceived source location in auditory stream segregation: Separation affects sound organization, common fate does not,” in Learning & Perception, 2013.
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      @inproceedings{54210602,
      title = {The role of perceived source location in auditory stream segregation: Separation affects sound organization, common fate does not},
      author = {{Tamás Bohm} and {L. Shestopalova} and {A. Bendixen} and {A. Andreou} and {J. Georgiou} and {Guillaume Garreau} and {P. Pouliquen} and {A. Cassidy} and {S. Denham} and {I. Winkler}},
      year = 2013,
      month = {6},
      booktitle = {Learning & Perception},
      url = {https://www.semanticscholar.org/paper/d085354dfe17f80bdd52435896c63faf019641ff},
      }

2460. Keith Kintzley, A. Jansen, and H. Hermansky, “Text-to-speech inspired duration modeling for improved whole-word acoustic models,” in Interspeech, 2013.
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      @inproceedings{14384242,
      title = {Text-to-speech inspired duration modeling for improved whole-word acoustic models},
      author = {{Keith Kintzley} and {A. Jansen} and {H. Hermansky}},
      year = 2013,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/e70051d7a9ce795e22f78e5b807108f6627c31db},
      }

2461. Guillaume Garreau, Eleni Proxenou, A. Andreou, and J. Georgiou, “Person localization through ground vibrations using a sand-scorpion inspired spiking neural network,” in Annual Conference on Information Sciences and Systems, 2013.
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      @inproceedings{2683521,
      title = {Person localization through ground vibrations using a sand-scorpion inspired spiking neural network},
      author = {{Guillaume Garreau} and {Eleni Proxenou} and {A. Andreou} and {J. Georgiou}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/99669fcc500dfaf71212ca746692094fc44c174e},
      }

2462. Samuel Thomas, M. Seltzer, Kenneth Ward Church, and H. Hermansky, “Deep neural network features and semi-supervised training for low resource speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{16548630,
      title = {Deep neural network features and semi-supervised training for low resource speech recognition},
      author = {{Samuel Thomas} and {M. Seltzer} and {Kenneth Ward Church} and {H. Hermansky}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/23bd8ab0ec8c6a79d3c501daa7bb1284cd2c7849},
      }

2463. Jonathan Gordon and Benjamin Van Durme, “Reporting Bias and Knowledge Extraction.” 2013.
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      @inproceedings{86766538,
      title = {Reporting Bias and Knowledge Extraction},
      author = {{Jonathan Gordon} and {Benjamin Van Durme}},
      year = 2013,
      month = {6},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/46e123a8ba5d4277b935c97529d48f2e678a9b45},
      }

2464. Michael J. Paul, Byron C. Wallace, and Mark Dredze, “What Affects Patient (Dis)satisfaction? Analyzing Online Doctor Ratings with a Joint Topic-Sentiment Model,” in AAAI Conference on Artificial Intelligence, 2013.
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      @inproceedings{10786550,
      title = {What Affects Patient (Dis)satisfaction? Analyzing Online Doctor Ratings with a Joint Topic-Sentiment Model},
      author = {{Michael J. Paul} and {Byron C. Wallace} and {Mark Dredze}},
      year = 2013,
      month = {6},
      booktitle = {AAAI Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/a23628e9d88cacafc35454ba77047f3de2e69f86},
      }

2465. David Etter, Francis Ferraro, Ryan Cotterell, Olivia Buzek, and Benjamin Van Durme, “Nerit: Named Entity Recognition for Informal Text.” 2013.
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      @inproceedings{14935035,
      title = {Nerit: Named Entity Recognition for Informal Text},
      author = {{David Etter} and {Francis Ferraro} and {Ryan Cotterell} and {Olivia Buzek} and {Benjamin Van Durme}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5920903e1c2c7ea165ad84a8fe6dbafdd586cbe7},
      }

2466. Carolina Parada, Mark Dredze, A. Sethy, and A. Rastrow, “Sub-Lexical and Contextual Modeling of Out-of-Vocabulary Words in Speech Recognition.” 2013.
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      @inproceedings{9011125,
      title = {Sub-Lexical and Contextual Modeling of Out-of-Vocabulary Words in Speech Recognition},
      author = {{Carolina Parada} and {Mark Dredze} and {A. Sethy} and {A. Rastrow}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/06988c9227cd8328ce54fc243c77797d40976020},
      }

2467. Kailash Patil and Mounya Elhilali, “Task-driven attentional mechanisms for auditory scene recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{18693156,
      title = {Task-driven attentional mechanisms for auditory scene recognition},
      author = {{Kailash Patil} and {Mounya Elhilali}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a634074eabf17f29414fac0497fd38522777d277},
      }

2468. Sridhar Krishna Nemala, Kailash Patil, and Mounya Elhilali, “A Multistream Feature Framework Based on Bandpass Modulation Filtering for Robust Speech Recognition,” in IEEE Transactions on Audio, Speech, and Language Processing, 2013.
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      @inproceedings{512926,
      title = {A Multistream Feature Framework Based on Bandpass Modulation Filtering for Robust Speech Recognition},
      author = {{Sridhar Krishna Nemala} and {Kailash Patil} and {Mounya Elhilali}},
      year = 2013,
      month = {2},
      booktitle = {IEEE Transactions on Audio, Speech, and Language Processing},
      url = {https://www.semanticscholar.org/paper/99858bad1dcdadcb75c3a6a3f34bc4cc178bfccf},
      }

2469. Mounya Elhilali, S. Shamma, J. Simon, and J. Fritz, “A linear systems view to the concept of STRFs.” 2013.
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      @inproceedings{210169840,
      title = {A linear systems view to the concept of STRFs},
      author = {{Mounya Elhilali} and {S. Shamma} and {J. Simon} and {J. Fritz}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/51224996f4ccf98b7d371af6add04965fb1bfa66},
      }

2470. Thomas S. Murray, P. Pouliquen, and A. Andreou, “Design of configurable chipping sequence generator for high-speed parallel samplers,” in Electronics Letters, 2013.
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      @inproceedings{62711320,
      title = {Design of configurable chipping sequence generator for high-speed parallel samplers},
      author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
      year = 2013,
      month = {7},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/eeda41683e5fc2b3d051e734272ab7577b6d3488},
      }

2471. Michael A. Carlin and Mounya Elhilali, “Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds,” in PLoS Comput. Biol., 2013.
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      @inproceedings{5541073,
      title = {Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds},
      author = {{Michael A. Carlin} and {Mounya Elhilali}},
      year = 2013,
      month = {3},
      booktitle = {PLoS Comput. Biol.},
      url = {https://www.semanticscholar.org/paper/8f76f5349b56e978757844d2a012cd263bc85699},
      }

2472. Kailash Patil and Mounya Elhilali, “MULTIRESOLUTION AUDITORY REPRESENTATIONS FOR SCENE CLASS IFICATION.” 2013.
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      @inproceedings{16069287,
      title = {MULTIRESOLUTION AUDITORY REPRESENTATIONS FOR SCENE CLASS IFICATION},
      author = {{Kailash Patil} and {Mounya Elhilali}},
      year = 2013,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/032f134770a3ef46c1b1f757cc86877a83f5d040},
      }

2473. M. Post, G. Kumar, A. Lopez, D. Karakos, C. Callison-Burch, and S. Khudanpur, “Improved speech-to-text translation with the Fisher and Callhome Spanish-English speech translation corpus,” in Proceedings of the 10th International Workshop on Spoken Language Translation: Papers, Heidelberg, Germany, 2013.
      [BibTeX] [Abstract] [Link]
      

      Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon{‘}s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.

      @inproceedings{post-etal-2013-improved,
      title = "Improved speech-to-text translation with the Fisher and Callhome {S}panish-{E}nglish speech translation corpus",
      author = "Post, Matt and
      Kumar, Gaurav and
      Lopez, Adam and
      Karakos, Damianos and
      Callison-Burch, Chris and
      Khudanpur, Sanjeev",
      editor = "Zhang, Joy Ying",
      booktitle = "Proceedings of the 10th International Workshop on Spoken Language Translation: Papers",
      month = dec # " 5-6",
      year = "2013",
      address = "Heidelberg, Germany",
      url = "https://aclanthology.org/2013.iwslt-papers.14",
      abstract = "Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon{'}s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.",
      }

2474. H. Hermansky, Jordan Cohen, and R. Stern, “Perceptual Properties of Current Speech Recognition Technology,” in Proceedings of the IEEE, 2013.
      [BibTeX] [Link]

      @inproceedings{17049673,
      title = {Perceptual Properties of Current Speech Recognition Technology},
      author = {{H. Hermansky} and {Jordan Cohen} and {R. Stern}},
      year = 2013,
      month = {7},
      booktitle = {Proceedings of the IEEE},
      url = {https://www.semanticscholar.org/paper/7d6c8044d155c0b9d6a74cab21f68c04c3b82616},
      }

2475. Vijayaditya Peddinti and H. Hermansky, “Filter-bank optimization for Frequency Domain Linear Prediction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{18178418,
      title = {Filter-bank optimization for Frequency Domain Linear Prediction},
      author = {{Vijayaditya Peddinti} and {H. Hermansky}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/1d94cb19b4ca1860ae59a5c27951b7470d2ed069},
      }

2476. F. Morabito, A. Andreou, and E. Chicca, “Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems,” in Neural Networks, 2013.
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      @inproceedings{8535893,
      title = {Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems},
      author = {{F. Morabito} and {A. Andreou} and {E. Chicca}},
      year = 2013,
      month = {9},
      booktitle = {Neural Networks},
      url = {https://www.semanticscholar.org/paper/3264cbe12b2305543a7c1c14c61d509d3c945fa6},
      }

2477. Ehsan Variani, Feipeng Li, and H. Hermansky, “Multi-stream recognition of noisy speech with performance monitoring,” in Interspeech, 2013.
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      @inproceedings{1932370,
      title = {Multi-stream recognition of noisy speech with performance monitoring},
      author = {{Ehsan Variani} and {Feipeng Li} and {H. Hermansky}},
      year = 2013,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/0042da1086b720eaa81f715ee0de93054e3bf811},
      }

2478. Shuai Huang and Glen A. Coppersmith, “Discriminative feature extraction for language identification,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
      [BibTeX] [Link]

      @inproceedings{15860900,
      title = {Discriminative feature extraction for language identification},
      author = {{Shuai Huang} and {Glen A. Coppersmith}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/fdbaa0d7a4a20fa54591d271cf85e645c9be3dde},
      }

2479. Tetsuji Ogawa, Feipeng Li, and H. Hermansky, “Stream selection and integration in multistream ASR using GMM-based performance monitoring,” in Interspeech, 2013.
      [BibTeX] [Link]

      @inproceedings{40634023,
      title = {Stream selection and integration in multistream ASR using GMM-based performance monitoring},
      author = {{Tetsuji Ogawa} and {Feipeng Li} and {H. Hermansky}},
      year = 2013,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/fdfbb1adb0a72c0abb48f367901829eb10df0096},
      }

2480. A. Jansen, Emmanuel Dupoux, S. Goldwater, Mark Johnson, S. Khudanpur, Kenneth Ward Church, Naomi H Feldman, H. Hermansky, Florian Metze, R. Rose, M. Seltzer, P. Clark, Ian McGraw, Balakrishnan Varadarajan, Erin D. Bennett, Benjamin Börschinger, J. Chiu, Ewan Dunbar, Abdellah Fourtassi, David Harwath, Chia-ying Lee, Keith D. Levin, A. Norouzian, Vijayaditya Peddinti, Rachael Richardson, Thomas Schatz, and Samuel Thomas, “A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
      [BibTeX] [Link]

      @inproceedings{1222906,
      title = {A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition},
      author = {{A. Jansen} and {Emmanuel Dupoux} and {S. Goldwater} and {Mark Johnson} and {S. Khudanpur} and {Kenneth Ward Church} and {Naomi H Feldman} and {H. Hermansky} and {Florian Metze} and {R. Rose} and {M. Seltzer} and {P. Clark} and {Ian McGraw} and {Balakrishnan Varadarajan} and {Erin D. Bennett} and {Benjamin Börschinger} and {J. Chiu} and {Ewan Dunbar} and {Abdellah Fourtassi} and {David Harwath} and {Chia-ying Lee} and {Keith D. Levin} and {A. Norouzian} and {Vijayaditya Peddinti} and {Rachael Richardson} and {Thomas Schatz} and {Samuel Thomas}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a2a0f0adb2b61ba21c8146b554b4416fb96d7aae},
      }

2481. D. Chakrabarty and Mounya Elhilali, “Predictive analysis of two tone stream segregation via extended Kalman filter,” in Annual Conference on Information Sciences and Systems, 2013.
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      title = {Predictive analysis of two tone stream segregation via extended Kalman filter},
      author = {{D. Chakrabarty} and {Mounya Elhilali}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/dd77dc5f35168fd91dd3fa0ccf0a46333d7cf291},
      }

2482. Sudarshan Ramenahalli, Daniel R. Mendat, S. Dura-Bernal, E. Culurciello, E. Niebur, and A. Andreou, “Audio-visual saliency map: Overview, basic models and hardware implementation,” in Annual Conference on Information Sciences and Systems, 2013.
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      author = {{Sudarshan Ramenahalli} and {Daniel R. Mendat} and {S. Dura-Bernal} and {E. Culurciello} and {E. Niebur} and {A. Andreou}},
      year = 2013,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/ef483498459b9fbb64b24b807ce3743535d919d1},
      }

2483. Thomas S. Murray, P. Pouliquen, and A. Andreou, “8-channel 20 kHz to 200 MHz Nyquist and compressive sampler in 0.5 μm CMOS,” in Electronics Letters, 2013.
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      title = {8-channel 20 kHz to 200 MHz Nyquist and compressive sampler in 0.5 μm CMOS},
      author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
      year = 2013,
      month = {1},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/261ac06da691014ee1c0206956fda2a749f2248f},
      }

2484. H. Hermansky, “Multistream Recognition of Speech: Dealing With Unknown Unknowns,” in Proceedings of the IEEE, 2013.
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      title = {Multistream Recognition of Speech: Dealing With Unknown Unknowns},
      author = {{H. Hermansky}},
      year = 2013,
      month = {2},
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      url = {https://www.semanticscholar.org/paper/3771538560a23d6233ce17015ccd97fa3d88ba2f},
      }

2485. N. Ahmidi, Yixin Gao, B. B. Haro, S. Vedula, S. Khudanpur, R. Vidal, and Gregory Hager, “String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013.
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      @inproceedings{20167467,
      title = {String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery},
      author = {{N. Ahmidi} and {Yixin Gao} and {B. B. Haro} and {S. Vedula} and {S. Khudanpur} and {R. Vidal} and {Gregory Hager}},
      year = 2013,
      month = {9},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/555530521a391df62edca0c602c1df05af1e2761},
      }

2486. H. Hermansky, Ehsan Variani, and Vijayaditya Peddinti, “Mean temporal distance: Predicting ASR error from temporal properties of speech signal,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{14800378,
      title = {Mean temporal distance: Predicting ASR error from temporal properties of speech signal},
      author = {{H. Hermansky} and {Ehsan Variani} and {Vijayaditya Peddinti}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/cd6486d6a52ee1225ddc49676c71875f65289397},
      }

2487. Dimitra Emmanouilidou and Mounya Elhilali, “Characterization of noise contaminations in lung sound recordings,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013.
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      @inproceedings{9360664,
      title = {Characterization of noise contaminations in lung sound recordings},
      author = {{Dimitra Emmanouilidou} and {Mounya Elhilali}},
      year = 2013,
      month = {7},
      booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
      url = {https://www.semanticscholar.org/paper/3c937c10101d1cd2dcc13c28057a6e95658433e3},
      }

2488. A. Jansen, Samuel Thomas, and H. Hermansky, “Weak top-down constraints for unsupervised acoustic model training,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{824237,
      title = {Weak top-down constraints for unsupervised acoustic model training},
      author = {{A. Jansen} and {Samuel Thomas} and {H. Hermansky}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/2888b31b5b79a39c8b224d5eb577dc21af6d7443},
      }

2489. Feipeng Li and H. Hermansky, “Effect of filter bandwidth and spectral sampling rate of analysis filterbank on automatic phoneme recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{7761542,
      title = {Effect of filter bandwidth and spectral sampling rate of analysis filterbank on automatic phoneme recognition},
      author = {{Feipeng Li} and {H. Hermansky}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/d85e2f43ad4df178ba92ab23e1d491e34410bf8a},
      }

2490. P. Clark, Sri Harish Reddy Mallidi, A. Jansen, and H. Hermansky, “Frequency offset correction in speech without detecting pitch,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013.
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      @inproceedings{6134861,
      title = {Frequency offset correction in speech without detecting pitch},
      author = {{P. Clark} and {Sri Harish Reddy Mallidi} and {A. Jansen} and {H. Hermansky}},
      year = 2013,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/9f7928802113eaf8157e8150e5dbf6f055540985},
      }

2491. S. Garimella and H. Hermansky, “Factor Analysis of Auto-Associative Neural Networks With Application in Speaker Verification,” in IEEE Transactions on Neural Networks and Learning Systems, 2013.
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      @inproceedings{8630565,
      title = {Factor Analysis of Auto-Associative Neural Networks With Application in Speaker Verification},
      author = {{S. Garimella} and {H. Hermansky}},
      year = 2013,
      month = {1},
      booktitle = {IEEE Transactions on Neural Networks and Learning Systems},
      url = {https://www.semanticscholar.org/paper/168d011e5d94dca30ba6094f0dac12a48deccb9a},
      }

2492. A. Cassidy, J. Georgiou, and A. Andreou, “Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization,” in Neural Networks, 2013.
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      @inproceedings{7701315,
      title = {Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization},
      author = {{A. Cassidy} and {J. Georgiou} and {A. Andreou}},
      year = 2013,
      month = {9},
      booktitle = {Neural Networks},
      url = {https://www.semanticscholar.org/paper/4ad2573355f5f957faaf792153aa782254cbb31d},
      }

2493. Mona T. Diab, Mark Dredze, S. Harabagiu, and Dragomir R. Radev, “Overview of the special session on semantics and sociolinguistics in social media.” 2012.
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      @inproceedings{64230074,
      title = {Overview of the special session on semantics and sociolinguistics in social media},
      author = {{Mona T. Diab} and {Mark Dredze} and {S. Harabagiu} and {Dragomir R. Radev}},
      year = 2012,
      month = {12},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4a9d1cb69246bd3530d1eef08eaf666aa6ee1fdb},
      }

2494. Sridhar Krishna Nemala, Kailash Patil, and Mounya Elhilali, “Recognizing the message and the messenger: biomimetic spectral analysis for robust speech and speaker recognition,” in International Journal of Speech Technology, 2012.
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      @inproceedings{6682247,
      title = {Recognizing the message and the messenger: biomimetic spectral analysis for robust speech and speaker recognition},
      author = {{Sridhar Krishna Nemala} and {Kailash Patil} and {Mounya Elhilali}},
      year = 2012,
      month = {12},
      booktitle = {International Journal of Speech Technology},
      url = {https://www.semanticscholar.org/paper/e407e38bc238cc08842c621b69213268f579b85e},
      }

2495. Michael J. Paul and Mark Dredze, “Factorial LDA: Sparse Multi-Dimensional Text Models,” in Neural Information Processing Systems, 2012.
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      @inproceedings{1860841,
      title = {Factorial LDA: Sparse Multi-Dimensional Text Models},
      author = {{Michael J. Paul} and {Mark Dredze}},
      year = 2012,
      month = {12},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/8fefa7f27808f578ee6b01443dce8a658201f0c8},
      }

2496. J. Jiang, A. Teichert, H. {Daumé III}, and J. Eisner, “Learned Prioritization for Trading Off Accuracy and Speed,” in Advances in Neural Information Processing Systems 25 (NeurIPS), Lake Tahoe, NV, 2012, p. 1331–1339.
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      @InProceedings{jiang-et-al-2012-nips,
      author = "Jiarong Jiang and Adam Teichert and Hal {Daum\'{e}
      III} and Jason Eisner",
      title = "Learned Prioritization for Trading Off Accuracy and
      Speed",
      booktitle = "Advances in Neural Information Processing Systems 25
      (NeurIPS)",
      pages = "1331--1339",
      year = "2012",
      month = dec,
      address = "Lake Tahoe, NV",
      URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2012-nips",
      }

2497. H. He, H. Daumé III, and J. Eisner, “Imitation Learning by Coaching,” in Advances in Neural Information Processing Systems 25 (NeurIPS), Lake Tahoe, NV, 2012, p. 3149–3157.
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      @InProceedings{he-daume-eisner-2012-nips,
      author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
      title = "Imitation Learning by Coaching",
      booktitle = "Advances in Neural Information Processing Systems 25
      (NeurIPS)",
      pages = "3149--3157",
      year = "2012",
      month = dec,
      address = "Lake Tahoe, NV",
      URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2012-nips",
      }

2498. V. Stoyanov and J. Eisner, “Easy-first Coreference Resolution,” in Proceedings of the 24th International Conference on Computational Linguistics (COLING), Mumbai, 2012, p. 2519–2534.
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      @InProceedings{stoyanov-eisner-2012-coling,
      aclid = "C12-1154",
      author = "Veselin Stoyanov and Jason Eisner",
      title = "Easy-first Coreference Resolution",
      booktitle = "Proceedings of the 24th International Conference on
      Computational Linguistics (COLING)",
      pages = "2519--2534",
      year = "2012",
      month = dec,
      address = "Mumbai",
      URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-coling",
      }

2499. Kailash Patil, D. Pressnitzer, S. Shamma, and Mounya Elhilali, “Music in Our Ears: The Biological Bases of Musical Timbre Perception,” in PLoS Comput. Biol., 2012.
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      @inproceedings{16290687,
      title = {Music in Our Ears: The Biological Bases of Musical Timbre Perception},
      author = {{Kailash Patil} and {D. Pressnitzer} and {S. Shamma} and {Mounya Elhilali}},
      year = 2012,
      month = {11},
      booktitle = {PLoS Comput. Biol.},
      url = {https://www.semanticscholar.org/paper/a9008fe9176219b88c323366c8306b856db3a4aa},
      }

2500. Dimitra Emmanouilidou, Kailash Patil, James E. West, and Mounya Elhilali, “A multiresolution analysis for detection of abnormal lung sounds,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012.
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      @inproceedings{7104064,
      title = {A multiresolution analysis for detection of abnormal lung sounds},
      author = {{Dimitra Emmanouilidou} and {Kailash Patil} and {James E. West} and {Mounya Elhilali}},
      year = 2012,
      month = {11},
      booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
      url = {https://www.semanticscholar.org/paper/18c997f4ca5c35181770acdcae8ff1d16ba6c5e3},
      }

2501. Mark Dredze, “Models for Mining Public Health Information from Social Media.” 2012.
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      @inproceedings{157114152,
      title = {Models for Mining Public Health Information from Social Media},
      author = {{Mark Dredze}},
      year = 2012,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9a0f64733f6f68217932c5e4c9742fe3a02f1728},
      }

2502. Sriram Ganapathy and H. Hermansky, “Temporal resolution analysis in frequency domain linear prediction.,” in Journal of the Acoustical Society of America, 2012.
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      @inproceedings{18646289,
      title = {Temporal resolution analysis in frequency domain linear prediction.},
      author = {{Sriram Ganapathy} and {H. Hermansky}},
      year = 2012,
      month = {10},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/e09f801058e6ec59fb4ca18b0b8a969a6c39a92a},
      }

2503. S. Garimella, Sri Harish Reddy Mallidi, and H. Hermansky, “Regularized Auto-Associative Neural Networks for Speaker Verification,” in IEEE Signal Processing Letters, 2012.
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      @inproceedings{17766644,
      title = {Regularized Auto-Associative Neural Networks for Speaker Verification},
      author = {{S. Garimella} and {Sri Harish Reddy Mallidi} and {H. Hermansky}},
      year = 2012,
      month = {10},
      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/e43a3e614f0d48fbc0bdd22801ab606f0e8fa9eb},
      }

2504. Alex Lamb, Michael J. Paul, and Mark Dredze, “Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics,” in AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text, 2012.
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      @inproceedings{1573045,
      title = {Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics},
      author = {{Alex Lamb} and {Michael J. Paul} and {Mark Dredze}},
      year = 2012,
      month = {10},
      booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
      url = {https://www.semanticscholar.org/paper/0aa787fb15a9a5aef417eed43b07418410f2cfaa},
      }

2505. Michael J. Paul and Mark Dredze, “Experimenting with Drugs (and Topic Models): Multi-Dimensional Exploration of Recreational Drug Discussions,” in AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text, 2012.
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      @inproceedings{3048394,
      title = {Experimenting with Drugs (and Topic Models): Multi-Dimensional Exploration of Recreational Drug Discussions},
      author = {{Michael J. Paul} and {Mark Dredze}},
      year = 2012,
      month = {10},
      booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
      url = {https://www.semanticscholar.org/paper/8d7a70d094901d2bd700ab23fa6d2e9b066bde46},
      }

2506. H. Korth, K. Strohbehn, Francisco Tejada, A. Andreou, J. Kitching, and S. Knappe, “Miniature Absolute Scalar Magnetometer Based on the Rubidium Isotope 87Rb.” 2012.
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      @inproceedings{222602753,
      title = {Miniature Absolute Scalar Magnetometer Based on the Rubidium Isotope 87Rb},
      author = {{H. Korth} and {K. Strohbehn} and {Francisco Tejada} and {A. Andreou} and {J. Kitching} and {S. Knappe}},
      year = 2012,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/31417310e9bd7abd7ee5275ca73cc2bf4b7b7495},
      }

2507. Atul Nakhasi, R. Passarella, Sarah G. Bell, Michael J. Paul, Mark Dredze, and P. Pronovost, “Malpractice and Malcontent: Analyzing Medical Complaints in Twitter,” in AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text, 2012.
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      @inproceedings{31827846,
      title = {Malpractice and Malcontent: Analyzing Medical Complaints in Twitter},
      author = {{Atul Nakhasi} and {R. Passarella} and {Sarah G. Bell} and {Michael J. Paul} and {Mark Dredze} and {P. Pronovost}},
      year = 2012,
      month = {10},
      booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
      url = {https://www.semanticscholar.org/paper/fd7064b1f28ada678659656311879ab7c377d04c},
      }

2508. N. W. Filardo and J. Eisner, “A Flexible Solver for Finite Arithmetic Circuits,” in Technical Communications of the 28th International Conference on Logic Programming (ICLP), Budapest, 2012, p. 425–438.
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      @InProceedings{filardo-eisner-2012-iclp,
      author = "Nathaniel Wesley Filardo and Jason Eisner",
      title = "A Flexible Solver for Finite Arithmetic Circuits",
      booktitle = "Technical Communications of the 28th International
      Conference on Logic Programming (ICLP)",
      editor = "Agostino Dovier and V\'{\i}tor Santos Costa",
      series = "Leibniz International Proceedings in Informatics
      (LIPIcs)",
      volume = "17",
      pages = "425--438",
      ISBN = "978-3-939897-43-9",
      year = "2012",
      month = sep,
      address = "Budapest",
      URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2012-iclp",
      }

2509. N. Andrews, J. Eisner, and M. Dredze, “Name Phylogeny: A Generative Model of String Variation,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, 2012, p. 344–355.
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      @inproceedings{andrews-etal-2012-name,
      title = "Name Phylogeny: A Generative Model of String Variation",
      author = "Andrews, Nicholas and
      Eisner, Jason and
      Dredze, Mark",
      editor = "Tsujii, Jun{'}ichi and
      Henderson, James and
      Pa{\c{s}}ca, Marius",
      booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
      month = jul,
      year = "2012",
      address = "Jeju Island, Korea",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D12-1032",
      pages = "344--355",
      }

2510. M. Joshi, M. Dredze, W. W. Cohen, and C. Rosé, “Multi-Domain Learning: When Do Domains Matter?,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, 2012, p. 1302–1312.
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      @inproceedings{joshi-etal-2012-multi,
      title = "Multi-Domain Learning: When Do Domains Matter?",
      author = "Joshi, Mahesh and
      Dredze, Mark and
      Cohen, William W. and
      Ros{\'e}, Carolyn",
      editor = "Tsujii, Jun{'}ichi and
      Henderson, James and
      Pa{\c{s}}ca, Marius",
      booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
      month = jul,
      year = "2012",
      address = "Jeju Island, Korea",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D12-1119",
      pages = "1302--1312",
      }

2511. B. Van Durme, “Streaming Analysis of Discourse Participants,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, 2012, p. 48–58.
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      @inproceedings{van-durme-2012-streaming,
      title = "Streaming Analysis of Discourse Participants",
      author = "Van Durme, Benjamin",
      editor = "Tsujii, Jun{'}ichi and
      Henderson, James and
      Pa{\c{s}}ca, Marius",
      booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
      month = jul,
      year = "2012",
      address = "Jeju Island, Korea",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D12-1005",
      pages = "48--58",
      }

2512. A. Rastrow, M. Dredze, and S. Khudanpur, “Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jeju Island, Korea, 2012, p. 175–183.
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      @inproceedings{rastrow-etal-2012-fast,
      title = "Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining",
      author = "Rastrow, Ariya and
      Dredze, Mark and
      Khudanpur, Sanjeev",
      editor = "Li, Haizhou and
      Lin, Chin-Yew and
      Osborne, Miles and
      Lee, Gary Geunbae and
      Park, Jong C.",
      booktitle = "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
      month = jul,
      year = "2012",
      address = "Jeju Island, Korea",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P12-1019",
      pages = "175--183",
      }

2513. V. Prabhakaran, M. Bloodgood, M. Diab, B. Dorr, L. Levin, C. D. Piatko, O. Rambow, and B. Van Durme, “Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing,” in Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, Jeju, Republic of Korea, 2012, p. 57–64.
      [BibTeX] [Link]

      @inproceedings{prabhakaran-etal-2012-statistical,
      title = "Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing",
      author = "Prabhakaran, Vinodkumar and
      Bloodgood, Michael and
      Diab, Mona and
      Dorr, Bonnie and
      Levin, Lori and
      Piatko, Christine D. and
      Rambow, Owen and
      Van Durme, Benjamin",
      editor = "Morante, Roser and
      Sporleder, Caroline",
      booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics",
      month = jul,
      year = "2012",
      address = "Jeju, Republic of Korea",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3807",
      pages = "57--64",
      }

2514. N. Andrews, J. Eisner, and M. Dredze, “Name Phylogeny: A Generative Model of String Variation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Jeju, Korea, 2012, p. 344–355.
      [BibTeX] [Link]

      @InProceedings{andrews-eisner-dredze-2012,
      aclid = "D12-1032",
      author = "Nicholas Andrews and Jason Eisner and Mark Dredze",
      title = "Name Phylogeny: {A} Generative Model of String
      Variation",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing and Computational Natural
      Language Learning (EMNLP-CoNLL)",
      pages = "344--355",
      year = "2012",
      month = jul,
      address = "Jeju, Korea",
      URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-dredze-2012",
      }

2515. A. Irvine, J. Weese, and C. Callison-Burch, “Processing Informal, Romanized Pakistani Text Messages,” in Proceedings of the Second Workshop on Language in Social Media, Montréal, Canada, 2012, p. 75–78.
      [BibTeX] [Link]

      @inproceedings{irvine-etal-2012-processing,
      title = "Processing Informal, {R}omanized {P}akistani Text Messages",
      author = "Irvine, Ann and
      Weese, Jonathan and
      Callison-Burch, Chris",
      editor = "Sood, Sara Owsley and
      Nagarajan, Meenakshi and
      Gamon, Michael",
      booktitle = "Proceedings of the Second Workshop on Language in Social Media",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-2109",
      pages = "75--78",
      }

2516. M. Post, C. Callison-Burch, and M. Osborne, “Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing,” in Proceedings of the Seventh Workshop on Statistical Machine Translation, Montréal, Canada, 2012, p. 401–409.
      [BibTeX] [Link]

      @inproceedings{post-etal-2012-constructing,
      title = "Constructing Parallel Corpora for Six {I}ndian Languages via Crowdsourcing",
      author = "Post, Matt and
      Callison-Burch, Chris and
      Osborne, Miles",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3152",
      pages = "401--409",
      }

2517. C. Napoles, M. Gormley, and B. Van Durme, “Annotated Gigaword,” in Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX), Montréal, Canada, 2012, p. 95–100.
      [BibTeX] [Link]

      @inproceedings{napoles-etal-2012-annotated,
      title = "Annotated {G}igaword",
      author = "Napoles, Courtney and
      Gormley, Matthew and
      Van Durme, Benjamin",
      editor = "Fan, James and
      Hoffman, Raphael and
      Kalyanpur, Aditya and
      Riedel, Sebastian and
      Suchanek, Fabian and
      Talukdar, Partha Pratim",
      booktitle = "Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction ({AKBC}-{WEKEX})",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3018",
      pages = "95--100",
      }

2518. R. Zbib, E. Malchiodi, J. Devlin, D. Stallard, S. Matsoukas, R. Schwartz, J. Makhoul, O. F. Zaidan, and C. Callison-Burch, “Machine Translation of Arabic Dialects,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 49–59.
      [BibTeX] [Link]

      @inproceedings{zbib-etal-2012-machine,
      title = "Machine Translation of {A}rabic Dialects",
      author = "Zbib, Rabih and
      Malchiodi, Erika and
      Devlin, Jacob and
      Stallard, David and
      Matsoukas, Spyros and
      Schwartz, Richard and
      Makhoul, John and
      Zaidan, Omar F. and
      Callison-Burch, Chris",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1006",
      pages = "49--59",
      }

2519. S. Green, N. Andrews, M. R. Gormley, M. Dredze, and C. D. Manning, “Entity Clustering Across Languages,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 60–69.
      [BibTeX] [Link]

      @inproceedings{green-etal-2012-entity,
      title = "Entity Clustering Across Languages",
      author = "Green, Spence and
      Andrews, Nicholas and
      Gormley, Matthew R. and
      Dredze, Mark and
      Manning, Christopher D.",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1007",
      pages = "60--69",
      }

2520. J. Ganitkevitch, Y. Cao, J. Weese, M. Post, and C. Callison-Burch, “Joshua 4.0: Packing, PRO, and Paraphrases,” in Proceedings of the Seventh Workshop on Statistical Machine Translation, Montréal, Canada, 2012, p. 283–291.
      [BibTeX] [Link]

      @inproceedings{ganitkevitch-etal-2012-joshua,
      title = "{J}oshua 4.0: Packing, {PRO}, and Paraphrases",
      author = "Ganitkevitch, Juri and
      Cao, Yuan and
      Weese, Jonathan and
      Post, Matt and
      Callison-Burch, Chris",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3134",
      pages = "283--291",
      }

2521. X. Yao, B. Van Durme, and C. Callison-Burch, “Expectations of Word Sense in Parallel Corpora,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 621–625.
      [BibTeX] [Link]

      @inproceedings{yao-etal-2012-expectations,
      title = "Expectations of Word Sense in Parallel Corpora",
      author = "Yao, Xuchen and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1078",
      pages = "621--625",
      }

2522. M. R. Gormley, M. Dredze, B. Van Durme, and J. Eisner, “Shared Components Topic Models,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 783–792.
      [BibTeX] [Link]

      @inproceedings{gormley-etal-2012-shared,
      title = "Shared Components Topic Models",
      author = "Gormley, Matthew R. and
      Dredze, Mark and
      Van Durme, Benjamin and
      Eisner, Jason",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1096",
      pages = "783--792",
      }

2523. C. Callison-Burch, P. Koehn, C. Monz, M. Post, R. Soricut, and L. Specia, “Findings of the 2012 Workshop on Statistical Machine Translation,” in Proceedings of the Seventh Workshop on Statistical Machine Translation, Montréal, Canada, 2012, p. 10–51.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2012-findings,
      title = "Findings of the 2012 Workshop on Statistical Machine Translation",
      author = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3102",
      pages = "10--51",
      }

2524. J. Weese, C. Callison-Burch, and A. Lopez, “Using Categorial Grammar to Label Translation Rules,” in Proceedings of the Seventh Workshop on Statistical Machine Translation, Montréal, Canada, 2012, p. 222–231.
      [BibTeX] [Link]

      @inproceedings{weese-etal-2012-using,
      title = "Using Categorial Grammar to Label Translation Rules",
      author = "Weese, Jonathan and
      Callison-Burch, Chris and
      Lopez, Adam",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Post, Matt and
      Soricut, Radu and
      Specia, Lucia",
      booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-3127",
      pages = "222--231",
      }

2525. A. Rastrow, S. Khudanpur, and M. Dredze, “Revisiting the Case for Explicit Syntactic Information in Language Models,” in Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, Montréal, Canada, 2012, p. 50–58.
      [BibTeX] [Link]

      @inproceedings{rastrow-etal-2012-revisiting,
      title = "Revisiting the Case for Explicit Syntactic Information in Language Models",
      author = "Rastrow, Ariya and
      Khudanpur, Sanjeev and
      Dredze, Mark",
      editor = "Ramabhadran, Bhuvana and
      Khudanpur, Sanjeev and
      Arisoy, Ebru",
      booktitle = "Proceedings of the {NAACL}-{HLT} 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for {HLT}",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-2707",
      pages = "50--58",
      }

2526. F. Ferraro, M. Post, and B. Van Durme, “Judging Grammaticality with Count-Induced Tree Substitution Grammars,” in Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, Montréal, Canada, 2012, p. 116–121.
      [BibTeX] [Link]

      @inproceedings{ferraro-etal-2012-judging,
      title = "Judging Grammaticality with Count-Induced Tree Substitution Grammars",
      author = "Ferraro, Francis and
      Post, Matt and
      Van Durme, Benjamin",
      editor = "Tetreault, Joel and
      Burstein, Jill and
      Leacock, Claudia",
      booktitle = "Proceedings of the Seventh Workshop on Building Educational Applications Using {NLP}",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-2013",
      pages = "116--121",
      }

2527. F. Ferraro, B. Van Durme, and M. Post, “Toward Tree Substitution Grammars with Latent Annotations,” in Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure, Montréal, Canada, 2012, p. 23–30.
      [BibTeX] [Link]

      @inproceedings{ferraro-etal-2012-toward,
      title = "Toward Tree Substitution Grammars with Latent Annotations",
      author = "Ferraro, Francis and
      Van Durme, Benjamin and
      Post, Matt",
      editor = "Cohn, Trevor and
      Blunsom, Phil and
      Graca, Joao",
      booktitle = "Proceedings of the {NAACL}-{HLT} Workshop on the Induction of Linguistic Structure",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W12-1904",
      pages = "23--30",
      }

2528. B. Kjersten and B. Van Durme, “Space Efficiencies in Discourse Modeling via Conditional Random Sampling,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 513–517.
      [BibTeX] [Link]

      @inproceedings{kjersten-van-durme-2012-space,
      title = "Space Efficiencies in Discourse Modeling via Conditional Random Sampling",
      author = "Kjersten, Brian and
      Van Durme, Benjamin",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1056",
      pages = "513--517",
      }

2529. S. Bergsma, M. Post, and D. Yarowsky, “Stylometric Analysis of Scientific Articles,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montréal, Canada, 2012, p. 327–337.
      [BibTeX] [Link]

      @inproceedings{bergsma-etal-2012-stylometric,
      title = "Stylometric Analysis of Scientific Articles",
      author = "Bergsma, Shane and
      Post, Matt and
      Yarowsky, David",
      editor = "Fosler-Lussier, Eric and
      Riloff, Ellen and
      Bangalore, Srinivas",
      booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N12-1033",
      pages = "327--337",
      }

2530. J. Jiang, A. Teichert, H. {Daumé III}, and J. Eisner, “Learned Prioritization for Trading Off Accuracy and Speed,” in ICML Workshop on Inferning: Interactions between Inference and Learning, Edinburgh, 2012.
      [BibTeX] [Link]

      @InProceedings{jiang-et-al-2012-icmlw,
      author = "Jiarong Jiang and Adam Teichert and Hal {Daum\'{e}
      III} and Jason Eisner",
      title = "Learned Prioritization for Trading Off Accuracy and
      Speed",
      booktitle = "ICML Workshop on Inferning: Interactions between
      Inference and Learning",
      note = "7 pages",
      year = "2012",
      month = jun,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2012-icmlw",
      }

2531. H. He, H. Daumé III, and J. Eisner, “Cost-Sensitive Dynamic Feature Selection,” in ICML Workshop on Inferning: Interactions between Inference and Learning, Edinburgh, 2012.
      [BibTeX] [Link]

      @InProceedings{he-daume-eisner-2012-icmlw,
      author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
      title = "Cost-Sensitive Dynamic Feature Selection",
      booktitle = "ICML Workshop on Inferning: Interactions between
      Inference and Learning",
      note = "6 pages",
      year = "2012",
      month = jun,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2012-icmlw",
      }

2532. V. Stoyanov and J. Eisner, “Fast and Accurate Prediction via Evidence-Specific MRF Structure,” in ICML Workshop on Inferning: Interactions between Inference and Learning, Edinburgh, 2012.
      [BibTeX] [Link]

      @InProceedings{stoyanov-eisner-2012-icmlw,
      author = "Veselin Stoyanov and Jason Eisner",
      title = "Fast and Accurate Prediction via Evidence-Specific
      {MRF} Structure",
      booktitle = "ICML Workshop on Inferning: Interactions between
      Inference and Learning",
      note = "6 pages",
      year = "2012",
      month = jun,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-icmlw",
      }

2533. M. R. Gormley, M. Dredze, B. {Van Durme}, and J. Eisner, “Shared Components Topic Models,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Montreal, 2012, p. 783–792.
      [BibTeX] [Link]

      @InProceedings{gormley-et-al-2012,
      aclid = "N12-1096",
      author = "Matthew R. Gormley and Mark Dredze and Benjamin {Van
      Durme} and Jason Eisner",
      title = "Shared Components Topic Models",
      booktitle = "Proceedings of the 2012 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "783--792",
      year = "2012",
      month = jun,
      address = "Montreal",
      URL = "http://cs.jhu.edu/~jason/papers/#gormley-et-al-2012",
      }

2534. M. Paul and J. Eisner, “Implicitly Intersecting Weighted Automata using Dual Decomposition,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Montreal, 2012, p. 232–242.
      [BibTeX] [Link]

      @InProceedings{paul-eisner-2012,
      aclid = "N12-1024",
      author = "Michael Paul and Jason Eisner",
      title = "Implicitly Intersecting Weighted Automata using Dual
      Decomposition",
      booktitle = "Proceedings of the 2012 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "232--242",
      year = "2012",
      month = jun,
      address = "Montreal",
      URL = "http://cs.jhu.edu/~jason/papers/#paul-eisner-2012",
      }

2535. J. Smith and J. Eisner, “Unsupervised Learning on an Approximate Corpus,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Montreal, 2012, p. 131–141.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2012,
      aclid = "N12-1014",
      author = "Jason Smith and Jason Eisner",
      title = "Unsupervised Learning on an Approximate Corpus",
      booktitle = "Proceedings of the 2012 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "131--141",
      year = "2012",
      month = jun,
      address = "Montreal",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2012",
      }

2536. V. Stoyanov and J. Eisner, “Minimum-Risk Training of Approximate CRF-Based NLP Systems,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Montreal, 2012, p. 120–130.
      [BibTeX] [Link]

      @InProceedings{stoyanov-eisner-2012-naacl,
      aclid = "N12-1013",
      author = "Veselin Stoyanov and Jason Eisner",
      title = "Minimum-Risk Training of Approximate {CRF}-Based {NLP}
      Systems",
      booktitle = "Proceedings of the 2012 Conference of the North
      American Chapter of the Association for Computational
      Linguistics: Human Language Technologies (NAACL-HLT)",
      pages = "120--130",
      year = "2012",
      month = jun,
      address = "Montreal",
      URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-naacl",
      }

2537. A. Klementiev, A. Irvine, C. Callison-Burch, and D. Yarowsky, “Toward Statistical Machine Translation without Parallel Corpora,” in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, 2012, p. 130–140.
      [BibTeX] [Link]

      @inproceedings{klementiev-etal-2012-toward,
      title = "Toward Statistical Machine Translation without Parallel Corpora",
      author = "Klementiev, Alexandre and
      Irvine, Ann and
      Callison-Burch, Chris and
      Yarowsky, David",
      editor = "Daelemans, Walter",
      booktitle = "Proceedings of the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
      month = apr,
      year = "2012",
      address = "Avignon, France",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E12-1014",
      pages = "130--140",
      }

2538. Scott Novotney, I. Bulyko, R. Schwartz, S. Khudanpur, and O. Kimball, “Semi-Supervised Methods for Improving Keyword Search of Unseen Terms,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{14059022,
      title = {Semi-Supervised Methods for Improving Keyword Search of Unseen Terms},
      author = {{Scott Novotney} and {I. Bulyko} and {R. Schwartz} and {S. Khudanpur} and {O. Kimball}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b45f6b3f29316cd761fd1f4c6e433135590d0ebc},
      }

2539. Mounya Elhilali and Sridhar Krishna Nemala, “Robust speech processing by humans and machines: the role of spectro-temporal modulations.” 2012.
      [BibTeX] [Link]

      @inproceedings{64247657,
      title = {Robust speech processing by humans and machines: the role of spectro-temporal modulations},
      author = {{Mounya Elhilali} and {Sridhar Krishna Nemala}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/323d426a62e4e30fbda05b1a02a64dcad22676b7},
      }

2540. Sriram Ganapathy and H. Hermansky, “Analysis of Temporal Resolution in Frequency Domain Linear Prediction,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{35374331,
      title = {Analysis of Temporal Resolution in Frequency Domain Linear Prediction},
      author = {{Sriram Ganapathy} and {H. Hermansky}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/6653892d726de4f9d3f5e14694e2ed68c5a3b746},
      }

2541. Lane Schwartz, Chris Callison-Burch, William Schuler, and Stephen T Wu, “Erratum to Incremental Syntactic Language Models for Phrase-based Translation.” 2012.
      [BibTeX] [Link]

      @inproceedings{8374741,
      title = {Erratum to Incremental Syntactic Language Models for Phrase-based Translation},
      author = {{Lane Schwartz} and {Chris Callison-Burch} and {William Schuler} and {Stephen T Wu}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/8144b43346d0d143d2ac7ffb251bbb050c698706},
      }

2542. H. Hirsch, Sriram Ganapathy, and H. Hermansky, “Comparison of Different Approaches for Speech Recognition in Hands-free Mode,” in ITG Conference on Speech Communication, 2012.
      [BibTeX] [Link]

      @inproceedings{31665887,
      title = {Comparison of Different Approaches for Speech Recognition in Hands-free Mode},
      author = {{H. Hirsch} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2012,
      month = {9},
      booktitle = {ITG Conference on Speech Communication},
      url = {https://www.semanticscholar.org/paper/465cc9e43e3bd5b32047f5b7a08bce4b4b0769ee},
      }

2543. J. Ganitkevitch, B. Van Durme, and C. Callison-Burch, “Monolingual Distributional Similarity for Text-to-Text Generation,” in *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), Montréal, Canada, 2012, p. 256–264.
      [BibTeX] [Link]

      @inproceedings{ganitkevitch-etal-2012-monolingual,
      title = "Monolingual Distributional Similarity for Text-to-Text Generation",
      author = "Ganitkevitch, Juri and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Agirre, Eneko and
      Bos, Johan and
      Diab, Mona and
      Manandhar, Suresh and
      Marton, Yuval and
      Yuret, Deniz",
      booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
      month = "7-8 " # jun,
      year = "2012",
      address = "Montr{\'e}al, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S12-1034",
      pages = "256--264",
      }

2544. H. Rostro-González, Guillaume Garreau, A. Andreou, J. Georgiou, J. H. Barrón-Zambrano, and C. Torres-Huitzil, “An FPGA-based approach for parameter estimation in spiking neural networks,” in 2012 IEEE International Symposium on Circuits and Systems, 2012.
      [BibTeX] [Link]

      @inproceedings{40918504,
      title = {An FPGA-based approach for parameter estimation in spiking neural networks},
      author = {{H. Rostro-González} and {Guillaume Garreau} and {A. Andreou} and {J. Georgiou} and {J. H. Barrón-Zambrano} and {C. Torres-Huitzil}},
      year = 2012,
      month = {5},
      booktitle = {2012 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/77e14a1f04ef133cebb10ab9e8162ac4204cbe8e},
      }

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2546. Puyang Xu, S. Khudanpur, M. Lehr, Emily Tucker Prud’hommeaux, N. Glenn, Damianos G. Karakos, Brian Roark, Kenji Sagae, M. Saraçlar, Izhak Shafran, D. Bikel, Chris Callison-Burch, Yuan Cao, Keith B. Hall, E. Hasler, Philipp Koehn, Adam Lopez, Matt Post, and Darcey Riley, “Continuous space discriminative language modeling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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2550. L. E. Ellington, R. Gilman, J. Tielsch, M. Steinhoff, D. Figueroa, Shalim Rodriguez, B. Caffo, B. Tracey, Mounya Elhilali, James E. West, and W. Checkley, “Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study,” in BMJ Open, 2012.
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2552. Emine Merve Kaya and Mounya Elhilali, “A temporal saliency map for modeling auditory attention,” in Annual Conference on Information Sciences and Systems, 2012.
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2553. Vinodkumar Prabhakaran, Michael Bloodgood, Mona T. Diab, B. Dorr, Lori S. Levin, C. Piatko, Owen Rambow, and Benjamin Van Durme, “from Rule-based Annotations and Crowdsourcing.” 2012.
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2555. Sridhar Krishna Nemala, D. Zotkin, R. Duraiswami, and Mounya Elhilali, “Biomimetic multi-resolution analysis for robust speaker recognition,” in EURASIP Journal on Audio, Speech, and Music Processing, 2012.
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2556. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Multilingual MLP features for low-resource LVCSR systems,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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2557. Kenji Sagae, M. Lehr, Emily Tucker Prud’hommeaux, Puyang Xu, N. Glenn, Damianos G. Karakos, S. Khudanpur, Brian Roark, M. Saraçlar, Izhak Shafran, D. Bikel, Chris Callison-Burch, Yuan Cao, Keith B. Hall, E. Hasler, Philipp Koehn, Adam Lopez, Matt Post, and Darcey Riley, “Hallucinated n-best lists for discriminative language modeling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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2558. Charbel G. Rizk, Joseph H. Lin, S. Kennerly, P. Pouliquen, A. Goldberg, and A. Andreou, “High-performance, event-driven, low-cost, and SWaP imaging sensor for hostile fire detection, homeland protection, and border security,” in Defense + Commercial Sensing, 2012.
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2559. Chris Callison-Burch and M. Osborne, “Shiva Temple ? Mahadevi Varma Ganges Penis History of India Vedas Meera General knowledge Sex positions World War II Vishnu Mohammed Rafi Global warming Sexual intercourse Mohandas Karamchand Gandhi Pollution Vagina Mother Teresa Solar energy Navbharat Times Terrorism Table.” 2012.
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2562. Thomas J. Dawidczyk, G. Johns, R. Ozgun, Olivia Alley, A. Andreou, N. Marković, and H. Katz, “Kelvin probe microscopic visualization of charge storage at polystyrene interfaces with pentacene and gold,” in Applied Physics Letters, 2012.
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2563. Sridhar Krishna Nemala and Mounya Elhilali, “Multilevel speech intelligibility for robust speaker recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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2565. Puyang Xu, Brian Roark, and S. Khudanpur, “Phrasal Cohort Based Unsupervised Discriminative Language Modeling,” in Interspeech, 2012.
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2567. A. Jansen, Samuel Thomas, and H. Hermansky, “Intrinsic Spectral Analysis for Zero and High Resource Speech Recognition,” in Interspeech, 2012.
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2568. Mounya Elhilali, “Auditory scene analysis: It’s all about expectations!.” 2012.
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2569. Sriram Ganapathy and H. Hermansky, “Robust Phoneme Recognition Using High Resolution Temporal Envelopes.” 2012.
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2570. Kailash Patil and Mounya Elhilali, “Goal-Oriented Auditory Scene Recognition,” in Interspeech, 2012.
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2571. Damianos G. Karakos, Brian Roark, Izhak Shafran, Kenji Sagae, M. Lehr, Emily Tucker Prud’hommeaux, Puyang Xu, N. Glenn, S. Khudanpur, M. Saraçlar, D. Bikel, Mark Dredze, Chris Callison-Burch, Yuan Cao, Keith B. Hall, E. Hasler, Philipp Koehn, Adam Lopez, Matt Post, and Darcey Riley, “INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association, Portland, Oregon, USA, September 9-13, 2012.” 2012.
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2572. M. Wolmetz and Mounya Elhilali, “Prior probabilities tune attentional bandwidth.” 2012.
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2573. Damianos G. Karakos, Brian Roark, Izhak Shafran, Kenji Sagae, M. Lehr, Emily Tucker Prud’hommeaux, Puyang Xu, N. Glenn, S. Khudanpur, M. Saraçlar, D. Bikel, Mark Dredze, Chris Callison-Burch, Yuan Cao, Keith B. Hall, E. Hasler, Philipp Koehn, Adam Lopez, Matt Post, and Darcey Riley, “Deriving conversation-based features from unlabeled speech for discriminative language modeling,” in Interspeech, 2012.
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2574. K. Crammer, Alex Kulesza, and Mark Dredze, “New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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2575. Keith Kintzley, A. Jansen, and H. Hermansky, “MAP Estimation of Whole-Word Acoustic Models with Dictionary Priors,” in Interspeech, 2012.
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2576. J. Anemüller, B. Caputo, H. Hermansky, F. Ohl, T. Pajdla, M. Pavel, L. Gool, R. Vogels, S. Wabnik, and D. Weinshall, “DIRAC: Detection and Identification of Rare Audio-Visual Events,” in Detection and Identification of Rare Audiovisual Cues, 2012.
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2577. S. Ikbal, Hemant Misra, H. Hermansky, and M. Magimai-Doss, “Phase AutoCorrelation (PAC) features for noise robust speech recognition,” in Speech Communication, 2012.
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2578. A. Jansen and Benjamin Van Durme, “Indexing Raw Acoustic Features for Scalable Zero Resource Search,” in Interspeech, 2012.
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2579. K. Crammer, Mark Dredze, and Fernando C Pereira, “Confidence-Weighted Linear Classification for Text Categorization,” in Journal of machine learning research, 2012.
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2580. Hoyoul Kong, Thomas J. Dawidczyk, R. Ozgun, A. Andreou, and H. Katz, “Printed Organic Electronic Sensors.” 2012.
      [BibTeX] [Link]

      @inproceedings{97153351,
      title = {Printed Organic Electronic Sensors},
      author = {{Hoyoul Kong} and {Thomas J. Dawidczyk} and {R. Ozgun} and {A. Andreou} and {H. Katz}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d7fa8dd424a8bf5338765ebc6dc2696c1b37e86d},
      }

2581. R. Passarella, Atul Nakhasi, Sarah G. Bell, Michael J. Paul, P. Pronovost, and Mark Dredze, “Twitter as a Source for Learning about Patient Safety Events,” in American Medical Informatics Association Annual Symposium, 2012.
      [BibTeX] [Link]

      @inproceedings{46321860,
      title = {Twitter as a Source for Learning about Patient Safety Events},
      author = {{R. Passarella} and {Atul Nakhasi} and {Sarah G. Bell} and {Michael J. Paul} and {P. Pronovost} and {Mark Dredze}},
      year = 2012,
      booktitle = {American Medical Informatics Association Annual Symposium},
      url = {https://www.semanticscholar.org/paper/d663e0a7f5475d8c4902a2fd31f14725cf2a2ef7},
      }

2582. Jonathan Weese, Chris Callison-Burch, and Adam Lopez, “Proceedings of the Seventh Workshop on Statistical Machine Translation,” in The Association for Computational Linguistics, 2012.
      [BibTeX] [Link]

      @inproceedings{208023170,
      title = {Proceedings of the Seventh Workshop on Statistical Machine Translation},
      author = {{Jonathan Weese} and {Chris Callison-Burch} and {Adam Lopez}},
      year = 2012,
      month = {6},
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/13bdb72e9aa1cb37db75478d0a2945db9c30d733},
      }

2583. Samuel Thomas, Sri Harish Reddy Mallidi, Sriram Ganapathy, and H. Hermansky, “Adaptation transforms of auto-associative neural networks as features for speaker verification,” in The Speaker and Language Recognition Workshop, 2012.
      [BibTeX] [Link]

      @inproceedings{13943470,
      title = {Adaptation transforms of auto-associative neural networks as features for speaker verification},
      author = {{Samuel Thomas} and {Sri Harish Reddy Mallidi} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2012,
      booktitle = {The Speaker and Language Recognition Workshop},
      url = {https://www.semanticscholar.org/paper/ac0ffb98b45ce9c0aed3f33a823e6c4ef4443290},
      }

2584. S. Khudanpur and A. Rastrow, “Practical and efficient incorporation of syntactic features into statistical language models.” 2012.
      [BibTeX] [Link]

      @inproceedings{35285188,
      title = {Practical and efficient incorporation of syntactic features into statistical language models},
      author = {{S. Khudanpur} and {A. Rastrow}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d425c2baa9a6c7cfcc9442f0c86e45f0cb2d9924},
      }

2585. D. Garcia-Romero, Xinhui Zhou, D. Zotkin, Balaji Vasan Srinivasan, Yuancheng Luo, Sriram Ganapathy, Samuel Thomas, Sridhar Krishna Nemala, Garimella S. V. S. Sivaram, Majid Mirbagheri, Sri Harish Reddy Mallidi, Thomas Janu, Padmanabhan Rajan, N. Mesgarani, Mounya Elhilali, H. Hermansky, S. Shamma, and R. Duraiswami, “The UMD-JHU 2011 speaker recognition system,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
      [BibTeX] [Link]

      @inproceedings{15722713,
      title = {The UMD-JHU 2011 speaker recognition system},
      author = {{D. Garcia-Romero} and {Xinhui Zhou} and {D. Zotkin} and {Balaji Vasan Srinivasan} and {Yuancheng Luo} and {Sriram Ganapathy} and {Samuel Thomas} and {Sridhar Krishna Nemala} and {Garimella S. V. S. Sivaram} and {Majid Mirbagheri} and {Sri Harish Reddy Mallidi} and {Thomas Janu} and {Padmanabhan Rajan} and {N. Mesgarani} and {Mounya Elhilali} and {H. Hermansky} and {S. Shamma} and {R. Duraiswami}},
      year = 2012,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/134b3e191c3ad3708228efeb7eddbb939b11badc},
      }

2586. H. Hermansky, Ehsan Variani, and Vijayaditya Peddinti, “METHOD FOR ASR PERFORMANCE PREDICTION BASED ON TEMPORAL PROPERTIES OF SPEECH SIGNAL.” 2012.
      [BibTeX] [Link]

      @inproceedings{14202272,
      title = {METHOD FOR ASR PERFORMANCE PREDICTION BASED ON TEMPORAL PROPERTIES OF SPEECH SIGNAL},
      author = {{H. Hermansky} and {Ehsan Variani} and {Vijayaditya Peddinti}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e02a91ff8da71e1cea63be17d1eaa8b3eabecfbd},
      }

2587. Nicholas Andrews, Jason Eisner, and Mark Dredze, “A Generative Model of String Variation.” 2012.
      [BibTeX] [Link]

      @inproceedings{61990873,
      title = {A Generative Model of String Variation},
      author = {{Nicholas Andrews} and {Jason Eisner} and {Mark Dredze}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/be5b25c42bc584a4d66dae90680a370ca50d6b1c},
      }

2588. Glen A. Coppersmith and C. Priebe, “Vertex Nomination via Content and Context,” in arXiv.org, 2012.
      [BibTeX] [Link]

      @inproceedings{18594435,
      title = {Vertex Nomination via Content and Context},
      author = {{Glen A. Coppersmith} and {C. Priebe}},
      year = 2012,
      month = {1},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/9e3a9be7a3779cbab7f13f486bdb0601af4bb973},
      }

2589. Arda Çelebi, Hasim Sak, Erinç Dikici, M. Saraçlar, M. Lehr, Emily Tucker Prud’hommeaux, Puyang Xu, N. Glenn, Damianos G. Karakos, S. Khudanpur, Brian Roark, Kenji Sagae, Izhak Shafran, D. Bikel, Chris Callison-Burch, Yuan Cao, Keith B. Hall, E. Hasler, Philipp Koehn, Adam Lopez, Matt Post, and Darcey Riley, “Semi-supervised discriminative language modeling for Turkish ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
      [BibTeX] [Link]

      @inproceedings{10732099,
      title = {Semi-supervised discriminative language modeling for Turkish ASR},
      author = {{Arda Çelebi} and {Hasim Sak} and {Erinç Dikici} and {M. Saraçlar} and {M. Lehr} and {Emily Tucker Prud'hommeaux} and {Puyang Xu} and {N. Glenn} and {Damianos G. Karakos} and {S. Khudanpur} and {Brian Roark} and {Kenji Sagae} and {Izhak Shafran} and {D. Bikel} and {Chris Callison-Burch} and {Yuan Cao} and {Keith B. Hall} and {E. Hasler} and {Philipp Koehn} and {Adam Lopez} and {Matt Post} and {Darcey Riley}},
      year = 2012,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/1d79d055cf9711944f14e1388a9d054cbe81ddd0},
      }

2590. Lingling Tao, Ehsan Elhamifar, S. Khudanpur, Gregory Hager, and R. Vidal, “Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation,” in International Conference on Information Processing in Computer-Assisted Interventions, 2012.
      [BibTeX] [Link]

      @inproceedings{17952638,
      title = {Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation},
      author = {{Lingling Tao} and {Ehsan Elhamifar} and {S. Khudanpur} and {Gregory Hager} and {R. Vidal}},
      year = 2012,
      month = {6},
      booktitle = {International Conference on Information Processing in Computer-Assisted Interventions},
      url = {https://www.semanticscholar.org/paper/c11126a78b85341f98f482377ab2913491bf8f46},
      }

2591. A. Cassidy and A. Andreou, “Beyond Amdahl’s Law: An Objective Function That Links Multiprocessor Performance Gains to Delay and Energy,” in IEEE transactions on computers, 2012.
      [BibTeX] [Link]

      @inproceedings{8714713,
      title = {Beyond Amdahl's Law: An Objective Function That Links Multiprocessor Performance Gains to Delay and Energy},
      author = {{A. Cassidy} and {A. Andreou}},
      year = 2012,
      month = {8},
      booktitle = {IEEE transactions on computers},
      url = {https://www.semanticscholar.org/paper/c4466acbd9ba0a2d2e139ef5a793b613aaf02b1e},
      }

2592. Y. Cao and S. Khudanpur, “Sample Selection for Large-scale MT Discriminative Training,” in Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA, 2012.
      [BibTeX] [Abstract] [Link]
      

      Discriminative training for MT usually involves numerous features and requires large-scale training set to reach reliable parameter estimation. Other than using the expensive human-labeled parallel corpora for training, semi-supervised methods have been proposed to generate huge amount of {“}hallucinated{”} data which relieves the data sparsity problem. However the large training set contains both good samples which are suitable for training and bad ones harmful to the training. How to select training samples from vast amount of data can greatly affect the training performance. In this paper we propose a method for selecting samples that are most suitable for discriminative training according to a criterion measuring the dataset quality. Our experimental results show that by adding samples to the training set selectively, we are able to exceed the performance of system trained with the same amount of samples selected randomly.

      @inproceedings{cao-khudanpur-2012-sample,
      title = "Sample Selection for Large-scale {MT} Discriminative Training",
      author = "Cao, Yuan and
      Khudanpur, Sanjeev",
      booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers",
      month = oct # " 28-" # nov # " 1",
      year = "2012",
      address = "San Diego, California, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2012.amta-papers.3",
      abstract = "Discriminative training for MT usually involves numerous features and requires large-scale training set to reach reliable parameter estimation. Other than using the expensive human-labeled parallel corpora for training, semi-supervised methods have been proposed to generate huge amount of {``}hallucinated{''} data which relieves the data sparsity problem. However the large training set contains both good samples which are suitable for training and bad ones harmful to the training. How to select training samples from vast amount of data can greatly affect the training performance. In this paper we propose a method for selecting samples that are most suitable for discriminative training according to a criterion measuring the dataset quality. Our experimental results show that by adding samples to the training set selectively, we are able to exceed the performance of system trained with the same amount of samples selected randomly.",
      }

2593. Benjamin Van Durme, “Jerboa: A Toolkit for Randomized and Streaming Algorithms.” 2012.
      [BibTeX] [Link]

      @inproceedings{11874569,
      title = {Jerboa: A Toolkit for Randomized and Streaming Algorithms},
      author = {{Benjamin Van Durme}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/13707dd6fa50b4ca6f9a2470284e4f0f45f33e2d},
      }

2594. Samuel Thomas, Sri Harish Reddy Mallidi, Thomas Janu, H. Hermansky, N. Mesgarani, Xinhui Zhou, S. Shamma, Tim Ng, Bing Zhang, L. Nguyen, and S. Matsoukas, “Acoustic and Data-driven Features for Robust Speech Activity Detection,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{14404779,
      title = {Acoustic and Data-driven Features for Robust Speech Activity Detection},
      author = {{Samuel Thomas} and {Sri Harish Reddy Mallidi} and {Thomas Janu} and {H. Hermansky} and {N. Mesgarani} and {Xinhui Zhou} and {S. Shamma} and {Tim Ng} and {Bing Zhang} and {L. Nguyen} and {S. Matsoukas}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b4bffa0abd34e3ff6ba0d3f4e305a81df65ec719},
      }

2595. K. Button, Nathaniel Broadbent Lee, Matt Post, and Alyssa Ann Proia, “A Novel Design to Canine and Feline Bone Healing Using External Fixation.” 2012.
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      @inproceedings{232823668,
      title = {A Novel Design to Canine and Feline Bone Healing Using External Fixation},
      author = {{K. Button} and {Nathaniel Broadbent Lee} and {Matt Post} and {Alyssa Ann Proia}},
      year = 2012,
      month = {4},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e39323c319fd1d3dc0fee037b995125a4f4f268b},
      }

2596. T. Stouraitis, Zhi-Pei Liang, Gary G. Yen, Gert Cauwenberghs, R. Etienne-Cummings, A. Andreou, Amine Bermak, Alison Burdett, Sandro Carrara, Krishnendu Chakrabarty, S. Chakrabartty, Jie Chen, T. Delbruck, Timothy J. Denison, S. DeWeerth, Emmanuel M. Drakakis, Roman Genov, Julius Georgiou, Edmund Y. L Am, Steffen Leonhardt, Yong Lian, Shih-Chii Liu, Wentai Liu, A. J. Mason, Tamás Roska, Rahul Sarpeshkar, M. Sawan, Kenneth L. Shepard, Bertram E. Shi, M. Stanaćević, and J. V. D. Spiegel, “IEEE Transactions on Biomedical Circuits and Systems,” in IEEE Transactions on Biomedical Circuits and Systems, 2012.
      [BibTeX] [Link]

      @inproceedings{263807607,
      title = {IEEE Transactions on Biomedical Circuits and Systems},
      author = {{T. Stouraitis} and {Zhi-Pei Liang} and {Gary G. Yen} and {Gert Cauwenberghs} and {R. Etienne-Cummings} and {A. Andreou} and {Amine Bermak} and {Alison Burdett} and {Sandro Carrara} and {Krishnendu Chakrabarty} and {S. Chakrabartty} and {Jie Chen} and {T. Delbruck} and {Timothy J. Denison} and {S. DeWeerth} and {Emmanuel M. Drakakis} and {Roman Genov} and {Julius Georgiou} and {Edmund Y. L Am} and {Steffen Leonhardt} and {Yong Lian} and {Shih-Chii Liu} and {Wentai Liu} and {A. J. Mason} and {Tamás Roska} and {Rahul Sarpeshkar} and {M. Sawan} and {Kenneth L. Shepard} and {Bertram E. Shi} and {M. Stanaćević} and {J. V. D. Spiegel}},
      year = 2012,
      booktitle = {IEEE Transactions on Biomedical Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/5224c9da3353cdec21724c67ebdc22fb63c9cc9c},
      }

2597. Samuel Thomas, Sriram Ganapathy, A. Jansen, and H. Hermansky, “Data-driven Posterior Features for Low Resource Speech Recognition Applications,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{7560412,
      title = {Data-driven Posterior Features for Low Resource Speech Recognition Applications},
      author = {{Samuel Thomas} and {Sriram Ganapathy} and {A. Jansen} and {H. Hermansky}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b93109dbfd2c441f8adacbe80994b3d47b0e988c},
      }

2598. Joseph H. Lin, P. Pouliquen, A. Andreou, A. Goldberg, and Charbel G. Rizk, “Flexible readout and integration sensor (FRIS): a bio-inspired, system-on-chip, event-based readout architecture,” in Defense + Commercial Sensing, 2012.
      [BibTeX] [Link]

      @inproceedings{110257846,
      title = {Flexible readout and integration sensor (FRIS): a bio-inspired, system-on-chip, event-based readout architecture},
      author = {{Joseph H. Lin} and {P. Pouliquen} and {A. Andreou} and {A. Goldberg} and {Charbel G. Rizk}},
      year = 2012,
      month = {5},
      booktitle = {Defense + Commercial Sensing},
      url = {https://www.semanticscholar.org/paper/2b7257e3fb6ac6cd91972beb4ba8826008192ecf},
      }

2599. A. Jansen, Benjamin Van Durme, and P. Clark, “The JHU-HLTCOE Spoken Web Search System for MediaEval 2012,” in MediaEval Benchmarking Initiative for Multimedia Evaluation, 2012.
      [BibTeX] [Link]

      @inproceedings{16622396,
      title = {The JHU-HLTCOE Spoken Web Search System for MediaEval 2012},
      author = {{A. Jansen} and {Benjamin Van Durme} and {P. Clark}},
      year = 2012,
      booktitle = {MediaEval Benchmarking Initiative for Multimedia Evaluation},
      url = {https://www.semanticscholar.org/paper/cf0b403c09c8634837abf66bb4390299a067a8c4},
      }

2600. Ehsan Variani and H. Hermansky, “Estimating Classifier Performance in Unknown Noise,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{14474861,
      title = {Estimating Classifier Performance in Unknown Noise},
      author = {{Ehsan Variani} and {H. Hermansky}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/cef44e00eea814721f947f59ff10dd9b1e3bb441},
      }

2601. Keith Kintzley, A. Jansen, Kenneth Ward Church, and H. Hermansky, “Inverting the Point Process Model for Fast Phonetic Keyword Search,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{1755828,
      title = {Inverting the Point Process Model for Fast Phonetic Keyword Search},
      author = {{Keith Kintzley} and {A. Jansen} and {Kenneth Ward Church} and {H. Hermansky}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b27412142aeedad7113eac939e953f1578c8c81e},
      }

2602. Antti-Veikko I. Rosti, E. Matusov, Jason R. Smith, Necip Fazil Ayan, Jason Eisner, Damianos G. Karakos, S. Khudanpur, Gregor Leusch, Zhifei Li, Spyros Matsoukas, H. Ney, R. Schwartz, Bing Zhang, and Jing Zheng, “Confusion Network Decoding for MT System Combination.” 2012.
      [BibTeX] [Link]

      @inproceedings{872519,
      title = {Confusion Network Decoding for MT System Combination},
      author = {{Antti-Veikko I. Rosti} and {E. Matusov} and {Jason R. Smith} and {Necip Fazil Ayan} and {Jason Eisner} and {Damianos G. Karakos} and {S. Khudanpur} and {Gregor Leusch} and {Zhifei Li} and {Spyros Matsoukas} and {H. Ney} and {R. Schwartz} and {Bing Zhang} and {Jing Zheng}},
      year = 2012,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/beefc348211d531c208a09cf0ab02d3dbd32bddb},
      }

2603. Shuai Huang, Glen A. Coppersmith, and Damianos G. Karakos, “Constrained Maximum Mutual Information Dimensionality Reduction for Language Identification,” in Interspeech, 2012.
      [BibTeX] [Link]

      @inproceedings{28045737,
      title = {Constrained Maximum Mutual Information Dimensionality Reduction for Language Identification},
      author = {{Shuai Huang} and {Glen A. Coppersmith} and {Damianos G. Karakos}},
      year = 2012,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/255f055d8c678b53a8a0c9c73f5f6480b125bdb5},
      }

2604. M. Slaney, Trevor R. Agus, Shih-Chii Liu, Emine Merve Kaya, and Mounya Elhilali, “A model of attention-driven scene analysis,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
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      @inproceedings{2620588,
      title = {A model of attention-driven scene analysis},
      author = {{M. Slaney} and {Trevor R. Agus} and {Shih-Chii Liu} and {Emine Merve Kaya} and {Mounya Elhilali}},
      year = 2012,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/808dc1284696ce10538922f453104d54168777cd},
      }

2605. A. Rastrow, Mark Dredze, and S. Khudanpur, “Adapting n-gram maximum entropy language models with conditional entropy regularization,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
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      @inproceedings{14363701,
      title = {Adapting n-gram maximum entropy language models with conditional entropy regularization},
      author = {{A. Rastrow} and {Mark Dredze} and {S. Khudanpur}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/46e3aa6c828f372e6d43f0b1fb00613f02bb0a8e},
      }

2606. A. Rastrow, Mark Dredze, and S. Khudanpur, “Efficient discriminative training of long-span language models,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
      [BibTeX] [Link]

      @inproceedings{9645306,
      title = {Efficient discriminative training of long-span language models},
      author = {{A. Rastrow} and {Mark Dredze} and {S. Khudanpur}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/8924c38cdd8fed464a9808524e45930a6164ca37},
      }

2607. A. Jansen and Benjamin Van Durme, “Efficient spoken term discovery using randomized algorithms,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
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      @inproceedings{17498735,
      title = {Efficient spoken term discovery using randomized algorithms},
      author = {{A. Jansen} and {Benjamin Van Durme}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/5561d01b9cc08bac589bccdfc2f68019c58f36e7},
      }

2608. Damianos G. Karakos, Mark Dredze, K. Church, A. Jansen, and S. Khudanpur, “Estimating document frequencies in a speech corpus,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
      [BibTeX] [Link]

      @inproceedings{6914375,
      title = {Estimating document frequencies in a speech corpus},
      author = {{Damianos G. Karakos} and {Mark Dredze} and {K. Church} and {A. Jansen} and {S. Khudanpur}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/990c9260b6d2a33aeaba30e4640ec59d709864fc},
      }

2609. Guillaume Garreau, C. M. Andreou, A. Andreou, J. Georgiou, S. Dura-Bernal, T. Wennekers, and S. Denham, “Gait-based person and gender recognition using micro-doppler signatures,” in Biomedical Circuits and Systems Conference, 2011.
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      @inproceedings{8578649,
      title = {Gait-based person and gender recognition using micro-doppler signatures},
      author = {{Guillaume Garreau} and {C. M. Andreou} and {A. Andreou} and {J. Georgiou} and {S. Dura-Bernal} and {T. Wennekers} and {S. Denham}},
      year = 2011,
      month = {12},
      booktitle = {Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/10f61c695ecd47086397481753793b3dd0d264d7},
      }

2610. Puyang Xu, S. Khudanpur, and A. Gunawardana, “Randomized maximum entropy language models,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
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      @inproceedings{16825268,
      title = {Randomized maximum entropy language models},
      author = {{Puyang Xu} and {S. Khudanpur} and {A. Gunawardana}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/b4fec4831d83708f81ecf6f7296b105e081d2d4c},
      }

2611. A. Andreou, “Interview with Andreas G. Andreou,” in Electronics Letters, 2011.
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      @inproceedings{62620872,
      title = {Interview with Andreas G. Andreou},
      author = {{A. Andreou}},
      year = 2011,
      month = {12},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/f313ea547da8588136cd316f99601b0f1ee7f7bf},
      }

2612. Shuai Huang, Damianos G. Karakos, Glen A. Coppersmith, Kenneth Ward Church, and Sabato Marco Siniscalchi, “Bootstrapping a spoken language identification system using unsupervised integrated sensing and processing decision trees,” in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011.
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      @inproceedings{2674223,
      title = {Bootstrapping a spoken language identification system using unsupervised integrated sensing and processing decision trees},
      author = {{Shuai Huang} and {Damianos G. Karakos} and {Glen A. Coppersmith} and {Kenneth Ward Church} and {Sabato Marco Siniscalchi}},
      year = 2011,
      month = {12},
      booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/5ffa9b8521e17c4f13f3fc2eff8fcbe4cf7892f2},
      }

2613. J. Eisner and H. Daumé III, “Learning Speed-Accuracy Tradeoffs in Nondeterministic Inference Algorithms,” in COST: NeurIPS Workshop on Computational Trade-offs in Statistical Learning, Sierra Nevada, Spain, 2011.
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      @InProceedings{eisner-daume-2011,
      author = "Jason Eisner and Hal {Daum\'{e} III}",
      title = "Learning Speed-Accuracy Tradeoffs in Nondeterministic
      Inference Algorithms",
      booktitle = "COST: NeurIPS Workshop on Computational Trade-offs in
      Statistical Learning",
      note = "5 pages",
      year = "2011",
      month = dec,
      address = "Sierra Nevada, Spain",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-daume-2011",
      }

2614. V. Stoyanov and J. Eisner, “Learning Cost-Aware, Loss-Aware Approximate Inference Policies for Probabilistic Graphical Models,” in COST: NeurIPS Workshop on Computational Trade-offs in Statistical Learning, Sierra Nevada, Spain, 2011.
      [BibTeX] [Link]

      @InProceedings{stoyanov-eisner-2011,
      author = "Veselin Stoyanov and Jason Eisner",
      title = "Learning Cost-Aware, Loss-Aware Approximate Inference
      Policies for Probabilistic Graphical Models",
      booktitle = "COST: NeurIPS Workshop on Computational Trade-offs in
      Statistical Learning",
      note = "5 pages",
      year = "2011",
      month = dec,
      address = "Sierra Nevada, Spain",
      URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2011",
      }

2615. N. Andrews and J. Eisner, “Transformation Process Priors,” in NeurIPS Workshop on Bayesian Nonparametrics: Hope or Hype?, Sierra Nevada, Spain, 2011.
      [BibTeX] [Link]

      @InProceedings{andrews-eisner-2011,
      author = "Nicholas Andrews and Jason Eisner",
      title = "Transformation Process Priors",
      booktitle = "NeurIPS Workshop on {B}ayesian Nonparametrics: Hope or
      Hype?",
      note = "Extended abstract (3 pages)",
      year = "2011",
      month = dec,
      address = "Sierra Nevada, Spain",
      URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-2011",
      }

2616. M. R. Gormley, M. Dredze, B. {Van Durme}, and J. Eisner, “Shared Components Topic Models with Application to Selectional Preference,” in NeurIPS Workshop on Learning Semantics, Sierra Nevada, Spain, 2011.
      [BibTeX] [Link]

      @InProceedings{gormley-et-al-2011,
      author = "Matthew R. Gormley and Mark Dredze and Benjamin {Van
      Durme} and Jason Eisner",
      title = "Shared Components Topic Models with Application to
      Selectional Preference",
      booktitle = "NeurIPS Workshop on Learning Semantics",
      note = "Extended abstract (3 pages)",
      year = "2011",
      month = dec,
      address = "Sierra Nevada, Spain",
      URL = "http://cs.jhu.edu/~jason/papers/#gormley-et-al-2011",
      }

2617. S. Dura-Bernal, Guillaume Garreau, C. M. Andreou, A. Andreou, J. Georgiou, T. Wennekers, and S. Denham, “Human Action Categorization Using Ultrasound Micro-Doppler Signatures,” in International Workshop on Human Behavior Unterstanding, 2011.
      [BibTeX] [Link]

      @inproceedings{28206308,
      title = {Human Action Categorization Using Ultrasound Micro-Doppler Signatures},
      author = {{S. Dura-Bernal} and {Guillaume Garreau} and {C. M. Andreou} and {A. Andreou} and {J. Georgiou} and {T. Wennekers} and {S. Denham}},
      year = 2011,
      month = {11},
      booktitle = {International Workshop on Human Behavior Unterstanding},
      url = {https://www.semanticscholar.org/paper/c6afda2d2fa7306af39dd70c5395142daa8694a6},
      }

2618. Kenneth Ward Church, “A Pendulum Swung too Far,” in Linguistic Issues in Language Technology, 2011.
      [BibTeX] [Link]

      @inproceedings{65038808,
      title = {A Pendulum Swung too Far},
      author = {{Kenneth Ward Church}},
      year = 2011,
      month = {11},
      booktitle = {Linguistic Issues in Language Technology},
      url = {https://www.semanticscholar.org/paper/38f3a353652713ac478b9e5c80f1479816cc95b0},
      }

2619. Sriram Ganapathy, Padmanabhan Rajan, and H. Hermansky, “Multi-layer perceptron based speech activity detection for speaker verification,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2011.
      [BibTeX] [Link]

      @inproceedings{11550724,
      title = {Multi-layer perceptron based speech activity detection for speaker verification},
      author = {{Sriram Ganapathy} and {Padmanabhan Rajan} and {H. Hermansky}},
      year = 2011,
      month = {11},
      booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
      url = {https://www.semanticscholar.org/paper/65a27221bcef478ab1b7ef717de42c97d33807a7},
      }

2620. H. Hermansky, “Speech recognition from spectral dynamics,” in Sadhana, 2011.
      [BibTeX] [Link]

      @inproceedings{16306430,
      title = {Speech recognition from spectral dynamics},
      author = {{H. Hermansky}},
      year = 2011,
      month = {11},
      booktitle = {Sadhana},
      url = {https://www.semanticscholar.org/paper/2c2e22c0e23062fc1caecda3b9a5b00abf7fec56},
      }

2621. H. Hermansky, “Dealing with unknown unknowns in speech,” in Journal of the Acoustical Society of America, 2011.
      [BibTeX] [Link]

      @inproceedings{120916036,
      title = {Dealing with unknown unknowns in speech},
      author = {{H. Hermansky}},
      year = 2011,
      month = {10},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/61181890e46971de51977f3498fc9e6bf63cd937},
      }

2622. J. Georgiou and A. Andreou, “Guest Editorial – Special Issue on Selected Papers From BioCAS 2010,” in IEEE Trans. Biomed. Circuits Syst., 2011.
      [BibTeX] [Link]

      @inproceedings{28505411,
      title = {Guest Editorial - Special Issue on Selected Papers From BioCAS 2010},
      author = {{J. Georgiou} and {A. Andreou}},
      year = 2011,
      month = {10},
      booktitle = {IEEE Trans. Biomed. Circuits Syst.},
      url = {https://www.semanticscholar.org/paper/535c18dc7cf4b5ec4484183beca7618f230223c6},
      }

2623. A. Rohatgi, I. Cooper, H. Yang, Kenneth Ward Church, and X. Chen, “High Aspect Ratio Fine Gridline for Front Side Metallization of Industrial Silicon Solar Cells by Direct Printing.” 2011.
      [BibTeX] [Link]

      @inproceedings{137297745,
      title = {High Aspect Ratio Fine Gridline for Front Side Metallization of Industrial Silicon Solar Cells by Direct Printing},
      author = {{A. Rohatgi} and {I. Cooper} and {H. Yang} and {Kenneth Ward Church} and {X. Chen}},
      year = 2011,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/548a07ffc1b9f771b7fce1680b2a5755d9867362},
      }

2624. S. Bergsma and David Yarowsky, “NADA: A Robust System for Non-referential Pronoun Detection,” in Discourse Anaphora and Anaphor Resolution Colloquium, 2011.
      [BibTeX] [Link]

      @inproceedings{7808482,
      title = {NADA: A Robust System for Non-referential Pronoun Detection},
      author = {{S. Bergsma} and {David Yarowsky}},
      year = 2011,
      month = {10},
      booktitle = {Discourse Anaphora and Anaphor Resolution Colloquium},
      url = {https://www.semanticscholar.org/paper/f3fa7a194b813e57282c1503a0b27a91764bb9ee},
      }

2625. T. P. Chan, C. Callison-Burch, and B. Van Durme, “Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity,” in Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, Edinburgh, UK, 2011, p. 33–42.
      [BibTeX] [Link]

      @inproceedings{chan-etal-2011-reranking,
      title = "Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity",
      author = "Chan, Tsz Ping and
      Callison-Burch, Chris and
      Van Durme, Benjamin",
      editor = "Pado, Sebastian and
      Peirsman, Yves",
      booktitle = "Proceedings of the {GEMS} 2011 Workshop on {GE}ometrical Models of Natural Language Semantics",
      month = jul,
      year = "2011",
      address = "Edinburgh, UK",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-2504",
      pages = "33--42",
      }

2626. A. Deoras, T. Mikolov, and K. Church, “A Fast Re-scoring Strategy to Capture Long-Distance Dependencies,” in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK., 2011, p. 1116–1127.
      [BibTeX] [Link]

      @inproceedings{deoras-etal-2011-fast,
      title = "A Fast Re-scoring Strategy to Capture Long-Distance Dependencies",
      author = "Deoras, Anoop and
      Mikolov, Tom{\'a}{\v{s}} and
      Church, Kenneth",
      editor = "Barzilay, Regina and
      Johnson, Mark",
      booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland, UK.",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D11-1103",
      pages = "1116--1127",
      }

2627. C. Callison-Burch, P. Koehn, C. Monz, and O. Zaidan, “Findings of the 2011 Workshop on Statistical Machine Translation,” in Proceedings of the Sixth Workshop on Statistical Machine Translation, Edinburgh, Scotland, 2011, p. 22–64.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2011-findings,
      title = "Findings of the 2011 Workshop on Statistical Machine Translation",
      author = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Zaidan, Omar",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Zaidan, Omar F.",
      booktitle = "Proceedings of the Sixth Workshop on Statistical Machine Translation",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-2103",
      pages = "22--64",
      }

2628. J. Ganitkevitch, C. Callison-Burch, C. Napoles, and B. Van Durme, “Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation,” in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK., 2011, p. 1168–1179.
      [BibTeX] [Link]

      @inproceedings{ganitkevitch-etal-2011-learning,
      title = "Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation",
      author = "Ganitkevitch, Juri and
      Callison-Burch, Chris and
      Napoles, Courtney and
      Van Durme, Benjamin",
      editor = "Barzilay, Regina and
      Johnson, Mark",
      booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland, UK.",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D11-1108",
      pages = "1168--1179",
      }

2629. P. Xu, A. Gunawardana, and S. Khudanpur, “Efficient Subsampling for Training Complex Language Models,” in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK., 2011, p. 1128–1136.
      [BibTeX] [Link]

      @inproceedings{xu-etal-2011-efficient,
      title = "Efficient Subsampling for Training Complex Language Models",
      author = "Xu, Puyang and
      Gunawardana, Asela and
      Khudanpur, Sanjeev",
      editor = "Barzilay, Regina and
      Johnson, Mark",
      booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland, UK.",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D11-1104",
      pages = "1128--1136",
      }

2630. Z. Li, Z. Wang, J. Eisner, S. Khudanpur, and B. Roark, “Minimum Imputed-Risk: Unsupervised Discriminative Training for Machine Translation,” in Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK., 2011, p. 920–929.
      [BibTeX] [Link]

      @inproceedings{li-etal-2011-minimum,
      title = "Minimum Imputed-Risk: Unsupervised Discriminative Training for Machine Translation",
      author = "Li, Zhifei and
      Wang, Ziyuan and
      Eisner, Jason and
      Khudanpur, Sanjeev and
      Roark, Brian",
      editor = "Barzilay, Regina and
      Johnson, Mark",
      booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland, UK.",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D11-1085",
      pages = "920--929",
      }

2631. J. Weese, J. Ganitkevitch, C. Callison-Burch, M. Post, and A. Lopez, “Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor,” in Proceedings of the Sixth Workshop on Statistical Machine Translation, Edinburgh, Scotland, 2011, p. 478–484.
      [BibTeX] [Link]

      @inproceedings{weese-etal-2011-joshua,
      title = "{J}oshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor",
      author = "Weese, Jonathan and
      Ganitkevitch, Juri and
      Callison-Burch, Chris and
      Post, Matt and
      Lopez, Adam",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Zaidan, Omar F.",
      booktitle = "Proceedings of the Sixth Workshop on Statistical Machine Translation",
      month = jul,
      year = "2011",
      address = "Edinburgh, Scotland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-2160",
      pages = "478--484",
      }

2632. M. Dreyer and J. Eisner, “Discovering Morphological Paradigms from Plain Text Using a Dirichlet Process Mixture Model,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh, 2011, p. 616–627.
      [BibTeX] [Link]

      @InProceedings{dreyer-eisner-2011,
      aclid = "D11-1057",
      author = "Markus Dreyer and Jason Eisner",
      title = "Discovering Morphological Paradigms from Plain Text
      Using a {D}irichlet Process Mixture Model",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "616--627",
      note = "Supplementary material (9 pages) also available",
      year = "2011",
      month = jul,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2011",
      }

2633. Z. Li, J. Eisner, Z. Wang, Sanjeev Khudanpur, and B. Roark, “Minimum Imputed Risk: Unsupervised Discriminative Training for Machine Translation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh, 2011, p. 920–929.
      [BibTeX] [Link]

      @InProceedings{li-et-al-2011,
      aclid = "D11-1085",
      author = "Zhifei Li and Jason Eisner and Ziyuan Wang and Sanjeev
      Khudanpur and Brian Roark",
      title = "Minimum Imputed Risk: Unsupervised Discriminative
      Training for Machine Translation",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "920--929",
      year = "2011",
      month = jul,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2011",
      }

2634. K. Church, “How Many Multiword Expressions do People Know?,” in Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World, Portland, Oregon, USA, 2011, p. 137–144.
      [BibTeX] [Link]

      @inproceedings{church-2011-many,
      title = "How Many Multiword Expressions do People Know?",
      author = "Church, Kenneth",
      editor = "Kordoni, Valia and
      Ramisch, Carlos and
      Villavicencio, Aline",
      booktitle = "Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-0823",
      pages = "137--144",
      }

2635. D. Rao and D. Yarowsky, “Typed Graph Models for Learning Latent Attributes from Names,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 514–518.
      [BibTeX] [Link]

      @inproceedings{rao-yarowsky-2011-typed,
      title = "Typed Graph Models for Learning Latent Attributes from Names",
      author = "Rao, Delip and
      Yarowsky, David",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-2090",
      pages = "514--518",
      }

2636. C. Napoles, C. Callison-Burch, J. Ganitkevitch, and B. Van Durme, “Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion,” in Proceedings of the Workshop on Monolingual Text-To-Text Generation, Portland, Oregon, 2011, p. 84–90.
      [BibTeX] [Link]

      @inproceedings{napoles-etal-2011-paraphrastic,
      title = "Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion",
      author = "Napoles, Courtney and
      Callison-Burch, Chris and
      Ganitkevitch, Juri and
      Van Durme, Benjamin",
      editor = "Filippova, Katja and
      Wan, Stephen",
      booktitle = "Proceedings of the Workshop on Monolingual Text-To-Text Generation",
      month = jun,
      year = "2011",
      address = "Portland, Oregon",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-1610",
      pages = "84--90",
      }

2637. X. Yao and B. Van Durme, “Nonparametric Bayesian Word Sense Induction,” in Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing, Portland, Oregon, 2011, p. 10–14.
      [BibTeX] [Link]

      @inproceedings{yao-van-durme-2011-nonparametric,
      title = "Nonparametric {B}ayesian Word Sense Induction",
      author = "Yao, Xuchen and
      Van Durme, Benjamin",
      editor = "Matveeva, Irina and
      Moschitti, Alessandro and
      M{\`a}rquez, Llu{\'\i}s and
      Massimo Zanzotto, Fabio",
      booktitle = "Proceedings of {T}ext{G}raphs-6: Graph-based Methods for Natural Language Processing",
      month = jun,
      year = "2011",
      address = "Portland, Oregon",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-1102",
      pages = "10--14",
      }

2638. C. Parada, M. Dredze, A. Sethy, and A. Rastrow, “Learning Sub-Word Units for Open Vocabulary Speech Recognition,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 712–721.
      [BibTeX] [Link]

      @inproceedings{parada-etal-2011-learning,
      title = "Learning Sub-Word Units for Open Vocabulary Speech Recognition",
      author = "Parada, Carolina and
      Dredze, Mark and
      Sethy, Abhinav and
      Rastrow, Ariya",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-1072",
      pages = "712--721",
      }

2639. B. G. Ahn, B. Van Durme, and C. Callison-Burch, “WikiTopics: What is Popular on Wikipedia and Why,” in Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages, Portland, Oregon, 2011, p. 33–40.
      [BibTeX] [Link]

      @inproceedings{ahn-etal-2011-wikitopics,
      title = "{W}iki{T}opics: What is Popular on {W}ikipedia and Why",
      author = "Ahn, Byung Gyu and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Nenkova, Ani and
      Hirschberg, Julia and
      Liu, Yang",
      booktitle = "Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages",
      month = jun,
      year = "2011",
      address = "Portland, Oregon",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-0505",
      pages = "33--40",
      }

2640. S. Bergsma, D. Yarowsky, and K. Church, “Using Large Monolingual and Bilingual Corpora to Improve Coordination Disambiguation,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 1346–1355.
      [BibTeX] [Link]

      @inproceedings{bergsma-etal-2011-using,
      title = "Using Large Monolingual and Bilingual Corpora to Improve Coordination Disambiguation",
      author = "Bergsma, Shane and
      Yarowsky, David and
      Church, Kenneth",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-1135",
      pages = "1346--1355",
      }

2641. C. Napoles, B. Van Durme, and C. Callison-Burch, “Evaluating Sentence Compression: Pitfalls and Suggested Remedies,” in Proceedings of the Workshop on Monolingual Text-To-Text Generation, Portland, Oregon, 2011, p. 91–97.
      [BibTeX] [Link]

      @inproceedings{napoles-etal-2011-evaluating,
      title = "Evaluating Sentence Compression: Pitfalls and Suggested Remedies",
      author = "Napoles, Courtney and
      Van Durme, Benjamin and
      Callison-Burch, Chris",
      editor = "Filippova, Katja and
      Wan, Stephen",
      booktitle = "Proceedings of the Workshop on Monolingual Text-To-Text Generation",
      month = jun,
      year = "2011",
      address = "Portland, Oregon",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-1611",
      pages = "91--97",
      }

2642. B. Van Durme and A. Lall, “Efficient Online Locality Sensitive Hashing via Reservoir Counting,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 18–23.
      [BibTeX] [Link]

      @inproceedings{van-durme-lall-2011-efficient,
      title = "Efficient Online Locality Sensitive Hashing via Reservoir Counting",
      author = "Van Durme, Benjamin and
      Lall, Ashwin",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-2004",
      pages = "18--23",
      }

2643. O. F. Zaidan and C. Callison-Burch, “The Arabic Online Commentary Dataset: an Annotated Dataset of Informal Arabic with High Dialectal Content,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 37–41.
      [BibTeX] [Link]

      @inproceedings{zaidan-callison-burch-2011-arabic,
      title = "The {A}rabic Online Commentary Dataset: an Annotated Dataset of Informal {A}rabic with High Dialectal Content",
      author = "Zaidan, Omar F. and
      Callison-Burch, Chris",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-2007",
      pages = "37--41",
      }

2644. R. Wang and C. Callison-Burch, “Paraphrase Fragment Extraction from Monolingual Comparable Corpora,” in Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web, Portland, Oregon, 2011, p. 52–60.
      [BibTeX] [Link]

      @inproceedings{wang-callison-burch-2011-paraphrase,
      title = "Paraphrase Fragment Extraction from Monolingual Comparable Corpora",
      author = "Wang, Rui and
      Callison-Burch, Chris",
      editor = "Zweigenbaum, Pierre and
      Rapp, Reinhard and
      Sharoff, Serge",
      booktitle = "Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web",
      month = jun,
      year = "2011",
      address = "Portland, Oregon",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W11-1208",
      pages = "52--60",
      }

2645. O. F. Zaidan and C. Callison-Burch, “Crowdsourcing Translation: Professional Quality from Non-Professionals,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 1220–1229.
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      @inproceedings{zaidan-callison-burch-2011-crowdsourcing,
      title = "Crowdsourcing Translation: Professional Quality from Non-Professionals",
      author = "Zaidan, Omar F. and
      Callison-Burch, Chris",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-1122",
      pages = "1220--1229",
      }

2646. M. Post, “Judging Grammaticality with Tree Substitution Grammar Derivations,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, 2011, p. 217–222.
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      @inproceedings{post-2011-judging,
      title = "Judging Grammaticality with Tree Substitution Grammar Derivations",
      author = "Post, Matt",
      editor = "Lin, Dekang and
      Matsumoto, Yuji and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
      month = jun,
      year = "2011",
      address = "Portland, Oregon, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P11-2038",
      pages = "217--222",
      }

2647. V. Stoyanov, A. Ropson, and Jason Eisner, “Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, 2011, p. 725–733.
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      @InProceedings{stoyanov-ropson-eisner-2011,
      author = "Veselin Stoyanov and Alexander Ropson and Jason
      Eisner",
      title = "Empirical Risk Minimization of Graphical Model
      Parameters Given Approximate Inference, Decoding, and
      Model Structure",
      booktitle = "Proceedings of the 14th International Conference on
      Artificial Intelligence and Statistics (AISTATS)",
      series = "JMLR Workshop and Conference Proceedings",
      volume = "15",
      pages = "725--733",
      note = "Supplementary material (4 pages) also available",
      year = "2011",
      month = apr,
      address = "Fort Lauderdale",
      URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-ropson-eisner-2011",
      }

2648. Samuel Thomas, Patrick Nguyen, G. Zweig, and H. Hermansky, “MLP Based Phoneme Detectors for Speech Recognition.” 2011.
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      @inproceedings{59653108,
      title = {MLP Based Phoneme Detectors for Speech Recognition},
      author = {{Samuel Thomas} and {Patrick Nguyen} and {G. Zweig} and {H. Hermansky}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/f9574986559ec37286de4a65e556307634eaf0c7},
      }

2649. Tomas Mikolov, Stefan Kombrink, L. Burget, J. Černocký, and S. Khudanpur, “Extensions of recurrent neural network language model,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      @inproceedings{14850173,
      title = {Extensions of recurrent neural network language model},
      author = {{Tomas Mikolov} and {Stefan Kombrink} and {L. Burget} and {J. Černocký} and {S. Khudanpur}},
      year = 2011,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/07ca885cb5cc4328895bfaec9ab752d5801b14cd},
      }

2650. A. Cassidy, A. Andreou, and J. Georgiou, “A combinational digital logic approach to STDP,” in International Symposium on Circuits and Systems, 2011.
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      @inproceedings{19042349,
      title = {A combinational digital logic approach to STDP},
      author = {{A. Cassidy} and {A. Andreou} and {J. Georgiou}},
      year = 2011,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/c989515519bbc15483291360839c23df1428ffae},
      }

2651. Balakrishnan Varadarajan, Garimella S. V. S. Sivaram, and S. Khudanpur, “Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      @inproceedings{376777,
      title = {Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition},
      author = {{Balakrishnan Varadarajan} and {Garimella S. V. S. Sivaram} and {S. Khudanpur}},
      year = 2011,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/03c6dcb813b4779c79421a548cc15008c32208a9},
      }

2652. F. Valente and H. Hermansky, “Data-driven extraction of spectral-dynamics based posteriors.” 2011.
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      @inproceedings{69727508,
      title = {Data-driven extraction of spectral-dynamics based posteriors},
      author = {{F. Valente} and {H. Hermansky}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3e01beea8d5526e426a11812aa2b0b8f7eb09969},
      }

2653. J. Georgiou, P. Pouliquen, A. Cassidy, Guillaume Garreau, C. M. Andreou, G. Stuarts, Cyrille d’Urbal, A. Andreou, S. Denham, T. Wennekers, R. Mill, I. Winkler, Tamás Bohm, O. Szalárdy, G. Klump, Simon J. Jones, and A. Bendixen, “A multimodal-corpus data collection system for cognitive acoustic scene analysis,” in Annual Conference on Information Sciences and Systems, 2011.
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      @inproceedings{646791,
      title = {A multimodal-corpus data collection system for cognitive acoustic scene analysis},
      author = {{J. Georgiou} and {P. Pouliquen} and {A. Cassidy} and {Guillaume Garreau} and {C. M. Andreou} and {G. Stuarts} and {Cyrille d'Urbal} and {A. Andreou} and {S. Denham} and {T. Wennekers} and {R. Mill} and {I. Winkler} and {Tamás Bohm} and {O. Szalárdy} and {G. Klump} and {Simon J. Jones} and {A. Bendixen}},
      year = 2011,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/311d0fa5b9d9e40d195843c1540884857226eb40},
      }

2654. H. Hermansky, Mark Dredze, and Maria Carolina Parada, “Learning sub-word units and exploiting contextual information for open vocabulary speech recognition.” 2011.
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      @inproceedings{36628046,
      title = {Learning sub-word units and exploiting contextual information for open vocabulary speech recognition},
      author = {{H. Hermansky} and {Mark Dredze} and {Maria Carolina Parada}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0509ca142cb5d3cf11440e29b749d3bbe4b8139b},
      }

2655. A. Cassidy, Kai Yu, Haolang Zhou, and A. Andreou, “A high-level analytical model for application specific CMP design exploration,” in Design, Automation and Test in Europe, 2011.
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      @inproceedings{14606101,
      title = {A high-level analytical model for application specific CMP design exploration},
      author = {{A. Cassidy} and {Kai Yu} and {Haolang Zhou} and {A. Andreou}},
      year = 2011,
      month = {3},
      booktitle = {Design, Automation and Test in Europe},
      url = {https://www.semanticscholar.org/paper/0087d3f48a3014668dc6da36027c134d9850dc13},
      }

2656. Thomas S. Murray, P. Pouliquen, A. Andreou, and K. Lauritzen, “Design of a CMOS A2I data converter: Theory, architecture and implementation,” in Annual Conference on Information Sciences and Systems, 2011.
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      @inproceedings{16740428,
      title = {Design of a CMOS A2I data converter: Theory, architecture and implementation},
      author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou} and {K. Lauritzen}},
      year = 2011,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/308e93c9795ec743f30a99506657229f91c56b35},
      }

2657. F. Jelinek, Kenneth Ward Church, and Anoop Deoras, “Search and decoding strategies for complex lexical modeling in lvcsr.” 2011.
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      @inproceedings{11595816,
      title = {Search and decoding strategies for complex lexical modeling in lvcsr},
      author = {{F. Jelinek} and {Kenneth Ward Church} and {Anoop Deoras}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/08369fde25b3ec363b572c2ccf42da94ed8dd8e1},
      }

2658. P. Pouliquen, A. Cassidy, A. Andreou, Guillaume Garreau, and J. Georgiou, “A wireless architecture for distributed sensing/actuation and pre-processing with microsecond synchronization,” in Annual Conference on Information Sciences and Systems, 2011.
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      @inproceedings{1462340,
      title = {A wireless architecture for distributed sensing/actuation and pre-processing with microsecond synchronization},
      author = {{P. Pouliquen} and {A. Cassidy} and {A. Andreou} and {Guillaume Garreau} and {J. Georgiou}},
      year = 2011,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/1501b6884fd95804d80e81b8381963693a1d31c2},
      }

2659. N. Mesgarani, Samuel Thomas, and H. Hermansky, “Adaptive Stream Fusion in Multistream Recognition of Speech,” in Interspeech, 2011.
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      @inproceedings{5635123,
      title = {Adaptive Stream Fusion in Multistream Recognition of Speech},
      author = {{N. Mesgarani} and {Samuel Thomas} and {H. Hermansky}},
      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/e1d7f09e28d7c0e5899463d280ff6d3366e754ad},
      }

2660. Balakrishnan Varadarajan, C. Reiley, S. Khudanpur, and Gregory Hager, “Data-Driven Statistical Models for Computer Integrated Surgery.” 2011.
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      @inproceedings{209330577,
      title = {Data-Driven Statistical Models for Computer Integrated Surgery},
      author = {{Balakrishnan Varadarajan} and {C. Reiley} and {S. Khudanpur} and {Gregory Hager}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b01994696b2d7e0c475b5cb4ee6cb9ff359643ab},
      }

2661. Keith Kintzley, A. Jansen, and H. Hermansky, “Event Selection from Phone Posteriorgrams Using Matched Filters,” in Interspeech, 2011.
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      @inproceedings{18470446,
      title = {Event Selection from Phone Posteriorgrams Using Matched Filters},
      author = {{Keith Kintzley} and {A. Jansen} and {H. Hermansky}},
      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/05d02dffa108ce5810b610e77a024ffe955e5ff7},
      }

2662. D. Rao and David Yarowsky, “Typed Graph Models for Learning Latent Attributes from Names,” in Annual Meeting of the Association for Computational Linguistics, 2011.
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      @inproceedings{1945336,
      title = {Typed Graph Models for Learning Latent Attributes from Names},
      author = {{D. Rao} and {David Yarowsky}},
      year = 2011,
      month = {6},
      booktitle = {Annual Meeting of the Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/31bd391775eec097078c9d9a21d86c1d6d26752f},
      }

2663. A. Jansen and Kenneth Ward Church, “Towards Unsupervised Training of Speaker Independent Acoustic Models,” in Interspeech, 2011.
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      @inproceedings{16979706,
      title = {Towards Unsupervised Training of Speaker Independent Acoustic Models},
      author = {{A. Jansen} and {Kenneth Ward Church}},
      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/865bc77fcdea5dcd035768bcf512bbee34fdb73d},
      }

2664. S. Khudanpur and Balakrishnan Varadarajan, “Learning and inference algorithms for dynamical system models of dextrous motion.” 2011.
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      @inproceedings{61844025,
      title = {Learning and inference algorithms for dynamical system models of dextrous motion},
      author = {{S. Khudanpur} and {Balakrishnan Varadarajan}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b16c176bf9859a587afa393e16852aa514255ba8},
      }

2665. Joseph H. Lin and A. Andreou, “A 32×32 single photon avalanche diode imager with delay-insensitive address-event readout,” in International Symposium on Circuits and Systems, 2011.
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      @inproceedings{9193505,
      title = {A 32×32 single photon avalanche diode imager with delay-insensitive address-event readout},
      author = {{Joseph H. Lin} and {A. Andreou}},
      year = 2011,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/9635ca6330a305dd9a5d123b9a15642a6b7a8f2d},
      }

2666. Samuel Thomas, Patrick Nguyen, G. Zweig, and H. Hermansky, “MLP based phoneme detectors for Automatic Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      @inproceedings{6946634,
      title = {MLP based phoneme detectors for Automatic Speech Recognition},
      author = {{Samuel Thomas} and {Patrick Nguyen} and {G. Zweig} and {H. Hermansky}},
      year = 2011,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/90d41ab41215064c29e096f817f559a1657f2da8},
      }

2667. Scott Novotney, R. Schwartz, and S. Khudanpur, “Unsupervised Arabic Dialect Adaptation with Self-Training,” in Interspeech, 2011.
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      @inproceedings{2161780,
      title = {Unsupervised Arabic Dialect Adaptation with Self-Training},
      author = {{Scott Novotney} and {R. Schwartz} and {S. Khudanpur}},
      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/33ebb76b403b92f7c7add3b532e6f35993cfdb58},
      }

2668. Michael J. Paul and Mark Dredze, “You Are What You Tweet: Analyzing Twitter for Public Health,” in International Conference on Web and Social Media, 2011.
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      @inproceedings{9270435,
      title = {You Are What You Tweet: Analyzing Twitter for Public Health},
      author = {{Michael J. Paul} and {Mark Dredze}},
      year = 2011,
      month = {7},
      booktitle = {International Conference on Web and Social Media},
      url = {https://www.semanticscholar.org/paper/e4e3d5552a0071f4f677d06c672c31b402b1266c},
      }

2669. Joseph H. Lin, R. Ozgun, P. Pouliquen, A. Andreou, C. M. Andreou, and J. Georgiou, “A 3-pin 1V 115µW 176×144 autonomous active pixel image sensor in 0.18µm CMOS,” in International Symposium on Circuits and Systems, 2011.
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      @inproceedings{30336890,
      title = {A 3-pin 1V 115µW 176×144 autonomous active pixel image sensor in 0.18µm CMOS},
      author = {{Joseph H. Lin} and {R. Ozgun} and {P. Pouliquen} and {A. Andreou} and {C. M. Andreou} and {J. Georgiou}},
      year = 2011,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/09440eca3b48bd93d3c65bd1bc220d26e38c08be},
      }

2670. Joseph H. Lin, P. Pouliquen, A. Andreou, A. Goldberg, and Charbel G. Rizk, “A bio-inspired event-driven digital readout architecture with pixel-level A/D conversion and non-uniformity correction,” in Annual Conference on Information Sciences and Systems, 2011.
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      @inproceedings{17753471,
      title = {A bio-inspired event-driven digital readout architecture with pixel-level A/D conversion and non-uniformity correction},
      author = {{Joseph H. Lin} and {P. Pouliquen} and {A. Andreou} and {A. Goldberg} and {Charbel G. Rizk}},
      year = 2011,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/f5030491c9cb5ff567f4254cfb9e994651625169},
      }

2671. Nash M. Borges, Glen A. Coppersmith, G. Meyer, and C. Priebe, “Anomaly detection for random graphs using distributions of vertex invariants,” in Annual Conference on Information Sciences and Systems, 2011.
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      @inproceedings{9596079,
      title = {Anomaly detection for random graphs using distributions of vertex invariants},
      author = {{Nash M. Borges} and {Glen A. Coppersmith} and {G. Meyer} and {C. Priebe}},
      year = 2011,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/237a79b69b9c980bd4b699da3bbdf3e9bff061d0},
      }

2672. B. Dhar, R. Ozgun, Thomas J. Dawidczyk, A. Andreou, and H. Katz, “Threshold voltage shifting for memory and tuning in printed transistor circuits,” in Materials Science & Engineering R-reports, 2011.
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      @inproceedings{111276948,
      title = {Threshold voltage shifting for memory and tuning in printed transistor circuits},
      author = {{B. Dhar} and {R. Ozgun} and {Thomas J. Dawidczyk} and {A. Andreou} and {H. Katz}},
      year = 2011,
      month = {5},
      booktitle = {Materials Science & Engineering R-reports},
      url = {https://www.semanticscholar.org/paper/0deb3ecc444e81c8a44b2ae5441ed372a865bb20},
      }

2673. A. Cassidy, Thomas S. Murray, A. Andreou, and J. Georgiou, “Evaluating on-chip interconnects for low operating frequency silicon neuron arrays,” in International Symposium on Circuits and Systems, 2011.
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      @inproceedings{20185260,
      title = {Evaluating on-chip interconnects for low operating frequency silicon neuron arrays},
      author = {{A. Cassidy} and {Thomas S. Murray} and {A. Andreou} and {J. Georgiou}},
      year = 2011,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/85446bccf2ba8dfa48f8a762fc717a744bc2604b},
      }

2674. N. Mesgarani, Samuel Thomas, and H. Hermansky, “Toward optimizing stream fusion in multistream recognition of speech.,” in Journal of the Acoustical Society of America, 2011.
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      @inproceedings{263479313,
      title = {Toward optimizing stream fusion in multistream recognition of speech.},
      author = {{N. Mesgarani} and {Samuel Thomas} and {H. Hermansky}},
      year = 2011,
      month = {6},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/46ac4c31882963e8aaabea2bb12f6e38b40caf09},
      }

2675. Michael A. Carlin, Samuel Thomas, A. Jansen, and H. Hermansky, “Rapid Evaluation of Speech Representations for Spoken Term Discovery,” in Interspeech, 2011.
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      @inproceedings{12998642,
      title = {Rapid Evaluation of Speech Representations for Spoken Term Discovery},
      author = {{Michael A. Carlin} and {Samuel Thomas} and {A. Jansen} and {H. Hermansky}},
      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/9f1fcf447f0e69ed1b2bbfb90ecc9b4b269cdd0e},
      }

2676. R. Azevedo, Gautam Biswas, D. Bohus, Ted Carmichael, Mark A. Finlayson, M. Hadzikadic, Catherine Havasi, E. Horvitz, T. Kanda, Oluwasanmi Koyejo, W. Lawless, D. Lenat, Felipe Meneguzzi, Bilge Mutlu, Jean Oh, R. Pirrone, Antoine Raux, D. Sofge, G. Sukthankar, and Benjamin Van Durme, “Reports of the AAAI 2010 Fall Symposia,” in The AI Magazine, 2011.
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      title = {Reports of the AAAI 2010 Fall Symposia},
      author = {{R. Azevedo} and {Gautam Biswas} and {D. Bohus} and {Ted Carmichael} and {Mark A. Finlayson} and {M. Hadzikadic} and {Catherine Havasi} and {E. Horvitz} and {T. Kanda} and {Oluwasanmi Koyejo} and {W. Lawless} and {D. Lenat} and {Felipe Meneguzzi} and {Bilge Mutlu} and {Jean Oh} and {R. Pirrone} and {Antoine Raux} and {D. Sofge} and {G. Sukthankar} and {Benjamin Van Durme}},
      year = 2011,
      month = {3},
      booktitle = {The AI Magazine},
      url = {https://www.semanticscholar.org/paper/bc4c61f6c8c7ea1530c65eb5d09ebe3ab67c2f1d},
      }

2677. H. Pao, Glen A. Coppersmith, and C. Priebe, “Statistical Inference on Random Graphs: Comparative Power Analyses via Monte Carlo,” in Journal of Computational and Graphical Statistics, 2011.
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      @inproceedings{11674778,
      title = {Statistical Inference on Random Graphs: Comparative Power Analyses via Monte Carlo},
      author = {{H. Pao} and {Glen A. Coppersmith} and {C. Priebe}},
      year = 2011,
      month = {1},
      booktitle = {Journal of Computational and Graphical Statistics},
      url = {https://www.semanticscholar.org/paper/cfe7962ae47d449d608561a670e23ec6c48d6c71},
      }

2678. A. Andreou, Z. Zhang, R. Ozgun, E. Choi, Z. Kalayjian, M. Marwick, J. Christen, and L. Tung, “Contactless fluorescence imaging with a CMOS image sensor,” in International Symposium on Circuits and Systems, 2011.
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      @inproceedings{39879224,
      title = {Contactless fluorescence imaging with a CMOS image sensor},
      author = {{A. Andreou} and {Z. Zhang} and {R. Ozgun} and {E. Choi} and {Z. Kalayjian} and {M. Marwick} and {J. Christen} and {L. Tung}},
      year = 2011,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/1a0f3f6e745dfd197500b1e483d5e46525059b0a},
      }

2679. A. Rastrow, Markus Dreyer, A. Sethy, S. Khudanpur, B. Ramabhadran, and Mark Dredze, “Hill climbing on speech lattices: A new rescoring framework,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      @inproceedings{8264290,
      title = {Hill climbing on speech lattices: A new rescoring framework},
      author = {{A. Rastrow} and {Markus Dreyer} and {A. Sethy} and {S. Khudanpur} and {B. Ramabhadran} and {Mark Dredze}},
      year = 2011,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/e91558ce4e41d471ea7240b07a96e60b605733b7},
      }

2680. J. Georgiou, P. Pouliquen, A. Cassidy, Guillaume Garreau, C. M. Andreou, G. Stuarts, Cyrille d’Urbal, A. Andreou, S. Denham, T. Wennekers, R. Mill, G. Klump, Simon J. Jones, I. Winkler, Tamás Bohm, O. Szalárdy, and A. Bendixen, “for Cognitive Acoustic Scene Analysis.” 2011.
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      @inproceedings{110542813,
      title = {for Cognitive Acoustic Scene Analysis},
      author = {{J. Georgiou} and {P. Pouliquen} and {A. Cassidy} and {Guillaume Garreau} and {C. M. Andreou} and {G. Stuarts} and {Cyrille d'Urbal} and {A. Andreou} and {S. Denham} and {T. Wennekers} and {R. Mill} and {G. Klump} and {Simon J. Jones} and {I. Winkler} and {Tamás Bohm} and {O. Szalárdy} and {A. Bendixen}},
      year = 2011,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e3d95f53a481897bffc003e76abec8f2e0d0b25f},
      }

2681. A. Cassidy, A. Andreou, and J. Georgiou, “Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis,” in Annual Conference on Information Sciences and Systems, 2011.
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      title = {Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis},
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2682. ChengXiang Zhai, Kuansan Wang, David Yarowsky, S. Vogel, and E. Viegas, “Web N-gram workshop 2010,” in SIGF, 2011.
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      author = {{ChengXiang Zhai} and {Kuansan Wang} and {David Yarowsky} and {S. Vogel} and {E. Viegas}},
      year = 2011,
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2683. R. Ozgun, Joseph H. Lin, Francisco Tejada, P. Pouliquen, and A. Andreou, “A low-power 8-bit SAR ADC for a QCIF image sensor,” in International Symposium on Circuits and Systems, 2011.
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2684. Balakrishnan Varadarajan and S. Khudanpur, “Learning and inference algorithms for partially observed structured switching vector autoregressive models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      author = {{Balakrishnan Varadarajan} and {S. Khudanpur}},
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      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
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2685. Anoop Deoras, Tomas Mikolov, Stefan Kombrink, M. Karafiát, and S. Khudanpur, “Variational approximation of long-span language models for lvcsr,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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2686. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan, “Proceedings of the Sixth Workshop on Statistical Machine Translation,” in WMT@EMNLP, 2011.
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      author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {Omar Zaidan}},
      year = 2011,
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      url = {https://www.semanticscholar.org/paper/d7222b1b0f9617e7e486ee35fc26a3aead94e9f2},
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2687. H. Hermansky, “Dealing with Unexpected Words in Automatic Recognition of Speech,” in International Conference on Text, Speech and Dialogue, 2011.
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      url = {https://www.semanticscholar.org/paper/e4da2561770c2e6769d0b7af6b2cf383558ab1f0},
      }

2688. Carolina Parada, Mark Dredze, and F. Jelinek, “OOV Sensitive Named-Entity Recognition in Speech,” in Interspeech, 2011.
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      author = {{Carolina Parada} and {Mark Dredze} and {F. Jelinek}},
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2689. Michael J. Paul and Mark Dredze, “A Model for Mining.” 2011.
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2690. Joel Pinto, Garimella S. V. S. Sivaram, M. Magimai-Doss, H. Hermansky, and H. Bourlard, “Analysis of MLP-Based Hierarchical Phoneme Posterior Probability Estimator,” in IEEE Transactions on Audio, Speech, and Language Processing, 2011.
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2691. Garimella S. V. S. Sivaram and H. Hermansky, “Multilayer perceptron with sparse hidden outputs for phoneme recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      }

2692. R. Ozgun, Byung-Jun Jung, B. Dhar, H. Katz, and A. Andreou, “Silicon-on-insulator (SOI) integration for organic field effect transistor (OFET) based circuits,” in International Symposium on Circuits and Systems, 2011.
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      year = 2011,
      month = {5},
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2693. Chris Callison-Burch, Philipp Koehn, Christof Monz, and F. Zaidan, “Sixth workshop on statistical machine translation: proceedings of the workshop.” 2011.
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      year = 2011,
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      }

2694. Sri Harish Reddy Mallidi, Sriram Ganapathy, and H. Hermansky, “Modulation Spectrum Analysis for Recognition of Reverberant Speech,” in Interspeech, 2011.
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      author = {{Sri Harish Reddy Mallidi} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2011,
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      url = {https://www.semanticscholar.org/paper/eaa7a5c51f3cb19e95e2ca55a88ce4504e0787a7},
      }

2695. G. Zweig, Patrick Nguyen, Dirk Van Compernolle, Kris Demuynck, L. Atlas, P. Clark, Gregory Sell, Meihong Wang, Fei Sha, H. Hermansky, Damianos G. Karakos, A. Jansen, Samuel Thomas, S. SivaramG.S.V., Samuel R. Bowman, and Justine T. Kao, “Speech recognitionwith segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
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      author = {{G. Zweig} and {Patrick Nguyen} and {Dirk Van Compernolle} and {Kris Demuynck} and {L. Atlas} and {P. Clark} and {Gregory Sell} and {Meihong Wang} and {Fei Sha} and {H. Hermansky} and {Damianos G. Karakos} and {A. Jansen} and {Samuel Thomas} and {S. SivaramG.S.V.} and {Samuel R. Bowman} and {Justine T. Kao}},
      year = 2011,
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2696. H. Hermansky, N. Mesgarani, and Samuel Thomas, “Performance monitoring for robustness in automatic recognition of speechi,” in Symposium on Machine Learning in Speech and Language Processing, 2011.
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      }

2697. D. Rao, Michael J. Paul, Clayton Fink, David Yarowsky, Timothy Oates, and Glen A. Coppersmith, “Hierarchical Bayesian Models for Latent Attribute Detection in Social Media,” in International Conference on Web and Social Media, 2011.
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      author = {{D. Rao} and {Michael J. Paul} and {Clayton Fink} and {David Yarowsky} and {Timothy Oates} and {Glen A. Coppersmith}},
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      }

2698. Spence Green, Nicholas Andrews, Matthew R. Gormley, Mark Dredze, and Christopher D. Manning, “Cross-lingual Coreference Resolution : A New Task for Multilingual Comparable Corpora.” 2011.
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      year = 2011,
      booktitle = {},
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      }

2699. Xudong Chen and Kenneth Ward Church, “Direct Printing for Next Generation High Density Packaging.” 2011.
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      author = {{Xudong Chen} and {Kenneth Ward Church}},
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      }

2700. Garimella S. V. S. Sivaram, Samuel Thomas, and H. Hermansky, “Mixture of Auto-Associative Neural Networks for Speaker Verification,” in Interspeech, 2011.
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      year = 2011,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/ad62530d0477852ff90b636ba89cec04d9a93445},
      }

2701. Balakrishnan Varadarajan, S. Khudanpur, and T. Tran, “Stepwise Optimal Subspace Pursuit for Improving Sparse Recovery,” in IEEE Signal Processing Letters, 2011.
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      author = {{Balakrishnan Varadarajan} and {S. Khudanpur} and {T. Tran}},
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      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/e571db0da9751e3b80fce9c123f001c2a1d94372},
      }

2702. Anoop Deoras, Denis Filimonov, M. Harper, and F. Jelinek, “Model combination for Speech Recognition using Empirical Bayes Risk minimization,” in 2010 IEEE Spoken Language Technology Workshop, 2010.
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      author = {{Anoop Deoras} and {Denis Filimonov} and {M. Harper} and {F. Jelinek}},
      year = 2010,
      month = {12},
      booktitle = {2010 IEEE Spoken Language Technology Workshop},
      url = {https://www.semanticscholar.org/paper/c9a903827447a39a2b34669f9ffad2505dadec52},
      }

2703. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Temporal envelope compensation for robust phoneme recognition using modulation spectrum.,” in Journal of the Acoustical Society of America, 2010.
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      author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
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      }

2704. G. Zweig, Patrick Nguyen, Dirk Van Compernolle, Kris Demuynck, L. Atlas, P. Clark, Gregory Sell, Fei Sha, Meihong Wang, A. Jansen, H. Hermansky, Damianos G. Karakos, Keith Kintzley, Samuel Thomas, Sivaram Gsvs, Samuel R. Bowman, and Justine T. Kao, “Speech Recognition with Segmental Conditional Random Fields: Final Report from the 2010 JHU Summer Workshop.” 2010.
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      title = {Speech Recognition with Segmental Conditional Random Fields: Final Report from the 2010 JHU Summer Workshop},
      author = {{G. Zweig} and {Patrick Nguyen} and {Dirk Van Compernolle} and {Kris Demuynck} and {L. Atlas} and {P. Clark} and {Gregory Sell} and {Fei Sha} and {Meihong Wang} and {A. Jansen} and {H. Hermansky} and {Damianos G. Karakos} and {Keith Kintzley} and {Samuel Thomas} and {Sivaram Gsvs} and {Samuel R. Bowman} and {Justine T. Kao}},
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      }

2705. Andre Harrison, R. Ozgun, Joseph H. Lin, A. Andreou, and R. Etienne-Cummings, “A spike based 3D imager chip using a mixed mode encoding readout,” in Biomedical Circuits and Systems Conference, 2010.
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      author = {{Andre Harrison} and {R. Ozgun} and {Joseph H. Lin} and {A. Andreou} and {R. Etienne-Cummings}},
      year = 2010,
      month = {11},
      booktitle = {Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/0371f6816c8478fc69b80e2c766718d12959db5f},
      }

2706. Lenhart K. Schubert, Benjamin Van Durme, and Marzieh Bazrafshan, “Entailment Inference in a Natural Logic-like General Reasoner,” in AAAI Fall Symposium: Commonsense Knowledge, 2010.
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      title = {Entailment Inference in a Natural Logic-like General Reasoner},
      author = {{Lenhart K. Schubert} and {Benjamin Van Durme} and {Marzieh Bazrafshan}},
      year = 2010,
      month = {11},
      booktitle = {AAAI Fall Symposium: Commonsense Knowledge},
      url = {https://www.semanticscholar.org/paper/7f21805a4a34fd967d8578b52a102dce09aa5a07},
      }

2707. Steven P. Abney, S. Kurohashi, S. Bangalore, Irene Langkilde-Geary, Christopher Brew, Mirella Lapata, Sharon A. Caraballo, C. Leacock, Bob Carpenter, B. Levin, Stanley F. Chen, D. Litman, Kenneth Ward Church, I. Mani, Michael Collins, Christopher Manning, Ann A. Copestake, D. Marcu, M. Crocker, E. Marsi, P. Deane, Diana McCarthy, Mona T. Diab, I. D. Melamed, M. Dras, J. W. Minett, Jason Eisner, Robert C. Moore, E. Fosler-Lussier, Thomas Morton, George Foster, H. Ney, R. Frank, G. Ngai, Jianfeng Gao, Kemal Oflazer, Claire Gardent, Massimo Poesio, Tanja Gaustad van Zaanen, Judita Preiss, D. Gildea, Ehud Reiter, Andrew R. Golding, P. Resnik, Joshua Goodman, Roni Rosenfeld, G. Grefenstette, Frank Schilder, Mohammad Haji-Abdolhosseini, Lenhart K. Schubert, P. Heeman, Advaith Siddharthan, Derrick Higgins, R. Sproat, J. Hockenmaier, M. Strube, H. Horacek, M. Swerts, Diana Inkpen, Simone Teufel, Martin Jansche, Kees van Deemter, Mark Johnson, Ye-Yi Wang, Frank Keller, B. Webber, A. Kilgarriff, Janyce Wiebe, Kevin Knight, and Florian Wolf, “Reviewers for Volume 31,” in Computational Linguistics, 2010.
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      @inproceedings{51622068,
      title = {Reviewers for Volume 31},
      author = {{Steven P. Abney} and {S. Kurohashi} and {S. Bangalore} and {Irene Langkilde-Geary} and {Christopher Brew} and {Mirella Lapata} and {Sharon A. Caraballo} and {C. Leacock} and {Bob Carpenter} and {B. Levin} and {Stanley F. Chen} and {D. Litman} and {Kenneth Ward Church} and {I. Mani} and {Michael Collins} and {Christopher Manning} and {Ann A. Copestake} and {D. Marcu} and {M. Crocker} and {E. Marsi} and {P. Deane} and {Diana McCarthy} and {Mona T. Diab} and {I. D. Melamed} and {M. Dras} and {J. W. Minett} and {Jason Eisner} and {Robert C. Moore} and {E. Fosler-Lussier} and {Thomas Morton} and {George Foster} and {H. Ney} and {R. Frank} and {G. Ngai} and {Jianfeng Gao} and {Kemal Oflazer} and {Claire Gardent} and {Massimo Poesio} and {Tanja Gaustad van Zaanen} and {Judita Preiss} and {D. Gildea} and {Ehud Reiter} and {Andrew R. Golding} and {P. Resnik} and {Joshua Goodman} and {Roni Rosenfeld} and {G. Grefenstette} and {Frank Schilder} and {Mohammad Haji-Abdolhosseini} and {Lenhart K. Schubert} and {P. Heeman} and {Advaith Siddharthan} and {Derrick Higgins} and {R. Sproat} and {J. Hockenmaier} and {M. Strube} and {H. Horacek} and {M. Swerts} and {Diana Inkpen} and {Simone Teufel} and {Martin Jansche} and {Kees van Deemter} and {Mark Johnson} and {Ye-Yi Wang} and {Frank Keller} and {B. Webber} and {A. Kilgarriff} and {Janyce Wiebe} and {Kevin Knight} and {Florian Wolf}},
      year = 2010,
      month = {11},
      booktitle = {Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/d7c3435dfafa3f7fdc546de6dbd53dab74a604a4},
      }

2708. M. Dredze, T. Oates, and C. Piatko, “We’re Not in Kansas Anymore: Detecting Domain Changes in Streams,” in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, MA, 2010, p. 585–595.
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      @inproceedings{dredze-etal-2010-kansas,
      title = "We{'}re Not in {K}ansas Anymore: Detecting Domain Changes in Streams",
      author = "Dredze, Mark and
      Oates, Tim and
      Piatko, Christine",
      editor = "Li, Hang and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2010",
      address = "Cambridge, MA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D10-1057",
      pages = "585--595",
      }

2709. J. A. Rodriguez, P. Julián, and A. Andreou, “Frame and arithmetic pipelining for a radix-4 FFT streamed core,” in Argentine School of Micro-Nanoelectronics, Technology and Applications, 2010.
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      author = {{J. A. Rodriguez} and {P. Julián} and {A. Andreou}},
      year = 2010,
      month = {10},
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      url = {https://www.semanticscholar.org/paper/ef0d9f52109e7382b7a276b2930566ee25bb0204},
      }

2710. M. Dredze, A. Jansen, G. Coppersmith, and K. Church, “NLP on Spoken Documents Without ASR,” in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, MA, 2010, p. 460–470.
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      @inproceedings{dredze-etal-2010-nlp,
      title = "{NLP} on Spoken Documents Without {ASR}",
      author = "Dredze, Mark and
      Jansen, Aren and
      Coppersmith, Glen and
      Church, Ken",
      editor = "Li, Hang and
      M{\`a}rquez, Llu{\'\i}s",
      booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2010",
      address = "Cambridge, MA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D10-1045",
      pages = "460--470",
      }

2711. D. Rao, David Yarowsky, Abhishek Shreevats, and Manaswi Gupta, “Classifying latent user attributes in twitter,” in SMUC ’10, 2010.
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      author = {{D. Rao} and {David Yarowsky} and {Abhishek Shreevats} and {Manaswi Gupta}},
      year = 2010,
      month = {10},
      booktitle = {SMUC '10},
      url = {https://www.semanticscholar.org/paper/740f183eb134f75cb943fa9ae0bac97366c9cdcf},
      }

2712. M. Dredze, P. McNamee, D. Rao, A. Gerber, and T. Finin, “Entity Disambiguation for Knowledge Base Population,” in Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Beijing, China, 2010, p. 277–285.
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      @inproceedings{dredze-etal-2010-entity,
      title = "Entity Disambiguation for Knowledge Base Population",
      author = "Dredze, Mark and
      McNamee, Paul and
      Rao, Delip and
      Gerber, Adam and
      Finin, Tim",
      editor = "Huang, Chu-Ren and
      Jurafsky, Dan",
      booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)",
      month = aug,
      year = "2010",
      address = "Beijing, China",
      publisher = "Coling 2010 Organizing Committee",
      url = "https://aclanthology.org/C10-1032",
      pages = "277--285",
      }

2713. Z. Li, Z. Wang, S. Khudanpur, and J. Eisner, “Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets,” in Coling 2010: Posters, Beijing, China, 2010, p. 656–664.
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      @inproceedings{li-etal-2010-unsupervised,
      title = "Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets",
      author = "Li, Zhifei and
      Wang, Ziyuan and
      Khudanpur, Sanjeev and
      Eisner, Jason",
      editor = "Huang, Chu-Ren and
      Jurafsky, Dan",
      booktitle = "Coling 2010: Posters",
      month = aug,
      year = "2010",
      address = "Beijing, China",
      publisher = "Coling 2010 Organizing Committee",
      url = "https://aclanthology.org/C10-2075",
      pages = "656--664",
      }

2714. D. Rao, P. McNamee, and M. Dredze, “Streaming Cross Document Entity Coreference Resolution,” in Coling 2010: Posters, Beijing, China, 2010, p. 1050–1058.
      [BibTeX] [Link]

      @inproceedings{rao-etal-2010-streaming,
      title = "Streaming Cross Document Entity Coreference Resolution",
      author = "Rao, Delip and
      McNamee, Paul and
      Dredze, Mark",
      editor = "Huang, Chu-Ren and
      Jurafsky, Dan",
      booktitle = "Coling 2010: Posters",
      month = aug,
      year = "2010",
      address = "Beijing, China",
      publisher = "Coling 2010 Organizing Committee",
      url = "https://aclanthology.org/C10-2121",
      pages = "1050--1058",
      }

2715. E. Pitler, S. Bergsma, D. Lin, and K. Church, “Using Web-scale N-grams to Improve Base NP Parsing Performance,” in Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Beijing, China, 2010, p. 886–894.
      [BibTeX] [Link]

      @inproceedings{pitler-etal-2010-using,
      title = "Using Web-scale N-grams to Improve Base {NP} Parsing Performance",
      author = "Pitler, Emily and
      Bergsma, Shane and
      Lin, Dekang and
      Church, Kenneth",
      editor = "Huang, Chu-Ren and
      Jurafsky, Dan",
      booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)",
      month = aug,
      year = "2010",
      address = "Beijing, China",
      publisher = "Coling 2010 Organizing Committee",
      url = "https://aclanthology.org/C10-1100",
      pages = "886--894",
      }

2716. Z. Li, Z. Wang, S. Khudanpur, and J. Eisner, “Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets,” in Proceedings of the 23rd International Conference on Computational Linguistics (COLING), Beijing, 2010, p. 656–664.
      [BibTeX] [Link]

      @InProceedings{li-et-al-2010,
      aclid = "C10-2075",
      author = "Zhifei Li and Ziyuan Wang and Sanjeev Khudanpur and
      Jason Eisner",
      title = "Unsupervised Discriminative Language Model Training
      for Machine Translation using Simulated Confusion
      Sets",
      booktitle = "Proceedings of the 23rd International Conference on
      Computational Linguistics (COLING)",
      pages = "656--664",
      year = "2010",
      month = aug,
      address = "Beijing",
      URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2010",
      }

2717. Z. Li, C. Callison-Burch, C. Dyer, J. Ganitkevitch, A. Irvine, S. Khudanpur, L. Schwartz, W. Thornton, Z. Wang, J. Weese, and O. Zaidan, “Joshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies,” in Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, Uppsala, Sweden, 2010, p. 133–137.
      [BibTeX] [Link]

      @inproceedings{li-etal-2010-joshua,
      title = "{J}oshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies",
      author = "Li, Zhifei and
      Callison-Burch, Chris and
      Dyer, Chris and
      Ganitkevitch, Juri and
      Irvine, Ann and
      Khudanpur, Sanjeev and
      Schwartz, Lane and
      Thornton, Wren and
      Wang, Ziyuan and
      Weese, Jonathan and
      Zaidan, Omar",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Peterson, Kay and
      Zaidan, Omar",
      booktitle = "Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and {M}etrics{MATR}",
      month = jul,
      year = "2010",
      address = "Uppsala, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-1718",
      pages = "133--137",
      }

2718. C. Callison-Burch, P. Koehn, C. Monz, K. Peterson, M. Przybocki, and O. Zaidan, “Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation,” in Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, Uppsala, Sweden, 2010, p. 17–53.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2010-findings,
      title = "Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation",
      author = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Peterson, Kay and
      Przybocki, Mark and
      Zaidan, Omar",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Peterson, Kay and
      Zaidan, Omar",
      booktitle = "Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and {M}etrics{MATR}",
      month = jul,
      year = "2010",
      address = "Uppsala, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-1703",
      pages = "17--53",
      }

2719. B. Van Durme and A. Lall, “Online Generation of Locality Sensitive Hash Signatures,” in Proceedings of the ACL 2010 Conference Short Papers, Uppsala, Sweden, 2010, p. 231–235.
      [BibTeX] [Link]

      @inproceedings{van-durme-lall-2010-online,
      title = "Online Generation of Locality Sensitive Hash Signatures",
      author = "Van Durme, Benjamin and
      Lall, Ashwin",
      editor = "Haji{\v{c}}, Jan and
      Carberry, Sandra and
      Clark, Stephen and
      Nivre, Joakim",
      booktitle = "Proceedings of the {ACL} 2010 Conference Short Papers",
      month = jul,
      year = "2010",
      address = "Uppsala, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P10-2043",
      pages = "231--235",
      }

2720. M. Bloodgood and C. Callison-Burch, “Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation,” in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, 2010, p. 854–864.
      [BibTeX] [Link]

      @inproceedings{bloodgood-callison-burch-2010-bucking,
      title = "Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation",
      author = "Bloodgood, Michael and
      Callison-Burch, Chris",
      editor = "Haji{\v{c}}, Jan and
      Carberry, Sandra and
      Clark, Stephen and
      Nivre, Joakim",
      booktitle = "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2010",
      address = "Uppsala, Sweden",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P10-1088",
      pages = "854--864",
      }

2721. S. Momtazi, S. Khudanpur, and D. Klakow, “A Comparative Study of Word Co-occurrence for Term Clustering in Language Model-based Sentence Retrieval,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 325–328.
      [BibTeX] [Link]

      @inproceedings{momtazi-etal-2010-comparative,
      title = "A Comparative Study of Word Co-occurrence for Term Clustering in Language Model-based Sentence Retrieval",
      author = "Momtazi, Saeedeh and
      Khudanpur, Sanjeev and
      Klakow, Dietrich",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1046",
      pages = "325--328",
      }

2722. C. Parada, M. Dredze, D. Filimonov, and F. Jelinek, “Contextual Information Improves OOV Detection in Speech,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 216–224.
      [BibTeX] [Link]

      @inproceedings{parada-etal-2010-contextual,
      title = "Contextual Information Improves {OOV} Detection in Speech",
      author = "Parada, Carolina and
      Dredze, Mark and
      Filimonov, Denis and
      Jelinek, Frederick",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1025",
      pages = "216--224",
      }

2723. R. Wang and C. Callison-Burch, “Cheap Facts and Counter-Facts,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 163–167.
      [BibTeX] [Link]

      @inproceedings{wang-callison-burch-2010-cheap,
      title = "Cheap Facts and Counter-Facts",
      author = "Wang, Rui and
      Callison-Burch, Chris",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0725",
      pages = "163--167",
      }

2724. M. R. Gormley, A. Gerber, M. Harper, and M. Dredze, “Non-Expert Correction of Automatically Generated Relation Annotations,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 204–207.
      [BibTeX] [Link]

      @inproceedings{gormley-etal-2010-non,
      title = "Non-Expert Correction of Automatically Generated Relation Annotations",
      author = "Gormley, Matthew R. and
      Gerber, Adam and
      Harper, Mary and
      Dredze, Mark",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0732",
      pages = "204--207",
      }

2725. T. Finin, W. Murnane, A. Karandikar, N. Keller, J. Martineau, and M. Dredze, “Annotating Named Entities in Twitter Data with Crowdsourcing,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 80–88.
      [BibTeX] [Link]

      @inproceedings{finin-etal-2010-annotating,
      title = "Annotating Named Entities in {T}witter Data with Crowdsourcing",
      author = "Finin, Tim and
      Murnane, William and
      Karandikar, Anand and
      Keller, Nicholas and
      Martineau, Justin and
      Dredze, Mark",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0713",
      pages = "80--88",
      }

2726. A. Rastrow, F. Jelinek, A. Sethy, and B. Ramabhadran, “Unsupervised Model Adaptation using Information-Theoretic Criterion,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 190–197.
      [BibTeX] [Link]

      @inproceedings{rastrow-etal-2010-unsupervised,
      title = "Unsupervised Model Adaptation using Information-Theoretic Criterion",
      author = "Rastrow, Ariya and
      Jelinek, Frederick and
      Sethy, Abhinav and
      Ramabhadran, Bhuvana",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1022",
      pages = "190--197",
      }

2727. C. Callison-Burch and M. Dredze, “Creating Speech and Language Data With Amazon’s Mechanical Turk,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 1–12.
      [BibTeX] [Link]

      @inproceedings{callison-burch-dredze-2010-creating,
      title = "Creating Speech and Language Data With {A}mazon{'}s {M}echanical {T}urk",
      author = "Callison-Burch, Chris and
      Dredze, Mark",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0701",
      pages = "1--12",
      }

2728. T. Chung, M. Post, and D. Gildea, “Factors Affecting the Accuracy of Korean Parsing,” in Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages, Los Angeles, CA, USA, 2010, p. 49–57.
      [BibTeX] [Link]

      @inproceedings{chung-etal-2010-factors,
      title = "Factors Affecting the Accuracy of {K}orean Parsing",
      author = "Chung, Tagyoung and
      Post, Matt and
      Gildea, Daniel",
      editor = "Seddah, Djame and
      Koebler, Sandra and
      Tsarfaty, Reut",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages",
      month = jun,
      year = "2010",
      address = "Los Angeles, CA, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-1406",
      pages = "49--57",
      }

2729. S. Novotney and C. Callison-Burch, “Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 207–215.
      [BibTeX] [Link]

      @inproceedings{novotney-callison-burch-2010-cheap,
      title = "Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription",
      author = "Novotney, Scott and
      Callison-Burch, Chris",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1024",
      pages = "207--215",
      }

2730. C. Napoles and M. Dredze, “Learning Simple Wikipedia: A Cogitation in Ascertaining Abecedarian Language,” in Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids, Los Angeles, CA, USA, 2010, p. 42–50.
      [BibTeX] [Link]

      @inproceedings{napoles-dredze-2010-learning,
      title = "Learning {S}imple {W}ikipedia: A Cogitation in Ascertaining Abecedarian Language",
      author = "Napoles, Courtney and
      Dredze, Mark",
      editor = "Piotrowski, Michael and
      Mahlow, Cerstin and
      Dale, Robert",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids",
      month = jun,
      year = "2010",
      address = "Los Angeles, CA, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0406",
      pages = "42--50",
      }

2731. A. Levenberg, C. Callison-Burch, and M. Osborne, “Stream-based Translation Models for Statistical Machine Translation,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 394–402.
      [BibTeX] [Link]

      @inproceedings{levenberg-etal-2010-stream,
      title = "Stream-based Translation Models for Statistical Machine Translation",
      author = "Levenberg, Abby and
      Callison-Burch, Chris and
      Osborne, Miles",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1062",
      pages = "394--402",
      }

2732. J. Gordon, B. Van Durme, and L. Schubert, “Evaluation of Commonsense Knowledge with Mechanical Turk,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 159–162.
      [BibTeX] [Link]

      @inproceedings{gordon-etal-2010-evaluation,
      title = "Evaluation of Commonsense Knowledge with {M}echanical {T}urk",
      author = "Gordon, Jonathan and
      Van Durme, Benjamin and
      Schubert, Lenhart",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0724",
      pages = "159--162",
      }

2733. O. F. Zaidan and C. Callison-Burch, “Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, 2010, p. 369–372.
      [BibTeX] [Link]

      @inproceedings{zaidan-callison-burch-2010-predicting,
      title = "Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators",
      author = "Zaidan, Omar F. and
      Callison-Burch, Chris",
      editor = "Kaplan, Ron and
      Burstein, Jill and
      Harper, Mary and
      Penn, Gerald",
      booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      month = jun,
      year = "2010",
      address = "Los Angeles, California",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N10-1057",
      pages = "369--372",
      }

2734. M. Bloodgood and C. Callison-Burch, “Using Mechanical Turk to Build Machine Translation Evaluation Sets,” in Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, 2010, p. 208–211.
      [BibTeX] [Link]

      @inproceedings{bloodgood-callison-burch-2010-using,
      title = "Using {M}echanical {T}urk to Build Machine Translation Evaluation Sets",
      author = "Bloodgood, Michael and
      Callison-Burch, Chris",
      editor = "Callison-Burch, Chris and
      Dredze, Mark",
      booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon{'}s Mechanical Turk",
      month = jun,
      year = "2010",
      address = "Los Angeles",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W10-0733",
      pages = "208--211",
      }

2735. D. Lin, K. Church, H. Ji, S. Sekine, D. Yarowsky, S. Bergsma, K. Patil, E. Pitler, R. Lathbury, V. Rao, K. Dalwani, and S. Narsale, “New Tools for Web-Scale N-grams,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, 2010.
      [BibTeX] [Abstract] [Link]
      

      While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges.

      @inproceedings{lin-etal-2010-new,
      title = "New Tools for Web-Scale N-grams",
      author = "Lin, Dekang and
      Church, Kenneth and
      Ji, Heng and
      Sekine, Satoshi and
      Yarowsky, David and
      Bergsma, Shane and
      Patil, Kailash and
      Pitler, Emily and
      Lathbury, Rachel and
      Rao, Vikram and
      Dalwani, Kapil and
      Narsale, Sushant",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Maegaard, Bente and
      Mariani, Joseph and
      Odijk, Jan and
      Piperidis, Stelios and
      Rosner, Mike and
      Tapias, Daniel",
      booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
      month = may,
      year = "2010",
      address = "Valletta, Malta",
      publisher = "European Language Resources Association (ELRA)",
      url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/233_Paper.pdf",
      abstract = "While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges.",
      }

2736. Catherine Havasi, D. Lenat, and Benjamin Van Durme, “Commonsense knowledge : papers from the AAAI Fall Symposium.” 2010.
      [BibTeX] [Link]

      @inproceedings{57091916,
      title = {Commonsense knowledge : papers from the AAAI Fall Symposium},
      author = {{Catherine Havasi} and {D. Lenat} and {Benjamin Van Durme}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0121c30e08749c884975e6e346a663d01ff81ef1},
      }

2737. H. Korth, K. Strohbehn, Francisco Tejada, A. Andreou, S. Mcveigh, J. Kitching, and S. Knappe, “Chip-Scale Absolute Scalar Magnetometer for Space Applications,” in Johns Hopkins Apl Technical Digest, 2010.
      [BibTeX] [Link]

      @inproceedings{124482575,
      title = {Chip-Scale Absolute Scalar Magnetometer for Space Applications},
      author = {{H. Korth} and {K. Strohbehn} and {Francisco Tejada} and {A. Andreou} and {S. Mcveigh} and {J. Kitching} and {S. Knappe}},
      year = 2010,
      booktitle = {Johns Hopkins Apl Technical Digest},
      url = {https://www.semanticscholar.org/paper/70af5c414fda1d60416d275e8ab73837306a652f},
      }

2738. H. Hermansky, “History of modulation spectrum in ASR,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
      [BibTeX] [Link]

      @inproceedings{26704443,
      title = {History of modulation spectrum in ASR},
      author = {{H. Hermansky}},
      year = 2010,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/efe183fd3f3ae6cd5a7c0ca7fdc18918e3104888},
      }

2739. D. Gildea and Matt Post, “Syntax-based language models for statistical machine translation.” 2010.
      [BibTeX] [Link]

      @inproceedings{61231407,
      title = {Syntax-based language models for statistical machine translation},
      author = {{D. Gildea} and {Matt Post}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4611bb703286114d36a67a1853ac4b6f700d7439},
      }

2740. Garimella S. V. S. Sivaram, Sridhar Krishna Nemala, N. Mesgarani, and H. Hermansky, “Data-Driven and Feedback Based Spectro-Temporal Features for Speech Recognition,” in IEEE Signal Processing Letters, 2010.
      [BibTeX] [Link]

      @inproceedings{15099587,
      title = {Data-Driven and Feedback Based Spectro-Temporal Features for Speech Recognition},
      author = {{Garimella S. V. S. Sivaram} and {Sridhar Krishna Nemala} and {N. Mesgarani} and {H. Hermansky}},
      year = 2010,
      month = {9},
      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/a5716b84696755dc64fed8f3d89a393061989ce6},
      }

2741. Jonathan Weese and Chris Callison-Burch, “Visualizing Data Structures in Parsing-Based Machine Translation,” in Prague Bulletin of Mathematical Linguistics, 2010.
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      @inproceedings{12217795,
      title = {Visualizing Data Structures in Parsing-Based Machine Translation},
      author = {{Jonathan Weese} and {Chris Callison-Burch}},
      year = 2010,
      booktitle = {Prague Bulletin of Mathematical Linguistics},
      url = {https://www.semanticscholar.org/paper/2271401a236b80e9ab352067a2f363515b42f130},
      }

2742. Ann Irvine, Mike Kayser, Zhifei Li, Wren N. G. Thornton, and Chris Callison-Burch, “Integrating Output from Specialized Modules in Machine Translation: Transliterations in Joshua,” in Prague Bulletin of Mathematical Linguistics, 2010.
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      @inproceedings{5552588,
      title = {Integrating Output from Specialized Modules in Machine Translation: Transliterations in Joshua},
      author = {{Ann Irvine} and {Mike Kayser} and {Zhifei Li} and {Wren N. G. Thornton} and {Chris Callison-Burch}},
      year = 2010,
      booktitle = {Prague Bulletin of Mathematical Linguistics},
      url = {https://www.semanticscholar.org/paper/bfbe11202871ad92c6a82db05c165a15bd843359},
      }

2743. A. Andreou and A. Cassidy, “Three topics in single-chip parallel computing: theoretical foundations, speech recognition, and the silicon cortex.” 2010.
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      @inproceedings{65076078,
      title = {Three topics in single-chip parallel computing: theoretical foundations, speech recognition, and the silicon cortex},
      author = {{A. Andreou} and {A. Cassidy}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1acb073706f67208f9188ebe68285303079ac073},
      }

2744. Chris Callison-Burch, Trevor Cohn, Chris Dyer, Jonathan Graehl, Adam Lopez, Jan A. Botha, Vladimir Eidelman, ThuyLinh Nguyen, Ziyuan Wang, Jonathan Weese, Olivia Buzek, and Desai Chen, “Models for Synchronous Grammar Induction.” 2010.
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      @inproceedings{14461089,
      title = {Models for Synchronous Grammar Induction},
      author = {{Chris Callison-Burch} and {Trevor Cohn} and {Chris Dyer} and {Jonathan Graehl} and {Adam Lopez} and {Jan A. Botha} and {Vladimir Eidelman} and {ThuyLinh Nguyen} and {Ziyuan Wang} and {Jonathan Weese} and {Olivia Buzek} and {Desai Chen}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0565d24885fb253c45ce46d2411b0267743de810},
      }

2745. Kenneth Ward Church, “More is More,” in A Way with Words, 2010.
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      @inproceedings{218982630,
      title = {More is More},
      author = {{Kenneth Ward Church}},
      year = 2010,
      booktitle = {A Way with Words},
      url = {https://www.semanticscholar.org/paper/33d70a6cbf5fd15a865ebc09bfa134477b65b616},
      }

2746. Lane Schwartz and Chris Callison-Burch, “Hierarchical Phrase-Based Grammar Extraction in Joshua:,” in Prague Bulletin of Mathematical Linguistics, 2010.
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      @inproceedings{8384802,
      title = {Hierarchical Phrase-Based Grammar Extraction in Joshua:},
      author = {{Lane Schwartz} and {Chris Callison-Burch}},
      year = 2010,
      booktitle = {Prague Bulletin of Mathematical Linguistics},
      url = {https://www.semanticscholar.org/paper/f58bf7e514da19263743ec736a80f571cc30eb91},
      }

2747. K. Baker, M. Bloodgood, C. Callison-Burch, B. Dorr, N. Filardo, L. Levin, S. Miller, and C. Piatko, “Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach,” in Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA, 2010.
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      We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality{–-}and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.

      @inproceedings{baker-etal-2010-semantically,
      title = "Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach",
      author = "Baker, Kathryn and
      Bloodgood, Michael and
      Callison-Burch, Chris and
      Dorr, Bonnie and
      Filardo, Nathaniel and
      Levin, Lori and
      Miller, Scott and
      Piatko, Christine",
      booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
      month = oct # " 31-" # nov # " 4",
      year = "2010",
      address = "Denver, Colorado, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2010.amta-papers.7",
      abstract = "We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality{---}and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.",
      }

2748. Lane Schwartz and Chris Callison-Burch, “‘HFRGLQJ LQ -RVKXD.” 2010.
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      @inproceedings{88492782,
      title = {'HFRGLQJ LQ -RVKXD},
      author = {{Lane Schwartz} and {Chris Callison-Burch}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/08ebc9cff52a98a4573995e3d91a2cdd519d177c},
      }

2749. Chris Callison-Burch, Philipp Koehn, Christof Monz, Kay Peterson, and Omar Zaidan, “Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, WMT@ACL 2010, Uppsala, Sweden, July 15-16, 2010,” in WMT@ACL, 2010.
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      @inproceedings{7114327,
      title = {Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, WMT@ACL 2010, Uppsala, Sweden, July 15-16, 2010},
      author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {Kay Peterson} and {Omar Zaidan}},
      year = 2010,
      month = {7},
      booktitle = {WMT@ACL},
      url = {https://www.semanticscholar.org/paper/19c253bafd9fc88e00d3bb234f167cde5c732ed7},
      }

2750. Damianos G. Karakos, Haolang Zhou, Puyang Xu, S. Khudanpur, and A. Andreou, “Two Self-supervised Learning Techniques for Speech Recognition.” 2010.
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      @inproceedings{14073180,
      title = {Two Self-supervised Learning Techniques for Speech Recognition},
      author = {{Damianos G. Karakos} and {Haolang Zhou} and {Puyang Xu} and {S. Khudanpur} and {A. Andreou}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/59e9355329b0418f99608a9d5c615963bcd495f1},
      }

2751. Scott Novotney and Chris Callison-Burch, “Shared task: crowdsourced accessibility elicitation of Wikipedia articles,” in HLT-NAACL 2010, 2010.
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      @inproceedings{17247831,
      title = {Shared task: crowdsourced accessibility elicitation of Wikipedia articles},
      author = {{Scott Novotney} and {Chris Callison-Burch}},
      year = 2010,
      month = {6},
      booktitle = {HLT-NAACL 2010},
      url = {https://www.semanticscholar.org/paper/2327f9117d9c8daa98ca80d3d05cbcd827d91d80},
      }

2752. P. Motlícek, Sriram Ganapathy, H. Hermansky, and H. Garudadri, “Wide-Band Audio Coding Based on Frequency-Domain Linear Prediction,” in EURASIP Journal on Audio, Speech, and Music Processing, 2010.
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      @inproceedings{14790161,
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      author = {{P. Motlícek} and {Sriram Ganapathy} and {H. Hermansky} and {H. Garudadri}},
      year = 2010,
      booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
      url = {https://www.semanticscholar.org/paper/fdf0a0c45c3a4ebcd505815193770a398e2de5b9},
      }

2753. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Comparison of modulation features for phoneme recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
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      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/e5686dccacc769ea2765e70ba0a17725c14143a9},
      }

2754. B. Dhar, R. Ozgun, Byung-Jun Jung, H. Katz, and A. Andreou, “Optimum bias of CMOS organic field effect transistor inverter through threshold adjustment of both p- and n-type devices,” in Electronics Letters, 2010.
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      title = {Optimum bias of CMOS organic field effect transistor inverter through threshold adjustment of both p- and n-type devices},
      author = {{B. Dhar} and {R. Ozgun} and {Byung-Jun Jung} and {H. Katz} and {A. Andreou}},
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      month = {9},
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      }

2755. Sriram Ganapathy, P. Motlícek, and H. Hermansky, “Autoregressive Models of Amplitude Modulations in Audio Compression,” in IEEE Transactions on Audio, Speech, and Language Processing, 2010.
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      @inproceedings{417567,
      title = {Autoregressive Models of Amplitude Modulations in Audio Compression},
      author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky}},
      year = 2010,
      month = {8},
      booktitle = {IEEE Transactions on Audio, Speech, and Language Processing},
      url = {https://www.semanticscholar.org/paper/7d5e611f3c13b0fd1445b98563664e65f48a5c63},
      }

2756. Acm Sigir, ChengXiang Zhai, David Yarowsky, E. Viegas, Kuansan Wang, and S. Vogel, “Web N-gram Workshop Workshop of the 33 rd Annual International.” 2010.
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      title = {Web N-gram Workshop Workshop of the 33 rd Annual International},
      author = {{Acm Sigir} and {ChengXiang Zhai} and {David Yarowsky} and {E. Viegas} and {Kuansan Wang} and {S. Vogel}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b3709cce58b27b1f90fe4fab68e56cb8cb1c5291},
      }

2757. H. Hermansky, “Posterior‐based attributes in machine recognition of speech..” 2010.
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      @inproceedings{120830830,
      title = {Posterior‐based attributes in machine recognition of speech.},
      author = {{H. Hermansky}},
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      }

2758. Dekang Lin, Kenneth Ward Church, Heng Ji, S. Sekine, David Yarowsky, S. Bergsma, Kailash Patil, Emily Pitler, Rachel Lathbury, Vikram Rao, Kapil Dalwani, and Sushant Narsale, “Unsupervised Acquisition of Lexical Knowledge From N-grams : Final Report of the 2009 JHU CLSP Workshop.” 2010.
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      @inproceedings{15828105,
      title = {Unsupervised Acquisition of Lexical Knowledge From N-grams : Final Report of the 2009 JHU CLSP Workshop},
      author = {{Dekang Lin} and {Kenneth Ward Church} and {Heng Ji} and {S. Sekine} and {David Yarowsky} and {S. Bergsma} and {Kailash Patil} and {Emily Pitler} and {Rachel Lathbury} and {Vikram Rao} and {Kapil Dalwani} and {Sushant Narsale}},
      year = 2010,
      booktitle = {},
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      }

2759. Mark Dredze, Alex Kulesza, and K. Crammer, “Multi-domain learning by confidence-weighted parameter combination,” in Machine-mediated learning, 2010.
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      title = {Multi-domain learning by confidence-weighted parameter combination},
      author = {{Mark Dredze} and {Alex Kulesza} and {K. Crammer}},
      year = 2010,
      month = {5},
      booktitle = {Machine-mediated learning},
      url = {https://www.semanticscholar.org/paper/5959ca92fe68e5c06fa4feedc32d9a94d1b2c03a},
      }

2760. A. Jansen, Kenneth Ward Church, and H. Hermansky, “Towards spoken term discovery at scale with zero resources,” in Interspeech, 2010.
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      @inproceedings{7427096,
      title = {Towards spoken term discovery at scale with zero resources},
      author = {{A. Jansen} and {Kenneth Ward Church} and {H. Hermansky}},
      year = 2010,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/7a29bbb30bf72cfc7ac52a351a04a1178e29dd7f},
      }

2761. Xudong Chen and Kenneth Ward Church, “Direct Printing/Micro-dispensing Solution for 3D Coating Applications.” 2010.
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      @inproceedings{112934259,
      title = {Direct Printing/Micro-dispensing Solution for 3D Coating Applications},
      author = {{Xudong Chen} and {Kenneth Ward Church}},
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      url = {https://www.semanticscholar.org/paper/84a2929615a231151bb7177d1fe495181538fdcb},
      }

2762. Garimella S. V. S. Sivaram, Sridhar Krishna Nemala, Mounya Elhilali, T. Tran, and H. Hermansky, “Sparse coding for speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
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      @inproceedings{4632000,
      title = {Sparse coding for speech recognition},
      author = {{Garimella S. V. S. Sivaram} and {Sridhar Krishna Nemala} and {Mounya Elhilali} and {T. Tran} and {H. Hermansky}},
      year = 2010,
      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/365cabf42c8d5a57a5843ec52cc62d43f2e1bfba},
      }

2763. Chris Callison-Burch and Mark Dredze, “Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk.” 2010.
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      @inproceedings{12295680,
      title = {Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk},
      author = {{Chris Callison-Burch} and {Mark Dredze}},
      year = 2010,
      month = {6},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e79470389fe3f73d37e8fc099439cd17e1c7748d},
      }

2764. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Robust spectro-temporal features based on autoregressive models of Hilbert envelopes,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
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      @inproceedings{2045383,
      title = {Robust spectro-temporal features based on autoregressive models of Hilbert envelopes},
      author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
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      month = {3},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
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      }

2765. L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “PERFORMANCE OF ENHANCED-UMTS HSDPA USING TRANSMIT DIVERSITY AND POWER CONTROL SCHEMES.” 2010.
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      @inproceedings{4541668,
      title = {PERFORMANCE OF ENHANCED-UMTS HSDPA USING TRANSMIT DIVERSITY AND POWER CONTROL SCHEMES},
      author = {{L. Bahl} and {J. Cocke} and {F. Jelinek} and {J. Raviv}},
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      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b76a6509ead3f10a8d81e8c8ee306f2b374d6f77},
      }

2766. Charbel G. Rizk, P. Pouliquen, and A. Andreou, “Flexible Readout and Integration Sensor (FRIS): New Class of Imaging Sensor Arrays Optimized for Air and Missile Defense,” in Johns Hopkins Apl Technical Digest, 2010.
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      @inproceedings{16064355,
      title = {Flexible Readout and Integration Sensor (FRIS): New Class of Imaging Sensor Arrays Optimized for Air and Missile Defense},
      author = {{Charbel G. Rizk} and {P. Pouliquen} and {A. Andreou}},
      year = 2010,
      booktitle = {Johns Hopkins Apl Technical Digest},
      url = {https://www.semanticscholar.org/paper/8e20e16338c79dddcab19c1afd95c1fd30d7dfcf},
      }

2767. Justin Ma, Alex Kulesza, Mark Dredze, K. Crammer, L. Saul, and Fernando C Pereira, “Exploiting Feature Covariance in High-Dimensional Online Learning,” in International Conference on Artificial Intelligence and Statistics, 2010.
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      title = {Exploiting Feature Covariance in High-Dimensional Online Learning},
      author = {{Justin Ma} and {Alex Kulesza} and {Mark Dredze} and {K. Crammer} and {L. Saul} and {Fernando C Pereira}},
      year = 2010,
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      booktitle = {International Conference on Artificial Intelligence and Statistics},
      url = {https://www.semanticscholar.org/paper/6b5061fbbe1727c0dabbbed48012cbfac7e255c9},
      }

2768. Shih-Chii Liu, N. Mesgarani, J. Harris, and H. Hermansky, “The use of spike-based representations for hardware audition systems,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010.
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      title = {The use of spike-based representations for hardware audition systems},
      author = {{Shih-Chii Liu} and {N. Mesgarani} and {J. Harris} and {H. Hermansky}},
      year = 2010,
      month = {8},
      booktitle = {Proceedings of 2010 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/4a26d523c302fd5c9a1db152021f29e390df67db},
      }

2769. Carolina Parada, A. Sethy, Mark Dredze, and F. Jelinek, “A spoken term detection framework for recovering out-of-vocabulary words using the web,” in Interspeech, 2010.
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      author = {{Carolina Parada} and {A. Sethy} and {Mark Dredze} and {F. Jelinek}},
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      }

2770. Stefan Kombrink, M. Hannemann, L. Burget, and H. Hermansky, “Recovery of Rare Words in Lecture Speech,” in International Conference on Text, Speech and Dialogue, 2010.
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      author = {{Stefan Kombrink} and {M. Hannemann} and {L. Burget} and {H. Hermansky}},
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      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
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2771. David Yarowsky, “Word Sense Disambiguation,” in Handbook of Natural Language Processing, 2010.
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2772. A. Klementiev, Chris Callison-Burch, and Ann Irvine, “HLTCOE Technical Reports.” 2010.
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2773. Garimella S. V. S. Sivaram, Sriram Ganapathy, and H. Hermansky, “Sparse auto-associative neural networks: theory and application to speech recognition,” in Interspeech, 2010.
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2774. M. D. Federico, P. Julián, P. Mandolesi, and A. Andreou, “PWL cores for nonlinear array processing,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010.
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2775. Samuel Thomas, Kailash Patil, Sriram Ganapathy, N. Mesgarani, and H. Hermansky, “A phoneme recognition framework based on auditory spectro-temporal receptive fields,” in Interspeech, 2010.
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2776. Damianos G. Karakos, Jason R. Smith, and S. Khudanpur, “Hypothesis ranking and two-pass approaches for machine translation system combination,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010.
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2777. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Cross-lingual and multi-stream posterior features for low resource LVCSR systems,” in Interspeech, 2010.
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2778. Jonathan Gordon, Benjamin Van Durme, and Lenhart K. Schubert, “Learning from the Web: Extracting General World Knowledge from Noisy Text,” in Collaboratively-Built Knowledge Sources and AI, 2010.
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2779. N. Mesgarani, Samuel Thomas, and H. Hermansky, “A multistream multiresolution framework for phoneme recognition,” in Interspeech, 2010.
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      @inproceedings{12003886,
      title = {A multistream multiresolution framework for phoneme recognition},
      author = {{N. Mesgarani} and {Samuel Thomas} and {H. Hermansky}},
      year = 2010,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/4b76d644d4946438d34b40db30a61643c6c39bb0},
      }

2780. A. Irvine, C. Callison-Burch, and A. Klementiev, “Transliterating From All Languages,” in Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA, 2010.
      [BibTeX] [Abstract] [Link]
      

      Much of the previous work on transliteration has depended on resources and attributes specific to particular language pairs. In this work, rather than focus on a single language pair, we create robust models for transliterating from all languages in a large, diverse set to English. We create training data for 150 languages by mining name pairs from Wikipedia. We train 13 systems and analyze the effects of the amount of training data on transliteration performance. We also present an analysis of the types of errors that the systems make. Our analyses are particularly valuable for building machine translation systems for low resource languages, where creating and integrating a transliteration module for a language with few NLP resources may provide substantial gains in translation performance.

      @inproceedings{irvine-etal-2010-transliterating,
      title = "Transliterating From All Languages",
      author = "Irvine, Ann and
      Callison-Burch, Chris and
      Klementiev, Alexandre",
      booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
      month = oct # " 31-" # nov # " 4",
      year = "2010",
      address = "Denver, Colorado, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2010.amta-papers.12",
      abstract = "Much of the previous work on transliteration has depended on resources and attributes specific to particular language pairs. In this work, rather than focus on a single language pair, we create robust models for transliterating from all languages in a large, diverse set to English. We create training data for 150 languages by mining name pairs from Wikipedia. We train 13 systems and analyze the effects of the amount of training data on transliteration performance. We also present an analysis of the types of errors that the systems make. Our analyses are particularly valuable for building machine translation systems for low resource languages, where creating and integrating a transliteration module for a language with few NLP resources may provide substantial gains in translation performance.",
      }

2781. T. Delbrück, T. Koch, R. Berner, and H. Hermansky, “Fully integrated 500uW speech detection wake-up circuit,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010.
      [BibTeX] [Link]

      @inproceedings{11487120,
      title = {Fully integrated 500uW speech detection wake-up circuit},
      author = {{T. Delbrück} and {T. Koch} and {R. Berner} and {H. Hermansky}},
      year = 2010,
      month = {8},
      booktitle = {Proceedings of 2010 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/20d6446f45669e1130376a826bbc0423ba786a22},
      }

2782. Tomas Mikolov, M. Karafiát, L. Burget, J. Černocký, and S. Khudanpur, “Recurrent neural network based language model,” in Interspeech, 2010.
      [BibTeX] [Link]

      @inproceedings{17048224,
      title = {Recurrent neural network based language model},
      author = {{Tomas Mikolov} and {M. Karafiát} and {L. Burget} and {J. Černocký} and {S. Khudanpur}},
      year = 2010,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/9819b600a828a57e1cde047bbe710d3446b30da5},
      }

2783. Catherine Havasi, Douglas B. Lenat, and Benjamin Van Durme, “Preface,” in AAAI Fall Symposium: Commonsense Knowledge, 2010.
      [BibTeX] [Link]

      @inproceedings{263890750,
      title = {Preface},
      author = {{Catherine Havasi} and {Douglas B. Lenat} and {Benjamin Van Durme}},
      year = 2010,
      booktitle = {AAAI Fall Symposium: Commonsense Knowledge},
      url = {https://www.semanticscholar.org/paper/699748263b85a2dba4fa2a203e079e545695b711},
      }

2784. S. Khudanpur and Zhifei Li, “Discriminative training and variational decoding in machine translation via novel algorithms for weighted hypergraphs.” 2010.
      [BibTeX] [Link]

      @inproceedings{122928409,
      title = {Discriminative training and variational decoding in machine translation via novel algorithms for weighted hypergraphs},
      author = {{S. Khudanpur} and {Zhifei Li}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/ab04d2d1fdd3c6744ce77b005b81b0cc460bdd89},
      }

2785. Lane Schwartz and Chris Callison-Burch, “NUMBER 93 JANUARY 2010 157 – 166 Hierarchical Phrase-Based Grammar Extraction in Joshua Suffix Arrays and Prefix Trees.” 2010.
      [BibTeX] [Link]

      @inproceedings{616568,
      title = {NUMBER 93 JANUARY 2010 157 – 166 Hierarchical Phrase-Based Grammar Extraction in Joshua Suffix Arrays and Prefix Trees},
      author = {{Lane Schwartz} and {Chris Callison-Burch}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3a97ec98f1678f4c2932e5fe20ca224c06125922},
      }

2786. Byung-Jun Jung, Kyu-Chul Lee, Jia Sun, A. Andreou, and H. Katz, “Air‐Operable, High‐Mobility Organic Transistors with Semifluorinated Side Chains and Unsubstituted Naphthalenetetracarboxylic Diimide Cores: High Mobility and Environmental and Bias Stress Stability from the Perfluorooctylpropyl Side Chain,” in Advanced Functional Materials, 2010.
      [BibTeX] [Link]

      @inproceedings{94175074,
      title = {Air‐Operable, High‐Mobility Organic Transistors with Semifluorinated Side Chains and Unsubstituted Naphthalenetetracarboxylic Diimide Cores: High Mobility and Environmental and Bias Stress Stability from the Perfluorooctylpropyl Side Chain},
      author = {{Byung-Jun Jung} and {Kyu-Chul Lee} and {Jia Sun} and {A. Andreou} and {H. Katz}},
      year = 2010,
      month = {9},
      booktitle = {Advanced Functional Materials},
      url = {https://www.semanticscholar.org/paper/ab3af82be39b3bd0a9b7685ecb6121e1fb42596e},
      }

2787. D. Rao and David Yarowsky, “Detecting Latent User Properties in Social Media.” 2010.
      [BibTeX] [Link]

      @inproceedings{38146728,
      title = {Detecting Latent User Properties in Social Media},
      author = {{D. Rao} and {David Yarowsky}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d815d10afdeb957aa688c47e2efdb107db0e921c},
      }

2788. Balakrishnan Varadarajan, S. Khudanpur, and T. Tran, “A greedy algorithm for sparse recovery using precise metrics.” 2010.
      [BibTeX] [Link]

      @inproceedings{209337018,
      title = {A greedy algorithm for sparse recovery using precise metrics},
      author = {{Balakrishnan Varadarajan} and {S. Khudanpur} and {T. Tran}},
      year = 2010,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9111c2e64cfa194d27fe2aaf869b4e65fed0be51},
      }

2789. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Temporal envelope subtraction for robust speech recognition using modulation spectrum,” in 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009.
      [BibTeX] [Link]

      @inproceedings{1338321,
      title = {Temporal envelope subtraction for robust speech recognition using modulation spectrum},
      author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
      year = 2009,
      month = {12},
      booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/6e8b74d0f228d3a75d04ea5cae222ca3522d5724},
      }

2790. Puyang Xu, Damianos G. Karakos, and S. Khudanpur, “Self-supervised discriminative training of statistical language models,” in 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009.
      [BibTeX] [Link]

      @inproceedings{6282404,
      title = {Self-supervised discriminative training of statistical language models},
      author = {{Puyang Xu} and {Damianos G. Karakos} and {S. Khudanpur}},
      year = 2009,
      month = {12},
      booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/0963942fdcba17f7b94d1d636431d4a772476711},
      }

2791. Sriram Ganapathy, Samuel Thomas, P. Motlícek, and H. Hermansky, “Applications of signal analysis using autoregressive models for amplitude modulation,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2009.
      [BibTeX] [Link]

      @inproceedings{14626552,
      title = {Applications of signal analysis using autoregressive models for amplitude modulation},
      author = {{Sriram Ganapathy} and {Samuel Thomas} and {P. Motlícek} and {H. Hermansky}},
      year = 2009,
      month = {12},
      booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
      url = {https://www.semanticscholar.org/paper/0d1708547eb4ebf51718eead4696eca4ce69a92b},
      }

2792. Anoop Deoras and F. Jelinek, “Iterative decoding: A novel re-scoring framework for confusion networks,” in 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009.
      [BibTeX] [Link]

      @inproceedings{15223401,
      title = {Iterative decoding: A novel re-scoring framework for confusion networks},
      author = {{Anoop Deoras} and {F. Jelinek}},
      year = 2009,
      month = {12},
      booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/1c4b03de4d1afea69833ff29d5a105de0ddab8d4},
      }

2793. Christopher M. White, S. Khudanpur, and P. Wolfe, “Likelihood-Based Semi-Supervised Model Selection With Applications to Speech Processing,” in IEEE Journal on Selected Topics in Signal Processing, 2009.
      [BibTeX] [Link]

      @inproceedings{2498421,
      title = {Likelihood-Based Semi-Supervised Model Selection With Applications to Speech Processing},
      author = {{Christopher M. White} and {S. Khudanpur} and {P. Wolfe}},
      year = 2009,
      month = {11},
      booktitle = {IEEE Journal on Selected Topics in Signal Processing},
      url = {https://www.semanticscholar.org/paper/81893362c863261dcdaa495eda0509f52d9c2b03},
      }

2794. Paul McNamee, Mark Dredze, Adam Gerber, Nikesh Garera, Timothy W. Finin, J. Mayfield, C. Piatko, D. Rao, David Yarowsky, and Markus Dreyer, “HLTCOE Approaches to Knowledge Base Population at TAC 2009,” in Text Analysis Conference, 2009.
      [BibTeX] [Link]

      @inproceedings{1067273,
      title = {HLTCOE Approaches to Knowledge Base Population at TAC 2009},
      author = {{Paul McNamee} and {Mark Dredze} and {Adam Gerber} and {Nikesh Garera} and {Timothy W. Finin} and {J. Mayfield} and {C. Piatko} and {D. Rao} and {David Yarowsky} and {Markus Dreyer}},
      year = 2009,
      month = {11},
      booktitle = {Text Analysis Conference},
      url = {https://www.semanticscholar.org/paper/35cbf98266b94d7d31d67e09faf57f8ea6f2204f},
      }

2795. Balakrishnan Varadarajan, C. Reiley, Henry Lin, S. Khudanpur, and Gregory Hager, “Data-Derived Models for Segmentation with Application to Surgical Assessment and Training,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009.
      [BibTeX] [Link]

      @inproceedings{10609865,
      title = {Data-Derived Models for Segmentation with Application to Surgical Assessment and Training},
      author = {{Balakrishnan Varadarajan} and {C. Reiley} and {Henry Lin} and {S. Khudanpur} and {Gregory Hager}},
      year = 2009,
      month = {10},
      booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
      url = {https://www.semanticscholar.org/paper/067ec3a26063e20984d61aecfd93f36743a8f706},
      }

2796. Sriram Ganapathy, P. Motlícek, and H. Hermansky, “MDCT for Encoding Residual Signals in Frequency Domain Linear Prediction,” in Journal of The Audio Engineering Society, 2009.
      [BibTeX] [Link]

      @inproceedings{15673872,
      title = {MDCT for Encoding Residual Signals in Frequency Domain Linear Prediction},
      author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky}},
      year = 2009,
      month = {10},
      booktitle = {Journal of The Audio Engineering Society},
      url = {https://www.semanticscholar.org/paper/5b3e56109172a2bc6e3c6bb45d2a2277babd6145},
      }

2797. Y. Marton, C. Callison-Burch, and P. Resnik, “Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 2009, p. 381–390.
      [BibTeX] [Link]

      @inproceedings{marton-etal-2009-improved,
      title = "Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases",
      author = "Marton, Yuval and
      Callison-Burch, Chris and
      Resnik, Philip",
      editor = "Koehn, Philipp and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
      month = aug,
      year = "2009",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D09-1040",
      pages = "381--390",
      }

2798. N. Garera and D. Yarowsky, “Modeling Latent Biographic Attributes in Conversational Genres,” in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Suntec, Singapore, 2009, p. 710–718.
      [BibTeX] [Link]

      @inproceedings{garera-yarowsky-2009-modeling,
      title = "Modeling Latent Biographic Attributes in Conversational Genres",
      author = "Garera, Nikesh and
      Yarowsky, David",
      editor = "Su, Keh-Yih and
      Su, Jian and
      Wiebe, Janyce and
      Li, Haizhou",
      booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P09-1080",
      pages = "710--718",
      }

2799. O. F. Zaidan and C. Callison-Burch, “Feasibility of Human-in-the-loop Minimum Error Rate Training,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 2009, p. 52–61.
      [BibTeX] [Link]

      @inproceedings{zaidan-callison-burch-2009-feasibility,
      title = "Feasibility of Human-in-the-loop Minimum Error Rate Training",
      author = "Zaidan, Omar F. and
      Callison-Burch, Chris",
      editor = "Koehn, Philipp and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
      month = aug,
      year = "2009",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D09-1006",
      pages = "52--61",
      }

2800. Z. Li, J. Eisner, and S. Khudanpur, “Variational Decoding for Statistical Machine Translation,” in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Suntec, Singapore, 2009, p. 593–601.
      [BibTeX] [Link]

      @inproceedings{li-etal-2009-variational,
      title = "Variational Decoding for Statistical Machine Translation",
      author = "Li, Zhifei and
      Eisner, Jason and
      Khudanpur, Sanjeev",
      editor = "Su, Keh-Yih and
      Su, Jian and
      Wiebe, Janyce and
      Li, Haizhou",
      booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P09-1067",
      pages = "593--601",
      }

2801. K. Crammer, M. Dredze, and A. Kulesza, “Multi-Class Confidence Weighted Algorithms,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 2009, p. 496–504.
      [BibTeX] [Link]

      @inproceedings{crammer-etal-2009-multi,
      title = "Multi-Class Confidence Weighted Algorithms",
      author = "Crammer, Koby and
      Dredze, Mark and
      Kulesza, Alex",
      editor = "Koehn, Philipp and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
      month = aug,
      year = "2009",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D09-1052",
      pages = "496--504",
      }

2802. Z. Li, C. Callison-Burch, C. Dyer, J. Ganitkevitch, S. Khudanpur, L. Schwartz, W. N. G. Thornton, J. Weese, and O. F. Zaidan, “Demonstration of Joshua: An Open Source Toolkit for Parsing-based Machine Translation,” in Proceedings of the ACL-IJCNLP 2009 Software Demonstrations, Suntec, Singapore, 2009, p. 25–28.
      [BibTeX] [Link]

      @inproceedings{li-etal-2009-demonstration,
      title = "Demonstration of {J}oshua: An Open Source Toolkit for Parsing-based Machine Translation",
      author = "Li, Zhifei and
      Callison-Burch, Chris and
      Dyer, Chris and
      Ganitkevitch, Juri and
      Khudanpur, Sanjeev and
      Schwartz, Lane and
      Thornton, Wren N. G. and
      Weese, Jonathan and
      Zaidan, Omar F.",
      editor = "Lee, Gary Geunbae and
      Schulte im Walde, Sabine",
      booktitle = "Proceedings of the {ACL}-{IJCNLP} 2009 Software Demonstrations",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P09-4007",
      pages = "25--28",
      }

2803. D. Rao and D. Yarowsky, “Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-Reduce,” in Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4), Suntec, Singapore, 2009, p. 58–65.
      [BibTeX] [Link]

      @inproceedings{rao-yarowsky-2009-ranking,
      title = "Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-Reduce",
      author = "Rao, Delip and
      Yarowsky, David",
      editor = "Choudhury, Monojit and
      Hassan, Samer and
      Mukherjee, Animesh and
      Muresan, Smaranda",
      booktitle = "Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing ({T}ext{G}raphs-4)",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W09-3209",
      pages = "58--65",
      }

2804. A. Sayeed, T. Elsayed, N. Garera, D. Alexander, T. Xu, D. Oard, D. Yarowsky, and C. Piatko, “Arabic Cross-Document Coreference Resolution,” in Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, Suntec, Singapore, 2009, p. 357–360.
      [BibTeX] [Link]

      @inproceedings{sayeed-etal-2009-arabic,
      title = "{A}rabic Cross-Document Coreference Resolution",
      author = "Sayeed, Asad and
      Elsayed, Tamer and
      Garera, Nikesh and
      Alexander, David and
      Xu, Tan and
      Oard, Doug and
      Yarowsky, David and
      Piatko, Christine",
      editor = "Su, Keh-Yih and
      Su, Jian and
      Wiebe, Janyce and
      Li, Haizhou",
      booktitle = "Proceedings of the {ACL}-{IJCNLP} 2009 Conference Short Papers",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P09-2090",
      pages = "357--360",
      }

2805. E. Fitzgerald, F. Jelinek, and R. Frank, “What lies beneath: Semantic and syntactic analysis of manually reconstructed spontaneous speech,” in Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Suntec, Singapore, 2009, p. 746–754.
      [BibTeX] [Link]

      @inproceedings{fitzgerald-etal-2009-lies,
      title = "What lies beneath: Semantic and syntactic analysis of manually reconstructed spontaneous speech",
      author = "Fitzgerald, Erin and
      Jelinek, Frederick and
      Frank, Robert",
      editor = "Su, Keh-Yih and
      Su, Jian and
      Wiebe, Janyce and
      Li, Haizhou",
      booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
      month = aug,
      year = "2009",
      address = "Suntec, Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P09-1084",
      pages = "746--754",
      }

2806. C. Callison-Burch, “Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 2009, p. 286–295.
      [BibTeX] [Link]

      @inproceedings{callison-burch-2009-fast,
      title = "Fast, Cheap, and Creative: Evaluating Translation Quality Using {A}mazon{'}s {M}echanical {T}urk",
      author = "Callison-Burch, Chris",
      editor = "Koehn, Philipp and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
      month = aug,
      year = "2009",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D09-1030",
      pages = "286--295",
      }

2807. E. Fitzgerald, F. Jelinek, and K. Hall, “Integrating sentence- and word-level error identification for disfluency correction,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 2009, p. 765–774.
      [BibTeX] [Link]

      @inproceedings{fitzgerald-etal-2009-integrating,
      title = "Integrating sentence- and word-level error identification for disfluency correction",
      author = "Fitzgerald, Erin and
      Jelinek, Frederick and
      Hall, Keith",
      editor = "Koehn, Philipp and
      Mihalcea, Rada",
      booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
      month = aug,
      year = "2009",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D09-1080",
      pages = "765--774",
      }

2808. Z. Li and J. Eisner, “First- and Second-Order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2009, p. 40–51.
      [BibTeX] [Link]

      @InProceedings{li-eisner-2009,
      aclid = "D09-1005",
      author = "Zhifei Li and Jason Eisner",
      title = "First- and Second-Order Expectation Semirings with
      Applications to Minimum-Risk Training on Translation
      Forests",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "40--51",
      year = "2009",
      month = aug,
      address = "Singapore",
      URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-2009",
      }

2809. M. Dreyer and J. Eisner, “Graphical Models over Multiple Strings,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2009, p. 101–110.
      [BibTeX] [Link]

      @InProceedings{dreyer-eisner-2009,
      aclid = "D09-1011",
      author = "Markus Dreyer and Jason Eisner",
      title = "Graphical Models over Multiple Strings",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "101--110",
      year = "2009",
      month = aug,
      address = "Singapore",
      URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2009",
      }

2810. D. A. Smith and J. Eisner, “Parser Adaptation and Projection with Quasi-Synchronous Grammar Features,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2009, p. 822–831.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2009,
      aclid = "D09-1086",
      author = "David A. Smith and Jason Eisner",
      title = "Parser Adaptation and Projection with
      Quasi-Synchronous Grammar Features",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "822--831",
      year = "2009",
      month = aug,
      address = "Singapore",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2009",
      }

2811. R. Tromble and J. Eisner, “Learning Linear Ordering Problems for Better Translation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2009, p. 1007–1016.
      [BibTeX] [Link]

      @InProceedings{tromble-eisner-2009,
      aclid = "D09-1105",
      author = "Roy Tromble and Jason Eisner",
      title = "Learning Linear Ordering Problems for Better
      Translation",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "1007--1016",
      year = "2009",
      month = aug,
      address = "Singapore",
      URL = "http://cs.jhu.edu/~jason/papers/#tromble-eisner-2009",
      }

2812. Z. Li, J. Eisner, and S. Khudanpur, “Variational Decoding for Statistical Machine Translation,” in Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL), Singapore, 2009, p. 593–601.
      [BibTeX] [Link]

      @InProceedings{li-eisner-khudanpur-2009,
      aclid = "P09-1067",
      author = "Zhifei Li and Jason Eisner and Sanjeev Khudanpur",
      title = "Variational Decoding for Statistical Machine
      Translation",
      booktitle = "Proceedings of the 47th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "593--601",
      year = "2009",
      month = aug,
      address = "Singapore",
      note = "Nominated for Best Paper Award.",
      URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-khudanpur-2009",
      }

2813. N. Garera, C. Callison-Burch, and D. Yarowsky, “Improving Translation Lexicon Induction from Monolingual Corpora via Dependency Contexts and Part-of-Speech Equivalences,” in Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), Boulder, Colorado, 2009, p. 129–137.
      [BibTeX] [Link]

      @inproceedings{garera-etal-2009-improving,
      title = "Improving Translation Lexicon Induction from Monolingual Corpora via Dependency Contexts and Part-of-Speech Equivalences",
      author = "Garera, Nikesh and
      Callison-Burch, Chris and
      Yarowsky, David",
      editor = "Stevenson, Suzanne and
      Carreras, Xavier",
      booktitle = "Proceedings of the Thirteenth Conference on Computational Natural Language Learning ({C}o{NLL}-2009)",
      month = jun,
      year = "2009",
      address = "Boulder, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W09-1117",
      pages = "129--137",
      }

2814. Z. Li and S. Khudanpur, “Efficient Extraction of Oracle-best Translations from Hypergraphs,” in Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, Boulder, Colorado, 2009, p. 9–12.
      [BibTeX] [Link]

      @inproceedings{li-khudanpur-2009-efficient,
      title = "Efficient Extraction of Oracle-best Translations from Hypergraphs",
      author = "Li, Zhifei and
      Khudanpur, Sanjeev",
      editor = "Ostendorf, Mari and
      Collins, Michael and
      Narayanan, Shri and
      Oard, Douglas W. and
      Vanderwende, Lucy",
      booktitle = "Proceedings of Human Language Technologies: The 2009 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers",
      month = jun,
      year = "2009",
      address = "Boulder, Colorado",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N09-2003",
      pages = "9--12",
      }

2815. E. Fitzgerald, K. Hall, and F. Jelinek, “Reconstructing False Start Errors in Spontaneous Speech Text,” in Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), Athens, Greece, 2009, p. 255–263.
      [BibTeX] [Link]

      @inproceedings{fitzgerald-etal-2009-reconstructing,
      title = "Reconstructing False Start Errors in Spontaneous Speech Text",
      author = "Fitzgerald, Erin and
      Hall, Keith and
      Jelinek, Frederick",
      editor = "Lascarides, Alex and
      Gardent, Claire and
      Nivre, Joakim",
      booktitle = "Proceedings of the 12th Conference of the {E}uropean Chapter of the {ACL} ({EACL} 2009)",
      month = mar,
      year = "2009",
      address = "Athens, Greece",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E09-1030",
      pages = "255--263",
      }

2816. N. Garera and D. Yarowsky, “Structural, Transitive and Latent Models for Biographic Fact Extraction,” in Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), Athens, Greece, 2009, p. 300–308.
      [BibTeX] [Link]

      @inproceedings{garera-yarowsky-2009-structural,
      title = "Structural, Transitive and Latent Models for Biographic Fact Extraction",
      author = "Garera, Nikesh and
      Yarowsky, David",
      editor = "Lascarides, Alex and
      Gardent, Claire and
      Nivre, Joakim",
      booktitle = "Proceedings of the 12th Conference of the {E}uropean Chapter of the {ACL} ({EACL} 2009)",
      month = mar,
      year = "2009",
      address = "Athens, Greece",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/E09-1035",
      pages = "300--308",
      }

2817. Z. Li, C. Callison-Burch, C. Dyer, S. Khudanpur, L. Schwartz, W. Thornton, J. Weese, and O. Zaidan, “Joshua: An Open Source Toolkit for Parsing-Based Machine Translation,” in Proceedings of the Fourth Workshop on Statistical Machine Translation, Athens, Greece, 2009, p. 135–139.
      [BibTeX] [Link]

      @inproceedings{li-etal-2009-joshua,
      title = "{J}oshua: An Open Source Toolkit for Parsing-Based Machine Translation",
      author = "Li, Zhifei and
      Callison-Burch, Chris and
      Dyer, Chris and
      Khudanpur, Sanjeev and
      Schwartz, Lane and
      Thornton, Wren and
      Weese, Jonathan and
      Zaidan, Omar",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh",
      booktitle = "Proceedings of the Fourth Workshop on Statistical Machine Translation",
      month = mar,
      year = "2009",
      address = "Athens, Greece",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W09-0424",
      pages = "135--139",
      }

2818. C. Callison-Burch, P. Koehn, C. Monz, and J. Schroeder, “Findings of the 2009 Workshop on Statistical Machine Translation,” in Proceedings of the Fourth Workshop on Statistical Machine Translation, Athens, Greece, 2009, p. 1–28.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2009-findings,
      title = "Findings of the 2009 {W}orkshop on {S}tatistical {M}achine {T}ranslation",
      author = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh",
      booktitle = "Proceedings of the Fourth Workshop on Statistical Machine Translation",
      month = mar,
      year = "2009",
      address = "Athens, Greece",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W09-0401",
      pages = "1--28",
      }

2819. J. Mayfield, D. Alexander, B. Dorr, J. Eisner, T. Elsayed, T. Finin, Clay Fink, M. Freedman, N. Garera, Paul McNamee, S. Mohammad, D. Oard, C. Piatko, A. Sayeed, Z. Syed, R. Weischedel, T. Xu, and D. Yarowsky, “Cross-Document Coreference Resolution: A Key Technology for Learning by Reading,” in Proceedings of the AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read, Stanford, 2009.
      [BibTeX] [Link]

      @InProceedings{mayfield-et-al-2009,
      author = "James Mayfield and David Alexander and Bonnie Dorr and
      Jason Eisner and Tamer Elsayed and Tim Finin and Clay
      Fink and Marjorie Freedman and Nikesh Garera and Paul
      McNamee and Saif Mohammad and Douglas Oard and
      Christine Piatko and Asad Sayeed and Zareen Syed and
      Ralph Weischedel and Tan Xu and David Yarowsky",
      title = "Cross-Document Coreference Resolution: {A} Key
      Technology for Learning by Reading",
      booktitle = "Proceedings of the AAAI 2009 Spring Symposium on
      Learning by Reading and Learning to Read",
      year = "2009",
      month = mar,
      address = "Stanford",
      note = "AAAI Technical Report SS-09-07",
      URL = "http://cs.jhu.edu/~jason/papers/#mayfield-et-al-2009",
      }

2820. Razvan C. Bunescu, Vitor R. Carvalho, J. Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Z. Dodds, Mark Dredze, David Furcy, E. Gabrilovich, M. Göker, H. Guesgen, H. Hirsh, D. Jannach, Ulrich Junker, W. Ketter, A. Kobsa, Sven Koenig, T. Lau, Lundy M. Lewis, E. Matson, Ted Metzler, Rada Mihalcea, B. Mobasher, Joelle Pineau, P. Poupart, A. Raja, Wheeler Ruml, N. Sadeh, Guy Shani, D. Shapiro, S. Anand, Matthew E. Taylor, K. Wagstaff, Trey Smith, W. E. Walsh, and R. Zhou, “AAAI 2008 Workshop Reports,” in The AI Magazine, 2009.
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      @inproceedings{7218649,
      title = {AAAI 2008 Workshop Reports},
      author = {{Razvan C. Bunescu} and {Vitor R. Carvalho} and {J. Chomicki} and {Vincent Conitzer} and {Michael T. Cox} and {Virginia Dignum} and {Z. Dodds} and {Mark Dredze} and {David Furcy} and {E. Gabrilovich} and {M. Göker} and {H. Guesgen} and {H. Hirsh} and {D. Jannach} and {Ulrich Junker} and {W. Ketter} and {A. Kobsa} and {Sven Koenig} and {T. Lau} and {Lundy M. Lewis} and {E. Matson} and {Ted Metzler} and {Rada Mihalcea} and {B. Mobasher} and {Joelle Pineau} and {P. Poupart} and {A. Raja} and {Wheeler Ruml} and {N. Sadeh} and {Guy Shani} and {D. Shapiro} and {S. Anand} and {Matthew E. Taylor} and {K. Wagstaff} and {Trey Smith} and {W. E. Walsh} and {R. Zhou}},
      year = 2009,
      month = {1},
      booktitle = {The AI Magazine},
      url = {https://www.semanticscholar.org/paper/103cfa1847ac1d1d7212c0dfb7f2c3f85e570dad},
      }

2821. J. Baker, Li Deng, S. Khudanpur, Chin-Hui Lee, James R. Glass, Nelson Morgan, and Douglas D. O’Shaughnessy, “Updated MINDS report on speech recognition and understanding, Part 2 [DSP Education],” in IEEE Signal Processing Magazine, 2009.
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      @inproceedings{267836083,
      title = {Updated MINDS report on speech recognition and understanding, Part 2 [DSP Education]},
      author = {{J. Baker} and {Li Deng} and {S. Khudanpur} and {Chin-Hui Lee} and {James R. Glass} and {Nelson Morgan} and {Douglas D. O'Shaughnessy}},
      year = 2009,
      booktitle = {IEEE Signal Processing Magazine},
      url = {https://www.semanticscholar.org/paper/d49de67491faaeea51e33a66b2a3ec8c71bca3eb},
      }

2822. Mark Dredze, Bill N. Schilit, and Peter Norvig, “Suggesting Email View Filters for Triage and Search,” in International Joint Conference on Artificial Intelligence, 2009.
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      @inproceedings{59819689,
      title = {Suggesting Email View Filters for Triage and Search},
      author = {{Mark Dredze} and {Bill N. Schilit} and {Peter Norvig}},
      year = 2009,
      month = {7},
      booktitle = {International Joint Conference on Artificial Intelligence},
      url = {https://www.semanticscholar.org/paper/c9ac7e404b5b7bbbb303ad8c3c2994541cbf2406},
      }

2823. M. Pavel, M. Slaney, and H. Hermansky, “Reconciliation of human and machine speech recognition performance,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009.
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      @inproceedings{14815456,
      title = {Reconciliation of human and machine speech recognition performance},
      author = {{M. Pavel} and {M. Slaney} and {H. Hermansky}},
      year = 2009,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/44cac5b76d9463f31f1c685066e475d1d9e2981c},
      }

2824. Edward Choi and A. Andreou, “Architecture of a , uRFID with integrated antenna in.” 2009.
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      @inproceedings{17100540,
      title = {Architecture of a , uRFID with integrated antenna in},
      author = {{Edward Choi} and {A. Andreou}},
      year = 2009,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9e11d3a65c90f6b54b7fab2adfaf6b11609e5de7},
      }

2825. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Static and dynamic modulation spectrum for speech recognition,” in Interspeech, 2009.
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      @inproceedings{2030225,
      title = {Static and dynamic modulation spectrum for speech recognition},
      author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
      year = 2009,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/0a9f1641f87a7300f8b4008217ad9e1f4a26477d},
      }

2826. Mark Dredze, P. Talukdar, and K. Crammer, “Sequence Learning from Data with Multiple Labels.” 2009.
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      @inproceedings{1707818,
      title = {Sequence Learning from Data with Multiple Labels},
      author = {{Mark Dredze} and {P. Talukdar} and {K. Crammer}},
      year = 2009,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/893107da9d13f0abb8c66cd351d056990a94674a},
      }

2827. C. Stricker, J. Wagen, Guillermo Aradilla, H. Bourlard, H. Hermansky, Joel Pinto, P. Rey, and Jérôme Théraulaz, “Intelligent Multi-modal Interfaces for Mobile Applications in Hostile Environment(IM-HOST),” in Human Machine Interaction, 2009.
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      @inproceedings{2588998,
      title = {Intelligent Multi-modal Interfaces for Mobile Applications in Hostile Environment(IM-HOST)},
      author = {{C. Stricker} and {J. Wagen} and {Guillermo Aradilla} and {H. Bourlard} and {H. Hermansky} and {Joel Pinto} and {P. Rey} and {Jérôme Théraulaz}},
      year = 2009,
      month = {3},
      booktitle = {Human Machine Interaction},
      url = {https://www.semanticscholar.org/paper/64dd43b2fa3f408bcf2eba6ff86efd102e04a219},
      }

2828. J. Baker, L. Deng, James R. Glass, S. Khudanpur, Chin-Hul Lee, N. Morgan, and Douglas O’Shgughnessy, “Research Developments and Directions in Speech Recognition and Understanding, Part 1,” in IEEE Signal Processing Magazine, 2009.
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      @inproceedings{63630945,
      title = {Research Developments and Directions in Speech Recognition and Understanding, Part 1},
      author = {{J. Baker} and {L. Deng} and {James R. Glass} and {S. Khudanpur} and {Chin-Hul Lee} and {N. Morgan} and {Douglas O'Shgughnessy}},
      year = 2009,
      month = {5},
      booktitle = {IEEE Signal Processing Magazine},
      url = {https://www.semanticscholar.org/paper/37c6843c66a0e18fbbc383a7cf344b7a7482746d},
      }

2829. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Phoneme recognition using spectral envelope and modulation frequency features,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009.
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      @inproceedings{7827952,
      title = {Phoneme recognition using spectral envelope and modulation frequency features},
      author = {{Samuel Thomas} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2009,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/a9bd6d459d0ac43c1f8708a635d7a6b5007fdcd4},
      }

2830. Arnab Ghoshal, Martin Jansche, S. Khudanpur, M. Riley, and Morgan Ulinski, “WEB-derived pronunciations,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009.
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      @inproceedings{1512535,
      title = {WEB-derived pronunciations},
      author = {{Arnab Ghoshal} and {Martin Jansche} and {S. Khudanpur} and {M. Riley} and {Morgan Ulinski}},
      year = 2009,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/f6efa55f8628ea48bb58fd3866206761288f7202},
      }

2831. J. Baker, L. Deng, S. Khudanpur, Chin-Hui Lee, James R. Glass, N. Morgan, and D. O’Shaughnessy, “Updated MINDS Report on Speech Recognition and Understanding, Part 2.” 2009.
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      @inproceedings{19014381,
      title = {Updated MINDS Report on Speech Recognition and Understanding, Part 2},
      author = {{J. Baker} and {L. Deng} and {S. Khudanpur} and {Chin-Hui Lee} and {James R. Glass} and {N. Morgan} and {D. O'Shaughnessy}},
      year = 2009,
      month = {7},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/019f149065f7a88dc82ffdd22004e8a212f384f1},
      }

2832. Arnab Ghoshal, S. Khudanpur, and D. Klakow, “Impact of novel sources on content-based image and video retrieval,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009.
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      @inproceedings{1577679,
      title = {Impact of novel sources on content-based image and video retrieval},
      author = {{Arnab Ghoshal} and {S. Khudanpur} and {D. Klakow}},
      year = 2009,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/aa99cf6f78d6a986c78b09a124c9eb7882cb4ab0},
      }

2833. A. Andreou, P. Pouliquen, and Charbel G. Rizk, “Noise analysis and comparison of analog and digital readout integrated circuits for infrared focal plane arrays,” in Annual Conference on Information Sciences and Systems, 2009.
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      @inproceedings{18660239,
      title = {Noise analysis and comparison of analog and digital readout integrated circuits for infrared focal plane arrays},
      author = {{A. Andreou} and {P. Pouliquen} and {Charbel G. Rizk}},
      year = 2009,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/c0c63f21b5bee5e7424029f2ef7a2e7934e60d05},
      }

2834. A. Rastrow, A. Sethy, B. Ramabhadran, and F. Jelinek, “Towards using hybrid word and fragment units for vocabulary independent LVCSR systems,” in Interspeech, 2009.
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      @inproceedings{2485902,
      title = {Towards using hybrid word and fragment units for vocabulary independent LVCSR systems},
      author = {{A. Rastrow} and {A. Sethy} and {B. Ramabhadran} and {F. Jelinek}},
      year = 2009,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/96ba5f76c1bb49bc1a8c294382a30be8dbf6c3d2},
      }

2835. Gary Geunbae, Pohang Lee, Paul Buitelaar, Ireland Deri, Massimiliano Ciaramita, Switzerland Google, Sadao Kurohasi, Jong Park, Korea Kaist, Ted Pedersen, Dan Roth, Ming Zhou, China, Heike Zinsmeister, Ant’onio Branco, Francisco Costa, Eduardo Ferreira, Pedro Martins, Filipe Nunes, João Silva, David K Elson, Kathleen R, Lianhau Lee, Aiti Aw, Thuy Vu, Sharifah Aljunied Mahani, Min Zhang, Haizhou Li, Zhifei Li, Chris Callison-Burch, Chris Dyery, Juri Ganitkevitch, S. Khudanpur, Lane Schwartz, Wren N. G. Thornton, Jonathan Weese, Omar F, A. Kumaran, K. Saravanan, Naren Datha, B. Ashok, Vikram Dendi, S. Varges, S. Quarteroni, Giuseppe Riccardi, A. Ivanov, Pierluigi Roberti, Susumu Akamine, Daisuke Kawahara, Yoshikiyo Kato, Tetsuji Nakagawa, Kentaro Inui, S. Kurohashi, and Yutaka Kidawara, “ACL-IJCNLP 2009 Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP Proceedings of Software Demonstrations.” 2009.
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      @inproceedings{262045498,
      title = {ACL-IJCNLP 2009 Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP Proceedings of Software Demonstrations},
      author = {{Gary Geunbae} and {Pohang Lee} and {Paul Buitelaar} and {Ireland Deri} and {Massimiliano Ciaramita} and {Switzerland Google} and {Sadao Kurohasi} and {Jong Park} and {Korea Kaist} and {Ted Pedersen} and {Dan Roth} and {Ming Zhou} and {China} and {Heike Zinsmeister} and {Ant'onio Branco} and {Francisco Costa} and {Eduardo Ferreira} and {Pedro Martins} and {Filipe Nunes} and {João Silva} and {David K Elson} and {Kathleen R} and {Lianhau Lee} and {Aiti Aw} and {Thuy Vu} and {Sharifah Aljunied Mahani} and {Min Zhang} and {Haizhou Li} and {Zhifei Li} and {Chris Callison-Burch} and {Chris Dyery} and {Juri Ganitkevitch} and {S. Khudanpur} and {Lane Schwartz} and {Wren N. G. Thornton} and {Jonathan Weese} and {Omar F} and {A. Kumaran} and {K. Saravanan} and {Naren Datha} and {B. Ashok} and {Vikram Dendi} and {S. Varges} and {S. Quarteroni} and {Giuseppe Riccardi} and {A. Ivanov} and {Pierluigi Roberti} and {Susumu Akamine} and {Daisuke Kawahara} and {Yoshikiyo Kato} and {Tetsuji Nakagawa} and {Kentaro Inui} and {S. Kurohashi} and {Yutaka Kidawara}},
      year = 2009,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/02a2039b1037520da9f8c415790b30cec39d5381},
      }

2836. M. Carrillo, L. Ricci, Glen A. Coppersmith, and R. Melloni, “The effect of increased serotonergic neurotransmission on aggression: a critical meta-analytical review of preclinical studies,” in Psychopharmacology, 2009.
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      @inproceedings{34303162,
      title = {The effect of increased serotonergic neurotransmission on aggression: a critical meta-analytical review of preclinical studies},
      author = {{M. Carrillo} and {L. Ricci} and {Glen A. Coppersmith} and {R. Melloni}},
      year = 2009,
      month = {4},
      booktitle = {Psychopharmacology},
      url = {https://www.semanticscholar.org/paper/7cb6aad993259ceaa329a78f6c9a3435d2f3ddfe},
      }

2837. J. Mayfield, David Alexander, B. Dorr, Jason Eisner, T. Elsayed, Timothy W. Finin, Clayton Fink, Marjorie Freedman, Nikesh Garera, Paul McNamee, Saif M. Mohammad, Douglas W. Oard, C. Piatko, A. Sayeed, Zareen Syed, R. Weischedel, Tan Xu, and David Yarowsky, “Cross-Document Coreference Resolution: A Key Technology for Learning by Reading,” in AAAI Spring Symposium: Learning by Reading and Learning to Read, 2009.
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      @inproceedings{2676716,
      title = {Cross-Document Coreference Resolution: A Key Technology for Learning by Reading},
      author = {{J. Mayfield} and {David Alexander} and {B. Dorr} and {Jason Eisner} and {T. Elsayed} and {Timothy W. Finin} and {Clayton Fink} and {Marjorie Freedman} and {Nikesh Garera} and {Paul McNamee} and {Saif M. Mohammad} and {Douglas W. Oard} and {C. Piatko} and {A. Sayeed} and {Zareen Syed} and {R. Weischedel} and {Tan Xu} and {David Yarowsky}},
      year = 2009,
      month = {3},
      booktitle = {AAAI Spring Symposium: Learning by Reading and Learning to Read},
      url = {https://www.semanticscholar.org/paper/a07dd42643b17c08ee3821cce19318e66349805f},
      }

2838. Haolang Zhou, Damianos G. Karakos, S. Khudanpur, A. Andreou, and C. Priebe, “On projections of Gaussian distributions using maximum likelihood criteria,” in Information Theory and Applications Workshop, 2009.
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      @inproceedings{8642318,
      title = {On projections of Gaussian distributions using maximum likelihood criteria},
      author = {{Haolang Zhou} and {Damianos G. Karakos} and {S. Khudanpur} and {A. Andreou} and {C. Priebe}},
      year = 2009,
      month = {5},
      booktitle = {Information Theory and Applications Workshop},
      url = {https://www.semanticscholar.org/paper/05b75a1b51a4a0b387d4a3d51ce322cf0116bad1},
      }

2839. M. Choudhury, Samer Hassan, Animesh Mukherjee, S. Muresan, Microsoft Research, India, A. Barrat, France, Niloy Ganguly, Iit Kharagpur, L. Getoor, Simon Kirby, Ben Leong, Alexander Mehler, Universitt Bielefeld, Roberto Navigli, V. Loreto, S. Sinha, R. K. Pan, Nisha Yadav, M. Vahia, I. Mahadevan, Daniel Ramage, Anna N. Rafferty, Christopher D, Eric Yeh, Christopher D. Manning, Eneko Agirre, Swapna Somasundaran, Galileo Namata, Janyce Wiebe, David Ellis, Iravatham Ma-Hadevan, Aitor Soroa Etxabe, Amaç Herda, K. Erk, Marco Baroni, Friday, Zheng Chen, Heng Ji, D. Rao, David Yarowsky, Linlin Li, C. Sporleder, and S. Martens, “Random Walks for Text Semantic Similarity Wikiwalk: Random Walks on Wikipedia for Semantic Relatedness Measuring Semantic Relatedness with Vector Space Models and Random Walks Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-reduce Opinion Graphs for Polarity and Discourse.” 2009.
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      @inproceedings{14265181,
      title = {Random Walks for Text Semantic Similarity Wikiwalk: Random Walks on Wikipedia for Semantic Relatedness Measuring Semantic Relatedness with Vector Space Models and Random Walks Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-reduce Opinion Graphs for Polarity and Discourse },
      author = {{M. Choudhury} and {Samer Hassan} and {Animesh Mukherjee} and {S. Muresan} and {Microsoft Research} and {India} and {A. Barrat} and {France} and {Niloy Ganguly} and {Iit Kharagpur} and {L. Getoor} and {Simon Kirby} and {Ben Leong} and {Alexander Mehler} and {Universitt Bielefeld} and {Roberto Navigli} and {V. Loreto} and {S. Sinha} and {R. K. Pan} and {Nisha Yadav} and {M. Vahia} and {I. Mahadevan} and {Daniel Ramage} and {Anna N. Rafferty} and {Christopher D} and {Eric Yeh} and {Christopher D. Manning} and {Eneko Agirre} and {Swapna Somasundaran} and {Galileo Namata} and {Janyce Wiebe} and {David Ellis} and {Iravatham Ma-Hadevan} and {Aitor Soroa Etxabe} and {Amaç Herda} and {K. Erk} and {Marco Baroni} and {Friday} and {Zheng Chen} and {Heng Ji} and {D. Rao} and {David Yarowsky} and {Linlin Li} and {C. Sporleder} and {S. Martens}},
      year = 2009,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b854e3c44b71ff4d4d46f215dce2b4045e269f5c},
      }

2840. J. Baker, Li Deng, S. Khudanpur, Chin-Hui Lee, James R. Glass, and Nelson Morgan, “Updated MINDS Report on Speech Recognition and Understanding,” in IEEE Signal Processing Magazine, 2009.
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      title = {Updated MINDS Report on Speech Recognition and Understanding},
      author = {{J. Baker} and {Li Deng} and {S. Khudanpur} and {Chin-Hui Lee} and {James R. Glass} and {Nelson Morgan}},
      year = 2009,
      month = {7},
      booktitle = {IEEE Signal Processing Magazine},
      url = {https://www.semanticscholar.org/paper/534e0528b5eb06c287c5281f41f9c92408d54d34},
      }

2841. Dogan Can, Erica Cooper, Arnab Ghoshal, Martin Jansche, S. Khudanpur, B. Ramabhadran, M. Riley, M. Saraçlar, A. Sethy, Morgan Ulinski, and Christopher M. White, “Web derived pronunciations for spoken term detection,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2009.
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      author = {{Dogan Can} and {Erica Cooper} and {Arnab Ghoshal} and {Martin Jansche} and {S. Khudanpur} and {B. Ramabhadran} and {M. Riley} and {M. Saraçlar} and {A. Sethy} and {Morgan Ulinski} and {Christopher M. White}},
      year = 2009,
      month = {7},
      booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
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      }

2842. Kathy A Baker, Steven Bethard, Michael Bloodgood, Ralf D. Brown, Chris Callison-Burch, Glen A. Coppersmith, B. Dorr, Wes Filardo, Kendall Giles, Ann Irvine, Mike Kayser, Lori S. Levin, Justin Martineau, J. Mayfield, Scott Miller, Aaron B. Phillips, A. Philpot, C. Piatko, Lane Schwartz, and David M. Zajic, “Semantically Informed Machine Translation ( SIMT ).” 2009.
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      }

2843. Christopher M. White, A. Rastrow, S. Khudanpur, and F. Jelinek, “Unsupervised estimation of the language model scaling factor,” in Interspeech, 2009.
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      author = {{Christopher M. White} and {A. Rastrow} and {S. Khudanpur} and {F. Jelinek}},
      year = 2009,
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      url = {https://www.semanticscholar.org/paper/d1eb05029396c041159e9213da629be75adc93aa},
      }

2844. Zhifei Li, Chris Callison-Burch, S. Khudanpur, and Wren N. G. Thornton, “Decoding in Joshua: Open Source, Parsing-Based Machine Translation,” in Prague Bulletin of Mathematical Linguistics, 2009.
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      author = {{Zhifei Li} and {Chris Callison-Burch} and {S. Khudanpur} and {Wren N. G. Thornton}},
      year = 2009,
      month = {1},
      booktitle = {Prague Bulletin of Mathematical Linguistics},
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      }

2845. Chris Callison-Burch, “WMT 2009 : proceedings of the fourth Workshop on Statistical Machine Translation : 30 and 31 March 2009, Athens, Greece.” 2009.
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      }

2846. Joel Pinto, Garimella S. V. S. Sivaram, H. Hermansky, and M. Magimai-Doss, “Volterra series for analyzing MLP based phoneme posterior estimator,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009.
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      year = 2009,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
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      }

2847. Fopefolu O. Folowosele, Andre Harrison, A. Cassidy, A. Andreou, R. Etienne-Cummings, Stefan Mihalas, E. Niebur, and T. Hamilton, “A switched capacitor implementation of the generalized linear integrate-and-fire neuron,” in 2009 IEEE International Symposium on Circuits and Systems, 2009.
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      year = 2009,
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2848. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Josh Schroeder, “Proceedings of the Fourth Workshop on Statistical Machine Translation, WMT@EACL 2009, Athens, Greece, March 30-31, 2009,” in WMT@EACL, 2009.
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      author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {Josh Schroeder}},
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      month = {3},
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      }

2849. A. Cassidy and A. Andreou, “Analytical methods for the design and optimization of chip-multiprocessor architectures,” in Annual Conference on Information Sciences and Systems, 2009.
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      }

2850. N. Mesgarani, Garimella S. V. S. Sivaram, Sridhar Krishna Nemala, Mounya Elhilali, and H. Hermansky, “Discriminant spectrotemporal features for phoneme recognition,” in Interspeech, 2009.
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      }

2851. Chris Callison-Burch and Fabio Massimo Zanzotto, “Proceedings of the 2009 Workshop on Applied Textual Inference, TextInfer@ACL 2009, Suntec, Singapore, August 6, 2009,” in TextInfer@ACL, 2009.
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      }

2852. Stefan Kombrink, L. Burget, P. Matejka, M. Karafiát, and H. Hermansky, “Posterior-based out of vocabulary word detection in telephone speech,” in Interspeech, 2009.
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      author = {{Stefan Kombrink} and {L. Burget} and {P. Matejka} and {M. Karafiát} and {H. Hermansky}},
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      url = {https://www.semanticscholar.org/paper/7c285e54ca30c1afabe1f98c260137dc2051418d},
      }

2853. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Modulation frequency features for phoneme recognition in noisy speech.,” in Journal of the Acoustical Society of America, 2009.
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      author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
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      month = {1},
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      url = {https://www.semanticscholar.org/paper/63c746fd024329386699b1cfdc3570db08959215},
      }

2854. A. Sayeed, T. Elsayed, Nikesh Garera, David Alexander, Tan Xu, Douglas W. Oard, David Yarowsky, and C. Piatko, “Arabic Cross-Document Coreference Resolution,” in ACL/IJCNLP, 2009.
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2855. Kenneth Ward Church, “Through the Looking Glass . . . Mediating the Inevitable and the Impossible.” 2009.
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      title = {Through the Looking Glass . . . Mediating the Inevitable and the Impossible},
      author = {{Kenneth Ward Church}},
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      }

2856. P. Motlícek, Sriram Ganapathy, and H. Hermansky, “Arithmetic coding of sub-band residuals in FDLP speech/audio codec,” in Interspeech, 2009.
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      @inproceedings{3010290,
      title = {Arithmetic coding of sub-band residuals in FDLP speech/audio codec},
      author = {{P. Motlícek} and {Sriram Ganapathy} and {H. Hermansky}},
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      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/e4a2526184bf9fa784e2a5dfbd600a06cc300e46},
      }

2857. Kyoji Umemura and Kenneth Ward Church, “Substring Statistics,” in Conference on Intelligent Text Processing and Computational Linguistics, 2009.
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      @inproceedings{15249488,
      title = {Substring Statistics},
      author = {{Kyoji Umemura} and {Kenneth Ward Church}},
      year = 2009,
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      url = {https://www.semanticscholar.org/paper/7124edca72ff281f20562c4ff2c33ef1542a6103},
      }

2858. H. Hermansky, Sangita Sharma, and D. Ellis, “Nonlinear mapping for feature extraction in automatic speech recognition,” in Journal of the Acoustical Society of America, 2009.
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      title = {Nonlinear mapping for feature extraction in automatic speech recognition},
      author = {{H. Hermansky} and {Sangita Sharma} and {D. Ellis}},
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2859. Zhifei Li and S. Khudanpur, “Forest Reranking for Machine Translation with the Perceptron Algorithm.” 2009.
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      author = {{Zhifei Li} and {S. Khudanpur}},
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      }

2860. Sriram Ganapathy, P. Motlícek, and H. Hermansky, “Error Resilient Speech Coding Using Sub-band Hilbert Envelopes,” in International Conference on Text, Speech and Dialogue, 2009.
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      year = 2009,
      month = {8},
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      url = {https://www.semanticscholar.org/paper/29e4191583e83289400c062c2c2a9c391d7d3e08},
      }

2861. S. Khudanpur and Arnab Ghoshal, “Modeling data-source variability for content-based video retrieval using hidden markov models.” 2009.
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      }

2862. F. Jelinek and Erin Fitzgerald, “Reconstructing spontaneous speech.” 2009.
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      }

2863. D. Allstot, D. B. Fogel, T. Lande, R. Butera, A. Andreou, A. Bermak, S. Carrara, G. Cauwenberghs, K. Chakrabarty, K. Cheung, T. Delbruck, S. DeWeerth, G. Inst, E. Drakakis, David Cumming, Sanqing Hu, E. Jovanov, E. Lam, Yong Lian, P. Mohseni, R. Rieger, R. Sarpeshkar, M. Sawan, T. Roska, and Zhihua Wang, “Engineering in Medicine and Biology B. HE, President TECHNICAL CO-SPONSORING SOCIETIES Computational Intelligence.” 2009.
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2864. F. Jelinek and Yi Su, “Knowledge integration into language models: a random forest approach.” 2009.
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      }

2865. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Tandem representations of spectral envelope and modulation frequency features for ASR,” in Interspeech, 2009.
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      url = {https://www.semanticscholar.org/paper/18b89e9f3e2305d56de34911c60065ce38caef9b},
      }

2866. J. Baker, L. Deng, James R. Glass, S. Khudanpur, Chin-Hui Lee, N. Morgan, and D. O’Shaughnessy, “Developments and directions in speech recognition and understanding, Part 1 [DSP Education],” in IEEE Signal Processing Magazine, 2009.
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      }

2867. Haolang Zhou, Damianos G. Karakos, and A. Andreou, “A semi-supervised version of heteroscedastic linear discriminant analysis,” in Interspeech, 2009.
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2868. F. Jelinek, “Code Breaking for Automatic Speech Recognition,” in International Conference on Text, Speech and Dialogue, 2009.
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2869. Michael Bendersky and Kenneth Ward Church, “Priors in Web Search.” 2009.
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      }

2870. J. Baker, Li Deng, S. Khudanpur, Chin-Hui Lee, James R. Glass, Nelson Morgan, and Douglas D. O’Shaughnessy, “Updated MINDS report on speech recognition and understanding, Part 2 [DSP Education],” in IEEE Signal Processing Magazine, 2009.
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      year = 2009,
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      }

2871. D. Weinshall, H. Hermansky, Alon Zweig, Jie Luo, H. Jimison, F. Ohl, and M. Pavel, “Beyond Novelty Detection: Incongruent Events, When General and Specific Classifiers Disagree,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
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      author = {{D. Weinshall} and {H. Hermansky} and {Alon Zweig} and {Jie Luo} and {H. Jimison} and {F. Ohl} and {M. Pavel}},
      year = 2008,
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      }

2872. Damianos G. Karakos and S. Khudanpur, “Sequential system combination for machine translation of speech,” in 2008 IEEE Spoken Language Technology Workshop, 2008.
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      }

2873. O. F. Zaidan, J. Eisner, and C. Piatko, “Machine Learning with Annotator Rationales to Reduce Annotation Cost,” in Proceedings of the NeurIPS*2008 Workshop on Cost Sensitive Learning, Whistler, BC, 2008.
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      @InProceedings{zaidan-eisner-piatko-2008,
      author = "Omar F. Zaidan and Jason Eisner and Christine Piatko",
      title = "Machine Learning with Annotator Rationales to Reduce
      Annotation Cost",
      booktitle = "Proceedings of the NeurIPS*2008 Workshop on Cost
      Sensitive Learning",
      note = "10 pages",
      year = "2008",
      month = dec,
      address = "Whistler, BC",
      URL = "http://cs.jhu.edu/~jason/papers/#zaidan-eisner-piatko-2008",
      }

2874. A. Cassidy, Z. Zhang, and A. Andreou, “Neuromorphic interconnects using Ultra Wideband radio,” in 2008 IEEE Biomedical Circuits and Systems Conference, 2008.
      [BibTeX] [Link]

      @inproceedings{6024302,
      title = {Neuromorphic interconnects using Ultra Wideband radio},
      author = {{A. Cassidy} and {Z. Zhang} and {A. Andreou}},
      year = 2008,
      month = {11},
      booktitle = {2008 IEEE Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/fbe9895986a4cad7fef4848d2d46eeb45d3dc9e4},
      }

2875. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Recognition of Reverberant Speech Using Frequency Domain Linear Prediction,” in IEEE Signal Processing Letters, 2008.
      [BibTeX] [Link]

      @inproceedings{1655589,
      title = {Recognition of Reverberant Speech Using Frequency Domain Linear Prediction},
      author = {{Samuel Thomas} and {Sriram Ganapathy} and {H. Hermansky}},
      year = 2008,
      month = {11},
      booktitle = {IEEE Signal Processing Letters},
      url = {https://www.semanticscholar.org/paper/6f108efd3aebf25f40a162fdb4f7ac8882f3d215},
      }

2876. A. Cassidy and A. Andreou, “Dynamical digital silicon neurons,” in 2008 IEEE Biomedical Circuits and Systems Conference, 2008.
      [BibTeX] [Link]

      @inproceedings{1623171,
      title = {Dynamical digital silicon neurons},
      author = {{A. Cassidy} and {A. Andreou}},
      year = 2008,
      month = {11},
      booktitle = {2008 IEEE Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/057ed292b9a95b1bf6efc8b22cfe55d0a266dafe},
      }

2877. A. Cassidy, Z. Zhang, and A. Andreou, “Impulse Radio Address Event Interconnects for body area networks and neural prostheses,” in Argentine School of Micro-Nanoelectronics, Technology and Applications, 2008.
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      @inproceedings{7784406,
      title = {Impulse Radio Address Event Interconnects for body area networks and neural prostheses},
      author = {{A. Cassidy} and {Z. Zhang} and {A. Andreou}},
      year = 2008,
      month = {10},
      booktitle = {Argentine School of Micro-Nanoelectronics, Technology and Applications},
      url = {https://www.semanticscholar.org/paper/a478e781f85913c349fee9718dda5c896ca3d0c4},
      }

2878. Z. Zhang and A. Andreou, “Human identification experiments using acoustic micro-Doppler signatures,” in Argentine School of Micro-Nanoelectronics, Technology and Applications, 2008.
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      @inproceedings{14144876,
      title = {Human identification experiments using acoustic micro-Doppler signatures},
      author = {{Z. Zhang} and {A. Andreou}},
      year = 2008,
      month = {10},
      booktitle = {Argentine School of Micro-Nanoelectronics, Technology and Applications},
      url = {https://www.semanticscholar.org/paper/dc69d5cde5eb260c71b72ca4d794bc8664a58174},
      }

2879. C. Callison-Burch, “Syntactic Constraints on Paraphrases Extracted from Parallel Corpora,” in Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii, 2008, p. 196–205.
      [BibTeX] [Link]

      @inproceedings{callison-burch-2008-syntactic,
      title = "Syntactic Constraints on Paraphrases Extracted from Parallel Corpora",
      author = "Callison-Burch, Chris",
      editor = "Lapata, Mirella and
      Ng, Hwee Tou",
      booktitle = "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2008",
      address = "Honolulu, Hawaii",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D08-1021",
      pages = "196--205",
      }

2880. A. Andreou, “Silicon-on-sapphire CMOS and opportunities in niche markets: Old wine in a new bottle,” in 2008 IEEE International SOI Conference, 2008.
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      @inproceedings{34651814,
      title = {Silicon-on-sapphire CMOS and opportunities in niche markets: Old wine in a new bottle},
      author = {{A. Andreou}},
      year = 2008,
      month = {10},
      booktitle = {2008 IEEE International SOI Conference},
      url = {https://www.semanticscholar.org/paper/849d93b3b615e9157faab4d5f46bdb29bdbff728},
      }

2881. E. Choi, R. Ozgun, B. Dhar, H. Katz, and A. Andreou, “Fabrication process design for complementary metal-cytop-organic-semiconductor integrated circuits,” in Argentine School of Micro-Nanoelectronics, Technology and Applications, 2008.
      [BibTeX] [Link]

      @inproceedings{15697569,
      title = {Fabrication process design for complementary metal-cytop-organic-semiconductor integrated circuits},
      author = {{E. Choi} and {R. Ozgun} and {B. Dhar} and {H. Katz} and {A. Andreou}},
      year = 2008,
      month = {10},
      booktitle = {Argentine School of Micro-Nanoelectronics, Technology and Applications},
      url = {https://www.semanticscholar.org/paper/7b584f80c98f6642571120aea5583ccece453ce9},
      }

2882. Z. Li and D. Yarowsky, “Mining and Modeling Relations between Formal and Informal Chinese Phrases from Web Corpora,” in Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii, 2008, p. 1031–1040.
      [BibTeX] [Link]

      @inproceedings{li-yarowsky-2008-mining,
      title = "Mining and Modeling Relations between Formal and Informal {C}hinese Phrases from Web Corpora",
      author = "Li, Zhifei and
      Yarowsky, David",
      editor = "Lapata, Mirella and
      Ng, Hwee Tou",
      booktitle = "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing",
      month = oct,
      year = "2008",
      address = "Honolulu, Hawaii",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/D08-1108",
      pages = "1031--1040",
      }

2883. J. Anemüller, Jörg-Hendrik Bach, B. Caputo, M. Havlena, Jie Luo, H. Kayser, B. Leibe, P. Motlícek, T. Pajdla, M. Pavel, A. Torii, L. Gool, Alon Zweig, and H. Hermansky, “The DIRAC AWEAR audio-visual platform for detection of unexpected and incongruent events,” in International Conference on Multimodal Interaction, 2008.
      [BibTeX] [Link]

      @inproceedings{12517620,
      title = {The DIRAC AWEAR audio-visual platform for detection of unexpected and incongruent events},
      author = {{J. Anemüller} and {Jörg-Hendrik Bach} and {B. Caputo} and {M. Havlena} and {Jie Luo} and {H. Kayser} and {B. Leibe} and {P. Motlícek} and {T. Pajdla} and {M. Pavel} and {A. Torii} and {L. Gool} and {Alon Zweig} and {H. Hermansky}},
      year = 2008,
      month = {10},
      booktitle = {International Conference on Multimodal Interaction},
      url = {https://www.semanticscholar.org/paper/0ce86354c621a029ce28462f9f24a715b3e83294},
      }

2884. Z. Zhang and A. Andreou, “Slow moving vehicles using the microphone arrays in the Hopkins Acoustic Surveillance Unit,” in Argentine School of Micro-Nanoelectronics, Technology and Applications, 2008.
      [BibTeX] [Link]

      @inproceedings{18700849,
      title = {Slow moving vehicles using the microphone arrays in the Hopkins Acoustic Surveillance Unit},
      author = {{Z. Zhang} and {A. Andreou}},
      year = 2008,
      month = {10},
      booktitle = {Argentine School of Micro-Nanoelectronics, Technology and Applications},
      url = {https://www.semanticscholar.org/paper/cafad54a832309b7b5c5a49c6f0d7ef97d794f0a},
      }

2885. D. A. Smith and J. Eisner, “Dependency Parsing by Belief Propagation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, 2008, p. 145–156.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2008-bp,
      aclid = "D08-1016",
      author = "David A. Smith and Jason Eisner",
      title = "Dependency Parsing by Belief Propagation",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "145--156",
      year = "2008",
      month = oct,
      address = "Honolulu",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2008-bp",
      }

2886. O. F. Zaidan and J. Eisner, “Modeling Annotators: A Generative Approach to Learning from Annotator Rationales,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, 2008, p. 31–40.
      [BibTeX] [Link]

      @InProceedings{zaidan-eisner-2008,
      aclid = "D08-1004",
      author = "Omar F. Zaidan and Jason Eisner",
      title = "Modeling Annotators: {A} Generative Approach to
      Learning from Annotator Rationales",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "31--40",
      year = "2008",
      month = oct,
      address = "Honolulu",
      URL = "http://cs.jhu.edu/~jason/papers/#zaidan-eisner-2008",
      }

2887. M. Dreyer, J. R. Smith, and J. Eisner, “Latent-Variable Modeling of String Transductions with Finite-State Methods,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, 2008, p. 1080–1089.
      [BibTeX] [Link]

      @InProceedings{dreyer-smith-eisner-2008,
      aclid = "D08-1113",
      author = "Markus Dreyer and Jason R. Smith and Jason Eisner",
      title = "Latent-Variable Modeling of String Transductions with
      Finite-State Methods",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "1080--1089",
      year = "2008",
      month = oct,
      address = "Honolulu",
      URL = "http://cs.jhu.edu/~jason/papers/#dreyer-smith-eisner-2008",
      }

2888. D. Rao, D. Yarowsky, and C. Callison-Burch, “Affinity Measures Based on the Graph Laplacian,” in Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing, Manchester, UK, 2008, p. 41–48.
      [BibTeX] [Link]

      @inproceedings{rao-etal-2008-affinity,
      title = "Affinity Measures Based on the Graph {L}aplacian",
      author = "Rao, Delip and
      Yarowsky, David and
      Callison-Burch, Chris",
      editor = "Matveeva, Irina and
      Biemann, Chris and
      Choudhury, Monojit and
      Diab, Mona",
      booktitle = "Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing",
      month = aug,
      year = "2008",
      address = "Manchester, UK",
      publisher = "Coling 2008 Organizing Committee",
      url = "https://aclanthology.org/W08-2006",
      pages = "41--48",
      }

2889. C. Callison-Burch, T. Cohn, and M. Lapata, “ParaMetric: An Automatic Evaluation Metric for Paraphrasing,” in Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK, 2008, p. 97–104.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2008-parametric,
      title = "{P}ara{M}etric: An Automatic Evaluation Metric for Paraphrasing",
      author = "Callison-Burch, Chris and
      Cohn, Trevor and
      Lapata, Mirella",
      editor = "Scott, Donia and
      Uszkoreit, Hans",
      booktitle = "Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)",
      month = aug,
      year = "2008",
      address = "Manchester, UK",
      publisher = "Coling 2008 Organizing Committee",
      url = "https://aclanthology.org/C08-1013",
      pages = "97--104",
      }

2890. Z. Li and S. Khudanpur, “A Scalable Decoder for Parsing-Based Machine Translation with Equivalent Language Model State Maintenance,” in Proceedings of the ACL-08: HLT Second Workshop on Syntax and Structure in Statistical Translation (SSST-2), Columbus, Ohio, 2008, p. 10–18.
      [BibTeX] [Link]

      @inproceedings{li-khudanpur-2008-scalable,
      title = "A Scalable Decoder for Parsing-Based Machine Translation with Equivalent Language Model State Maintenance",
      author = "Li, Zhifei and
      Khudanpur, Sanjeev",
      editor = "Chiang, David and
      Wu, Dekai",
      booktitle = "Proceedings of the {ACL}-08: {HLT} Second Workshop on Syntax and Structure in Statistical Translation ({SSST}-2)",
      month = jun,
      year = "2008",
      address = "Columbus, Ohio",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W08-0402",
      pages = "10--18",
      }

2891. B. Varadarajan, S. Khudanpur, and E. Dupoux, “Unsupervised Learning of Acoustic Sub-word Units,” in Proceedings of ACL-08: HLT, Short Papers, Columbus, Ohio, 2008, p. 165–168.
      [BibTeX] [Link]

      @inproceedings{varadarajan-etal-2008-unsupervised,
      title = "Unsupervised Learning of Acoustic Sub-word Units",
      author = "Varadarajan, Balakrishnan and
      Khudanpur, Sanjeev and
      Dupoux, Emmanuel",
      editor = "Moore, Johanna D. and
      Teufel, Simone and
      Allan, James and
      Furui, Sadaoki",
      booktitle = "Proceedings of ACL-08: HLT, Short Papers",
      month = jun,
      year = "2008",
      address = "Columbus, Ohio",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P08-2042",
      pages = "165--168",
      }

2892. C. Callison-Burch, C. Fordyce, P. Koehn, C. Monz, and J. Schroeder, “Further Meta-Evaluation of Machine Translation,” in Proceedings of the Third Workshop on Statistical Machine Translation, Columbus, Ohio, 2008, p. 70–106.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2008-meta,
      title = "Further Meta-Evaluation of Machine Translation",
      author = "Callison-Burch, Chris and
      Fordyce, Cameron and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh and
      Fordyce, Cameron Shaw",
      booktitle = "Proceedings of the Third Workshop on Statistical Machine Translation",
      month = jun,
      year = "2008",
      address = "Columbus, Ohio",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W08-0309",
      pages = "70--106",
      }

2893. Z. Li and D. Yarowsky, “Unsupervised Translation Induction for Chinese Abbreviations using Monolingual Corpora,” in Proceedings of ACL-08: HLT, Columbus, Ohio, 2008, p. 425–433.
      [BibTeX] [Link]

      @inproceedings{li-yarowsky-2008-unsupervised,
      title = "Unsupervised Translation Induction for {C}hinese Abbreviations using Monolingual Corpora",
      author = "Li, Zhifei and
      Yarowsky, David",
      editor = "Moore, Johanna D. and
      Teufel, Simone and
      Allan, James and
      Furui, Sadaoki",
      booktitle = "Proceedings of ACL-08: HLT",
      month = jun,
      year = "2008",
      address = "Columbus, Ohio",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P08-1049",
      pages = "425--433",
      }

2894. D. Karakos, J. Eisner, S. Khudanpur, and M. Dreyer, “Machine Translation System Combination using ITG-based Alignments,” in Proceedings of ACL-08: HLT, Short Papers, Columbus, Ohio, 2008, p. 81–84.
      [BibTeX] [Link]

      @inproceedings{karakos-etal-2008-machine,
      title = "Machine Translation System Combination using {ITG}-based Alignments",
      author = "Karakos, Damianos and
      Eisner, Jason and
      Khudanpur, Sanjeev and
      Dreyer, Markus",
      editor = "Moore, Johanna D. and
      Teufel, Simone and
      Allan, James and
      Furui, Sadaoki",
      booktitle = "Proceedings of ACL-08: HLT, Short Papers",
      month = jun,
      year = "2008",
      address = "Columbus, Ohio",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P08-2021",
      pages = "81--84",
      }

2895. J. Eisner and N. A. Smith, “Competitive Grammar Writing,” in Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics, Columbus, Ohio, 2008, p. 97–105.
      [BibTeX] [Link]

      @InProceedings{eisner-smith-2008-tnlp,
      aclid = "W08-0212",
      author = "Jason Eisner and Noah A. Smith",
      title = "Competitive Grammar Writing",
      booktitle = "Proceedings of the Third Workshop on Issues in
      Teaching Computational Linguistics",
      pages = "97--105",
      year = "2008",
      month = jun,
      address = "Columbus, Ohio",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-smith-2008-tnlp",
      }

2896. D. Karakos, J. Eisner, Sanjeev Khudanpur, and M. Dreyer, “Machine Translation System Combination using ITG-based Alignments,” in Proceedings of ACL-08: HLT, Short Papers, Columbus, Ohio, 2008, p. 81–84.
      [BibTeX] [Link]

      @InProceedings{karakos-et-al-2008,
      aclid = "P08-2021",
      author = "Damianos Karakos and Jason Eisner and Sanjeev
      Khudanpur and Markus Dreyer",
      title = "Machine Translation System Combination using
      {ITG}-based Alignments",
      booktitle = "Proceedings of ACL-08: HLT, Short Papers",
      pages = "81--84",
      year = "2008",
      month = jun,
      address = "Columbus, Ohio",
      URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2008",
      }

2897. E. Fitzgerald and F. Jelinek, “Linguistic Resources for Reconstructing Spontaneous Speech Text,” in Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco, 2008.
      [BibTeX] [Abstract] [Link]
      

      The output of a speech recognition system is not always ideal for subsequent downstream processing, in part because speakers themselves often make mistakes. A system would accomplish speech reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content-preserving English, the message that the speaker intended to convey. These cleaner speech transcripts would allow for more accurate language processing as needed for NLP tasks such as machine translation and conversation summarization, which often rely on grammatical input. Recognizing that supervised statistical methods to identify and transform ill-formed areas of the transcript will require richly labeled resources, we have built the Spontaneous Speech Reconstruction corpus. This small corpus of reconstructed and aligned conversational telephone speech transcriptions for the Fisher conversational telephone speech corpus (Strassel and Walker, 2004) was annotated on several levels including string transformations and predicate-argument structure, and will be shared with the linguistic research community.

      @inproceedings{fitzgerald-jelinek-2008-linguistic,
      title = "Linguistic Resources for Reconstructing Spontaneous Speech Text",
      author = "Fitzgerald, Erin and
      Jelinek, Frederick",
      editor = "Calzolari, Nicoletta and
      Choukri, Khalid and
      Maegaard, Bente and
      Mariani, Joseph and
      Odijk, Jan and
      Piperidis, Stelios and
      Tapias, Daniel",
      booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
      month = may,
      year = "2008",
      address = "Marrakech, Morocco",
      publisher = "European Language Resources Association (ELRA)",
      url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/874_paper.pdf",
      abstract = "The output of a speech recognition system is not always ideal for subsequent downstream processing, in part because speakers themselves often make mistakes. A system would accomplish speech reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content-preserving English, the message that the speaker intended to convey. These cleaner speech transcripts would allow for more accurate language processing as needed for NLP tasks such as machine translation and conversation summarization, which often rely on grammatical input. Recognizing that supervised statistical methods to identify and transform ill-formed areas of the transcript will require richly labeled resources, we have built the Spontaneous Speech Reconstruction corpus. This small corpus of reconstructed and aligned conversational telephone speech transcriptions for the Fisher conversational telephone speech corpus (Strassel and Walker, 2004) was annotated on several levels including string transformations and predicate-argument structure, and will be shared with the linguistic research community.",
      }

2898. Damianos G. Karakos, S. Khudanpur, and C. Priebe, “Computation of Csiszár’s mutual Information of order α,” in 2008 IEEE International Symposium on Information Theory, 2008.
      [BibTeX] [Link]

      @inproceedings{13846529,
      title = {Computation of Csiszár’s mutual Information of order α},
      author = {{Damianos G. Karakos} and {S. Khudanpur} and {C. Priebe}},
      year = 2008,
      month = {7},
      booktitle = {2008 IEEE International Symposium on Information Theory},
      url = {https://www.semanticscholar.org/paper/6adc75a7dfc64ac21d7b2ca3c0ce7a9d326035a4},
      }

2899. Joel Pinto, Garimella S. V. S. Sivaram, and H. Hermansky, “Reverse Correlation for Analyzing MLP Posterior Features in ASR,” in International Conference on Text, Speech and Dialogue, 2008.
      [BibTeX] [Link]

      @inproceedings{2502917,
      title = {Reverse Correlation for Analyzing MLP Posterior Features in ASR},
      author = {{Joel Pinto} and {Garimella S. V. S. Sivaram} and {H. Hermansky}},
      year = 2008,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/1c7aa21f8f785408385d711e47f844a70acd04f1},
      }

2900. N. Garera and D. Yarowsky, “Minimally Supervised Multilingual Taxonomy and Translation Lexicon Induction,” in Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I, 2008.
      [BibTeX] [Link]

      @inproceedings{garera-yarowsky-2008-minimally,
      title = "Minimally Supervised Multilingual Taxonomy and Translation Lexicon Induction",
      author = "Garera, Nikesh and
      Yarowsky, David",
      booktitle = "Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-{I}",
      year = "2008",
      url = "https://aclanthology.org/I08-1061",
      }

2901. Garimella S. V. S. Sivaram and H. Hermansky, “Introducing temporal asymmetries in feature extraction for automatic speech recognition,” in Interspeech, 2008.
      [BibTeX] [Link]

      @inproceedings{7386241,
      title = {Introducing temporal asymmetries in feature extraction for automatic speech recognition},
      author = {{Garimella S. V. S. Sivaram} and {H. Hermansky}},
      year = 2008,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/bf56690f6f6ebb7212bcb514da62e188e7fa0fac},
      }

2902. Joel Pinto and H. Hermansky, “Combining evidence from a generative and a discriminative model in phoneme recognition,” in Interspeech, 2008.
      [BibTeX] [Link]

      @inproceedings{14678152,
      title = {Combining evidence from a generative and a discriminative model in phoneme recognition},
      author = {{Joel Pinto} and {H. Hermansky}},
      year = 2008,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/8807d4234754a6ef53573da0af0d3436b69bf501},
      }

2903. S. Parthasarathi, P. Motlícek, and H. Hermansky, “Exploiting temporal context for speech/non-speech detection.” 2008.
      [BibTeX] [Link]

      @inproceedings{13562623,
      title = {Exploiting temporal context for speech/non-speech detection},
      author = {{S. Parthasarathi} and {P. Motlícek} and {H. Hermansky}},
      year = 2008,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/bc7ad43abaae286d65ba59e8979db24b71603ccf},
      }

2904. Chris Callison-Burch, C. Fordyce, Philipp Koehn, Christof Monz, and Josh Schroeder, “Proceedings of the Third Workshop on Statistical Machine Translation (StatMT ’08),” in The Association for Computational Linguistics, 2008.
      [BibTeX] [Link]

      @inproceedings{195718969,
      title = {Proceedings of the Third Workshop on Statistical Machine Translation (StatMT '08)},
      author = {{Chris Callison-Burch} and {C. Fordyce} and {Philipp Koehn} and {Christof Monz} and {Josh Schroeder}},
      year = 2008,
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/282c69ba6e6b38ea2df099974586a22b760c2500},
      }

2905. S. Parthasarathi and H. Hermansky, “A Data-driven Approach to Speech/Non-speech Detection.” 2008.
      [BibTeX] [Link]

      @inproceedings{7315061,
      title = {A Data-driven Approach to Speech/Non-speech Detection},
      author = {{S. Parthasarathi} and {H. Hermansky}},
      year = 2008,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9539f77375a5d99ee9fb7975905cddfd48f16eea},
      }

2906. Christopher M. White, G. Zweig, L. Burget, Petr Schwarz, and H. Hermansky, “Confidence estimation, OOV detection and language ID using phone-to-word transduction and phone-level alignments,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
      [BibTeX] [Link]

      @inproceedings{1473258,
      title = {Confidence estimation, OOV detection and language ID using phone-to-word transduction and phone-level alignments},
      author = {{Christopher M. White} and {G. Zweig} and {L. Burget} and {Petr Schwarz} and {H. Hermansky}},
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2907. P. Motlícek, Sriram Ganapathy, H. Hermansky, H. Garudadri, and M. Athineos, “Perceptually Motivated Sub-band Decomposition for FDLP Audio Coding,” in International Conference on Text, Speech and Dialogue, 2008.
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2908. Chris Callison-Burch, Philipp Koehn, Christof Monz, Josh Schroeder, and C. Fordyce, “Proceedings of the Third Workshop on Statistical Machine Translation,” in WMT@ACL, 2008.
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2909. Balakrishnan Varadarajan and S. Khudanpur, “Automatically learning speaker-independent acoustic subword units,” in Interspeech, 2008.
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2910. R. Sproat, J. Baker, Martin Jansche, B. Ramabhadran, M. Riley, A. Sethy, P. Wolfe, S. Khudanpur, Arnab Ghoshal, Kristy Hollingshead, Christopher M. White, Ting Qian, Erica Cooper, and Morgan Ulinski, “Multilingual Spoken Term Detection: Finding and Testing New Pronunciations.” 2008.
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2911. Sriram Ganapathy, P. Motlícek, and H. Hermansky, “MODIFIED DISCRETE COSINE TRANSFORM FOR ENCODING RESIDUAL SIGNALS IN FREQUENCY DOMAIN LINEAR PREDICTION.” 2008.
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      }

2912. David Farris, Christopher M. White, and S. Khudanpur, “Sample selection for automatic language identification,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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2913. Damianos G. Karakos, S. Khudanpur, D. Marchette, A. Papamarcou, and C. Priebe, “On the minimization of concave information functionals for unsupervised classification via decision trees,” in Statistics & Probability Letters, 2008.
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2914. Garimella S. V. S. Sivaram and H. Hermansky, “Emulating temporal receptive fields of auditory mid-brain neurons for automatic speech recognition,” in European Signal Processing Conference, 2008.
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2915. P. Motlícek, Sriram Ganapathy, and H. Hermansky, “Entropy coding of Quantized Spectral Components in FDLP audio codec.” 2008.
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2916. M. Soma, Zhi-Pei Liang, G. Yen, G. Cauwenberghs, R. Etienne-Cummings, A. Andreou, A. Bermak, A. Burdett, Toumaz Uk Ltd, S. Carrara, K. Chakrabarty, S. Chakrabartty, P. Chiang, David Cumming, T. Delbruck, T. Denison, S. DeWeerth, E. Drakakis, D. Ham, E. Jovanov, Edmund Y. L Am, Yong Lian, Shih-Chii Liu, Wentai Liu, A. J. Mason, T. Roska, R. Sarpeshkar, M. Sawan, K. Shepard, Bertram E. Shi, M. Stanaćević, and J. Spiegel, “TECHNICAL CO-SPONSORING SOCIETIES Computational Intelligence.” 2008.
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2917. Sriram Ganapathy, P. Motlícek, H. Hermansky, and H. Garudadri, “Autoregressive Modelling of Hilbert Envelopes for Wide-band Audio Coding,” in Journal of The Audio Engineering Society, 2008.
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2918. H. Hermansky, “Sangita Tibrewala and Hynek Hermansky, “multi-stream Approach in Acoustic Modeling ,” in Proc. Lvcsr-hub5 Workshop, Multi-stream Approach in Acoustic Modeling 1. the Multi-stream Concept.” 2008.
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2919. F. Valente and H. Hermansky, “Hierarchical and parallel processing of modulation spectrum for ASR applications,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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2920. Joel Pinto, Igor Szöke, S. Prasanna, and H. Hermansky, “Fast Approximate Spoken Term Detection from Sequence of Phonemes,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008.
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2921. Yi Su and F. Jelinek, “Exploiting prosodic breaks in language modeling with random forests,” in Proceedings of the International Conference on Speech Prosody, 2008.
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2922. B. Pal, Jia Sun, Byung-Jun Jung, E. Choi, A. Andreou, and H. Katz, “Pentacene‐Zinc Oxide Vertical Diode with Compatible Grains and 15‐MHz Rectification,” in Advanced Materials, 2008.
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2923. F. Jelinek and Carolina Parada, “Toward the Ultimate ASR Language Model,” in International Conference on Text, Speech and Dialogue, 2008.
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2924. R. Melloni and Glen A. Coppersmith, “A computational investigation into maladaptive aggression.” 2008.
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2925. N. Garera and D. Yarowsky, “Translating Compounds by Learning Component Gloss Translation Models via Multiple Languages,” in Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I, 2008.
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2926. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Hilbert Envelope Based Features for Far-Field Speech Recognition,” in Machine Learning for Multimodal Interaction, 2008.
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2927. Joel Pinto, Garimella S. V. S. Sivaram, H. Hermansky, and M. Magimai-Doss, “Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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2928. Wei Tang, A. Andreou, and E. Culurciello, “A low-power silicon-on-sapphire tunable ultra-wideband transmitter,” in 2008 IEEE International Symposium on Circuits and Systems, 2008.
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2929. Sriram Ganapathy, Samuel Thomas, and H. Hermansky, “Front-end for far-field speech recognition based on frequency domain linear prediction,” in Interspeech, 2008.
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2930. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Hilbert envelope based spectro-temporal features for phoneme recognition in telephone speech,” in Interspeech, 2008.
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2931. M. D. Federico, P. Mandolesi, P. Julián, and A. Andreou, “Experimental results of simplicial cnn digital pixel processor,” in Electronics Letters, 2008.
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2932. Sriram Ganapathy, P. Motlícek, H. Hermansky, and H. Garudadri, “Spectral noise shaping: improvements in speech/audio codec based on linear prediction in spectral domain,” in Interspeech, 2008.
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2933. L. Burget, Petr Schwarz, P. Matejka, M. Hannemann, A. Rastrow, Christopher M. White, S. Khudanpur, H. Hermansky, and J. Černocký, “Combination of strongly and weakly constrained recognizers for reliable detection of OOVS,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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2934. M. Marwick and A. Andreou, “Single photon avalanche photodetector with integrated quenching fabricated in TSMC 0.18 μm 1.8 V CMOS process,” in Electronics Letters, 2008.
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2935. A. Andreou, “An Electronically Tunable Linear or Nonlinear MOS Resistor.” 2008.
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2936. Garimella S. V. S. Sivaram and H. Hermansky, “Emulating Temporal Receptive Fields of Higher Level Auditory Neurons for ASR,” in International Conference on Text, Speech and Dialogue, 2008.
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2937. A. Andreou, “Detection Methods of Foot Shape and Pressure Distribution.” 2008.
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2938. Samuel Thomas, Sriram Ganapathy, and H. Hermansky, “Spectro-temporal features for Automatic Speech Recognition using Linear Prediction in spectral domain,” in European Signal Processing Conference, 2008.
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2939. S. Parthasarathi, P. Motlícek, and H. Hermansky, “Exploiting Contextual Information for Speech/Non-Speech Detection,” in International Conference on Text, Speech and Dialogue, 2008.
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2940. Sriram Ganapathy, P. Motlícek, H. Hermansky, and H. Garudadri, “Temporal masking for bit-rate reduction in audio codec based on Frequency Domain Linear Prediction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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2941. C. Reiley, Henry C. Lin, Balakrishnan Varadarajan, Balázs P. Vágvölgyi, S. Khudanpur, D. Yuh, and Gregory Hager, “Automatic Recognition of Surgical Motions Using Statistical Modeling for Capturing Variability,” in Medicine Meets Virtual Reality, 2008.
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2942. N. Morgan, Q. Zhu, A. Stolcke, Kemal Sönmez, S. Sivadas, T. Shinozaki, Mari Ostendorf, P. Jain, H. Hermansky, D. Ellis, G. Doddington, Barry Y. Chen, Ö. Çetin, H. Bourlard, and M. Athineos, “Pushing the Envelope – Aside : Beyond the Spectral Envelope as the Fundamental Representation for Speech Recognition.” 2008.
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      year = 2008,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4818ba90b1c5db6bbd8f857695f8117130babfce},
      }

2943. K. Kohler, W. Barry, Didier Demolin, R. Diehl, O. Engstrand, Nina Grønnum, Sarah Hawkins, H. Hermansky, V. V. Heuven, J. Kingston, Francis Nolan, J. Ohala, D. Recasens, A. Simpson, J. Vaissière, and Yi Xu, “Contents Vol. 64, 2007,” in Phonetica: International Journal of Phonetic Science, 2008.
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      @inproceedings{202653002,
      title = {Contents Vol. 64, 2007},
      author = {{K. Kohler} and {W. Barry} and {Didier Demolin} and {R. Diehl} and {O. Engstrand} and {Nina Grønnum} and {Sarah Hawkins} and {H. Hermansky} and {V. V. Heuven} and {J. Kingston} and {Francis Nolan} and {J. Ohala} and {D. Recasens} and {A. Simpson} and {J. Vaissière} and {Yi Xu}},
      year = 2008,
      month = {4},
      booktitle = {Phonetica: International Journal of Phonetic Science},
      url = {https://www.semanticscholar.org/paper/d83330c41f4dbd60e197b8088fca4f07c856f28a},
      }

2944. Joel Pinto, B. Yegnanarayana, H. Hermansky, and M. Magimai-Doss, “Exploiting contextual information for improved phoneme recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008.
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      @inproceedings{5905702,
      title = {Exploiting contextual information for improved phoneme recognition},
      author = {{Joel Pinto} and {B. Yegnanarayana} and {H. Hermansky} and {M. Magimai-Doss}},
      year = 2008,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/4c93e00e9b54d1c30143b9f005460acb0f643fe4},
      }

2945. F. Valente and H. Hermansky, “On the combination of auditory and modulation frequency channels for ASR applications,” in Interspeech, 2008.
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      @inproceedings{12994161,
      title = {On the combination of auditory and modulation frequency channels for ASR applications},
      author = {{F. Valente} and {H. Hermansky}},
      year = 2008,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/5b115a575f558b5b76625d10477786b1bfadfd40},
      }

2946. Z. Li and S. Khudanpur, “Large-scale Discriminative n-gram Language Models for Statistical Machine Translation,” in Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, Waikiki, USA, 2008, p. 133–142.
      [BibTeX] [Abstract] [Link]
      

      We extend discriminative n-gram language modeling techniques originally proposed for automatic speech recognition to a statistical machine translation task. In this context, we propose a novel data selection method that leads to good models using a fraction of the training data. We carry out systematic experiments on several benchmark tests for Chinese to English translation using a hierarchical phrase-based machine translation system, and show that a discriminative language model significantly improves upon a state-of-the-art baseline. The experiments also highlight the benefits of our data selection method.

      @inproceedings{li-khudanpur-2008-large,
      title = "Large-scale Discriminative n-gram Language Models for Statistical Machine Translation",
      author = "Li, Zhifei and
      Khudanpur, Sanjeev",
      booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
      month = oct # " 21-25",
      year = "2008",
      address = "Waikiki, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2008.amta-papers.12",
      pages = "133--142",
      abstract = "We extend discriminative n-gram language modeling techniques originally proposed for automatic speech recognition to a statistical machine translation task. In this context, we propose a novel data selection method that leads to good models using a fraction of the training data. We carry out systematic experiments on several benchmark tests for Chinese to English translation using a hierarchical phrase-based machine translation system, and show that a discriminative language model significantly improves upon a state-of-the-art baseline. The experiments also highlight the benefits of our data selection method.",
      }

2947. Christopher M. White, S. Khudanpur, and J. Baker, “An investigation of acoustic models for multilingual code-switching,” in Interspeech, 2008.
      [BibTeX] [Link]

      @inproceedings{18661445,
      title = {An investigation of acoustic models for multilingual code-switching},
      author = {{Christopher M. White} and {S. Khudanpur} and {J. Baker}},
      year = 2008,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/5a71064aae459c32e64b7d880e36ff588963baae},
      }

2948. Tamara Tosic, M. Magimai-Doss, and H. Hermansky, “Using comparison of parallel phoneme probability streams for OOV word detection,” in European Signal Processing Conference, 2008.
      [BibTeX] [Link]

      @inproceedings{10605182,
      title = {Using comparison of parallel phoneme probability streams for OOV word detection},
      author = {{Tamara Tosic} and {M. Magimai-Doss} and {H. Hermansky}},
      year = 2008,
      month = {8},
      booktitle = {European Signal Processing Conference},
      url = {https://www.semanticscholar.org/paper/25396b14e1fafaea1e9b5bfe29b468f1f3ec1cc8},
      }

2949. M. Marwick and A. Andreou, “Photo-battery fabricated in silicon on sapphire CMOS,” in Electronics Letters, 2008.
      [BibTeX] [Link]

      @inproceedings{110935662,
      title = {Photo-battery fabricated in silicon on sapphire CMOS},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2008,
      month = {6},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/96233510e6db4c6f00d7c8b5cea6f9600412644f},
      }

2950. M. Marwick and A. Andreou, “A high voltage PMOS transistor for quenching of geiger-mode avalanche photodiodes in deep submicron CMOS technologies,” in International Semiconductor Device Research Symposium, 2007.
      [BibTeX] [Link]

      @inproceedings{39956050,
      title = {A high voltage PMOS transistor for quenching of geiger-mode avalanche photodiodes in deep submicron CMOS technologies},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2007,
      month = {12},
      booktitle = {International Semiconductor Device Research Symposium},
      url = {https://www.semanticscholar.org/paper/0ddc680ab5e0243dc1c341910322a4141102394c},
      }

2951. A. Andreou, “Microsystems engineering from nano to micro and macro,” in 2007 14th IEEE International Conference on Electronics, Circuits and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{19696628,
      title = {Microsystems engineering from nano to micro and macro},
      author = {{A. Andreou}},
      year = 2007,
      month = {12},
      booktitle = {2007 14th IEEE International Conference on Electronics, Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/a40c9af77cf73e410911fd3b6eeeafbbad30658e},
      }

2952. Jia Cui, Yi Su, Keith B. Hall, and F. Jelinek, “Investigating linguistic knowledge in a maximum entropy token-based language model,” in Automatic Speech Recognition & Understanding, 2007.
      [BibTeX] [Link]

      @inproceedings{1984160,
      title = {Investigating linguistic knowledge in a maximum entropy token-based language model},
      author = {{Jia Cui} and {Yi Su} and {Keith B. Hall} and {F. Jelinek}},
      year = 2007,
      month = {12},
      booktitle = {Automatic Speech Recognition & Understanding},
      url = {https://www.semanticscholar.org/paper/a83d497045fc89f9a0d4bd3a97156a824824cc87},
      }

2953. M. Marwick and A. Andreou, “A UV Photodetector with Internal Gain Fabricated in Silicon on Sapphire CMOS,” in Italian National Conference on Sensors, 2007.
      [BibTeX] [Link]

      @inproceedings{6694549,
      title = {A UV Photodetector with Internal Gain Fabricated in Silicon on Sapphire CMOS},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2007,
      month = {12},
      booktitle = {Italian National Conference on Sensors},
      url = {https://www.semanticscholar.org/paper/95127189ab08324cb3922f0a6c592cc574301675},
      }

2954. M. Marwick and A. Andreou, “Design and characterization of a gain-enhanced floating gate-body tied photodetector in Silicon on Sapphire CMOS,” in International Semiconductor Device Research Symposium, 2007.
      [BibTeX] [Link]

      @inproceedings{33132178,
      title = {Design and characterization of a gain-enhanced floating gate-body tied photodetector in Silicon on Sapphire CMOS},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2007,
      month = {12},
      booktitle = {International Semiconductor Device Research Symposium},
      url = {https://www.semanticscholar.org/paper/eb0bbb601458c8ff5ccbda23614fbce38f93ee84},
      }

2955. A. Cassidy, S. Denham, P. Kanold, and A. Andreou, “FPGA Based Silicon Spiking Neural Array,” in 2007 IEEE Biomedical Circuits and Systems Conference, 2007.
      [BibTeX] [Link]

      @inproceedings{535106,
      title = {FPGA Based Silicon Spiking Neural Array},
      author = {{A. Cassidy} and {S. Denham} and {P. Kanold} and {A. Andreou}},
      year = 2007,
      month = {11},
      booktitle = {2007 IEEE Biomedical Circuits and Systems Conference},
      url = {https://www.semanticscholar.org/paper/cdb1d3a8fc09856480d9d838cd5fa1c7ce12aa43},
      }

2956. David H. Goldberg and A. Andreou, “Distortion of Neural Signals by Spike Coding,” in Neural Computation, 2007.
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      @inproceedings{17225429,
      title = {Distortion of Neural Signals by Spike Coding},
      author = {{David H. Goldberg} and {A. Andreou}},
      year = 2007,
      month = {10},
      booktitle = {Neural Computation},
      url = {https://www.semanticscholar.org/paper/a7262bf92982eb518d27a4f17f1922d997007e7d},
      }

2957. J. Baker, Li Deng, James R. Glass, S. Khudanpur, Chin-Hui Lee, and J. Baker, “Historical Development and Future Directions in Speech Recognition and Understanding.” 2007.
      [BibTeX] [Link]

      @inproceedings{268087285,
      title = {Historical Development and Future Directions in Speech Recognition and Understanding},
      author = {{J. Baker} and {Li Deng} and {James R. Glass} and {S. Khudanpur} and {Chin-Hui Lee} and {J. Baker}},
      year = 2007,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/03fa761a42c9c9a74ea85cae60b2cfff107e1190},
      }

2958. C. Callison-Burch, C. Fordyce, P. Koehn, C. Monz, and J. Schroeder, “(Meta-) Evaluation of Machine Translation,” in Proceedings of the Second Workshop on Statistical Machine Translation, Prague, Czech Republic, 2007, p. 136–158.
      [BibTeX] [Link]

      @inproceedings{callison-burch-etal-2007-meta,
      title = "(Meta-) Evaluation of Machine Translation",
      author = "Callison-Burch, Chris and
      Fordyce, Cameron and
      Koehn, Philipp and
      Monz, Christof and
      Schroeder, Josh",
      editor = "Callison-Burch, Chris and
      Koehn, Philipp and
      Fordyce, Cameron Shaw and
      Monz, Christof",
      booktitle = "Proceedings of the Second Workshop on Statistical Machine Translation",
      month = jun,
      year = "2007",
      address = "Prague, Czech Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W07-0718",
      pages = "136--158",
      }

2959. D. Rao, N. Garera, and D. Yarowsky, “JHU1 : An Unsupervised Approach to Person Name Disambiguation using Web Snippets,” in Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic, 2007, p. 199–202.
      [BibTeX] [Link]

      @inproceedings{rao-etal-2007-jhu1,
      title = "{JHU}1 : An Unsupervised Approach to Person Name Disambiguation using Web Snippets",
      author = "Rao, Delip and
      Garera, Nikesh and
      Yarowsky, David",
      editor = "Agirre, Eneko and
      M{\`a}rquez, Llu{\'\i}s and
      Wicentowski, Richard",
      booktitle = "Proceedings of the Fourth International Workshop on Semantic Evaluations ({S}em{E}val-2007)",
      month = jun,
      year = "2007",
      address = "Prague, Czech Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S07-1042",
      pages = "199--202",
      }

2960. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst, “Moses: Open Source Toolkit for Statistical Machine Translation,” in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, Prague, Czech Republic, 2007, p. 177–180.
      [BibTeX] [Link]

      @inproceedings{koehn-etal-2007-moses,
      title = "{M}oses: Open Source Toolkit for Statistical Machine Translation",
      author = "Koehn, Philipp and
      Hoang, Hieu and
      Birch, Alexandra and
      Callison-Burch, Chris and
      Federico, Marcello and
      Bertoldi, Nicola and
      Cowan, Brooke and
      Shen, Wade and
      Moran, Christine and
      Zens, Richard and
      Dyer, Chris and
      Bojar, Ond{\v{r}}ej and
      Constantin, Alexandra and
      Herbst, Evan",
      editor = "Ananiadou, Sophia",
      booktitle = "Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions",
      month = jun,
      year = "2007",
      address = "Prague, Czech Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P07-2045",
      pages = "177--180",
      }

2961. D. A. Smith and J. Eisner, “Bootstrapping Feature-Rich Dependency Parsers with Entropic Priors,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, 2007, p. 667–677.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2007,
      aclid = "D07-1070",
      author = "David A. Smith and Jason Eisner",
      title = "Bootstrapping Feature-Rich Dependency Parsers with
      Entropic Priors",
      booktitle = "Proceedings of the 2007 Joint Conference on Empirical
      Methods in Natural Language Processing and
      Computational Natural Language Learning (EMNLP-CoNLL)",
      pages = "667--677",
      year = "2007",
      month = jun,
      address = "Prague",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2007",
      }

2962. D. Karakos, J. Eisner, S. Khudanpur, and C. Priebe, “Cross-Instance Tuning of Unsupervised Document Clustering Algorithms,” in Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, Rochester, New York, 2007, p. 252–259.
      [BibTeX] [Link]

      @inproceedings{karakos-etal-2007-cross,
      title = "Cross-Instance Tuning of Unsupervised Document Clustering Algorithms",
      author = "Karakos, Damianos and
      Eisner, Jason and
      Khudanpur, Sanjeev and
      Priebe, Carey",
      editor = "Sidner, Candace and
      Schultz, Tanja and
      Stone, Matthew and
      Zhai, ChengXiang",
      booktitle = "Human Language Technologies 2007: The Conference of the North {A}merican Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference",
      month = apr,
      year = "2007",
      address = "Rochester, New York",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N07-1032",
      pages = "252--259",
      }

2963. M. Dreyer, K. Hall, and S. Khudanpur, “Comparing Reordering Constraints for SMT Using Efficient BLEU Oracle Computation,” in Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation, Rochester, New York, 2007, p. 103–110.
      [BibTeX] [Link]

      @inproceedings{dreyer-etal-2007-comparing,
      title = "Comparing Reordering Constraints for {SMT} Using Efficient {BLEU} Oracle Computation",
      author = "Dreyer, Markus and
      Hall, Keith and
      Khudanpur, Sanjeev",
      editor = "Wu, Dekai and
      Chiang, David",
      booktitle = "Proceedings of {SSST}, {NAACL}-{HLT} 2007 / {AMTA} Workshop on Syntax and Structure in Statistical Translation",
      month = apr,
      year = "2007",
      address = "Rochester, New York",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W07-0414",
      pages = "103--110",
      }

2964. O. Zaidan, J. Eisner, and C. Piatko, “Using “Annotator Rationales” to Improve Machine Learning for Text Categorization,” in Human Language Technologies: Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY, 2007, p. 260–267.
      [BibTeX] [Link]

      @InProceedings{zaidan-eisner-piatko-2007,
      aclid = "N07-1033",
      author = "Omar Zaidan and Jason Eisner and Christine Piatko",
      title = "Using ``Annotator Rationales'' to Improve Machine
      Learning for Text Categorization",
      booktitle = "Human Language Technologies: Proceedings of the Annual
      Conference of the North American Chapter of the
      Association for Computational Linguistics (NAACL-HLT)",
      pages = "260--267",
      year = "2007",
      month = apr,
      address = "Rochester, NY",
      URL = "http://cs.jhu.edu/~jason/papers/#zaidan-eisner-piatko-2007",
      }

2965. D. Karakos, J. Eisner, Sanjeev Khudanpur, and C. E. Priebe, “Cross-Instance Tuning of Unsupervised Document Clustering Algorithms,” in Human Language Technologies: Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY, 2007, p. 252–259.
      [BibTeX] [Link]

      @InProceedings{karakos-et-al-2007-naacl,
      aclid = "N07-1032",
      author = "Damianos Karakos and Jason Eisner and Sanjeev
      Khudanpur and Carey E. Priebe",
      title = "Cross-Instance Tuning of Unsupervised Document
      Clustering Algorithms",
      booktitle = "Human Language Technologies: Proceedings of the Annual
      Conference of the North American Chapter of the
      Association for Computational Linguistics (NAACL-HLT)",
      year = "2007",
      month = apr,
      address = "Rochester, NY",
      pages = "252--259",
      URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2007-naacl",
      }

2966. D. Karakos, S. Khudanpur, Jason Eisner, and C. E. Priebe, “Iterative Denoising Using Jensen-Renyí Divergences with an Application to Unsupervised Document Categorization,” in Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honolulu, 2007.
      [BibTeX] [Link]

      @InProceedings{karakos-et-al-2007-icassp,
      author = "Damianos Karakos and Sanjeev Khudanpur and Jason
      Eisner and Carey E. Priebe",
      title = "Iterative Denoising Using {J}ensen-{R}eny\'{\i}
      Divergences with an Application to Unsupervised
      Document Categorization",
      booktitle = "Proceedings of the International Conference on
      Acoustics, Speech and Signal Processing (ICASSP)",
      note = "4 pages",
      year = "2007",
      month = apr,
      address = "Honolulu",
      URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2007-icassp",
      }

2967. F. Jelinek and Jia Cui, “Language Modeling with Linguistic Cluster Constraints,” in International Conference on Text, Speech and Dialogue, 2007.
      [BibTeX] [Link]

      @inproceedings{27296686,
      title = {Language Modeling with Linguistic Cluster Constraints},
      author = {{F. Jelinek} and {Jia Cui}},
      year = 2007,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/0e9cf845db82ed7961d1817e80cff9ef36b8482e},
      }

2968. M. Marwick and A. Andreou, “Fabrication and Testing of Single Photon Avalanche Detectors in the TSMC 0.18μm CMOS Technology,” in Annual Conference on Information Sciences and Systems, 2007.
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      @inproceedings{17087252,
      title = {Fabrication and Testing of Single Photon Avalanche Detectors in the TSMC 0.18μm CMOS Technology},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2007,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/0a1d9d8ff27e1aa8371bba67cee89a44687ab285},
      }

2969. Chris Callison-Burch, Philipp Koehn, Christof Monz, and C. Fordyce, “Proceedings of the Second Workshop on Statistical Machine Translation,” in The Association for Computational Linguistics, 2007.
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      @inproceedings{195960044,
      title = {Proceedings of the Second Workshop on Statistical Machine Translation},
      author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {C. Fordyce}},
      year = 2007,
      month = {6},
      booktitle = {The Association for Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/d34385e2deafc44f32dbd4f7c4046e8e63bf8702},
      }

2970. Chris Callison-Burch, “Paraphrasing and translation.” 2007.
      [BibTeX] [Link]

      @inproceedings{29653179,
      title = {Paraphrasing and translation},
      author = {{Chris Callison-Burch}},
      year = 2007,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a37b078041b89e09e36ceb04d59755870678c3be},
      }

2971. J. Christen and A. Andreou, “Design, Fabrication, and Testing of a Hybrid CMOS/PDMS Microsystem for Cell Culture and Incubation,” in IEEE Transactions on Biomedical Circuits and Systems, 2007.
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      @inproceedings{1252647,
      title = {Design, Fabrication, and Testing of a Hybrid CMOS/PDMS Microsystem for Cell Culture and Incubation},
      author = {{J. Christen} and {A. Andreou}},
      year = 2007,
      month = {7},
      booktitle = {IEEE Transactions on Biomedical Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/fffa9180c2f2749a171b25a50a0ad65820744de4},
      }

2972. Damianos G. Karakos and S. Khudanpur, “Error Bounds and Improved Probability Estimation using the Maximum Likelihood Set,” in 2007 IEEE International Symposium on Information Theory, 2007.
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      @inproceedings{11900023,
      title = {Error Bounds and Improved Probability Estimation using the Maximum Likelihood Set},
      author = {{Damianos G. Karakos} and {S. Khudanpur}},
      year = 2007,
      month = {6},
      booktitle = {2007 IEEE International Symposium on Information Theory},
      url = {https://www.semanticscholar.org/paper/fccde205cbe2115f103d19ab954b1b8c3b0c3fe0},
      }

2973. Chris Callison-Burch, Philipp Koehn, Christof Monz, and C. Fordyce, “Proceedings of the Second Workshop on Statistical Machine Translation, WMT@ACL 2007, Prague, Czech Republic, June 23, 2007,” in WMT@ACL, 2007.
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      @inproceedings{39379903,
      title = {Proceedings of the Second Workshop on Statistical Machine Translation, WMT@ACL 2007, Prague, Czech Republic, June 23, 2007},
      author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {C. Fordyce}},
      year = 2007,
      booktitle = {WMT@ACL},
      url = {https://www.semanticscholar.org/paper/eb04d3dc680b76b6538f383bf0a52f6db2af402b},
      }

2974. A. Lavie, David Yarowsky, Kevin Knight, Chris Callison-Burch, Nizar Habash, and T. Mitamura, “Machine Translation Working Group Final Report.” 2007.
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      @inproceedings{203657369,
      title = {Machine Translation Working Group Final Report},
      author = {{A. Lavie} and {David Yarowsky} and {Kevin Knight} and {Chris Callison-Burch} and {Nizar Habash} and {T. Mitamura}},
      year = 2007,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/8475eae421574b7e1208c92ec02b5bcbdeb601ea},
      }

2975. Damianos G. Karakos, S. Khudanpur, Jason Eisner, and C. Priebe, “Iterative Denoising using Jensen-Renyi Divergences with an Application to Unsupervised Document Categorization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2007.
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      @inproceedings{2000978,
      title = {Iterative Denoising using Jensen-Renyi Divergences with an Application to Unsupervised Document Categorization},
      author = {{Damianos G. Karakos} and {S. Khudanpur} and {Jason Eisner} and {C. Priebe}},
      year = 2007,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/71b552b685aa73311036a44ecfbf69b1c9fb1c28},
      }

2976. Patricia Driscoll and David Yarowsky, “Disambiguation of Standardized Personal Name Variants.” 2007.
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      @inproceedings{14583345,
      title = {Disambiguation of Standardized Personal Name Variants},
      author = {{Patricia Driscoll} and {David Yarowsky}},
      year = 2007,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/6acb002de451741af96dd24c99adb56d21dab969},
      }

2977. E. Culurciello, P. Pouliquen, and A. Andreou, “Digital isolation amplifier in silicon-on-sapphire CMOS,” in Electronics Letters, 2007.
      [BibTeX] [Link]

      @inproceedings{110077446,
      title = {Digital isolation amplifier in silicon-on-sapphire CMOS},
      author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou}},
      year = 2007,
      month = {4},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/e8bbf1f1f93eb96bcc618ef64e2e855502cb017b},
      }

2978. J. Christen and A. Andreou, “Design, Analysis and Implementation of Integrated Micro-Thermal Control Systems,” in 2007 IEEE International Symposium on Circuits and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{2075156,
      title = {Design, Analysis and Implementation of Integrated Micro-Thermal Control Systems},
      author = {{J. Christen} and {A. Andreou}},
      year = 2007,
      month = {5},
      booktitle = {2007 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/659b86a5822e3bb09e839d70871f302c7908b81e},
      }

2979. Z. Zhang, P. Pouliquen, A. Waxman, and A. Andreou, “Acoustic Micro-Doppler Gait Signatures of Humans and Animals,” in Annual Conference on Information Sciences and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{396554,
      title = {Acoustic Micro-Doppler Gait Signatures of Humans and Animals},
      author = {{Z. Zhang} and {P. Pouliquen} and {A. Waxman} and {A. Andreou}},
      year = 2007,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/a5e97fb77acfad82403eff3d6bd758288f22066e},
      }

2980. E. Choi and A. Andreou, “Architecture of a μRFID with integrated antenna in 3D SOI-CMOS,” in Annual Conference on Information Sciences and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{16685473,
      title = {Architecture of a μRFID with integrated antenna in 3D SOI-CMOS},
      author = {{E. Choi} and {A. Andreou}},
      year = 2007,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/41eb662a44bdc1e2e66fd745fded8bf13b298f1d},
      }

2981. Trevor Cohn, Philipp Koehn, Kevin Knight, Chris Callison-Burch, David Chiang, and Phil Blunsom, “Statistical machine translation,” in Machine Translation Summit, 2007.
      [BibTeX] [Link]

      @inproceedings{264714672,
      title = {Statistical machine translation},
      author = {{Trevor Cohn} and {Philipp Koehn} and {Kevin Knight} and {Chris Callison-Burch} and {David Chiang} and {Phil Blunsom}},
      year = 2007,
      booktitle = {Machine Translation Summit},
      url = {https://www.semanticscholar.org/paper/bc4cff47e4c0ce5f6eff8a77d6dd12c7dd3d8a21},
      }

2982. A. Andreou and J. Christen, “Hybrid integration of silicon/silicone microsystems: a closed-loop, autonomous micro-incubator.” 2007.
      [BibTeX] [Link]

      @inproceedings{114165149,
      title = {Hybrid integration of silicon/silicone microsystems: a closed-loop, autonomous micro-incubator},
      author = {{A. Andreou} and {J. Christen}},
      year = 2007,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9475e7c5a385c89e6c95ef670eb82ef53b33a625},
      }

2983. J. Christen and A. Andreou, “A Self-Biased Operational Transconductance Amplifier in 0.18 micron 3D SOI-CMOS,” in 2007 IEEE International Symposium on Circuits and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{20142766,
      title = {A Self-Biased Operational Transconductance Amplifier in 0.18 micron 3D SOI-CMOS},
      author = {{J. Christen} and {A. Andreou}},
      year = 2007,
      month = {5},
      booktitle = {2007 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/07af8f2c812e1799671ccfba911a513819c6d52e},
      }

2984. J. Christen, A. Andreou, and B. Iglehart, “Localized closed-loop temperature control and regulation in hybrid silicon/silicone life science microsystems,” in 2007 IEEE International Symposium on Circuits and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{23535312,
      title = {Localized closed-loop temperature control and regulation in hybrid silicon/silicone life science microsystems},
      author = {{J. Christen} and {A. Andreou} and {B. Iglehart}},
      year = 2007,
      month = {5},
      booktitle = {2007 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/7077438a107986aeedffd3bb5a5f01904a5e089f},
      }

2985. Yi Su, F. Jelinek, and S. Khudanpur, “Large-scale random forest language models for speech recognition,” in Interspeech, 2007.
      [BibTeX] [Link]

      @inproceedings{20201889,
      title = {Large-scale random forest language models for speech recognition},
      author = {{Yi Su} and {F. Jelinek} and {S. Khudanpur}},
      year = 2007,
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/b2b65bd8f4ab849a2ae0d93b74a18c0015b28f0a},
      }

2986. A. Andreou, Jie Chen, P. Chung, and Stephen T. C. Wong, “Enabling Technologies in Drug Delivery and Clinical Care,” in 2007 IEEE International Symposium on Circuits and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{11927906,
      title = {Enabling Technologies in Drug Delivery and Clinical Care},
      author = {{A. Andreou} and {Jie Chen} and {P. Chung} and {Stephen T. C. Wong}},
      year = 2007,
      month = {5},
      booktitle = {2007 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/edc84a68dc38bee9d9363dedebe6d78df26a3e2f},
      }

2987. Z. Zhang, P. Pouliquen, A. Waxman, and A. Andreou, “Acoustic micro-Doppler radar for human gait imaging.,” in Journal of the Acoustical Society of America, 2007.
      [BibTeX] [Link]

      @inproceedings{43367566,
      title = {Acoustic micro-Doppler radar for human gait imaging.},
      author = {{Z. Zhang} and {P. Pouliquen} and {A. Waxman} and {A. Andreou}},
      year = 2007,
      month = {2},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/cf3b1b15293afd79a6feac97a89b1b6e1d1d69db},
      }

2988. P. Xu and F. Jelinek, “Random forests and the data sparseness problem in language modeling,” in Computer Speech and Language, 2007.
      [BibTeX] [Link]

      @inproceedings{27115686,
      title = {Random forests and the data sparseness problem in language modeling},
      author = {{P. Xu} and {F. Jelinek}},
      year = 2007,
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/ff80a400198f0ce26887672407d8872825e663bf},
      }

2989. Z. Zhang and A. Andreou, “Design of An Ultra Wideband Transmitter in 0.18μm 3D Silicon on Insulator CMOS,” in Annual Conference on Information Sciences and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{17540825,
      title = {Design of An Ultra Wideband Transmitter in 0.18μm 3D Silicon on Insulator CMOS},
      author = {{Z. Zhang} and {A. Andreou}},
      year = 2007,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/c93033a2484406a5cad6bce5b4fc45bae2714867},
      }

2990. J. Georgiou and A. Andreou, “Address-data event representation for communication in multichip neuromorphic system architectures,” in Electronics Letters, 2007.
      [BibTeX] [Link]

      @inproceedings{58464138,
      title = {Address-data event representation for communication in multichip neuromorphic system architectures},
      author = {{J. Georgiou} and {A. Andreou}},
      year = 2007,
      month = {7},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/15e555ff7a0483466bcb3870fb85b37190bc7003},
      }

2991. P. Koehn and C. Callison-Burch, “Evaluating evaluation – lessons from the WMT 2007 shared task,” in Proceedings of the Workshop on Automatic procedures in MT evaluation, Copenhagen, Denmark, 2007.
      [BibTeX] [Link]

      @inproceedings{koehn-callison-burch-2007-evaluating,
      title = "Evaluating evaluation {--} lessons from the {WMT} 2007 shared task",
      author = "Koehn, Philipp and
      Callison-Burch, Chris",
      editor = "Thurmair, Gregor and
      Choukri, Khalid and
      Maegaard, Bente",
      booktitle = "Proceedings of the Workshop on Automatic procedures in MT evaluation",
      month = sep # " 11",
      year = "2007",
      address = "Copenhagen, Denmark",
      url = "https://aclanthology.org/2007.mtsummit-aptme.2",
      }

2992. J. Baker, L. Deng, James R. Glass, S. Khudanpur, Chin-Hui Lee, and J. Baker, “Historical Development and Future Directions in Speech Recognition and Understanding.” 2007.
      [BibTeX] [Link]

      @inproceedings{12324205,
      title = {Historical Development and Future Directions in Speech Recognition and Understanding},
      author = {{J. Baker} and {L. Deng} and {James R. Glass} and {S. Khudanpur} and {Chin-Hui Lee} and {J. Baker}},
      year = 2007,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1d43749353e3bef215bc9494e666f4105ad59a55},
      }

2993. J. Georgiou and A. Andreou, “Address Data Event Representation (ADER) for Efficient Neuromorphic Communication,” in Annual Conference on Information Sciences and Systems, 2007.
      [BibTeX] [Link]

      @inproceedings{774299,
      title = {Address Data Event Representation (ADER) for Efficient Neuromorphic Communication},
      author = {{J. Georgiou} and {A. Andreou}},
      year = 2007,
      month = {3},
      booktitle = {Annual Conference on Information Sciences and Systems},
      url = {https://www.semanticscholar.org/paper/9f277ef9ea827d95dfd3ee52910f8130bb240638},
      }

2994. J. Eisner and J. Blatz, “Program Transformations for Optimization of Parsing Algorithms and Other Weighted Logic Programs,” in Proceedings of FG 2006: The 11th Conference on Formal Grammar, 2007, p. 45–85.
      [BibTeX] [Link]

      @InProceedings{eisner-blatz-2007,
      author = "Jason Eisner and John Blatz",
      title = "Program Transformations for Optimization of Parsing
      Algorithms and Other Weighted Logic Programs",
      booktitle = "Proceedings of FG 2006: The 11th Conference on Formal
      Grammar",
      pages = "45--85",
      year = "2007",
      editor = "Shuly Wintner",
      publisher = "CSLI Publications",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-blatz-2007",
      }

2995. J. Christen and A. Andreou, “Hybrid integration for autonomous, closed-loop cell culture and incubation,” in Nanomedicine: Nanotechnology, Biology and Medicine, 2006.
      [BibTeX] [Link]

      @inproceedings{135774967,
      title = {Hybrid integration for autonomous, closed-loop cell culture and incubation},
      author = {{J. Christen} and {A. Andreou}},
      year = 2006,
      month = {12},
      booktitle = {Nanomedicine: Nanotechnology, Biology and Medicine},
      url = {https://www.semanticscholar.org/paper/e40043919c5a4ec0fb0c201f0eb1ade9e0183866},
      }

2996. E. Culurciello and A. Andreou, “Capacitive Inter-Chip Data and Power Transfer for 3-D VLSI,” in IEEE Transactions on Circuits and Systems – II – Express Briefs, 2006.
      [BibTeX] [Link]

      @inproceedings{28828770,
      title = {Capacitive Inter-Chip Data and Power Transfer for 3-D VLSI},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2006,
      month = {12},
      booktitle = {IEEE Transactions on Circuits and Systems - II - Express Briefs},
      url = {https://www.semanticscholar.org/paper/bf171d71b22d5cf219ad410fd2a9c06c55c3bbb8},
      }

2997. David H. Goldberg, A. Andreou, P. Julián, P. Pouliquen, Laurence Riddle, and Rich Rosasco, “VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network,” in TOSN, 2006.
      [BibTeX] [Link]

      @inproceedings{6736227,
      title = {VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network},
      author = {{David H. Goldberg} and {A. Andreou} and {P. Julián} and {P. Pouliquen} and {Laurence Riddle} and {Rich Rosasco}},
      year = 2006,
      month = {11},
      booktitle = {TOSN},
      url = {https://www.semanticscholar.org/paper/e3bb4400e6eedfedc9f37f687a86039bca8757cd},
      }

2998. A. Andreou, P. Chung, Guang‐Zhong Yang, and S. Wong, “Special issue on advances in life science systems and applications: Guest editorial.” 2006.
      [BibTeX] [Link]

      @inproceedings{16677717,
      title = {Special issue on advances in life science systems and applications: Guest editorial},
      author = {{A. Andreou} and {P. Chung} and {Guang‐Zhong Yang} and {S. Wong}},
      year = 2006,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/97f8eb3bdda51a7285c049eb3a79d90da3a56f52},
      }

2999. J.M. Blain Christen and A. Andreou, “Integrated PDMS/CMOS Microsystem for Autonomous Incubation and Imaging in Cell Culture Studies,” in 2006 IEEE/NLM Life Science Systems and Applications Workshop, 2006.
      [BibTeX] [Link]

      @inproceedings{17549385,
      title = {Integrated PDMS/CMOS Microsystem for Autonomous Incubation and Imaging in Cell Culture Studies},
      author = {{J.M. Blain Christen} and {A. Andreou}},
      year = 2006,
      month = {11},
      booktitle = {2006 IEEE/NLM Life Science Systems and Applications Workshop},
      url = {https://www.semanticscholar.org/paper/05cdbf3c044bc1bae5f318e24c4d97f1588819bb},
      }

3000. G. Marcus, Kim Strohben, S. Jaskulek, A. Andreou, and E. Culurciello, “A monolithic isolation amplifier in silicon-on-insulator CMOS: Testing and applications,” in Analog Integrated Circuits and Signal Processing, 2006.
      [BibTeX] [Link]

      @inproceedings{62187386,
      title = {A monolithic isolation amplifier in silicon-on-insulator CMOS: Testing and applications},
      author = {{G. Marcus} and {Kim Strohben} and {S. Jaskulek} and {A. Andreou} and {E. Culurciello}},
      year = 2006,
      month = {10},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/0baff22901722ea21b0614228f2bee905d25d91c},
      }

3001. P. Julián, F. N. M. Pirchio, and A. Andreou, “Experimental results for cascadable micropower time delay estimator,” in Electronics Letters, 2006.
      [BibTeX] [Link]

      @inproceedings{111002903,
      title = {Experimental results for cascadable micropower time delay estimator},
      author = {{P. Julián} and {F. N. M. Pirchio} and {A. Andreou}},
      year = 2006,
      month = {10},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/dbb64ee184793bacc681318b35892e1fd5439e6c},
      }

3002. E. Culurciello and A. Andreou, “CMOS image sensors for sensor networks,” in Analog Integrated Circuits and Signal Processing, 2006.
      [BibTeX] [Link]

      @inproceedings{41929501,
      title = {CMOS image sensors for sensor networks},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2006,
      month = {10},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/5560fd5e58c25ce864738cf764341305ba758f90},
      }

3003. J. Eisner, M. Kornbluh, G. Woodhull, R. Buse, S. Huang, Constantinos Michael, and G. Shafer, “Visual Navigation Through Large Directed Graphs and Hypergraphs,” in Proceedings of the IEEE Symposium on Information Visualization (InfoVis’06), Poster/Demo Session, Baltimore, 2006, p. 116–117.
      [BibTeX] [Link]

      @InProceedings{DYNASTY-2006,
      author = "Jason Eisner and Michael Kornbluh and Gordon Woodhull
      and Raymond Buse and Samuel Huang and Constantinos
      Michael and George Shafer",
      title = "Visual Navigation Through Large Directed Graphs and
      Hypergraphs",
      booktitle = "Proceedings of the IEEE Symposium on Information
      Visualization (InfoVis'06), Poster/Demo Session",
      pages = "116--117",
      year = "2006",
      month = oct,
      address = "Baltimore",
      URL = "http://cs.jhu.edu/~jason/papers/#DYNASTY-2006",
      }

3004. J. Mason, K. Watkins, J. Eisner, and A. Stubblefield, “A Natural-Language Approach to Automated Cryptanalysis of Two-Time Pads,” in Proceedings of the ACM Conference on Computer and Communications Security (ACM CCS), Alexandria, VA, 2006, p. 235–244.
      [BibTeX] [Link]

      @InProceedings{mason-et-al-2006,
      author = "Joshua Mason and Kathryn Watkins and Jason Eisner and
      Adam Stubblefield",
      title = "A Natural-Language Approach to Automated Cryptanalysis
      of Two-Time Pads",
      booktitle = "Proceedings of the ACM Conference on Computer and
      Communications Security (ACM CCS)",
      pages = "235--244",
      year = "2006",
      month = oct,
      address = "Alexandria, VA",
      URL = "http://cs.jhu.edu/~jason/papers/#mason-et-al-2006",
      }

3005. M. Dreyer and J. Eisner, “Better Informed Training of Latent Syntactic Features,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, 2006, p. 317–326.
      [BibTeX] [Link]

      @InProceedings{dreyer-eisner-2006,
      aclid = "W06-1638",
      author = "Markus Dreyer and Jason Eisner",
      title = "Better Informed Training of Latent Syntactic
      Features",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "317--326",
      year = "2006",
      month = jul,
      address = "Sydney",
      URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2006",
      }

3006. D. A. Smith and J. Eisner, “Minimum-Risk Annealing for Training Log-Linear Models,” in Proceedings of the International Conference on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL), Companion Volume, Sydney, 2006, p. 787–794.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2006-acl-risk,
      aclid = "P06-2101",
      author = "David A. Smith and Jason Eisner",
      title = "Minimum-Risk Annealing for Training Log-Linear
      Models",
      booktitle = "Proceedings of the International Conference on
      Computational Linguistics and the Association for
      Computational Linguistics (COLING-ACL), Companion
      Volume",
      pages = "787--794",
      year = "2006",
      month = jul,
      address = "Sydney",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-acl-risk",
      }

3007. N. A. Smith and J. Eisner, “Annealing Structural Bias in Multilingual Weighted Grammar Induction,” in Proceedings of the International Conference on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL), Sydney, 2006, p. 569–576.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2006-acl-sa,
      aclid = "P06-1072",
      author = "Noah A. Smith and Jason Eisner",
      title = "Annealing Structural Bias in Multilingual Weighted
      Grammar Induction",
      booktitle = "Proceedings of the International Conference on
      Computational Linguistics and the Association for
      Computational Linguistics (COLING-ACL)",
      pages = "569--576",
      year = "2006",
      month = jul,
      address = "Sydney",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-acl-sa",
      }

3008. N. Garera and D. Yarowsky, “Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora,” in Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), New York City, 2006, p. 37–44.
      [BibTeX] [Link]

      @inproceedings{garera-yarowsky-2006-resolving,
      title = "Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora",
      author = "Garera, Nikesh and
      Yarowsky, David",
      editor = "M{\`a}rquez, Llu{\'\i}s and
      Klein, Dan",
      booktitle = "Proceedings of the Tenth Conference on Computational Natural Language Learning ({C}o{NLL}-X)",
      month = jun,
      year = "2006",
      address = "New York City",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W06-2906",
      pages = "37--44",
      }

3009. J. Lin, D. Karakos, D. Demner-Fushman, and S. Khudanpur, “Generative Content Models for Structural Analysis of Medical Abstracts,” in Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology, New York, New York, 2006, p. 65–72.
      [BibTeX] [Link]

      @inproceedings{lin-etal-2006-generative,
      title = "Generative Content Models for Structural Analysis of Medical Abstracts",
      author = "Lin, Jimmy and
      Karakos, Damianos and
      Demner-Fushman, Dina and
      Khudanpur, Sanjeev",
      editor = "Verspoor, Karin and
      Cohen, Kevin Bretonnel and
      Goertzel, Ben and
      Mani, Inderjeet",
      booktitle = "Proceedings of the {HLT}-{NAACL} {B}io{NLP} Workshop on Linking Natural Language and Biology",
      month = jun,
      year = "2006",
      address = "New York, New York",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W06-3309",
      pages = "65--72",
      }

3010. J. Eisner and R. W. Tromble, “Local Search with Very Large-Scale Neighborhoods for Optimal Permutations in Machine Translation,” in Proceedings of the HLT-NAACL Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing, New York, 2006, p. 57–75.
      [BibTeX] [Link]

      @InProceedings{eisner-tromble-2006,
      author = "Jason Eisner and Roy W. Tromble",
      title = "Local Search with Very Large-Scale Neighborhoods for
      Optimal Permutations in Machine Translation",
      booktitle = "Proceedings of the HLT-NAACL Workshop on
      Computationally Hard Problems and Joint Inference in
      Speech and Language Processing",
      pages = "57--75",
      year = "2006",
      month = jun,
      address = "New York",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-tromble-2006",
      }

3011. D. A. Smith and J. Eisner, “Quasi-Synchronous Grammars: Alignment by Soft Projection of Syntactic Dependencies,” in Proceedings of the HLT-NAACL Workshop on Statistical Machine Translation, New York, 2006, p. 23–30.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2006-smt,
      aclid = "W06-3104",
      author = "David A. Smith and Jason Eisner",
      title = "Quasi-Synchronous Grammars: Alignment by Soft
      Projection of Syntactic Dependencies",
      booktitle = "Proceedings of the HLT-NAACL Workshop on Statistical
      Machine Translation",
      pages = "23--30",
      year = "2006",
      month = jun,
      address = "New York",
      note = "Nominated for 5-year retrospective Best Paper award.",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-smt",
      }

3012. R. W. Tromble and J. Eisner, “A Fast Finite-State Relaxation Method for Enforcing Global Constraints on Sequence Decoding,” in Proceedings of the Human Language Technology Conference of the North American Association for Computational Linguistics (HLT-NAACL), New York, 2006, p. 423–430.
      [BibTeX] [Link]

      @InProceedings{tromble-eisner-2006,
      aclid = "N06-1054",
      author = "Roy W. Tromble and Jason Eisner",
      title = "A Fast Finite-State Relaxation Method for Enforcing
      Global Constraints on Sequence Decoding",
      booktitle = "Proceedings of the Human Language Technology
      Conference of the North American Association for
      Computational Linguistics (HLT-NAACL)",
      pages = "423--430",
      year = "2006",
      month = jun,
      address = "New York",
      URL = "http://cs.jhu.edu/~jason/papers/#tromble-eisner-2006",
      }

3013. J. Georgiou, A. Andreou, and P. Pouliquen, “A mixed analog/digital asynchronous processor for cortical computations in 3D SOI-CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{18402065,
      title = {A mixed analog/digital asynchronous processor for cortical computations in 3D SOI-CMOS},
      author = {{J. Georgiou} and {A. Andreou} and {P. Pouliquen}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/47083729fba1bc4296ce60fc8e3fb9faf418fe02},
      }

3014. David Yarowsky and C. Schafer, “Translation discovery using diverse similarity measures.” 2006.
      [BibTeX] [Link]

      @inproceedings{61476379,
      title = {Translation discovery using diverse similarity measures},
      author = {{David Yarowsky} and {C. Schafer}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/cf56cd1fed212f5ad2cbdc7a07b58377df07c917},
      }

3015. Damianos G. Karakos and S. Khudanpur, “Estimating Conditional Densities from Sparse Data for Statistical Language Modeling.” 2006.
      [BibTeX] [Link]

      @inproceedings{375582,
      title = {Estimating Conditional Densities from Sparse Data for Statistical Language Modeling},
      author = {{Damianos G. Karakos} and {S. Khudanpur}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b10cbdc743d8f2210685007e65101436c764fb72},
      }

3016. J. Georgiou and A. Andreou, “High-speed, address-encoding arbiter architecture,” in Electronics Letters, 2006.
      [BibTeX] [Link]

      @inproceedings{62152556,
      title = {High-speed, address-encoding arbiter architecture},
      author = {{J. Georgiou} and {A. Andreou}},
      year = 2006,
      month = {2},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/bbab19fec5a003aa7223c7ef629824427bc77db8},
      }

3017. J. Riesa and D. Yarowsky, “Minimally Supervised Morphological Segmentation with Applications to Machine Translation,” in Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, Cambridge, Massachusetts, USA, 2006, p. 185–192.
      [BibTeX] [Abstract] [Link]
      

      Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50{\%}, total vocabulary size by over 40{\%}, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system.

      @inproceedings{riesa-yarowsky-2006-minimally,
      title = "Minimally Supervised Morphological Segmentation with Applications to Machine Translation",
      author = "Riesa, Jason and
      Yarowsky, David",
      booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
      month = aug # " 8-12",
      year = "2006",
      address = "Cambridge, Massachusetts, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2006.amta-papers.21",
      pages = "185--192",
      abstract = "Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50{\%}, total vocabulary size by over 40{\%}, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system.",
      }

3018. P. Mandolesi, P. Julián, and A. Andreou, “A simplicial CNN visual processor in 3D SOI-CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{15013898,
      title = {A simplicial CNN visual processor in 3D SOI-CMOS},
      author = {{P. Mandolesi} and {P. Julián} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/01144a80c37c7b79ed7841748479cd6bc8db5d9b},
      }

3019. Arnab Ghoshal and S. Khudanpur, “Source Adaptation for Improved Content-Based Video Retrieval,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006.
      [BibTeX] [Link]

      @inproceedings{18032935,
      title = {Source Adaptation for Improved Content-Based Video Retrieval},
      author = {{Arnab Ghoshal} and {S. Khudanpur}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings},
      url = {https://www.semanticscholar.org/paper/58f69f3d5c124ade69d2f2fb52bd69acf77fce2e},
      }

3020. David Yarowsky and Gideon S. Mann, “Multi-document statistical fact extraction and fusion.” 2006.
      [BibTeX] [Link]

      @inproceedings{57804249,
      title = {Multi-document statistical fact extraction and fusion},
      author = {{David Yarowsky} and {Gideon S. Mann}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3c1e8a4a290a85002c8fb997707f859088673ae6},
      }

3021. E. Choi, Yingkai Liu, E. Smela, and A. Andreou, “System for deposition and characterization of polypyrrole/gold bilayer hinges,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{2861907,
      title = {System for deposition and characterization of polypyrrole/gold bilayer hinges},
      author = {{E. Choi} and {Yingkai Liu} and {E. Smela} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/2a188b09f820905a1cdc2fc973a476df9e13cea7},
      }

3022. S. Khudanpur, “Multilingual Language Modeling.” 2006.
      [BibTeX] [Link]

      @inproceedings{61313353,
      title = {Multilingual Language Modeling},
      author = {{S. Khudanpur}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1ca79b1efec424969fb7d06d0576ba4efd2d8f7a},
      }

3023. M. Marwick, Francisco Tejada, P. Pouliquen, E. Culurciello, K. Strohbehn, and A. Andreou, “Dark current and noise of 100nm thick silicon on sapphire CMOS lateral PIN photodiodes,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{22413744,
      title = {Dark current and noise of 100nm thick silicon on sapphire CMOS lateral PIN photodiodes},
      author = {{M. Marwick} and {Francisco Tejada} and {P. Pouliquen} and {E. Culurciello} and {K. Strohbehn} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/728e8535977fe37f6754dd73f60dfbe7e1603d78},
      }

3024. F. Jelinek, “DR AF T Speech and Language Processing : An introduction to speech recognition , computational linguistics and natural language processing.” 2006.
      [BibTeX] [Link]

      @inproceedings{42849210,
      title = {DR AF T Speech and Language Processing : An introduction to speech recognition , computational linguistics and natural language processing},
      author = {{F. Jelinek}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/bc30ea1eacc49db8a01572679a0a07ee52a85cb7},
      }

3025. E. Culurciello and A. Andreou, “An 8-bit 800-$muhboxW$1.23-MS/s Successive Approximation ADC in SOI CMOS,” in IEEE Transactions on Circuits and Systems – II – Express Briefs, 2006.
      [BibTeX] [Link]

      @inproceedings{25906118,
      title = {An 8-bit 800-$muhboxW$1.23-MS/s Successive Approximation ADC in SOI CMOS},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2006,
      month = {9},
      booktitle = {IEEE Transactions on Circuits and Systems - II - Express Briefs},
      url = {https://www.semanticscholar.org/paper/869169a56616c156fb4b2775e2d3fd885870b1ef},
      }

3026. P. Julián, A. Andreou, and David H. Goldberg, “A low-power correlation-derivative CMOS VLSI circuit for bearing estimation,” in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{6053956,
      title = {A low-power correlation-derivative CMOS VLSI circuit for bearing estimation},
      author = {{P. Julián} and {A. Andreou} and {David H. Goldberg}},
      year = 2006,
      month = {2},
      booktitle = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
      url = {https://www.semanticscholar.org/paper/8659e1260299d11e89c80f2201f16faee1b86c9d},
      }

3027. Damianos G. Karakos and S. Khudanpur, “Language Modeling with the Maximum Likelihood Set: Complexity Issues and the Back-off Formula,” in 2006 IEEE International Symposium on Information Theory, 2006.
      [BibTeX] [Link]

      @inproceedings{191944,
      title = {Language Modeling with the Maximum Likelihood Set: Complexity Issues and the Back-off Formula},
      author = {{Damianos G. Karakos} and {S. Khudanpur}},
      year = 2006,
      month = {7},
      booktitle = {2006 IEEE International Symposium on Information Theory},
      url = {https://www.semanticscholar.org/paper/576ef236ed4c553eaa32943ad782e11e22e0ea17},
      }

3028. D. Rao and David Yarowsky, “Part of Speech Tagging and Shallow Parsing of Indian Languages.” 2006.
      [BibTeX] [Link]

      @inproceedings{15369849,
      title = {Part of Speech Tagging and Shallow Parsing of Indian Languages},
      author = {{D. Rao} and {David Yarowsky}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9d3ab3e2ef54774f5bf8d505247b62d259e440a6},
      }

3029. E. Culurciello, P. Pouliquen, and A. Andreou, “Digital phase-shift modulation for an isolation buffer in silicon-on-sapphire CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{16707225,
      title = {Digital phase-shift modulation for an isolation buffer in silicon-on-sapphire CMOS},
      author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/714a9c9fadc59ebd150222f0ad9c4067c436b031},
      }

3030. J. Christen and A. Andreou, “Hybrid silicon/silicone (polydimethylsiloxane) microsystem for cell culture,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{1094509,
      title = {Hybrid silicon/silicone (polydimethylsiloxane) microsystem for cell culture},
      author = {{J. Christen} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/2d5278f4e9b8ae905379c5008e4282ae4420919c},
      }

3031. Francisco Tejada, A. Andreou, and P. Pouliquen, “Stacked, standing wave detectors in 3D SOI-CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{23193472,
      title = {Stacked, standing wave detectors in 3D SOI-CMOS},
      author = {{Francisco Tejada} and {A. Andreou} and {P. Pouliquen}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/630a6ed484c28f2329523712460ff398f249d112},
      }

3032. B. Pytlik and D. Yarowsky, “Machine Translation for Languages Lacking Bitext via Multilingual Gloss Transduction,” in Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, Cambridge, Massachusetts, USA, 2006, p. 156–165.
      [BibTeX] [Abstract] [Link]
      

      We propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses.

      @inproceedings{pytlik-yarowsky-2006-machine,
      title = "Machine Translation for Languages Lacking Bitext via Multilingual Gloss Transduction",
      author = "Pytlik, Brock and
      Yarowsky, David",
      booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
      month = aug # " 8-12",
      year = "2006",
      address = "Cambridge, Massachusetts, USA",
      publisher = "Association for Machine Translation in the Americas",
      url = "https://aclanthology.org/2006.amta-papers.18",
      pages = "156--165",
      abstract = "We propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses.",
      }

3033. Francisco Tejada and A. Andreou, “Microelectromechanical systems in 3D SOI-CMOS: sensing electronics embedded in mechanical structures,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{18057741,
      title = {Microelectromechanical systems in 3D SOI-CMOS: sensing electronics embedded in mechanical structures},
      author = {{Francisco Tejada} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/ef40a56e2434aa0d9db45241354ce14d6d95c7b7},
      }

3034. Arnab Ghoshal, S. Khudanpur, João Magalhães, Simon E. Overell, S. Rüger, and Alexei Yavlinsky, “Imperial College and Johns Hopkins University at TRECVID,” in TREC Video Retrieval Evaluation, 2006.
      [BibTeX] [Link]

      @inproceedings{11906980,
      title = {Imperial College and Johns Hopkins University at TRECVID},
      author = {{Arnab Ghoshal} and {S. Khudanpur} and {João Magalhães} and {Simon E. Overell} and {S. Rüger} and {Alexei Yavlinsky}},
      year = 2006,
      booktitle = {TREC Video Retrieval Evaluation},
      url = {https://www.semanticscholar.org/paper/70a5d0b63bac342c2aab9abea37cad4f2a054889},
      }

3035. Jennifer M. BlainChristen and A. Andreou, “Integrated PDMS/CMOSMicrosystem forAutonomous Incubation and Imaging inCellCulture Studies.” 2006.
      [BibTeX] [Link]

      @inproceedings{138266011,
      title = {Integrated PDMS/CMOSMicrosystem forAutonomous Incubation and Imaging inCellCulture Studies},
      author = {{Jennifer M. BlainChristen} and {A. Andreou}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9defd0f90c96bd00b0bbc1f37967df3c40a71fce},
      }

3036. M. Marwick and A. Andreou, “Retinomorphic system design in three dimensional SOI-CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{8853781,
      title = {Retinomorphic system design in three dimensional SOI-CMOS},
      author = {{M. Marwick} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/962aa0ce67f8b64061c7878a9073025f118148ff},
      }

3037. J. Christen and A. Andreou, “Hybrid Silicon/Silicone (polydimethylsiloxane) Microsystem for Cell Culture,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006.
      [BibTeX] [Link]

      @inproceedings{47118668,
      title = {Hybrid Silicon/Silicone (polydimethylsiloxane) Microsystem for Cell Culture},
      author = {{J. Christen} and {A. Andreou}},
      year = 2006,
      booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
      url = {https://www.semanticscholar.org/paper/1493f1a2d1a18286aa6a2c170ee1076b2b4cb665},
      }

3038. E. Choi, Zhiyong Gu, D. Gracias, and A. Andreou, “Chip-scale magnetic sensing and control of nanoparticles and nanorods,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{11496371,
      title = {Chip-scale magnetic sensing and control of nanoparticles and nanorods},
      author = {{E. Choi} and {Zhiyong Gu} and {D. Gracias} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/a97df2f3acf9689b414abf170266a7f45773819b},
      }

3039. Thiago Teixeira, E. Culurciello, and A. Andreou, “An Address-Event Image Sensor Network,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{6132660,
      title = {An Address-Event Image Sensor Network},
      author = {{Thiago Teixeira} and {E. Culurciello} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/e2d47210a12160afecf78eb30ca3275b696cf9f6},
      }

3040. R. Basili, Nicola Cancedda, Marcello Federico, Marko Grobelink, Slovenia Ljubljana, F. Jelinek, D. Roth, J. Shawe, Taylor, Alessandro Moschitti, Vanessa Sandrini, M. Cettolo, S. Canisius, Antal van den Bosch, Walter Daelemans, Fabrizio Costa, Sauro Menchetti, Alessio Ceroni, Andrea Passerini, P. Frasconi, Ana Zelaia, I. Alegria, Olatz Arregi, B. Sierra, R. Subba, B. Di, Eugênio, Su Nam Kim, Sa S. Sa, Oliver Hasan, Hermann Bender, Ney, Daniele Pighin, C. Giuliano, A. Gliozzo, C. Strapparava, T. Lassen, T. V. Terney, Ana-Maria Giuglea, S. Nam, and M. Bender, “Learning Structured Information in Natural Language Applications.” 2006.
      [BibTeX] [Link]

      @inproceedings{69602970,
      title = {Learning Structured Information in Natural Language Applications},
      author = {{R. Basili} and {Nicola Cancedda} and {Marcello Federico} and {Marko Grobelink} and {Slovenia Ljubljana} and {F. Jelinek} and {D. Roth} and {J. Shawe} and {Taylor} and {Alessandro Moschitti} and {Vanessa Sandrini} and {M. Cettolo} and {S. Canisius} and {Antal van den Bosch} and {Walter Daelemans} and {Fabrizio Costa} and {Sauro Menchetti} and {Alessio Ceroni} and {Andrea Passerini} and {P. Frasconi} and {Ana Zelaia} and {I. Alegria} and {Olatz Arregi} and {B. Sierra} and {R. Subba} and {B. Di} and {Eugênio} and {Su Nam Kim} and {Sa S. Sa} and {Oliver Hasan} and {Hermann Bender} and {Ney} and {Daniele Pighin} and {C. Giuliano} and {A. Gliozzo} and {C. Strapparava} and {T. Lassen} and {T. V. Terney} and {Ana-Maria Giuglea} and {S. Nam} and {M. Bender}},
      year = 2006,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/caec881d2a889a2372b35d0ba1e0fe3e58c79b39},
      }

3041. E. Culurciello and A. Andreou, “3D integrated sensors in silicon-on-sapphire CMOS,” in 2006 IEEE International Symposium on Circuits and Systems, 2006.
      [BibTeX] [Link]

      @inproceedings{2705729,
      title = {3D integrated sensors in silicon-on-sapphire CMOS},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2006,
      month = {5},
      booktitle = {2006 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/03fdd243a89c134ffd51cabca98be2fd359aa17e},
      }

3042. G. Iyengar, P. D. Sahin, Shaolei Feng, P. Ircing, S. Khudanpur, D. Klakow, M. R. Krause, R. Manmatha, H. Nock, D. Petkova, Brock Pytlik, and Paola Virga, “Joint visual-text modeling for automatic retrieval of multimedia documents,” in ACM Multimedia, 2005.
      [BibTeX] [Link]

      @inproceedings{3833221,
      title = {Joint visual-text modeling for automatic retrieval of multimedia documents},
      author = {{G. Iyengar} and {P. D. Sahin} and {Shaolei Feng} and {P. Ircing} and {S. Khudanpur} and {D. Klakow} and {M. R. Krause} and {R. Manmatha} and {H. Nock} and {D. Petkova} and {Brock Pytlik} and {Paola Virga}},
      year = 2005,
      month = {11},
      booktitle = {ACM Multimedia},
      url = {https://www.semanticscholar.org/paper/d55de6dcdfde200b9a975578ffe8cb5c056e2c76},
      }

3043. J. Eisner and N. A. Smith, “Parsing with Soft and Hard Constraints on Dependency Length,” in Proceedings of the International Workshop on Parsing Technologies (IWPT), Vancouver, 2005, p. 30–41.
      [BibTeX] [Link]

      @InProceedings{eisner-smith-2005,
      aclid = "W05-1504",
      author = "Jason Eisner and Noah A. Smith",
      title = "Parsing with Soft and Hard Constraints on Dependency
      Length",
      booktitle = "Proceedings of the International Workshop on Parsing
      Technologies (IWPT)",
      pages = "30--41",
      year = "2005",
      month = oct,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-smith-2005",
      }

3044. J. Eisner and D. Karakos, “Bootstrapping Without the Boot,” in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP), Vancouver, 2005, p. 395–402.
      [BibTeX] [Link]

      @InProceedings{eisner-karakos-2005,
      aclid = "H05-1050",
      author = "Jason Eisner and Damianos Karakos",
      title = "Bootstrapping Without the Boot",
      booktitle = "Proceedings of Human Language Technology Conference
      and Conference on Empirical Methods in Natural Language
      Processing (HLT-EMNLP)",
      pages = "395--402",
      year = "2005",
      month = oct,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-karakos-2005",
      }

3045. J. Eisner, E. Goldlust, and N. A. Smith, “Compiling Comp Ling: Weighted Dynamic Programming and the Dyna Language,” in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP), Vancouver, 2005, p. 281–290.
      [BibTeX] [Link]

      @InProceedings{eisner-goldlust-smith-2005,
      aclid = "H05-1036",
      author = "Jason Eisner and Eric Goldlust and Noah A. Smith",
      title = "Compiling Comp Ling: Weighted Dynamic Programming and
      the {D}yna Language",
      booktitle = "Proceedings of Human Language Technology Conference
      and Conference on Empirical Methods in Natural Language
      Processing (HLT-EMNLP)",
      pages = "281--290",
      year = "2005",
      month = oct,
      address = "Vancouver",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-goldlust-smith-2005",
      }

3046. N. A. Smith and J. Eisner, “Guiding Unsupervised Grammar Induction Using Contrastive Estimation,” in International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Grammatical Inference Applications, Edinburgh, 2005, p. 73–82.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2005-gia,
      author = "Noah A. Smith and Jason Eisner",
      title = "Guiding Unsupervised Grammar Induction Using
      Contrastive Estimation",
      booktitle = "International Joint Conference on Artificial
      Intelligence (IJCAI) Workshop on Grammatical Inference
      Applications",
      pages = "73--82",
      year = "2005",
      month = jul,
      address = "Edinburgh",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2005-gia",
      }

3047. E. Drábek and D. Yarowsky, “Induction of Fine-Grained Part-of-Speech Taggers via Classifier Combination and Crosslingual Projection,” in Proceedings of the ACL Workshop on Building and Using Parallel Texts, Ann Arbor, Michigan, 2005, p. 49–56.
      [BibTeX] [Link]

      @inproceedings{drabek-yarowsky-2005-induction,
      title = "Induction of Fine-Grained Part-of-Speech Taggers via Classifier Combination and Crosslingual Projection",
      author = "Dr{\'a}bek, Elliott and
      Yarowsky, David",
      editor = "Koehn, Philipp and
      Martin, Joel and
      Mihalcea, Rada and
      Monz, Christof and
      Pedersen, Ted",
      booktitle = "Proceedings of the {ACL} Workshop on Building and Using Parallel Texts",
      month = jun,
      year = "2005",
      address = "Ann Arbor, Michigan",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W05-0807",
      pages = "49--56",
      }

3048. G. Mann and D. Yarowsky, “Multi-Field Information Extraction and Cross-Document Fusion,” in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, Michigan, 2005, p. 483–490. doi:10.3115/1219840.1219900
      [BibTeX] [Link]

      @inproceedings{mann-yarowsky-2005-multi,
      title = "Multi-Field Information Extraction and Cross-Document Fusion",
      author = "Mann, Gideon and
      Yarowsky, David",
      editor = "Knight, Kevin and
      Ng, Hwee Tou and
      Oflazer, Kemal",
      booktitle = "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics ({ACL}{'}05)",
      month = jun,
      year = "2005",
      address = "Ann Arbor, Michigan",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P05-1060",
      doi = "10.3115/1219840.1219900",
      pages = "483--490",
      }

3049. N. A. Smith and J. Eisner, “Contrastive Estimation: Training Log-Linear Models on Unlabeled Data,” in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, Michigan, 2005, p. 354–362.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2005-acl,
      aclid = "P05-1044",
      author = "Noah A. Smith and Jason Eisner",
      title = "Contrastive Estimation: Training Log-Linear Models on
      Unlabeled Data",
      booktitle = "Proceedings of the 43rd Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "354--362",
      year = "2005",
      month = jun,
      address = "Ann Arbor, Michigan",
      note = "Nominated for Best Paper Award.",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2005-acl",
      }

3050. A. Kempe, J. Champarnaud, Jason Eisner, F. Guingne, and F. Nicart, “A Class of Rational $n$-WFSM Auto-Intersections,” in Proceedings of the Tenth International Conference on Implementation and Application of Automata (CIAA-2005), Sophia Antipolis, France, 2005, p. 189–200.
      [BibTeX] [Link]

      @InProceedings{kempe-et-al-2005,
      author = "Andr\'{e} Kempe and Jean-Marc Champarnaud and Jason
      Eisner and Franck Guingne and Florent Nicart",
      title = "A Class of Rational {$n$-WFSM} Auto-Intersections",
      booktitle = "Proceedings of the Tenth International Conference on
      Implementation and Application of Automata
      (CIAA-2005)",
      pages = "189--200",
      series = "Lecture Notes in Computer Science",
      number = "3845",
      publisher = "Springer-Verlag",
      year = "2005",
      month = jun,
      address = "Sophia Antipolis, France",
      URL = "http://cs.jhu.edu/~jason/papers/#kempe-et-al-2005",
      }

3051. D. Karakos, S. Khudanpur, Jason Eisner, and C. E. Priebe, “Unsupervised Classification via Decision Trees: An Information-Theoretic Perspective,” in Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, 2005, p. 1081–1084.
      [BibTeX] [Link]

      @InProceedings{karakos-et-al-2005,
      author = "Damianos Karakos and Sanjeev Khudanpur and Jason
      Eisner and Carey E. Priebe",
      title = "Unsupervised Classification via Decision Trees: An
      Information-Theoretic Perspective",
      booktitle = "Proceedings of the 2005 IEEE International Conference
      on Acoustics, Speech and Signal Processing (ICASSP)",
      volume = "5",
      pages = "1081--1084",
      year = "2005",
      month = mar,
      address = "Philadelphia",
      note = "Invited talk",
      URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2005",
      }

3052. M. Cohen, A. Andreou, A. Paulraj, D. Gore, R. Nabar, H. Bolcskei, G. Stuber, J. R. Barry, S. McLaughlin, Ye Li, Ingram, and M. A. Pratt, “Top Articles.” 2005.
      [BibTeX] [Link]

      @inproceedings{262502050,
      title = {Top Articles},
      author = {{M. Cohen} and {A. Andreou} and {A. Paulraj} and {D. Gore} and {R. Nabar} and {H. Bolcskei} and {G. Stuber} and {J. R. Barry} and {S. McLaughlin} and {Ye Li} and {Ingram} and {M. A. Pratt}},
      year = 2005,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/773a8e9bfc5b8039d4a65d5a360ebf4e66503950},
      }

3053. P. Julián, A. Andreou, G. Cauwenberghs, M. Stanaćević, David H. Goldberg, P. Mandolesi, Laurence Riddle, and S. Shamma, “Field test results for low power bearing estimator sensor nodes,” in 2005 IEEE International Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{10633458,
      title = {Field test results for low power bearing estimator sensor nodes},
      author = {{P. Julián} and {A. Andreou} and {G. Cauwenberghs} and {M. Stanaćević} and {David H. Goldberg} and {P. Mandolesi} and {Laurence Riddle} and {S. Shamma}},
      year = 2005,
      month = {5},
      booktitle = {2005 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/fdb5fbcd34502706f361c2014636a44b32ba3d78},
      }

3054. F. Jelinek, “Some of my Best Friends are Linguists,” in Language Resources and Evaluation, 2005.
      [BibTeX] [Link]

      @inproceedings{10024464,
      title = {Some of my Best Friends are Linguists},
      author = {{F. Jelinek}},
      year = 2005,
      month = {2},
      booktitle = {Language Resources and Evaluation},
      url = {https://www.semanticscholar.org/paper/4d546894d3f8bc28d13b6a2eeca500b47b970db4},
      }

3055. F. Jelinek, “Language Modeling Experiments with Random Forests,” in International Conference on Text, Speech and Dialogue, 2005.
      [BibTeX] [Link]

      @inproceedings{35162850,
      title = {Language Modeling Experiments with Random Forests},
      author = {{F. Jelinek}},
      year = 2005,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/e15cbadea77e9ea17cea31647d646f49adaececf},
      }

3056. Brock Pytlik, Arnab Ghoshal, Damianos G. Karakos, and S. Khudanpur, “TRECVID 2005 Experiment at Johns Hopkins University: Using Hidden Markov Models for Video Retrieval,” in TREC Video Retrieval Evaluation, 2005.
      [BibTeX] [Link]

      @inproceedings{8747388,
      title = {TRECVID 2005 Experiment at Johns Hopkins University: Using Hidden Markov Models for Video Retrieval},
      author = {{Brock Pytlik} and {Arnab Ghoshal} and {Damianos G. Karakos} and {S. Khudanpur}},
      year = 2005,
      booktitle = {TREC Video Retrieval Evaluation},
      url = {https://www.semanticscholar.org/paper/14276bb64fde89475f28f011e04bc54234cf32ff},
      }

3057. B. Jedynak and S. Khudanpur, “Maximum Likelihood Set for Estimating a Probability Mass Function,” in Neural Computation, 2005.
      [BibTeX] [Link]

      @inproceedings{668989,
      title = {Maximum Likelihood Set for Estimating a Probability Mass Function},
      author = {{B. Jedynak} and {S. Khudanpur}},
      year = 2005,
      month = {7},
      booktitle = {Neural Computation},
      url = {https://www.semanticscholar.org/paper/0d9ccb67111dbf9d0318b4460cd1204983784ae3},
      }

3058. Arnab Ghoshal, P. Ircing, and S. Khudanpur, “Hidden Markov models for automatic annotation and content-based retrieval of images and video,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005.
      [BibTeX] [Link]

      @inproceedings{15072142,
      title = {Hidden Markov models for automatic annotation and content-based retrieval of images and video},
      author = {{Arnab Ghoshal} and {P. Ircing} and {S. Khudanpur}},
      year = 2005,
      month = {8},
      booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
      url = {https://www.semanticscholar.org/paper/80afcc0fbcc30ae4436094fc3a06fed0515dc70b},
      }

3059. E. Culurciello, Thiago Teixeira, and A. Andreou, “Event-based imaging with active illumination in sensor networks,” in 2005 IEEE International Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{8091076,
      title = {Event-based imaging with active illumination in sensor networks},
      author = {{E. Culurciello} and {Thiago Teixeira} and {A. Andreou}},
      year = 2005,
      month = {5},
      booktitle = {2005 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/fc32c185187bec2ea579554345c019aea13bda2b},
      }

3060. E. Culurciello, P. Pouliquen, A. Andreou, K. Strohbehn, and S. Jaskulek, “A monolithic isolation amplifier in silicon-on-insulator CMOS,” in 2005 IEEE International Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{26439714,
      title = {A monolithic isolation amplifier in silicon-on-insulator CMOS},
      author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou} and {K. Strohbehn} and {S. Jaskulek}},
      year = 2005,
      month = {5},
      booktitle = {2005 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/5cad0ae329636d2d5714afcd0121e7b03930e749},
      }

3061. E. Culurciello, P. Pouliquen, A. Andreou, K. Strohbehn, and S. Jaskulek, “Monolithic digital galvanic isolation buffer fabricated in silicon on sapphire CMOS,” in Electronics Letters, 2005.
      [BibTeX] [Link]

      @inproceedings{109920157,
      title = {Monolithic digital galvanic isolation buffer fabricated in silicon on sapphire CMOS},
      author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou} and {K. Strohbehn} and {S. Jaskulek}},
      year = 2005,
      month = {4},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/11077920601a5d4869703b0473bdab5cbd25e052},
      }

3062. A. Apsel and A. Andreou, “A low-power silicon on sapphire CMOS optoelectronic receiver using low- and high-threshold devices,” in IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2005.
      [BibTeX] [Link]

      @inproceedings{3035508,
      title = {A low-power silicon on sapphire CMOS optoelectronic receiver using low- and high-threshold devices},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2005,
      month = {2},
      booktitle = {IEEE Transactions on Circuits and Systems Part 1: Regular Papers},
      url = {https://www.semanticscholar.org/paper/e6f12c4fd211f6d31271618664eb6b31daf69bfd},
      }

3063. Damianos G. Karakos, S. Khudanpur, Jason Eisner, and C. Priebe, “Unsupervised classification via decision trees: an information-theoretic perspective,” in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005.
      [BibTeX] [Link]

      @inproceedings{7112668,
      title = {Unsupervised classification via decision trees: an information-theoretic perspective},
      author = {{Damianos G. Karakos} and {S. Khudanpur} and {Jason Eisner} and {C. Priebe}},
      year = 2005,
      month = {3},
      booktitle = {Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.},
      url = {https://www.semanticscholar.org/paper/498194c017435df0fe09538f1dd3526b4e002d68},
      }

3064. E. Culurciello, P. Pouliquen, and A. Andreou, “Isolation charge pump fabricated in silicon on sapphire CMOS technology,” in Electronics Letters, 2005.
      [BibTeX] [Link]

      @inproceedings{110244379,
      title = {Isolation charge pump fabricated in silicon on sapphire CMOS technology},
      author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou}},
      year = 2005,
      month = {5},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/93127462f2b89823f3bea93f252c49874391be45},
      }

3065. G. Cauwenberghs, A. Andreou, J. West, M. Stanaćević, Abdullah Celik, P. Julián, Thiago Teixeira, C. Diehl, and Laurence Riddle, “A miniature low-power intelligent sensor node for persistent acoustic surveillance,” in SPIE Defense + Commercial Sensing, 2005.
      [BibTeX] [Link]

      @inproceedings{17301696,
      title = {A miniature low-power intelligent sensor node for persistent acoustic surveillance},
      author = {{G. Cauwenberghs} and {A. Andreou} and {J. West} and {M. Stanaćević} and {Abdullah Celik} and {P. Julián} and {Thiago Teixeira} and {C. Diehl} and {Laurence Riddle}},
      year = 2005,
      month = {5},
      booktitle = {SPIE Defense + Commercial Sensing},
      url = {https://www.semanticscholar.org/paper/af4e2125e4fe69992147b3ace38e39e6c4534a70},
      }

3066. E. Culurciello and A. Andreou, “Capacitive coupling of data and power for 3D silicon-on-insulator VLSI,” in 2005 IEEE International Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{8219009,
      title = {Capacitive coupling of data and power for 3D silicon-on-insulator VLSI},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2005,
      month = {5},
      booktitle = {2005 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/878f30c393ec0468c8de7d945580fcd1a28fae90},
      }

3067. Ahmad Emami and F. Jelinek, “Random clusterings for language modeling,” in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005.
      [BibTeX] [Link]

      @inproceedings{5779171,
      title = {Random clusterings for language modeling},
      author = {{Ahmad Emami} and {F. Jelinek}},
      year = 2005,
      month = {3},
      booktitle = {Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.},
      url = {https://www.semanticscholar.org/paper/a493a23b86192aa74e6f394061288082e1e7cdb7},
      }

3068. F. Masson, P. Julián, D. Puschini, P. Crocce, L. Arlenghi, A. Andreou, and P. Mandolesi, “Hybrid sensor network and fusion algorithm for sound source localization,” in 2005 IEEE International Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{34076984,
      title = {Hybrid sensor network and fusion algorithm for sound source localization},
      author = {{F. Masson} and {P. Julián} and {D. Puschini} and {P. Crocce} and {L. Arlenghi} and {A. Andreou} and {P. Mandolesi}},
      year = 2005,
      month = {5},
      booktitle = {2005 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/d6e20e15223e3441e74ebb62e6306128baff7551},
      }

3069. J.M. Blain Christen and A. Andreou, “CMOS heater array for incubation environment cellular study,” in Midwest Symposium on Circuits and Systems, 2005.
      [BibTeX] [Link]

      @inproceedings{3051229,
      title = {CMOS heater array for incubation environment cellular study},
      author = {{J.M. Blain Christen} and {A. Andreou}},
      year = 2005,
      booktitle = {Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/413bbebc9f64ba8af7443fcb60ab27942ec140f6},
      }

3070. Ahmad Emami and F. Jelinek, “A Neural Syntactic Language Model,” in Machine-mediated learning, 2005.
      [BibTeX] [Link]

      @inproceedings{2503423,
      title = {A Neural Syntactic Language Model},
      author = {{Ahmad Emami} and {F. Jelinek}},
      year = 2005,
      month = {9},
      booktitle = {Machine-mediated learning},
      url = {https://www.semanticscholar.org/paper/ffea7f0fd89dc940107cdf94f7decfcc42315c67},
      }

3071. P. Xu and F. Jelinek, “Using Random Forests in the Structured Language Model,” in Neural Information Processing Systems, 2004.
      [BibTeX] [Link]

      @inproceedings{14695689,
      title = {Using Random Forests in the Structured Language Model},
      author = {{P. Xu} and {F. Jelinek}},
      year = 2004,
      month = {12},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/2099818ee725771f033e987b92272d368a491a55},
      }

3072. A. Kempe, J. Champarnaud, and Jason Eisner, “A Note on Join and Auto-Intersection of $n$-ary Rational Relations,” in Proceedings of the Eindhoven FASTAR Days (Computer Science Technical Report 04-40), 2004, p. 64–78.
      [BibTeX] [Link]

      @InProceedings{kempe-champarnaud-eisner-2004,
      author = "Andr\'{e} Kempe and Jean-Marc Champarnaud and Jason
      Eisner",
      title = "A Note on Join and Auto-Intersection of $n$-ary
      Rational Relations",
      booktitle = "Proceedings of the Eindhoven FASTAR Days (Computer
      Science Technical Report 04-40)",
      editor = "Loek Cleophas and Bruce Watson",
      pages = "64--78",
      year = "2004",
      month = dec,
      organization = "Department of Mathematics and Computer Science,
      Technische Universiteit Eindhoven, Netherlands",
      URL = "http://cs.jhu.edu/~jason/papers/#kempe-champarnaud-eisner-2004",
      }

3073. M. Saraçlar and S. Khudanpur, “Pronunciation change in conversational speech and its implications for automatic speech recognition,” in Computer Speech and Language, 2004.
      [BibTeX] [Link]

      @inproceedings{6558963,
      title = {Pronunciation change in conversational speech and its implications for automatic speech recognition},
      author = {{M. Saraçlar} and {S. Khudanpur}},
      year = 2004,
      month = {10},
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/4ff0579f18caff30fe9620e71fbdc22ffe33f09a},
      }

3074. P. Xu and F. Jelinek, “Random Forests in Language Modelin,” in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, 2004, p. 325–332.
      [BibTeX] [Link]

      @inproceedings{xu-jelinek-2004-random,
      title = "Random Forests in Language Modelin",
      author = "Xu, Peng and
      Jelinek, Frederick",
      editor = "Lin, Dekang and
      Wu, Dekai",
      booktitle = "Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing",
      month = jul,
      year = "2004",
      address = "Barcelona, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W04-3242",
      pages = "325--332",
      }

3075. E. F. Drabek and D. Yarowsky, “Improving Bitext Word Alignments via Syntax-based Reordering of English,” in Proceedings of the ACL Interactive Poster and Demonstration Sessions, Barcelona, Spain, 2004, p. 146–149.
      [BibTeX] [Link]

      @inproceedings{drabek-yarowsky-2004-improving,
      title = "Improving Bitext Word Alignments via Syntax-based Reordering of {E}nglish",
      author = "Drabek, Elliott Franco and
      Yarowsky, David",
      booktitle = "Proceedings of the {ACL} Interactive Poster and Demonstration Sessions",
      month = jul,
      year = "2004",
      address = "Barcelona, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P04-3014",
      pages = "146--149",
      }

3076. C. Schafer and D. Yarowsky, “Exploiting Aggregate Properties of Bilingual Dictionaries For Distinguishing Senses of English Words and Inducing English Sense Clusters,” in Proceedings of the ACL Interactive Poster and Demonstration Sessions, Barcelona, Spain, 2004, p. 118–121.
      [BibTeX] [Link]

      @inproceedings{schafer-yarowsky-2004-exploiting,
      title = "Exploiting Aggregate Properties of Bilingual Dictionaries For Distinguishing Senses of {E}nglish Words and Inducing {E}nglish Sense Clusters",
      author = "Schafer, Charles and
      Yarowsky, David",
      booktitle = "Proceedings of the {ACL} Interactive Poster and Demonstration Sessions",
      month = jul,
      year = "2004",
      address = "Barcelona, Spain",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P04-3007",
      pages = "118--121",
      }

3077. J. Eisner, E. Goldlust, and N. A. Smith, “Dyna: A Declarative Language for Implementing Dynamic Programs,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Companion Volume, Barcelona, 2004, p. 218–221.
      [BibTeX] [Link]

      @InProceedings{eisner-goldlust-smith-2004,
      aclid = "P04-3032",
      author = "Jason Eisner and Eric Goldlust and Noah A. Smith",
      title = "Dyna: {A} Declarative Language for Implementing
      Dynamic Programs",
      booktitle = "Proceedings of the 42nd Annual Meeting of the
      Association for Computational Linguistics (ACL),
      Companion Volume",
      pages = "218--221",
      year = "2004",
      month = jul,
      address = "Barcelona",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-goldlust-smith-2004",
      }

3078. N. A. Smith and J. Eisner, “Annealing Techniques for Unsupervised Statistical Language Learning,” in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, 2004, p. 486–493.
      [BibTeX] [Link]

      @InProceedings{smith-eisner-2004,
      aclid = "P04-1062",
      author = "Noah A. Smith and Jason Eisner",
      title = "Annealing Techniques for Unsupervised Statistical
      Language Learning",
      booktitle = "Proceedings of the 42nd Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "486--493",
      year = "2004",
      month = jul,
      address = "Barcelona",
      URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2004",
      }

3079. L. Guthrie, R. Basili, F. Zanzotto, K. Bontcheva, H. Cunningham, D. Guthrie, J. Cui, M. Cammisa, J. C. Liu, C. F. Martin, K. Haralambiev, M. Holub, K. Macherey, and F. Jelinek, “Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text,” in Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal, 2004.
      [BibTeX] [Link]

      @inproceedings{guthrie-etal-2004-large,
      title = "Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text",
      author = "Guthrie, Louise and
      Basili, Roberto and
      Zanzotto, Fabio and
      Bontcheva, Kalina and
      Cunningham, Hamish and
      Guthrie, David and
      Cui, Jia and
      Cammisa, Marco and
      Liu, Jerry Cheng-Chieh and
      Martin, Cassia Farria and
      Haralambiev, Kristiyan and
      Holub, Martin and
      Macherey, Klaus and
      Jelinek, Fredrick",
      editor = "Lino, Maria Teresa and
      Xavier, Maria Francisca and
      Ferreira, F{\'a}tima and
      Costa, Rute and
      Silva, Raquel",
      booktitle = "Proceedings of the Fourth International Conference on Language Resources and Evaluation ({LREC}{'}04)",
      month = may,
      year = "2004",
      address = "Lisbon, Portugal",
      publisher = "European Language Resources Association (ELRA)",
      url = "http://www.lrec-conf.org/proceedings/lrec2004/pdf/666.pdf",
      }

3080. E. Culurciello and A. Andreou, “A 16 /spl times/ 16 pixel silicon on sapphire CMOS photosensor array with a digital interface for adaptive wavefront correction,” in 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004.
      [BibTeX] [Link]

      @inproceedings{24950713,
      title = {A 16 /spl times/ 16 pixel silicon on sapphire CMOS photosensor array with a digital interface for adaptive wavefront correction},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2004,
      month = {5},
      booktitle = {2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)},
      url = {https://www.semanticscholar.org/paper/f1863992cce1af15b1246986f1a100c92db2c1a1},
      }

3081. David H. Goldberg and A. Andreou, “Spike communication of dynamic stimuli: rate decoding versus temporal decoding,” in Neurocomputing, 2004.
      [BibTeX] [Link]

      @inproceedings{36350590,
      title = {Spike communication of dynamic stimuli: rate decoding versus temporal decoding},
      author = {{David H. Goldberg} and {A. Andreou}},
      year = 2004,
      month = {6},
      booktitle = {Neurocomputing},
      url = {https://www.semanticscholar.org/paper/4a550a5c7b6337110de6aab644967c82969cb472},
      }

3082. P. Mandolesi, P. Julián, and A. Andreou, “A scalable and programmable simplicial CNN digital pixel processor architecture,” in IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2004.
      [BibTeX] [Link]

      @inproceedings{12260491,
      title = {A scalable and programmable simplicial CNN digital pixel processor architecture},
      author = {{P. Mandolesi} and {P. Julián} and {A. Andreou}},
      year = 2004,
      month = {5},
      booktitle = {IEEE Transactions on Circuits and Systems Part 1: Regular Papers},
      url = {https://www.semanticscholar.org/paper/3fb5574553e1c3ae6b4c48bd8c170da984a709a0},
      }

3083. A. Apsel, Zhongtao Fu, and A. Andreou, “A 2.5-mW SOS CMOS optical receiver for chip-to-chip interconnect,” in Journal of Lightwave Technology, 2004.
      [BibTeX] [Link]

      @inproceedings{31449595,
      title = {A 2.5-mW SOS CMOS optical receiver for chip-to-chip interconnect},
      author = {{A. Apsel} and {Zhongtao Fu} and {A. Andreou}},
      year = 2004,
      month = {9},
      booktitle = {Journal of Lightwave Technology},
      url = {https://www.semanticscholar.org/paper/d4211067322dc2a0df8236d703b390b036774c4b},
      }

3084. A. Andreou and David H. Goldberg, “Efficient spike communication and computation in biological and engineered systems.” 2004.
      [BibTeX] [Link]

      @inproceedings{63717901,
      title = {Efficient spike communication and computation in biological and engineered systems},
      author = {{A. Andreou} and {David H. Goldberg}},
      year = 2004,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4f7786d86a5d72c8194137e486ae7af00f52db49},
      }

3085. E. Culurciello and A. Andreou, “A 16 × 16 pixel silicon on sapphire CMOS photosensor array with a digital interface for adaptive wavefront correction,” in International Symposium on Circuits and Systems, 2004.
      [BibTeX] [Link]

      @inproceedings{236460365,
      title = {A 16 × 16 pixel silicon on sapphire CMOS photosensor array with a digital interface for adaptive wavefront correction},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2004,
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/cf2be860f30ac5d7d621a1ceba3a7fb9340ba896},
      }

3086. A. Andreou, “A JSSC classic paper: Sigma-Delta converters,” in IEEE Solid-State Circuits Society Newsletter, 2004.
      [BibTeX] [Link]

      @inproceedings{41880831,
      title = {A JSSC classic paper: Sigma-Delta converters},
      author = {{A. Andreou}},
      year = 2004,
      booktitle = {IEEE Solid-State Circuits Society Newsletter},
      url = {https://www.semanticscholar.org/paper/78d85e0625b4f36b6ca9c5ff9d3a148aec8e36e8},
      }

3087. E. Culurciello and A. Andreou, “16/spl times/16 pixel silicon on sapphire CMOS digital pixel photosensor array,” in Electronics Letters, 2004.
      [BibTeX] [Link]

      @inproceedings{113530045,
      title = {16/spl times/16 pixel silicon on sapphire CMOS digital pixel photosensor array},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2004,
      month = {1},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/2dc87534fa55d9ff535f66d28823d576d16656bc},
      }

3088. A. Kanlis, S. Khudanpur, and P. Narayan, “Typicality of a Good Rate-Distortion Code.” 2004.
      [BibTeX] [Link]

      @inproceedings{7392740,
      title = {Typicality of a Good Rate-Distortion Code},
      author = {{A. Kanlis} and {S. Khudanpur} and {P. Narayan}},
      year = 2004,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/20d46cff812a4f74cfad63c94b2eadd1a10881d2},
      }

3089. Francisco Tejada, A. Andreou, D. Wickenden, and A. Francomacaro, “Surface micromachining in Silicon on Sapphire CMOS technology,” in 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004.
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      @inproceedings{5946240,
      title = {Surface micromachining in Silicon on Sapphire CMOS technology},
      author = {{Francisco Tejada} and {A. Andreou} and {D. Wickenden} and {A. Francomacaro}},
      year = 2004,
      month = {5},
      booktitle = {2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)},
      url = {https://www.semanticscholar.org/paper/8c4e401e6086573a2defdbcd73426455cad26a38},
      }

3090. F. Jelinek, “Stochastic Analysis of Structured Language Modeling.” 2004.
      [BibTeX] [Link]

      @inproceedings{60461065,
      title = {Stochastic Analysis of Structured Language Modeling},
      author = {{F. Jelinek}},
      year = 2004,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/66eeac450b7dd7c5363cb044a1f3b31801b81365},
      }

3091. P. Julián, A. Andreou, Laurence Riddle, S. Shamma, David H. Goldberg, and G. Cauwenberghs, “A comparative study of sound localization algorithms for energy aware sensor network nodes,” in IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2004.
      [BibTeX] [Link]

      @inproceedings{6708545,
      title = {A comparative study of sound localization algorithms for energy aware sensor network nodes},
      author = {{P. Julián} and {A. Andreou} and {Laurence Riddle} and {S. Shamma} and {David H. Goldberg} and {G. Cauwenberghs}},
      year = 2004,
      month = {4},
      booktitle = {IEEE Transactions on Circuits and Systems Part 1: Regular Papers},
      url = {https://www.semanticscholar.org/paper/b7ec04aebae69ebc0bebb1bb8d7026b39452a2b0},
      }

3092. Woosung Kim and S. Khudanpur, “Lexical triggers and latent semantic analysis for cross-lingual language model adaptation,” in TALIP, 2004.
      [BibTeX] [Link]

      @inproceedings{15539014,
      title = {Lexical triggers and latent semantic analysis for cross-lingual language model adaptation},
      author = {{Woosung Kim} and {S. Khudanpur}},
      year = 2004,
      month = {6},
      booktitle = {TALIP},
      url = {https://www.semanticscholar.org/paper/906dcda9128e2132dc5d301790a0cf176cbb6631},
      }

3093. G. Iyengar, Pinar Duygulu, Shi Feng, P. Ircing, S. Khudanpur, D. Klakow, M. R. Krause, R. Manmatha, D. Petkova, Brock Pytlik, and Paola Virga, “Joint Visual-Text Modeling for Multimedia Retrieval.” 2004.
      [BibTeX] [Link]

      @inproceedings{59665405,
      title = {Joint Visual-Text Modeling for Multimedia Retrieval},
      author = {{G. Iyengar} and {Pinar Duygulu} and {Shi Feng} and {P. Ircing} and {S. Khudanpur} and {D. Klakow} and {M. R. Krause} and {R. Manmatha} and {D. Petkova} and {Brock Pytlik} and {Paola Virga}},
      year = 2004,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/8adc36ee813283473947312cd00d3e5d7cd1280a},
      }

3094. Ahmad Emami and F. Jelinek, “Exact training of a neural syntactic language model,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004.
      [BibTeX] [Link]

      @inproceedings{1976732,
      title = {Exact training of a neural syntactic language model},
      author = {{Ahmad Emami} and {F. Jelinek}},
      year = 2004,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/9319ca5a532462f9f3515ac3d317668aa9650d5b},
      }

3095. Woosung Kim and S. Khudanpur, “Cross-lingual latent semantic analysis for language modeling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004.
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      @inproceedings{1381027,
      title = {Cross-lingual latent semantic analysis for language modeling},
      author = {{Woosung Kim} and {S. Khudanpur}},
      year = 2004,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/f850844cd5a4b9b807f28f072db52c2f1e80d9fd},
      }

3096. Francisco Tejada, D. Wesolek, J. Lehtonen, J. Miragliotta, A. Andreou, and R. Osiander, “An SOS MEMS interferometer,” in SPIE MOEMS-MEMS, 2004.
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      @inproceedings{120043495,
      title = {An SOS MEMS interferometer},
      author = {{Francisco Tejada} and {D. Wesolek} and {J. Lehtonen} and {J. Miragliotta} and {A. Andreou} and {R. Osiander}},
      year = 2004,
      month = {1},
      booktitle = {SPIE MOEMS-MEMS},
      url = {https://www.semanticscholar.org/paper/1ea3a55d626166368105787bae8553f6d654b280},
      }

3097. F. J. Och, D. Gildea, S. Khudanpur, A. Sarkar, K. Yamada, A. Fraser, S. Kumar, L. Shen, D. Smith, K. Eng, V. Jain, Z. Jin, and D. Radev, “A Smorgasbord of Features for Statistical Machine Translation,” in Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004, Boston, Massachusetts, USA, 2004, p. 161–168.
      [BibTeX] [Link]

      @inproceedings{och-etal-2004-smorgasbord,
      title = "A Smorgasbord of Features for Statistical Machine Translation",
      author = "Och, Franz Josef and
      Gildea, Daniel and
      Khudanpur, Sanjeev and
      Sarkar, Anoop and
      Yamada, Kenji and
      Fraser, Alex and
      Kumar, Shankar and
      Shen, Libin and
      Smith, David and
      Eng, Katherine and
      Jain, Viren and
      Jin, Zhen and
      Radev, Dragomir",
      booktitle = "Proceedings of the Human Language Technology Conference of the North {A}merican Chapter of the Association for Computational Linguistics: {HLT}-{NAACL} 2004",
      month = may # " 2 - " # may # " 7",
      year = "2004",
      address = "Boston, Massachusetts, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/N04-1021",
      pages = "161--168",
      }

3098. S. Khudanpur and Woosung Kim, “Contemporaneous text as side-information in statistical language modeling,” in Computer Speech and Language, 2004.
      [BibTeX] [Link]

      @inproceedings{8485968,
      title = {Contemporaneous text as side-information in statistical language modeling},
      author = {{S. Khudanpur} and {Woosung Kim}},
      year = 2004,
      month = {4},
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/5f97843c5313b76adb73ff9bf3c02d1b79d5947b},
      }

3099. P. Mandolesi, P. Julián, and A. Andreou, “A simplicial CNN architecture for on-chip image processing,” in 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004.
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      @inproceedings{31188038,
      title = {A simplicial CNN architecture for on-chip image processing},
      author = {{P. Mandolesi} and {P. Julián} and {A. Andreou}},
      year = 2004,
      month = {5},
      booktitle = {2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)},
      url = {https://www.semanticscholar.org/paper/dd3d26b0cfc6db1c577aa499be83e446b1d9cd5e},
      }

3100. Helen M. Meng, Berlin Chen, S. Khudanpur, Gina-Anne Levow, W. Lo, Douglas W. Oard, Patrick Schone, Karen Tang, Hsin-Min Wang, and Jianqiang Wang, “Mandarin-English Information (MEI): investigating translingual speech retrieval,” in Computer Speech and Language, 2004.
      [BibTeX] [Link]

      @inproceedings{267872915,
      title = {Mandarin-English Information (MEI): investigating translingual speech retrieval},
      author = {{Helen M. Meng} and {Berlin Chen} and {S. Khudanpur} and {Gina-Anne Levow} and {W. Lo} and {Douglas W. Oard} and {Patrick Schone} and {Karen Tang} and {Hsin-Min Wang} and {Jianqiang Wang}},
      year = 2004,
      month = {4},
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/b8742df1adce11b82648a3deab412c92fc58b70e},
      }

3101. David H. Goldberg, A. Andreou, P. Julián, P. Pouliquen, Laurence Riddle, and Rich Rosasco, “A wake-up detector for an acoustic surveillance sensor network: algorithm and VLSI implementation,” in Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004, 2004.
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      @inproceedings{2599649,
      title = {A wake-up detector for an acoustic surveillance sensor network: algorithm and VLSI implementation},
      author = {{David H. Goldberg} and {A. Andreou} and {P. Julián} and {P. Pouliquen} and {Laurence Riddle} and {Rich Rosasco}},
      year = 2004,
      month = {4},
      booktitle = {Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004},
      url = {https://www.semanticscholar.org/paper/afc7898078b08930d18ad0c2181e68a719458fc6},
      }

3102. Francisco Tejada, A. Andreou, J. Miragliotta, R. Osiander, and D. Wesolek, “Silicon on sapphire CMOS architectures for interferometric array readout,” in 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004.
      [BibTeX] [Link]

      @inproceedings{17231760,
      title = {Silicon on sapphire CMOS architectures for interferometric array readout},
      author = {{Francisco Tejada} and {A. Andreou} and {J. Miragliotta} and {R. Osiander} and {D. Wesolek}},
      year = 2004,
      month = {9},
      booktitle = {2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)},
      url = {https://www.semanticscholar.org/paper/4fb38bc75018ca80a0b8826d954a3a1b5bc27a0d},
      }

3103. David Yarowsky and Silviu Cucerzan, “Language independent, minimally supervised methods in natural language ambiguity resolution.” 2004.
      [BibTeX] [Link]

      @inproceedings{63928511,
      title = {Language independent, minimally supervised methods in natural language ambiguity resolution},
      author = {{David Yarowsky} and {Silviu Cucerzan}},
      year = 2004,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e7722aa5bd2a11d33fefa8981492eedceed37c6f},
      }

3104. Daqing He, Dina Demner-Fushman, Douglas W. Oard, Damianos G. Karakos, and S. Khudanpur, “Improving Passage Retrieval Using Interactive Elicition and Statistical Modeling,” in Text Retrieval Conference, 2004.
      [BibTeX] [Link]

      @inproceedings{7402537,
      title = {Improving Passage Retrieval Using Interactive Elicition and Statistical Modeling},
      author = {{Daqing He} and {Dina Demner-Fushman} and {Douglas W. Oard} and {Damianos G. Karakos} and {S. Khudanpur}},
      year = 2004,
      booktitle = {Text Retrieval Conference},
      url = {https://www.semanticscholar.org/paper/db241eb085446466b3dd32ff248fcf966a33458f},
      }

3105. J. J. Liu, Z. Kalayjian, B. Riely, W. Chang, G. Simonis, A. Apsel, and A. Andreou, “Multichannel ultrathin silicon-on-sapphire optical interconnects,” in IEEE Journal of Selected Topics in Quantum Electronics, 2003.
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      @inproceedings{122758259,
      title = {Multichannel ultrathin silicon-on-sapphire optical interconnects},
      author = {{J. J. Liu} and {Z. Kalayjian} and {B. Riely} and {W. Chang} and {G. Simonis} and {A. Apsel} and {A. Andreou}},
      year = 2003,
      month = {10},
      booktitle = {IEEE Journal of Selected Topics in Quantum Electronics},
      url = {https://www.semanticscholar.org/paper/eb2ea5e1185cd3e70cffa630c12405c4c079cb1c},
      }

3106. P. Virga and S. Khudanpur, “Transliteration of Proper Names in Cross-Lingual Information Retrieval,” in Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition, Sapporo, Japan, 2003, p. 57–64. doi:10.3115/1119384.1119392
      [BibTeX] [Link]

      @inproceedings{virga-khudanpur-2003-transliteration,
      title = "Transliteration of Proper Names in Cross-Lingual Information Retrieval",
      author = "Virga, Paola and
      Khudanpur, Sanjeev",
      booktitle = "Proceedings of the {ACL} 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition",
      month = jul,
      year = "2003",
      address = "Sapporo, Japan",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W03-1508",
      doi = "10.3115/1119384.1119392",
      pages = "57--64",
      }

3107. J. Eisner, “Learning Non-Isomorphic Tree Mappings for Machine Translation,” in Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL), Companion Volume, Sapporo, 2003, p. 205–208.
      [BibTeX] [Link]

      @InProceedings{eisner-2003-acl,
      aclid = "P03-2041",
      author = "Jason Eisner",
      title = "Learning Non-Isomorphic Tree Mappings for Machine
      Translation",
      booktitle = "Proceedings of the 41st Annual Meeting of the
      Association for Computational Linguistics (ACL),
      Companion Volume",
      pages = "205--208",
      year = "2003",
      month = jul,
      address = "Sapporo",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2003-acl",
      }

3108. J. Eisner, “Simpler and More General Minimization for Weighted Finite-State Automata,” in Proceedings of the Joint Meeting of the Human Language Technology Conference and the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), Edmonton, 2003, p. 64–71.
      [BibTeX] [Link]

      @InProceedings{eisner-2003-hlt,
      aclid = "N03-1009",
      author = "Jason Eisner",
      title = "Simpler and More General Minimization for Weighted
      Finite-State Automata",
      booktitle = "Proceedings of the Joint Meeting of the Human Language
      Technology Conference and the North American Chapter of
      the Association for Computational Linguistics
      (HLT-NAACL)",
      pages = "64--71",
      year = "2003",
      month = may,
      address = "Edmonton",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2003-hlt",
      }

3109. F. Och, D. Gildea, S. Khudanpur, Kenji Yamada, Alexander M. Fraser, Shankar Kumar, David A. Smith, Katherine Eng, Viren Jain, Zhenglin Jin, and Dragomir R. Radev, “Syntax for Statistical Machine Translation.” 2003.
      [BibTeX] [Link]

      @inproceedings{5838856,
      title = {Syntax for Statistical Machine Translation},
      author = {{F. Och} and {D. Gildea} and {S. Khudanpur} and {Kenji Yamada} and {Alexander M. Fraser} and {Shankar Kumar} and {David A. Smith} and {Katherine Eng} and {Viren Jain} and {Zhenglin Jin} and {Dragomir R. Radev}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fefd66ad4e74c333a47ed726be66f9c0e440f5e1},
      }

3110. F. Jelinek, “Combating the Sparse Data Problem of Language Modelling,” in International Conference on Text, Speech and Dialogue, 2003.
      [BibTeX] [Link]

      @inproceedings{39697252,
      title = {Combating the Sparse Data Problem of Language Modelling},
      author = {{F. Jelinek}},
      year = 2003,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/cbf801d00bf0f0020b17645874108012909e6a99},
      }

3111. Ahmad Emami, P. Xu, and F. Jelinek, “Using a connectionist model in a syntactical based language model,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003.
      [BibTeX] [Link]

      @inproceedings{6529491,
      title = {Using a connectionist model in a syntactical based language model},
      author = {{Ahmad Emami} and {P. Xu} and {F. Jelinek}},
      year = 2003,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/3d6036af971c1f11ab712cc41487376a94e63673},
      }

3112. C. Schafer and D. Yarowsky, “Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction,” in Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, p. 9–16.
      [BibTeX] [Link]

      @inproceedings{schafer-yarowsky-2003-statistical,
      title = "Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction",
      author = "Schafer, Charles and
      Yarowsky, David",
      booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
      year = "2003",
      url = "https://aclanthology.org/W03-1002",
      pages = "9--16",
      }

3113. E. Culurciello and A. Andreou, “A comparative study of access topologies for chip-level address-event communication channels,” in IEEE Trans. Neural Networks, 2003.
      [BibTeX] [Link]

      @inproceedings{21766789,
      title = {A comparative study of access topologies for chip-level address-event communication channels},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2003,
      month = {9},
      booktitle = {IEEE Trans. Neural Networks},
      url = {https://www.semanticscholar.org/paper/8f1bfd136a506bca6be6b6b7bf427bfd37844971},
      }

3114. S. Khudanpur and Jun Wu, “Maximum entropy language modeling with non-local dependencies.” 2003.
      [BibTeX] [Link]

      @inproceedings{61050622,
      title = {Maximum entropy language modeling with non-local dependencies},
      author = {{S. Khudanpur} and {Jun Wu}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/39c3b39e7fd10efd59a114c8ed7a02e4071ddbf5},
      }

3115. P. Xu, A. Emami, and F. Jelinek, “Training Connectionist Models for the Structured Language Model,” in Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, p. 160–167.
      [BibTeX] [Link]

      @inproceedings{xu-etal-2003-training,
      title = "Training Connectionist Models for the {S}tructured {L}anguage {M}odel",
      author = "Xu, Peng and
      Emami, Ahmad and
      Jelinek, Frederick",
      booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
      year = "2003",
      url = "https://aclanthology.org/W03-1021",
      pages = "160--167",
      }

3116. Woosung Kim and S. Khudanpur, “Language model adaptation using cross-lingual information,” in Interspeech, 2003.
      [BibTeX] [Link]

      @inproceedings{1862811,
      title = {Language model adaptation using cross-lingual information},
      author = {{Woosung Kim} and {S. Khudanpur}},
      year = 2003,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/23d9233dd26768acbea6c6bfe2270d313d33fef2},
      }

3117. C. Schafer and D. Yarowsky, “A two-level syntax-based approach to Arabic-English statistical machine translation,” in Workshop on Machine Translation for Semitic languages: issues and approaches, New Orleans, USA, 2003.
      [BibTeX] [Abstract] [Link]
      

      We formulate an original model for statistical machine translation (SMT) inspired by characteristics of the Arabic-English translation task. Our approach incorporates part-of-speech tags and linguistically motivated phrase chunks in a 2-level shallow syntactic model of reordering. We implement and evaluate this model, showing it to have advantageous properties and to be competitive with an existing SMT baseline. We also describe cross-categorial lexical translation coercion, an interesting component and side-effect of our approach. Finally, we discuss the novel implementation of decoding for this model which saves much development work by constructing finite-state machine (FSM) representations of translation probability distributions and using generic FSM operations for search. Algorithmic details, examples and results focus on Arabic, and the paper includes discussion on the issues and challenges of Arabic statistical machine translation.

      @inproceedings{schafer-yarowsky-2003-two,
      title = "A two-level syntax-based approach to {A}rabic-{E}nglish statistical machine translation",
      author = "Schafer, Charles and
      Yarowsky, David",
      booktitle = "Workshop on Machine Translation for Semitic languages: issues and approaches",
      month = sep # " 23-27",
      year = "2003",
      address = "New Orleans, USA",
      url = "https://aclanthology.org/2003.mtsummit-semit.11",
      abstract = "We formulate an original model for statistical machine translation (SMT) inspired by characteristics of the Arabic-English translation task. Our approach incorporates part-of-speech tags and linguistically motivated phrase chunks in a 2-level shallow syntactic model of reordering. We implement and evaluate this model, showing it to have advantageous properties and to be competitive with an existing SMT baseline. We also describe cross-categorial lexical translation coercion, an interesting component and side-effect of our approach. Finally, we discuss the novel implementation of decoding for this model which saves much development work by constructing finite-state machine (FSM) representations of translation probability distributions and using generic FSM operations for search. Algorithmic details, examples and results focus on Arabic, and the paper includes discussion on the issues and challenges of Arabic statistical machine translation.",
      }

3118. David H. Goldberg, A. Sripati, and A. Andreou, “Energy efficiency in a channel model for the spiking axon,” in Neurocomputing, 2003.
      [BibTeX] [Link]

      @inproceedings{36729490,
      title = {Energy efficiency in a channel model for the spiking axon},
      author = {{David H. Goldberg} and {A. Sripati} and {A. Andreou}},
      year = 2003,
      month = {6},
      booktitle = {Neurocomputing},
      url = {https://www.semanticscholar.org/paper/a7110da29070e2fd2b5ad8fb76ceef9f0f8056d4},
      }

3119. Y. Deng and S. Khudanpur, “Latent Semantic Information in Maximum Entropy Language Models for Conversational Speech Recognition,” in Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, 2003, p. 56–63.
      [BibTeX] [Link]

      @inproceedings{deng-khudanpur-2003-latent,
      title = "Latent Semantic Information in Maximum Entropy Language Models for Conversational Speech Recognition",
      author = "Deng, Yonggang and
      Khudanpur, Sanjeev",
      booktitle = "Proceedings of the 2003 Human Language Technology Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      year = "2003",
      url = "https://aclanthology.org/N03-1008",
      pages = "56--63",
      }

3120. Daqing He, Douglas W. Oard, Jianqiang Wang, Jun Luo, Dina Demner-Fushman, Kareem Darwish, P. Resnik, S. Khudanpur, Michael Nossal, M. Subotin, and Anton Leuski, “Making MIRACLEs: Interactive translingual search for Cebuano and Hindi,” in TALIP, 2003.
      [BibTeX] [Link]

      @inproceedings{14753351,
      title = {Making MIRACLEs: Interactive translingual search for Cebuano and Hindi},
      author = {{Daqing He} and {Douglas W. Oard} and {Jianqiang Wang} and {Jun Luo} and {Dina Demner-Fushman} and {Kareem Darwish} and {P. Resnik} and {S. Khudanpur} and {Michael Nossal} and {M. Subotin} and {Anton Leuski}},
      year = 2003,
      month = {9},
      booktitle = {TALIP},
      url = {https://www.semanticscholar.org/paper/79896d701850b6edb354f56dea4955c785062f33},
      }

3121. S. Cucerzan and D. Yarowsky, “Minimally Supervised Induction of Grammatical Gender,” in Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, 2003, p. 40–47.
      [BibTeX] [Link]

      @inproceedings{cucerzan-yarowsky-2003-minimally,
      title = "Minimally Supervised Induction of Grammatical Gender",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "Proceedings of the 2003 Human Language Technology Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
      year = "2003",
      url = "https://aclanthology.org/N03-1006",
      pages = "40--47",
      }

3122. W. Byrne, S. Khudanpur, W. Kim, S. Kumar, P. Pecina, P. Virga, P. Xu, and D. Yarowsky, “The Johns Hopkins University 2003 Chinese-English machine translation system,” in Proceedings of Machine Translation Summit IX: System Presentations, New Orleans, USA, 2003.
      [BibTeX] [Abstract] [Link]
      

      We describe a Chinese to English Machine Translation system developed at the Johns Hopkins University for the NIST 2003 MT evaluation. The system is based on a Weighted Finite State Transducer implementation of the alignment template translation model for statistical machine translation. The baseline MT system was trained using 100,000 sentence pairs selected from a static bitext training collection. Information retrieval techniques were then used to create specific training collections for each document to be translated. This document-specific training set included bitext and name entities that were then added to the baseline system by augmenting the library of alignment templates. We report translation performance of baseline and IR-based systems on two NIST MT evaluation test sets.

      @inproceedings{byrne-etal-2003-johns,
      title = "The {J}ohns {H}opkins {U}niversity 2003 {C}hinese-{E}nglish machine translation system",
      author = "Byrne, W. and
      Khudanpur, S. and
      Kim, W. and
      Kumar, S. and
      Pecina, P. and
      Virga, P. and
      Xu, P. and
      Yarowsky, D.",
      booktitle = "Proceedings of Machine Translation Summit IX: System Presentations",
      month = sep # " 23-27",
      year = "2003",
      address = "New Orleans, USA",
      url = "https://aclanthology.org/2003.mtsummit-systems.3",
      abstract = "We describe a Chinese to English Machine Translation system developed at the Johns Hopkins University for the NIST 2003 MT evaluation. The system is based on a Weighted Finite State Transducer implementation of the alignment template translation model for statistical machine translation. The baseline MT system was trained using 100,000 sentence pairs selected from a static bitext training collection. Information retrieval techniques were then used to create specific training collections for each document to be translated. This document-specific training set included bitext and name entities that were then added to the baseline system by augmenting the library of alignment templates. We report translation performance of baseline and IR-based systems on two NIST MT evaluation test sets.",
      }

3123. Woosung Kim and S. Khudanpur, “Cross-LingualLexical Triggers in Statistical LanguageModeling.” 2003.
      [BibTeX] [Link]

      @inproceedings{267839748,
      title = {Cross-LingualLexical Triggers in Statistical LanguageModeling},
      author = {{Woosung Kim} and {S. Khudanpur}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b32d55a3184e2f27c8d68fb82e09561484986f1c},
      }

3124. A. Apsel and A. Andreou, “A 7 milliwatt 1GBPS CMOS optical receiver for through wafer communication,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{40026731,
      title = {A 7 milliwatt 1GBPS CMOS optical receiver for through wafer communication},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/a20e20995fbe939ea3b41645daea08459d2e31d4},
      }

3125. N. Sgouros, A. Andreou, M. Sangriotis, P. Papageorgas, D. Maroulis, and NG Theofanous, “Compression of IP images for autostereoscopic 3D imaging applications,” in 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2003.
      [BibTeX] [Link]

      @inproceedings{14571038,
      title = {Compression of IP images for autostereoscopic 3D imaging applications},
      author = {{N. Sgouros} and {A. Andreou} and {M. Sangriotis} and {P. Papageorgas} and {D. Maroulis} and {NG Theofanous}},
      year = 2003,
      month = {9},
      booktitle = {3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the},
      url = {https://www.semanticscholar.org/paper/8c3f905a15cad55747b223abf0c61bbc91101adc},
      }

3126. G. Mann and D. Yarowsky, “Unsupervised Personal Name Disambiguation,” in Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, 2003, p. 33–40.
      [BibTeX] [Link]

      @inproceedings{mann-yarowsky-2003-unsupervised,
      title = "Unsupervised Personal Name Disambiguation",
      author = "Mann, Gideon and
      Yarowsky, David",
      booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
      year = "2003",
      url = "https://aclanthology.org/W03-0405",
      pages = "33--40",
      }

3127. David Yarowsky and Radu Florian, “Transformation based learning and data-driven lexical disambiguation: syntactic and semantic ambiguity resolution.” 2003.
      [BibTeX] [Link]

      @inproceedings{58130901,
      title = {Transformation based learning and data-driven lexical disambiguation: syntactic and semantic ambiguity resolution},
      author = {{David Yarowsky} and {Radu Florian}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9a5caa9c826264663dd7254dba8529b1a6748073},
      }

3128. E. Culurciello and A. Andreou, “An 8-bit, 1mW successive approximation ADC in SOI CMOS,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{37397472,
      title = {An 8-bit, 1mW successive approximation ADC in SOI CMOS},
      author = {{E. Culurciello} and {A. Andreou}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/5b8e5af2063e4316296244353854ca8363f57a58},
      }

3129. D. W. Oard, D. Doermann, B. Dorr, D. He, P. Resnik, A. Weinberg, W. Byrne, S. Khudanpur, D. Yarowsky, A. Leuski, P. Koehn, and K. Knight, “Desparately Seeking Cebuano,” in Companion Volume of the Proceedings of HLT-NAACL 2003 – Short Papers, 2003, p. 76–78.
      [BibTeX] [Link]

      @inproceedings{oard-etal-2003-desparately,
      title = "Desparately Seeking {C}ebuano",
      author = "Oard, Douglas W. and
      Doermann, David and
      Dorr, Bonnie and
      He, Daqing and
      Resnik, Philip and
      Weinberg, Amy and
      Byrne, William and
      Khudanpur, Sanjeev and
      Yarowsky, David and
      Leuski, Anton and
      Koehn, Philipp and
      Knight, Kevin",
      booktitle = "Companion Volume of the Proceedings of {HLT}-{NAACL} 2003 - Short Papers",
      year = "2003",
      url = "https://aclanthology.org/N03-2026",
      pages = "76--78",
      }

3130. B. Linares-Barranco, A. Andreou, G. Indiveri, and T. Shibata, “Guest editorial – Special issue on neural networks hardware implementations,” in IEEE Trans. Neural Networks, 2003.
      [BibTeX] [Link]

      @inproceedings{26975520,
      title = {Guest editorial - Special issue on neural networks hardware implementations},
      author = {{B. Linares-Barranco} and {A. Andreou} and {G. Indiveri} and {T. Shibata}},
      year = 2003,
      month = {9},
      booktitle = {IEEE Trans. Neural Networks},
      url = {https://www.semanticscholar.org/paper/f5513abf7b72220b9757b16342730748775f362e},
      }

3131. A. Apsel and A. Andreou, “A 10 milliwatt 2 Gbps CMOS optical receiver for optoelectronic interconnect,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{42004952,
      title = {A 10 milliwatt 2 Gbps CMOS optical receiver for optoelectronic interconnect},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/a58bd9def113605925c3e3a32c95df94a5f44223},
      }

3132. Paola Virga and S. Khudanpur, “Transliteration of proper names in cross-language applications,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003.
      [BibTeX] [Link]

      @inproceedings{18061996,
      title = {Transliteration of proper names in cross-language applications},
      author = {{Paola Virga} and {S. Khudanpur}},
      year = 2003,
      month = {7},
      booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
      url = {https://www.semanticscholar.org/paper/b279fe7b24107a04c47fc792db3ea48bef7c532e},
      }

3133. Jia Cui and F. Jelinek, “Maximum Entropy Modeling in Semantic Tagging.” 2003.
      [BibTeX] [Link]

      @inproceedings{16056099,
      title = {Maximum Entropy Modeling in Semantic Tagging},
      author = {{Jia Cui} and {F. Jelinek}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/2e11d4c492b52d56bca01a21a74981b1087c444f},
      }

3134. A. Apsel, Jiang Liu, A. Andreou, W. Chang, and G. Simonis, “Integrated arrays of low power SOS chip-to-chip interconnects for efficient parallel communication in CMOS.” 2003.
      [BibTeX] [Link]

      @inproceedings{111718664,
      title = {Integrated arrays of low power SOS chip-to-chip interconnects for efficient parallel communication in CMOS},
      author = {{A. Apsel} and {Jiang Liu} and {A. Andreou} and {W. Chang} and {G. Simonis}},
      year = 2003,
      month = {6},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/176cfdf52c792a258161fadc2e134c981749c01e},
      }

3135. W. Kim and S. Khudanpur, “Cross-Lingual Lexical Triggers in Statistical Language Modeling,” in Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, p. 17–24.
      [BibTeX] [Link]

      @inproceedings{kim-khudanpur-2003-cross,
      title = "Cross-Lingual Lexical Triggers in Statistical Language Modeling",
      author = "Kim, Woosung and
      Khudanpur, Sanjeev",
      booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
      year = "2003",
      url = "https://aclanthology.org/W03-1003",
      pages = "17--24",
      }

3136. David Yarowsky and R. Wicentowski, “Modeling and learning multilingual inflectional morphology in a minimally supervised framework.” 2003.
      [BibTeX] [Link]

      @inproceedings{62258158,
      title = {Modeling and learning multilingual inflectional morphology in a minimally supervised framework},
      author = {{David Yarowsky} and {R. Wicentowski}},
      year = 2003,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/2fda0456d2a3a3008206f5c0ec2da0e95cb8e20d},
      }

3137. P. Julián, A. Andreou, Laurence Riddle, S. Shamma, and G. Cauwenberghs, “A comparison of algorithms for sound localization,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{41499820,
      title = {A comparison of algorithms for sound localization},
      author = {{P. Julián} and {A. Andreou} and {Laurence Riddle} and {S. Shamma} and {G. Cauwenberghs}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/c6959240792e8cda2e324df58f68764d312b8f4c},
      }

3138. A. Apsel, E. Culurciello, A. Andreou, and K. Aliberti, “Thin film PIN photodiodes for optoelectronic silicon on sapphire CMOS,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{2009128,
      title = {Thin film PIN photodiodes for optoelectronic silicon on sapphire CMOS},
      author = {{A. Apsel} and {E. Culurciello} and {A. Andreou} and {K. Aliberti}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/3b35f4c94262f6856d79158851b3825145c759da},
      }

3139. P. Julián, A. Andreou, P. Mandolesi, and David H. Goldberg, “A low-power CMOS integrated circuit for bearing estimation,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{30699245,
      title = {A low-power CMOS integrated circuit for bearing estimation},
      author = {{P. Julián} and {A. Andreou} and {P. Mandolesi} and {David H. Goldberg}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/8ed454310941f4a4a68f6941256bcd0efd90c22a},
      }

3140. A. Apsel and A. Andreou, “Analysis of short distance optoelectronic link architectures,” in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS ’03., 2003.
      [BibTeX] [Link]

      @inproceedings{35403518,
      title = {Analysis of short distance optoelectronic link architectures},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2003,
      month = {5},
      booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
      url = {https://www.semanticscholar.org/paper/31473674b33933ff83740bc21936f80fbec4122b},
      }

3141. A. Andreou and Z. Kalayjian, “Polarization imaging: principles and integrated polarimeters,” in IEEE Sensors Journal, 2002.
      [BibTeX] [Link]

      @inproceedings{122879363,
      title = {Polarization imaging: principles and integrated polarimeters},
      author = {{A. Andreou} and {Z. Kalayjian}},
      year = 2002,
      month = {12},
      booktitle = {IEEE Sensors Journal},
      url = {https://www.semanticscholar.org/paper/08912d533bdc5a6e259bb5c3cb1e76a990be1580},
      }

3142. Radu Florian, Silviu Cucerzan, C. Schafer, and David Yarowsky, “Combining Classifiers for word sense disambiguation,” in Natural Language Engineering, 2002.
      [BibTeX] [Link]

      @inproceedings{43025066,
      title = {Combining Classifiers for word sense disambiguation},
      author = {{Radu Florian} and {Silviu Cucerzan} and {C. Schafer} and {David Yarowsky}},
      year = 2002,
      month = {12},
      booktitle = {Natural Language Engineering},
      url = {https://www.semanticscholar.org/paper/b1e64153e0eccea699d01b094020f3424598cd94},
      }

3143. David Yarowsky and Radu Florian, “Evaluating sense disambiguation across diverse parameter spaces,” in Natural Language Engineering, 2002.
      [BibTeX] [Link]

      @inproceedings{15605004,
      title = {Evaluating sense disambiguation across diverse parameter spaces},
      author = {{David Yarowsky} and {Radu Florian}},
      year = 2002,
      month = {12},
      booktitle = {Natural Language Engineering},
      url = {https://www.semanticscholar.org/paper/a9b2040cc48c41cf3ccd85e4e95b3baefc1b0459},
      }

3144. R. Florian and D. Yarowsky, “Modeling Consensus: Classifier Combination for Word Sense Disambiguation,” in Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), 2002, p. 25–32. doi:10.3115/1118693.1118697
      [BibTeX] [Link]

      @inproceedings{florian-yarowsky-2002-modeling,
      title = "Modeling Consensus: Classifier Combination for Word Sense Disambiguation",
      author = "Florian, Radu and
      Yarowsky, David",
      booktitle = "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002)",
      month = jul,
      year = "2002",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W02-1004",
      doi = "10.3115/1118693.1118697",
      pages = "25--32",
      }

3145. P. Xu, C. Chelba, and F. Jelinek, “A Study on Richer Syntactic Dependencies for Structured Language Modeling,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 2002, p. 191–198. doi:10.3115/1073083.1073116
      [BibTeX] [Link]

      @inproceedings{xu-etal-2002-study,
      title = "A Study on Richer Syntactic Dependencies for Structured Language Modeling",
      author = "Xu, Peng and
      Chelba, Ciprian and
      Jelinek, Frederick",
      editor = "Isabelle, Pierre and
      Charniak, Eugene and
      Lin, Dekang",
      booktitle = "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
      month = jul,
      year = "2002",
      address = "Philadelphia, Pennsylvania, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P02-1025",
      doi = "10.3115/1073083.1073116",
      pages = "191--198",
      }

3146. S. Cucerzan and D. Yarowsky, “Augmented Mixture Models for Lexical Disambiguation,” in Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), 2002, p. 33–40. doi:10.3115/1118693.1118698
      [BibTeX] [Link]

      @inproceedings{cucerzan-yarowsky-2002-augmented,
      title = "Augmented Mixture Models for Lexical Disambiguation",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002)",
      month = jul,
      year = "2002",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/W02-1005",
      doi = "10.3115/1118693.1118698",
      pages = "33--40",
      }

3147. J. Eisner, “Parameter Estimation for Probabilistic Finite-State Transducers,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, 2002, p. 1–8.
      [BibTeX] [Link]

      @InProceedings{eisner-2002-acl-fst,
      aclid = "P02-1001",
      author = "Jason Eisner",
      title = "Parameter Estimation for Probabilistic Finite-State
      Transducers",
      booktitle = "Proceedings of the 40th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "1--8",
      year = "2002",
      month = jul,
      address = "Philadelphia",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-acl-fst",
      }

3148. J. Eisner, “Comprehension and Compilation in Optimality Theory,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, 2002, p. 56–63.
      [BibTeX] [Link]

      @InProceedings{eisner-2002-acl-ot,
      aclid = "P02-1008",
      author = "Jason Eisner",
      title = "Comprehension and Compilation in {O}ptimality
      {T}heory",
      booktitle = "Proceedings of the 40th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "56--63",
      year = "2002",
      month = jul,
      address = "Philadelphia",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-acl-ot",
      }

3149. J. Eisner, “An Interactive Spreadsheet for Teaching the Forward-Backward Algorithm,” in Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching NLP and CL, Philadelphia, 2002, p. 10–18.
      [BibTeX] [Link]

      @InProceedings{eisner-2002-tnlp,
      aclid = "W02-0102",
      author = "Jason Eisner",
      title = "An Interactive Spreadsheet for Teaching the
      Forward-Backward Algorithm",
      booktitle = "Proceedings of the ACL Workshop on Effective Tools and
      Methodologies for Teaching NLP and CL",
      editor = "Dragomir Radev and Chris Brew",
      pages = "10--18",
      year = "2002",
      month = jul,
      address = "Philadelphia",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-tnlp",
      }

3150. J. Eisner, “Transformational Priors Over Grammars,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, 2002, p. 63–70.
      [BibTeX] [Link]

      @InProceedings{eisner-2002-emnlp,
      aclid = "W02-1009",
      author = "Jason Eisner",
      title = "Transformational Priors Over Grammars",
      booktitle = "Proceedings of the Conference on Empirical Methods in
      Natural Language Processing (EMNLP)",
      pages = "63--70",
      year = "2002",
      month = jul,
      address = "Philadelphia",
      note = "Nominated for Best Paper Award.",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-emnlp",
      }

3151. A. Apsel, A. Andreou, and J. Liu, “A 6 channel array of 5 milliwatt, 500 MHz optical receivers in .5 /spl mu/m SOS CMOS,” in IEEE International Symposium on Circuits and Systems proceedings, 2002.
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      @inproceedings{21149766,
      title = {A 6 channel array of 5 milliwatt, 500 MHz optical receivers in .5 /spl mu/m SOS CMOS},
      author = {{A. Apsel} and {A. Andreou} and {J. Liu}},
      year = 2002,
      month = {8},
      booktitle = {IEEE International Symposium on Circuits and Systems proceedings},
      url = {https://www.semanticscholar.org/paper/fd80450ae8cadd7d435fd065a436f636976c1d5a},
      }

3152. S. Cucerzan and D. Yarowsky, “Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day,” in COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002), 2002.
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      @inproceedings{cucerzan-yarowsky-2002-bootstrapping,
      title = "Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
      year = "2002",
      url = "https://aclanthology.org/W02-2006",
      }

3153. J. J. Liu, Z. Kalayjian, W. Chang, G. Simonis, A. Apsel, and A. Andreou, “Ultra-thin silicon-on-sapphire multi-channel optical interconnects,” in Conference on Lasers and Electro-Optics, 2002.
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      @inproceedings{117749207,
      title = {Ultra-thin silicon-on-sapphire multi-channel optical interconnects},
      author = {{J. J. Liu} and {Z. Kalayjian} and {W. Chang} and {G. Simonis} and {A. Apsel} and {A. Andreou}},
      year = 2002,
      month = {5},
      booktitle = {Conference on Lasers and Electro-Optics},
      url = {https://www.semanticscholar.org/paper/7dbbf1741af1475cc60f6c82b282477ce66be41f},
      }

3154. C. Schafer and D. Yarowsky, “Inducing Translation Lexicons via Diverse Similarity Measures and Bridge Languages,” in COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002), 2002.
      [BibTeX] [Link]

      @inproceedings{schafer-yarowsky-2002-inducing,
      title = "Inducing Translation Lexicons via Diverse Similarity Measures and Bridge Languages",
      author = "Schafer, Charles and
      Yarowsky, David",
      booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
      year = "2002",
      url = "https://aclanthology.org/W02-2026",
      }

3155. S. Khudanpur and P. Narayan, “Order estimation for a special class of hidden Markov sources and binary renewal processes,” in IEEE Transactions on Information Theory, 2002.
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      @inproceedings{14041212,
      title = {Order estimation for a special class of hidden Markov sources and binary renewal processes},
      author = {{S. Khudanpur} and {P. Narayan}},
      year = 2002,
      month = {6},
      booktitle = {IEEE Transactions on Information Theory},
      url = {https://www.semanticscholar.org/paper/a4282dca310b448a329eb4b2c5fb1e313e033745},
      }

3156. E. Culurciello, A. Andreou, and P. Pouliquen, “Modeling hot-electrons effects in silicon-on-sapphire MOSFETs,” in IEEE International Symposium on Circuits and Systems proceedings, 2002.
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      @inproceedings{15662068,
      title = {Modeling hot-electrons effects in silicon-on-sapphire MOSFETs},
      author = {{E. Culurciello} and {A. Andreou} and {P. Pouliquen}},
      year = 2002,
      month = {8},
      booktitle = {IEEE International Symposium on Circuits and Systems proceedings},
      url = {https://www.semanticscholar.org/paper/9dc4fb378b646999a9589c09f0c824b9ab38e1a7},
      }

3157. K. V. Dang, P. Pouliquen, M. Grenn, A. Andreou, Paul Blase, and Sanh Phu, “Low Cost Microbolometer Development Using Commercially Available CMOS Foundry Processes.” 2002.
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      @inproceedings{110543057,
      title = {Low Cost Microbolometer Development Using Commercially Available CMOS Foundry Processes},
      author = {{K. V. Dang} and {P. Pouliquen} and {M. Grenn} and {A. Andreou} and {Paul Blase} and {Sanh Phu}},
      year = 2002,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/650c8ade0fc4aea7b908c04808653a1368553a65},
      }

3158. Jun Wu and S. Khudanpur, “Maximum Entropy Language Modeling with Non-local and Syntactic Dependencies.” 2002.
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      @inproceedings{17923970,
      title = {Maximum Entropy Language Modeling with Non-local and Syntactic Dependencies},
      author = {{Jun Wu} and {S. Khudanpur}},
      year = 2002,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fe686c6813569d202b9cb8046822396d3c1a37cf},
      }

3159. Jun Wu and S. Khudanpur, “Building a topic-dependent maximum entropy model for very large corpora,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002.
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      @inproceedings{5098446,
      title = {Building a topic-dependent maximum entropy model for very large corpora},
      author = {{Jun Wu} and {S. Khudanpur}},
      year = 2002,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/26040ab24407312bb4d244178f8f0f2793c0f5a2},
      }

3160. J. Christen, Cristina E. Davis, Min Li, and A. Andreou, “Design, double sided post-processing, and packaging of CMOS compatible bio-MEMS device arrays,” in IEEE International Symposium on Circuits and Systems proceedings, 2002.
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      @inproceedings{32482235,
      title = {Design, double sided post-processing, and packaging of CMOS compatible bio-MEMS device arrays},
      author = {{J. Christen} and {Cristina E. Davis} and {Min Li} and {A. Andreou}},
      year = 2002,
      month = {8},
      booktitle = {IEEE International Symposium on Circuits and Systems proceedings},
      url = {https://www.semanticscholar.org/paper/2688d86b31663735cfde25445dfe0d1d645c31d9},
      }

3161. S. Cucerzan and D. Yarowsky, “Language Independent NER using a Unified Model of Internal and Contextual Evidence,” in COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002), 2002.
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      @inproceedings{cucerzan-yarowsky-2002-language,
      title = "Language Independent {NER} using a Unified Model of Internal and Contextual Evidence",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
      year = "2002",
      url = "https://aclanthology.org/W02-2007",
      }

3162. E. Riloff, C. Schafer, and D. Yarowsky, “Inducing Information Extraction Systems for New Languages via Cross-language Projection,” in COLING 2002: The 19th International Conference on Computational Linguistics, 2002.
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      @inproceedings{riloff-etal-2002-inducing,
      title = "Inducing Information Extraction Systems for New Languages via Cross-language Projection",
      author = "Riloff, Ellen and
      Schafer, Charles and
      Yarowsky, David",
      booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics",
      year = "2002",
      url = "https://aclanthology.org/C02-1070",
      }

3163. S. Khudanpur and Woosung Kim, “Using cross-language cues for story-specific language modeling,” in Interspeech, 2002.
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      @inproceedings{5831379,
      title = {Using cross-language cues for story-specific language modeling},
      author = {{S. Khudanpur} and {Woosung Kim}},
      year = 2002,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/7be21ebb963a2d6bbfbdd315a7c9e9bd52c740de},
      }

3164. K. V. Dang, W. Blase, S. Horn, P. Pouliquen, A. Andreou, G. Cauwenberghs, and J. Caulfield, “Advanced on-FPA signal processing for staring IRFPAs,” in SPIE Optics + Photonics, 2001.
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      @inproceedings{109277592,
      title = {Advanced on-FPA signal processing for staring IRFPAs},
      author = {{K. V. Dang} and {W. Blase} and {S. Horn} and {P. Pouliquen} and {A. Andreou} and {G. Cauwenberghs} and {J. Caulfield}},
      year = 2001,
      month = {12},
      booktitle = {SPIE Optics + Photonics},
      url = {https://www.semanticscholar.org/paper/a153cc97b70dab9758d9f280b577530fb7c4bad8},
      }

3165. M. Martin, D. Roth, A. Garrison-Darrin, P. Mcnulty, and A. Andreou, “FGMOS dosimetry: design and implementation,” in IEEE Transactions on Nuclear Science, 2001.
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      @inproceedings{111368078,
      title = {FGMOS dosimetry: design and implementation},
      author = {{M. Martin} and {D. Roth} and {A. Garrison-Darrin} and {P. Mcnulty} and {A. Andreou}},
      year = 2001,
      month = {12},
      booktitle = {IEEE Transactions on Nuclear Science},
      url = {https://www.semanticscholar.org/paper/a7756dd74fd6ea1610f1fa81ff8d601ea4965111},
      }

3166. A. Apsel and A. Andreou, “Analysis of data reconstruction efficiency using stochastic encoding and an integrating receiver,” in IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 2001.
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      @inproceedings{62316916,
      title = {Analysis of data reconstruction efficiency using stochastic encoding and an integrating receiver},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2001,
      month = {10},
      booktitle = {IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing},
      url = {https://www.semanticscholar.org/paper/6dd2419df16eb7d0f3f2a2e0f807ead86a48623c},
      }

3167. J. Eisner, “Expectation Semirings: Flexible EM for Finite-State Transducers,” in Proceedings of the ESSLLI Workshop on Finite-State Methods in Natural Language Processing (FSMNLP), Helsinki, 2001.
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      @InProceedings{eisner-2001-fsmnlp,
      author = "Jason Eisner",
      title = "Expectation Semirings: Flexible {EM} for Finite-State
      Transducers",
      booktitle = "Proceedings of the ESSLLI Workshop on Finite-State
      Methods in Natural Language Processing (FSMNLP)",
      editor = "Gertjan van Noord",
      year = "2001",
      month = aug,
      address = "Helsinki",
      note = "Extended abstract (5 pages)",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2001-fsmnlp",
      }

3168. D. Yarowsky, S. Cucerzan, R. Florian, C. Schafer, and R. Wicentowski, “The John Hopkins SENSEVAL-2 System Descriptions,” in Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France, 2001, p. 163–166.
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      @inproceedings{yarowsky-etal-2001-john,
      title = "{T}he {J}ohn {H}opkins {SENSEVAL}-2 System Descriptions",
      author = "Yarowsky, David and
      Cucerzan, Silviu and
      Florian, Radu and
      Schafer, Charles and
      Wicentowski, Richard",
      editor = "Preiss, Judita and
      Yarowsky, David",
      booktitle = "Proceedings of {SENSEVAL}-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems",
      month = jul,
      year = "2001",
      address = "Toulouse, France",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/S01-1040",
      pages = "163--166",
      }

3169. D. Yarowsky and G. Ngai, “Inducing Multilingual POS Taggers and NP Bracketers via Robust Projection Across Aligned Corpora,” in Second Meeting of the North American Chapter of the Association for Computational Linguistics, 2001.
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      @inproceedings{yarowsky-ngai-2001-inducing,
      title = "Inducing Multilingual {POS} Taggers and {NP} Bracketers via Robust Projection Across Aligned Corpora",
      author = "Yarowsky, David and
      Ngai, Grace",
      booktitle = "Second Meeting of the North {A}merican Chapter of the Association for Computational Linguistics",
      year = "2001",
      url = "https://aclanthology.org/N01-1026",
      }

3170. G. S. Mann and D. Yarowsky, “Multipath Translation Lexicon Induction via Bridge Languages,” in Second Meeting of the North American Chapter of the Association for Computational Linguistics, 2001.
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      @inproceedings{mann-yarowsky-2001-multipath,
      title = "Multipath Translation Lexicon Induction via Bridge Languages",
      author = "Mann, Gideon S. and
      Yarowsky, David",
      booktitle = "Second Meeting of the North {A}merican Chapter of the Association for Computational Linguistics",
      year = "2001",
      url = "https://aclanthology.org/N01-1020",
      }

3171. Woosung Kim, S. Khudanpur, and Jun Wu, “Smoothing issues in the structured language model,” in Interspeech, 2001.
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      @inproceedings{1114072,
      title = {Smoothing issues in the structured language model},
      author = {{Woosung Kim} and {S. Khudanpur} and {Jun Wu}},
      year = 2001,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/9fa6a4cc42de3274729a89df7db728da18e1fb46},
      }

3172. A. Andreou, Z. Kalayjian, A. Apsel, P. Pouliquen, R. Athale, G. Simonis, and R. Reedy, “Silicon on sapphire CMOS for optoelectronic microsystems,” in IEEE Circuits and Systems Magazine, 2001.
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      @inproceedings{62480671,
      title = {Silicon on sapphire CMOS for optoelectronic microsystems},
      author = {{A. Andreou} and {Z. Kalayjian} and {A. Apsel} and {P. Pouliquen} and {R. Athale} and {G. Simonis} and {R. Reedy}},
      year = 2001,
      booktitle = {IEEE Circuits and Systems Magazine},
      url = {https://www.semanticscholar.org/paper/b5d04538a5b139ad3596eb14e0e580abc0e23e99},
      }

3173. David H. Goldberg, G. Cauwenberghs, and A. Andreou, “Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons,” in Neural Networks, 2001.
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      @inproceedings{7913359,
      title = {Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons},
      author = {{David H. Goldberg} and {G. Cauwenberghs} and {A. Andreou}},
      year = 2001,
      month = {7},
      booktitle = {Neural Networks},
      url = {https://www.semanticscholar.org/paper/6d3fa11af0d13120c2bb941b04a2982ca3fc9561},
      }

3174. A. Apsel and A. Andreou, “5 mV, Gbit/s silicon on sapphire CMOS optical receiver,” in Electronics Letters, 2001.
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      @inproceedings{111207115,
      title = {5 mV, Gbit/s silicon on sapphire CMOS optical receiver},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2001,
      month = {9},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/e5247a91c2cacdb3821f4bc1f18731b8dba09be6},
      }

3175. D. Yarowsky, G. Ngai, and R. Wicentowski, “Inducing Multilingual Text Analysis Tools via Robust Projection across Aligned Corpora,” in Proceedings of the First International Conference on Human Language Technology Research, 2001.
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      @inproceedings{yarowsky-etal-2001-inducing,
      title = "Inducing Multilingual Text Analysis Tools via Robust Projection across Aligned Corpora",
      author = "Yarowsky, David and
      Ngai, Grace and
      Wicentowski, Richard",
      booktitle = "Proceedings of the First International Conference on Human Language Technology Research",
      year = "2001",
      url = "https://aclanthology.org/H01-1035",
      }

3176. H. Meng, Berlin Chen, S. Khudanpur, Gina-Anne Levow, W. Lo, Douglas W. Oard, Patrick Schone, Karen Tang, H. Wang, and Jianqiang Wang, “Mandarin-English Information (MEI),” in Human Language Technology – The Baltic Perspectiv, 2001.
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      @inproceedings{1573620,
      title = {Mandarin-English Information (MEI)},
      author = {{H. Meng} and {Berlin Chen} and {S. Khudanpur} and {Gina-Anne Levow} and {W. Lo} and {Douglas W. Oard} and {Patrick Schone} and {Karen Tang} and {H. Wang} and {Jianqiang Wang}},
      year = 2001,
      month = {3},
      booktitle = {Human Language Technology - The Baltic Perspectiv},
      url = {https://www.semanticscholar.org/paper/244e2b96fd1f02ed6dd1baa132b408821f2a7304},
      }

3177. P. Abshire and A. Andreou, “A communication channel model for information transmission in the blowfly photoreceptor.,” in Bio Systems, 2001.
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      @inproceedings{18742393,
      title = {A communication channel model for information transmission in the blowfly photoreceptor.},
      author = {{P. Abshire} and {A. Andreou}},
      year = 2001,
      month = {9},
      booktitle = {Bio Systems},
      url = {https://www.semanticscholar.org/paper/a4c5f83ffc31e969d4eeed09dca205cc2ec05bba},
      }

3178. A. Apsel and A. Andreou, “Analysis of Data Reconstruction Efficiency Using.” 2001.
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      @inproceedings{61453218,
      title = {Analysis of Data Reconstruction Efficiency Using},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2001,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/58faa289c89dc0142f499304fb733d8aa47e1448},
      }

3179. P. Ircing, Pavel Krbec, Jan Hajic, J. Psutka, S. Khudanpur, F. Jelinek, and W. Byrne, “On large vocabulary continuous speech recognition of highly inflectional language – czech,” in Interspeech, 2001.
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      @inproceedings{8037739,
      title = {On large vocabulary continuous speech recognition of highly inflectional language - czech},
      author = {{P. Ircing} and {Pavel Krbec} and {Jan Hajic} and {J. Psutka} and {S. Khudanpur} and {F. Jelinek} and {W. Byrne}},
      year = 2001,
      month = {9},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/4ac7a2247cffb243e8f7148eb62cbf133ba9f020},
      }

3180. A. Andreou, David H. Goldberg, E. Culurciello, M. Stanaćević, G. Cauwenberghs, and Laurence Riddle, “Heterogeneous integration of biomimetic acoustic microsystems,” in ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001.
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      @inproceedings{16600244,
      title = {Heterogeneous integration of biomimetic acoustic microsystems},
      author = {{A. Andreou} and {David H. Goldberg} and {E. Culurciello} and {M. Stanaćević} and {G. Cauwenberghs} and {Laurence Riddle}},
      year = 2001,
      month = {5},
      booktitle = {ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196)},
      url = {https://www.semanticscholar.org/paper/ce603e5e19380c3437f561c676e72db0af6aabad},
      }

3181. F. Jelinek, S. Khudanpur, and M. Saraçlar, “Pronunciation modeling for conversational speech recognition.” 2001.
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      @inproceedings{62513210,
      title = {Pronunciation modeling for conversational speech recognition},
      author = {{F. Jelinek} and {S. Khudanpur} and {M. Saraçlar}},
      year = 2001,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b15ecf1c19ba2bd7bc9c0134945ee0ce725e0baa},
      }

3182. David Yarowsky and G. Ngai, “Maximizing resources for corpus-based natural language processing.” 2001.
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      @inproceedings{57595060,
      title = {Maximizing resources for corpus-based natural language processing},
      author = {{David Yarowsky} and {G. Ngai}},
      year = 2001,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/49ea30d292d02473aa92c01bc83f8b37a5ba3b58},
      }

3183. David H. Goldberg, G. Cauwenberghs, and A. Andreou, “Analog VLSI spiking neural network with address domain probabilistic synapses,” in ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001.
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      @inproceedings{8543826,
      title = {Analog VLSI spiking neural network with address domain probabilistic synapses},
      author = {{David H. Goldberg} and {G. Cauwenberghs} and {A. Andreou}},
      year = 2001,
      month = {5},
      booktitle = {ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196)},
      url = {https://www.semanticscholar.org/paper/362209d46dba3b59fdd2bc1b19576dfc84fbb1ae},
      }

3184. F. Jelinek, W. Byrne, S. Khudanpur, B. Hladká, H. Ney, F. J. Och, J. Cuř{‘i}n, and J. Psutka, “Robust Knowledge Discovery from Parallel Speech and Text Sources,” in Proceedings of the First International Conference on Human Language Technology Research, 2001.
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      @inproceedings{jelinek-etal-2001-robust,
      title = "Robust Knowledge Discovery from Parallel Speech and Text Sources",
      author = "Jelinek, F. and
      Byrne, W. and
      Khudanpur, S. and
      Hladk{\'a}, B. and
      Ney, H. and
      Och, F. J. and
      Cu{\v{r}}{\'\i}n, J. and
      Psutka, J.",
      booktitle = "Proceedings of the First International Conference on Human Language Technology Research",
      year = "2001",
      url = "https://aclanthology.org/H01-1061",
      }

3185. Judita Preiss and David Yarowsky, “The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems.” 2001.
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      @inproceedings{67736355,
      title = {The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems},
      author = {{Judita Preiss} and {David Yarowsky}},
      year = 2001,
      month = {7},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5e58cd1baa55fd88a586047d1ead3dfb50cb1cda},
      }

3186. P. Abshire and A. Andreou, “Capacity and energy cost of information in biological and silicon photoreceptors,” in Proceedings of the IEEE, 2001.
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      @inproceedings{12766130,
      title = {Capacity and energy cost of information in biological and silicon photoreceptors},
      author = {{P. Abshire} and {A. Andreou}},
      year = 2001,
      month = {7},
      booktitle = {Proceedings of the IEEE},
      url = {https://www.semanticscholar.org/paper/59fa1c04cd742c545aec64ac0f0a22373f39fe1c},
      }

3187. G. Simonis, Z. Kalayjian, A. Apsel, P. Pouliquen, A. Andreou, R. Athale, and R. Reedy, “Silicon-on-sapphire CMOS for improved VCSEL/CMOS optoelectronic interconnects,” in LEOS 2000. 2000 IEEE Annual Meeting Conference Proceedings. 13th Annual Meeting. IEEE Lasers and Electro-Optics Society 2000 Annual Meeting (Cat. No.00CH37080), 2000.
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      @inproceedings{124920278,
      title = {Silicon-on-sapphire CMOS for improved VCSEL/CMOS optoelectronic interconnects},
      author = {{G. Simonis} and {Z. Kalayjian} and {A. Apsel} and {P. Pouliquen} and {A. Andreou} and {R. Athale} and {R. Reedy}},
      year = 2000,
      month = {11},
      booktitle = {LEOS 2000. 2000 IEEE Annual Meeting Conference Proceedings. 13th Annual Meeting. IEEE Lasers and Electro-Optics Society 2000 Annual Meeting (Cat. No.00CH37080)},
      url = {https://www.semanticscholar.org/paper/f495d04ff6fcf4e289f6a6c54882fd551562789f},
      }

3188. Ciprian Chelba and F. Jelinek, “Structured language modeling,” in Computer Speech and Language, 2000.
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      @inproceedings{14339957,
      title = {Structured language modeling},
      author = {{Ciprian Chelba} and {F. Jelinek}},
      year = 2000,
      month = {10},
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/a1c3748820d6b5ab4e7334524815df9bb6d20aed},
      }

3189. Jun Wu and S. Khudanpur, “Efficient training methods for maximum entropy language modeling,” in Interspeech, 2000.
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      @inproceedings{14484036,
      title = {Efficient training methods for maximum entropy language modeling},
      author = {{Jun Wu} and {S. Khudanpur}},
      year = 2000,
      month = {10},
      booktitle = {Interspeech},
      url = {https://www.semanticscholar.org/paper/92b99542f27ce2e899326ac167a07ee35991cfee},
      }

3190. G. Ngai and D. Yarowsky, “Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking,” in Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, 2000, p. 117–125. doi:10.3115/1075218.1075234
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      @inproceedings{ngai-yarowsky-2000-rule,
      title = "Rule Writing or Annotation: Cost-efficient Resource Usage for Base Noun Phrase Chunking",
      author = "Ngai, Grace and
      Yarowsky, David",
      booktitle = "Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics",
      month = oct,
      year = "2000",
      address = "Hong Kong",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P00-1016",
      doi = "10.3115/1075218.1075234",
      pages = "117--125",
      }

3191. S. Cucerzan and D. Yarowsky, “Language Independent, Minimally Supervised Induction of Lexical Probabilities,” in Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, 2000, p. 270–277. doi:10.3115/1075218.1075253
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      @inproceedings{cucerzan-yarowsky-2000-language,
      title = "Language Independent, Minimally Supervised Induction of Lexical Probabilities",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics",
      month = oct,
      year = "2000",
      address = "Hong Kong",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P00-1035",
      doi = "10.3115/1075218.1075253",
      pages = "270--277",
      }

3192. S. Khudanpur and Jun Wu, “Maximum entropy techniques for exploiting syntactic, semantic and collocational dependencies in language modeling,” in Computer Speech and Language, 2000.
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      @inproceedings{895577,
      title = {Maximum entropy techniques for exploiting syntactic, semantic and collocational dependencies in language modeling},
      author = {{S. Khudanpur} and {Jun Wu}},
      year = 2000,
      month = {10},
      booktitle = {Computer Speech and Language},
      url = {https://www.semanticscholar.org/paper/7ac0550daef2f936c4280aca87ff8e9c7e7baf69},
      }

3193. D. Yarowsky and R. Wicentowski, “Minimally Supervised Morphological Analysis by Multimodal Alignment,” in Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, 2000, p. 207–216. doi:10.3115/1075218.1075245
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      @inproceedings{yarowsky-wicentowski-2000-minimally,
      title = "Minimally Supervised Morphological Analysis by Multimodal Alignment",
      author = "Yarowsky, David and
      Wicentowski, Richard",
      booktitle = "Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics",
      month = oct,
      year = "2000",
      address = "Hong Kong",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P00-1027",
      doi = "10.3115/1075218.1075245",
      pages = "207--216",
      }

3194. J. Eisner, “Easy and Hard Constraint Ranking in Optimality Theory: Algorithms and Complexity,” in Finite-State Phonology: Proceedings of the 5th Workshop of the ACL Special Interest Group in Computational Phonology (SIGPHON), Luxembourg, 2000, p. 22–33.
      [BibTeX] [Link]

      @InProceedings{eisner-2000-sigphon,
      aclid = "W00-1803",
      author = "Jason Eisner",
      title = "Easy and Hard Constraint Ranking in {O}ptimality
      {T}heory: Algorithms and Complexity",
      booktitle = "Finite-State Phonology: Proceedings of the 5th
      Workshop of the ACL Special Interest Group in
      Computational Phonology (SIGPHON)",
      editor = "Jason Eisner and Lauri Karttunen and Alain
      Th\'{e}riault",
      pages = "22--33",
      year = "2000",
      month = aug,
      address = "Luxembourg",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2000-sigphon",
      }

3195. J. Eisner, “Directional Constraint Evaluation in Optimality Theory,” in Proceedings of the 18th International Conference on Computational Linguistics (COLING 2000), Saarbrücken, Germany, 2000, p. 257–263.
      [BibTeX] [Link]

      @InProceedings{eisner-2000-coling,
      aclid = "C00-1038",
      author = "Jason Eisner",
      title = "Directional Constraint Evaluation in {O}ptimality
      {T}heory",
      booktitle = "Proceedings of the 18th International Conference on
      Computational Linguistics (COLING 2000)",
      pages = "257--263",
      year = "2000",
      month = aug,
      address = "Saarbr{\"{u}}cken, Germany",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-2000-coling",
      }

3196. J. Eisner and G. Satta, “A Faster Parsing Algorithm for Lexicalized Tree-Adjoining Grammars,” in Proceedings of the 5th Workshop on Tree-Adjoining Grammars and Related Formalisms (TAG+5), Paris, 2000, p. 14–19.
      [BibTeX] [Link]

      @InProceedings{eisner-satta-2000,
      author = "Jason Eisner and Giorgio Satta",
      title = "A Faster Parsing Algorithm for Lexicalized
      Tree-Adjoining Grammars",
      booktitle = "Proceedings of the 5th Workshop on Tree-Adjoining
      Grammars and Related Formalisms (TAG+5)",
      pages = "14--19",
      year = "2000",
      month = may,
      address = "Paris",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-satta-2000",
      }

3197. Z. Kalayjian and A. Andreou, “Mismatch in photodiode and phototransistor arrays,” in 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000.
      [BibTeX] [Link]

      @inproceedings{1697748,
      title = {Mismatch in photodiode and phototransistor arrays},
      author = {{Z. Kalayjian} and {A. Andreou}},
      year = 2000,
      month = {5},
      booktitle = {2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)},
      url = {https://www.semanticscholar.org/paper/c410d1baa3730be9061f8365ddcf0a551cebb398},
      }

3198. P. Abshire and A. Andreou, “Relating information capacity to a biophysical model for blowfly photoreceptors,” in Neurocomputing, 2000.
      [BibTeX] [Link]

      @inproceedings{19011322,
      title = {Relating information capacity to a biophysical model for blowfly photoreceptors},
      author = {{P. Abshire} and {A. Andreou}},
      year = 2000,
      month = {6},
      booktitle = {Neurocomputing},
      url = {https://www.semanticscholar.org/paper/06cf0ef7b747127404fd0376648ca1409b69705f},
      }

3199. Jun Wu and S. Khudanpur, “Syntactic heads in statistical language modeling,” in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000.
      [BibTeX] [Link]

      @inproceedings{16153255,
      title = {Syntactic heads in statistical language modeling},
      author = {{Jun Wu} and {S. Khudanpur}},
      year = 2000,
      month = {6},
      booktitle = {2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)},
      url = {https://www.semanticscholar.org/paper/a9df554dab846bc8ec37e469e144a9e424be1bf6},
      }

3200. H. Meng, S. Khudanpur, G. Levow, D. W. Oard, and H. Wang, “Mandarin-English Information (MEI): Investigating Translingual Speech Retrieval,” in ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems, 2000.
      [BibTeX] [Link]

      @inproceedings{meng-etal-2000-mandarin,
      title = "{M}andarin-{E}nglish Information ({MEI}): Investigating Translingual Speech Retrieval",
      author = "Meng, Helen and
      Khudanpur, Sanjeev and
      Levow, Gina and
      Oard, Douglas W. and
      Wang, Hsin-Min",
      booktitle = "{ANLP}-{NAACL} 2000 Workshop: Embedded Machine Translation Systems",
      year = "2000",
      url = "https://aclanthology.org/W00-0504",
      }

3201. A. Apsel, Z. Kalayjian, A. Andreou, G. Simonis, W. Chang, M. Datta, and B. Koley, “Edge orientation enhancement using optoelectronic VLSI and asynchronous pulse coding,” in 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000.
      [BibTeX] [Link]

      @inproceedings{34180914,
      title = {Edge orientation enhancement using optoelectronic VLSI and asynchronous pulse coding},
      author = {{A. Apsel} and {Z. Kalayjian} and {A. Andreou} and {G. Simonis} and {W. Chang} and {M. Datta} and {B. Koley}},
      year = 2000,
      month = {5},
      booktitle = {2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)},
      url = {https://www.semanticscholar.org/paper/7a906d3f2b1e42fe8e1ac30dd6b932eeda158cc8},
      }

3202. W. Byrne, P. Beyerlein, J. Huerta, S. Khudanpur, B. Marthi, J. Morgan, N. Peterek, J. Picone, D. Vergyri, and W. Wang, “Towards language independent acoustic modeling,” in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000.
      [BibTeX] [Link]

      @inproceedings{6239225,
      title = {Towards language independent acoustic modeling},
      author = {{W. Byrne} and {P. Beyerlein} and {J. Huerta} and {S. Khudanpur} and {B. Marthi} and {J. Morgan} and {N. Peterek} and {J. Picone} and {D. Vergyri} and {W. Wang}},
      year = 2000,
      month = {6},
      booktitle = {2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)},
      url = {https://www.semanticscholar.org/paper/33a86acfa0252fb6bdcd1272467fcd96350810a8},
      }

3203. W. Millard, Z. Kalayjian, and A. Andreou, “Calibration and matching of floating gate devices,” in 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000.
      [BibTeX] [Link]

      @inproceedings{8853356,
      title = {Calibration and matching of floating gate devices},
      author = {{W. Millard} and {Z. Kalayjian} and {A. Andreou}},
      year = 2000,
      month = {5},
      booktitle = {2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)},
      url = {https://www.semanticscholar.org/paper/d80a1ac766852437a76641d553c23de13972d6b4},
      }

3204. A. Andreou and Z. Kalayjian, “Vlseye: optoelectronic vision and image processing.” 2000.
      [BibTeX] [Link]

      @inproceedings{137727200,
      title = {Vlseye: optoelectronic vision and image processing},
      author = {{A. Andreou} and {Z. Kalayjian}},
      year = 2000,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c4a6295961026a51f5f9284b650e1b65e0c09342},
      }

3205. T. Serrano-Gotarredona, A. Andreou, and B. Linares-Barranco, “A Programmable VLSI Filter Architecture for Application in Real-Time Vision Processing Systems,” in International Journal of Neural Systems, 2000.
      [BibTeX] [Link]

      @inproceedings{8910256,
      title = {A Programmable VLSI Filter Architecture for Application in Real-Time Vision Processing Systems},
      author = {{T. Serrano-Gotarredona} and {A. Andreou} and {B. Linares-Barranco}},
      year = 2000,
      month = {6},
      booktitle = {International Journal of Neural Systems},
      url = {https://www.semanticscholar.org/paper/48789f24d44bf6e9f9560b0e003181e743e4a216},
      }

3206. Ciprian Chelba and F. Jelinek, “Refinement of a Structured Language Model,” in arXiv.org, 2000.
      [BibTeX] [Link]

      @inproceedings{177,
      title = {Refinement of a Structured Language Model},
      author = {{Ciprian Chelba} and {F. Jelinek}},
      year = 2000,
      month = {1},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/c4e9d839e334477b348d7fc3734a7b15aa264008},
      }

3207. David Yarowsky, “Hierarchical Decision Lists for Word Sense Disambiguation,” in Computers and the Humanities, 2000.
      [BibTeX] [Link]

      @inproceedings{19568332,
      title = {Hierarchical Decision Lists for Word Sense Disambiguation},
      author = {{David Yarowsky}},
      year = 2000,
      month = {4},
      booktitle = {Computers and the Humanities},
      url = {https://www.semanticscholar.org/paper/8462d40bc3342fb73893b9a1e7f8380a7e84e24f},
      }

3208. A. Apsel and A. Andreou, “Quality of data reconstruction using stochastic encoding and an integrating receiver,” in Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144), 2000.
      [BibTeX] [Link]

      @inproceedings{62621220,
      title = {Quality of data reconstruction using stochastic encoding and an integrating receiver},
      author = {{A. Apsel} and {A. Andreou}},
      year = 2000,
      month = {8},
      booktitle = {Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)},
      url = {https://www.semanticscholar.org/paper/76260cc1e28e877572e276f8c76e545c5f260158},
      }

3209. Ciprian Chelba and F. Jelinek, “Recognition performance of a structured language model,” in EUROSPEECH, 2000.
      [BibTeX] [Link]

      @inproceedings{1467372,
      title = {Recognition performance of a structured language model},
      author = {{Ciprian Chelba} and {F. Jelinek}},
      year = 2000,
      month = {1},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/bccfe1f56cf0568e3c37f945d39faaabe3c1c44b},
      }

3210. H. Meng, S. Khudanpur, Gina-Anne Levow, Douglas W. Oard, and H. Wang, “MANDARIN-ENGLISH INFORMATION (MEI).” 2000.
      [BibTeX] [Link]

      @inproceedings{69747781,
      title = {MANDARIN-ENGLISH INFORMATION (MEI)},
      author = {{H. Meng} and {S. Khudanpur} and {Gina-Anne Levow} and {Douglas W. Oard} and {H. Wang}},
      year = 2000,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/ecd99124b76fbeadcf5d6c660f3104c1ccbb99fb},
      }

3211. Ciprian Chelba and F. Jelinek, “Structured Language Modeling for Speech Recognition,” in arXiv.org, 2000.
      [BibTeX] [Link]

      @inproceedings{18106285,
      title = {Structured Language Modeling for Speech Recognition},
      author = {{Ciprian Chelba} and {F. Jelinek}},
      year = 2000,
      month = {1},
      booktitle = {arXiv.org},
      url = {https://www.semanticscholar.org/paper/0080651dc62aaf3519a394e9ecfd800bba0ab768},
      }

3212. M. Saraçlar and S. Khudanpur, “Pronunciation ambiguity vs. pronunciation variability in speech recognition,” in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000.
      [BibTeX] [Link]

      @inproceedings{27590512,
      title = {Pronunciation ambiguity vs. pronunciation variability in speech recognition},
      author = {{M. Saraçlar} and {S. Khudanpur}},
      year = 2000,
      month = {6},
      booktitle = {2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)},
      url = {https://www.semanticscholar.org/paper/b89c5ae0c93e5f5d3638d43886f6be7628c9c005},
      }

3213. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Programmable kernel analog VLSI convolution chip for real time vision processing,” in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000.
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      @inproceedings{16406006,
      title = {Programmable kernel analog VLSI convolution chip for real time vision processing},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 2000,
      month = {7},
      booktitle = {Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium},
      url = {https://www.semanticscholar.org/paper/2028231bfa92a0c3442841aba3cf3215f65c8f39},
      }

3214. P. Pouliquen, A. Andreou, G. Cauwenberghs, and C. Terrill, “A CMOS smart focal plane for infra-red imagers,” in 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000.
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      @inproceedings{22665701,
      title = {A CMOS smart focal plane for infra-red imagers},
      author = {{P. Pouliquen} and {A. Andreou} and {G. Cauwenberghs} and {C. Terrill}},
      year = 2000,
      month = {5},
      booktitle = {2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)},
      url = {https://www.semanticscholar.org/paper/0174ca75074700bbe739e1b5fd8ab6c67848895e},
      }

3215. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Very Wide Range Tunable CMOS/Bipolar Current Mirrors with Voltage Clamped Input.” 1999.
      [BibTeX] [Link]

      @inproceedings{6885978,
      title = {Very Wide Range Tunable CMOS/Bipolar Current Mirrors with Voltage Clamped Input},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1999,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3433e9342bdd76df0acf4085f0b080ffc4759ce0},
      }

3216. M. Riley, W. Byrne, M. Finke, S. Khudanpur, A. Ljolje, J. McDonough, H. Nock, M. Saraçlar, Chuck Wooters, and G. Zavaliagkos, “Stochastic pronunciation modelling from hand-labelled phonetic corpora,” in Speech Communication, 1999.
      [BibTeX] [Link]

      @inproceedings{1816617,
      title = {Stochastic pronunciation modelling from hand-labelled phonetic corpora},
      author = {{M. Riley} and {W. Byrne} and {M. Finke} and {S. Khudanpur} and {A. Ljolje} and {J. McDonough} and {H. Nock} and {M. Saraçlar} and {Chuck Wooters} and {G. Zavaliagkos}},
      year = 1999,
      month = {11},
      booktitle = {Speech Communication},
      url = {https://www.semanticscholar.org/paper/b37ad11f5ab32480c290a74ee57a504e414cad7c},
      }

3217. R. Florian and D. Yarowsky, “Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation,” in Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, Maryland, USA, 1999, p. 167–174. doi:10.3115/1034678.1034711
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      @inproceedings{florian-yarowsky-1999-dynamic,
      title = "Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation",
      author = "Florian, Radu and
      Yarowsky, David",
      booktitle = "Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics",
      month = jun,
      year = "1999",
      address = "College Park, Maryland, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P99-1022",
      doi = "10.3115/1034678.1034711",
      pages = "167--174",
      }

3218. J. Eisner and G. Satta, “Efficient Parsing for Bilexical Context-Free Grammars and Head-Automaton Grammars,” in Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL), University of Maryland, 1999, p. 457–464.
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      @InProceedings{eisner-satta-1999,
      aclid = "P99-1059",
      author = "Jason Eisner and Giorgio Satta",
      title = "Efficient Parsing for Bilexical Context-Free Grammars
      and Head-Automaton Grammars",
      booktitle = "Proceedings of the 37th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "457--464",
      year = "1999",
      month = jun,
      address = "University of Maryland",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-satta-1999",
      }

3219. Z. Kalayjian and A. Andreou, “A silicon retina for polarization contrast vision,” in IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999.
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      @inproceedings{8179040,
      title = {A silicon retina for polarization contrast vision},
      author = {{Z. Kalayjian} and {A. Andreou}},
      year = 1999,
      month = {7},
      booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)},
      url = {https://www.semanticscholar.org/paper/bd0fbdcb357650ba6f2fad71affcbdcf3903c5d5},
      }

3220. S. Armstrong, Kenneth Ward Church, P. Isabelle, Sandra Manzi, E. Tzoukermann, and David Yarowsky, “Techniques in Speech Acoustics,” in Computational Linguistics, 1999.
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      @inproceedings{51620545,
      title = {Techniques in Speech Acoustics},
      author = {{S. Armstrong} and {Kenneth Ward Church} and {P. Isabelle} and {Sandra Manzi} and {E. Tzoukermann} and {David Yarowsky}},
      year = 1999,
      month = {7},
      booktitle = {Computational Linguistics},
      url = {https://www.semanticscholar.org/paper/ef847e14d63341274f5f2dcf978786b721bcfcd8},
      }

3221. Yaser Al-Onaizan, J. Curín, Michael E. Jahr, Kevin Knight, John La erty, D. Melamed, F. Och, D. Purdy, Noah A. Smith, and David Yarowsky, “Statistical Machine Translation: Final Report.” 1999.
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      @inproceedings{262327627,
      title = {Statistical Machine Translation: Final Report},
      author = {{Yaser Al-Onaizan} and {J. Curín} and {Michael E. Jahr} and {Kevin Knight} and {John La erty} and {D. Melamed} and {F. Och} and {D. Purdy} and {Noah A. Smith} and {David Yarowsky}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4ead7f9d42cf3b6f886dda0ae1a7f5f036457f8b},
      }

3222. A. Andreou, “Energy and information processing in biological and silicon sensory systems,” in Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems, 1999.
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      @inproceedings{60843611,
      title = {Energy and information processing in biological and silicon sensory systems},
      author = {{A. Andreou}},
      year = 1999,
      month = {4},
      booktitle = {Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems},
      url = {https://www.semanticscholar.org/paper/66d8d99457e22a31f2a5c27f8440a3bdee8f6d00},
      }

3223. E. Sánchez-Sinencio and A. Andreou, “LowPower Multiplierless YUVtoRGB Converter Based on Human Vision Perception.” 1999.
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      @inproceedings{61922489,
      title = {LowPower Multiplierless YUVtoRGB Converter Based on Human Vision Perception},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/96064ae9348fbc9ccfb05b0ddd2b1273c172e4b7},
      }

3224. E. Sánchez-Sinencio and A. Andreou, “ContinuousTime LowVoltage CurrentMode Filters.” 1999.
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      @inproceedings{177306889,
      title = {ContinuousTime LowVoltage CurrentMode Filters},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c7a45fd62987870faa2342fe365a00364f1bfb32},
      }

3225. Ciprian Chelba and F. Jelinek, “Structured Language Modeling for Speech Recognition Errata.” 1999.
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      @inproceedings{14588206,
      title = {Structured Language Modeling for Speech Recognition Errata},
      author = {{Ciprian Chelba} and {F. Jelinek}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/04ae85115f543df0fdc84e329516f89c1c60e097},
      }

3226. S. Cucerzan and D. Yarowsky, “Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence,” in 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.
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      @inproceedings{cucerzan-yarowsky-1999-language,
      title = "Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence",
      author = "Cucerzan, Silviu and
      Yarowsky, David",
      booktitle = "1999 Joint {SIGDAT} Conference on Empirical Methods in Natural Language Processing and Very Large Corpora",
      year = "1999",
      url = "https://aclanthology.org/W99-0612",
      }

3227. V. Digalakis, Heather Collier, Sid Berkowitz, Adrian Corduneanu, E. Bocchieri, Ashvin Kannan, Constantinos Boulis, S. Khudanpur, W. Byrne, and Ananth Sankar, “Rapid speech recognizer adaptation to new speakers,” in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
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      @inproceedings{2216967,
      title = {Rapid speech recognizer adaptation to new speakers},
      author = {{V. Digalakis} and {Heather Collier} and {Sid Berkowitz} and {Adrian Corduneanu} and {E. Bocchieri} and {Ashvin Kannan} and {Constantinos Boulis} and {S. Khudanpur} and {W. Byrne} and {Ananth Sankar}},
      year = 1999,
      month = {3},
      booktitle = {1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)},
      url = {https://www.semanticscholar.org/paper/49688e12b1735ac793494b8c894277eaa1c47b4a},
      }

3228. E. Sánchez-Sinencio and A. Andreou, “LowPower CMOS Data Conversion.” 1999.
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      @inproceedings{61667078,
      title = {LowPower CMOS Data Conversion},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a80364aac3891a167baec08cd6044165b80f95f1},
      }

3229. D. Yarowsky and R. Florian, “Taking the load off the conference chairs-towards a digital paper-routing assistant,” in 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.
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      @inproceedings{yarowsky-florian-1999-taking,
      title = "Taking the load off the conference chairs-towards a digital paper-routing assistant",
      author = "Yarowsky, David and
      Florian, Radu",
      booktitle = "1999 Joint {SIGDAT} Conference on Empirical Methods in Natural Language Processing and Very Large Corpora",
      year = "1999",
      url = "https://aclanthology.org/W99-0627",
      }

3230. E. Sánchez-Sinencio and A. Andreou, “Micropower Systems for Implantable Defibrillators and Pacemakers.” 1999.
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      @inproceedings{74143354,
      title = {Micropower Systems for Implantable Defibrillators and Pacemakers},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/c1dca39ea9ffb63fe0467b03604fc147dc29dce1},
      }

3231. E. Sánchez-Sinencio and A. Andreou, “An Information Theoretic Framework for Comparing the Bit Energy of Signal Representations at the Circuit Level.” 1999.
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      @inproceedings{123603313,
      title = {An Information Theoretic Framework for Comparing the Bit Energy of Signal Representations at the Circuit Level},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/374d8ffabbc8c89080b608ec098dadb966e198a6},
      }

3232. W. Byrne, Jan Hajic, P. Ircing, F. Jelinek, S. Khudanpur, Jerome McDonough, N. Peterek, and J. Psutka, “Large Vocabulary Speech Recognition for Read and Broadcast Czech,” in International Conference on Text, Speech and Dialogue, 1999.
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      @inproceedings{7622877,
      title = {Large Vocabulary Speech Recognition for Read and Broadcast Czech},
      author = {{W. Byrne} and {Jan Hajic} and {P. Ircing} and {F. Jelinek} and {S. Khudanpur} and {Jerome McDonough} and {N. Peterek} and {J. Psutka}},
      year = 1999,
      month = {9},
      booktitle = {International Conference on Text, Speech and Dialogue},
      url = {https://www.semanticscholar.org/paper/d000438a7a27184182a9e955d134d1137e8baa7d},
      }

3233. T. Serrano-Gotarredona, A. Andreou, and B. Linares-Barranco, “Programmable 2D image filter for AER vision processing,” in ICECS’99. Proceedings of ICECS ’99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), 1999.
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      @inproceedings{61145410,
      title = {Programmable 2D image filter for AER vision processing},
      author = {{T. Serrano-Gotarredona} and {A. Andreou} and {B. Linares-Barranco}},
      year = 1999,
      month = {9},
      booktitle = {ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357)},
      url = {https://www.semanticscholar.org/paper/5ea4dc0b4a109d7f468dfafbbdf42c95f69f7a38},
      }

3234. E. Sánchez-Sinencio and A. Andreou, “LowVoltage Analog CMOS Filter Design.” 1999.
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      @inproceedings{62427511,
      title = {LowVoltage Analog CMOS Filter Design},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1021bbcd1db8d890e04b3ef505e68f7e1f6eac68},
      }

3235. T. Serrano-Gotarredona, A. Andreou, and B. Linares-Barranco, “AER image filtering architecture for vision-processing systems,” in IEEE Transactions on Circuits and Systems I-regular Papers, 1999.
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      @inproceedings{3641670,
      title = {AER image filtering architecture for vision-processing systems},
      author = {{T. Serrano-Gotarredona} and {A. Andreou} and {B. Linares-Barranco}},
      year = 1999,
      month = {9},
      booktitle = {IEEE Transactions on Circuits and Systems I-regular Papers},
      url = {https://www.semanticscholar.org/paper/779367e5f41dbf67cffa65e7ad127f560278a898},
      }

3236. E. Sánchez-Sinencio and A. Andreou, “LowVoltage CMOS Operational Amplifiers.” 1999.
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      @inproceedings{62507561,
      title = {LowVoltage CMOS Operational Amplifiers},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/75acf24ef393fc9ec83109d2dd77285e96f61a19},
      }

3237. E. Sánchez-Sinencio and A. Andreou, “Low-voltage/low-power integrated circuits and systems : low-voltage mixed-signal circuits.” 1999.
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      @inproceedings{58156152,
      title = {Low-voltage/low-power integrated circuits and systems : low-voltage mixed-signal circuits},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d33dd6678be4868da708c5e8f673634aea37efe1},
      }

3238. P. Pouliquen, A. Andreou, G. Cauwenberghs, and C. Terrill, “Learning to compensate for sensor variability at the focal plane,” in IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999.
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      @inproceedings{35432293,
      title = {Learning to compensate for sensor variability at the focal plane},
      author = {{P. Pouliquen} and {A. Andreou} and {G. Cauwenberghs} and {C. Terrill}},
      year = 1999,
      month = {7},
      booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)},
      url = {https://www.semanticscholar.org/paper/71a33fdd6c6a7ee4bd3254ca275e73763e6b26fd},
      }

3239. E. Sánchez-Sinencio and A. Andreou, “LowVoltage Analog BiCMOS Circuit Building Blocks.” 1999.
      [BibTeX] [Link]

      @inproceedings{61252925,
      title = {LowVoltage Analog BiCMOS Circuit Building Blocks},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1c446c2ee21e6ec44d0873773c0961bb1b676a03},
      }

3240. E. Sánchez-Sinencio and A. Andreou, “A CurrentBased MOSFET Model for Integrated Circuit Design.” 1999.
      [BibTeX] [Link]

      @inproceedings{61188758,
      title = {A CurrentBased MOSFET Model for Integrated Circuit Design},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b852e9c19e0488f2edaca7afd17a77d6aa9e1365},
      }

3241. E. Sánchez-Sinencio and A. Andreou, “A Synchronous GatedClock Strategy for LowPower Design of Telecom ASICs.” 1999.
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      @inproceedings{110312024,
      title = {A Synchronous GatedClock Strategy for LowPower Design of Telecom ASICs},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5dfa5f118ca2cd72292a3fdf9da8ca68616c56d8},
      }

3242. MODELINGP. Beyerlein, W. Byrne, J. Huerta, S. Khudanpur, B. Marthi, J. Morgan, N. Peterek, J. P. one, D. Vergyri, and W. Wang, “TOWARDS LANGUAGE INDEPENDENT ACOUSTIC.” 1999.
      [BibTeX] [Link]

      @inproceedings{14763793,
      title = {TOWARDS LANGUAGE INDEPENDENT ACOUSTIC},
      author = {{MODELINGP. Beyerlein} and {W. Byrne} and {J. Huerta} and {S. Khudanpur} and {B. Marthi} and {J. Morgan} and {N. Peterek} and {J. P. one} and {D. Vergyri} and {W. Wang}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/e31526cebb4cc258f3f11a7b935d3461cd2ac923},
      }

3243. E. Sánchez-Sinencio and A. Andreou, “LowVoltage Circuit Techniques Using FloatingGate Transistors.” 1999.
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      @inproceedings{111217965,
      title = {LowVoltage Circuit Techniques Using FloatingGate Transistors},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1e6d564e8d25876aeb82bd153711106320f3ee09},
      }

3244. P. Abshire and A. Andreou, “Relating information capacity to a biophysical model for blowfly retina,” in IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999.
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      @inproceedings{186405,
      title = {Relating information capacity to a biophysical model for blowfly retina},
      author = {{P. Abshire} and {A. Andreou}},
      year = 1999,
      month = {7},
      booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)},
      url = {https://www.semanticscholar.org/paper/5f8360a147661199a0045a7dc76e4db44d4cdce6},
      }

3245. Philip Resnik and David Yarowsky, “Distinguishing systems and distinguishing senses: new evaluation methods for Word Sense Disambiguation,” in Natural Language Engineering, 1999.
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      @inproceedings{1983600,
      title = {Distinguishing systems and distinguishing senses: new evaluation methods for Word Sense Disambiguation},
      author = {{Philip Resnik} and {David Yarowsky}},
      year = 1999,
      month = {6},
      booktitle = {Natural Language Engineering},
      url = {https://www.semanticscholar.org/paper/6246f3f3f914ff37c261c1d85b8b6b52c97d33bf},
      }

3246. S. Khudanpur and Jun Wu, “A maximum entropy language model integrating N-grams and topic dependencies for conversational speech recognition,” in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
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      @inproceedings{16007031,
      title = {A maximum entropy language model integrating N-grams and topic dependencies for conversational speech recognition},
      author = {{S. Khudanpur} and {Jun Wu}},
      year = 1999,
      month = {3},
      booktitle = {1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)},
      url = {https://www.semanticscholar.org/paper/5209d1b3f57ee9fc6c08b1022cab5cb360eecc1f},
      }

3247. E. Sánchez-Sinencio and A. Andreou, “LowVoltage/LowPower Amplifiers with Optimized Dynamic Range and Bandwidth.” 1999.
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      @inproceedings{61574052,
      title = {LowVoltage/LowPower Amplifiers with Optimized Dynamic Range and Bandwidth},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/31f0a43e4b3530960d6bf6a33f8736cb48753336},
      }

3248. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “A general subthreshold MOS translinear theorem,” in ISCAS’99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 1999.
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      @inproceedings{36581757,
      title = {A general subthreshold MOS translinear theorem},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1999,
      month = {5},
      booktitle = {ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349)},
      url = {https://www.semanticscholar.org/paper/d3bc768a9e44da258796884f67fbaac3418a5113},
      }

3249. E. Sánchez-Sinencio and A. Andreou, “LowPower CMOS Digital Circuits.” 1999.
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      @inproceedings{61488609,
      title = {LowPower CMOS Digital Circuits},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/eeac3a9131fabae5b6adb2d7a5ed25ed970023b4},
      }

3250. E. Sánchez-Sinencio and A. Andreou, “Two New Directions in LowPower Digital CMOS VLSI Design.” 1999.
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      @inproceedings{59302418,
      title = {Two New Directions in LowPower Digital CMOS VLSI Design},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/4a92a726eb7a19ed68d5403b9ed824d28e303883},
      }

3251. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “A General Translinear Principle for Subthreshold MOS Transistors.” 1999.
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      @inproceedings{6299332,
      title = {A General Translinear Principle for Subthreshold MOS Transistors},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1999,
      month = {5},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fc0a27ea73e36944029dd19ee16a346ca947943c},
      }

3252. Á. Rodríguez-Vázquez, T. Roska, and A. Andreou, “Guest Editorial Special Issue On Bio-inspired Processors And Cellular Neural Networks For Vision,” in IEEE Transactions on Circuits and Systems I-regular Papers, 1999.
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      @inproceedings{62541548,
      title = {Guest Editorial Special Issue On Bio-inspired Processors And Cellular Neural Networks For Vision},
      author = {{Á. Rodríguez-Vázquez} and {T. Roska} and {A. Andreou}},
      year = 1999,
      month = {2},
      booktitle = {IEEE Transactions on Circuits and Systems I-regular Papers},
      url = {https://www.semanticscholar.org/paper/6f2bea100be2230742a642b3513bb456edaf553e},
      }

3253. Jun Wu and S. Khudanpur, “Combining nonlocal, syntactic and n-gram dependencies in language modeling,” in EUROSPEECH, 1999.
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      @inproceedings{8805888,
      title = {Combining nonlocal, syntactic and n-gram dependencies in language modeling},
      author = {{Jun Wu} and {S. Khudanpur}},
      year = 1999,
      month = {9},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/a63540c6eefdae0ac555bdd8a9bda7afea918974},
      }

3254. Xiaoqiang Luo and F. Jelinek, “Probabilistic classification of HMM states for large vocabulary continuous speech recognition,” in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
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      @inproceedings{29597956,
      title = {Probabilistic classification of HMM states for large vocabulary continuous speech recognition},
      author = {{Xiaoqiang Luo} and {F. Jelinek}},
      year = 1999,
      month = {3},
      booktitle = {1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)},
      url = {https://www.semanticscholar.org/paper/1e526daa5cbe919c731e071c78467b52abcf2fcc},
      }

3255. M. Saraçlar, H. Nock, and S. Khudanpur, “Pronunciation modeling by sharing gaussian densities across phonetic models,” in EUROSPEECH, 1999.
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      @inproceedings{6164277,
      title = {Pronunciation modeling by sharing gaussian densities across phonetic models},
      author = {{M. Saraçlar} and {H. Nock} and {S. Khudanpur}},
      year = 1999,
      month = {9},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/715908ccb336699656cc56916c4171ef8120153f},
      }

3256. E. Sánchez-Sinencio and A. Andreou, “A Review of the Performance of Available Integrated Circuit Components Under the Constraints of LowPower Operation.” 1999.
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      @inproceedings{62111478,
      title = {A Review of the Performance of Available Integrated Circuit Components Under the Constraints of LowPower Operation},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/335b3f2999b63da6bb20d212218a6031a10578af},
      }

3257. Xiaoqiang Luo and F. Jelinek, “Improved clustering techniques for class-based statistical language modeling.” 1999.
      [BibTeX] [Link]

      @inproceedings{59730944,
      title = {Improved clustering techniques for class-based statistical language modeling},
      author = {{Xiaoqiang Luo} and {F. Jelinek}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/dc4f12712491bf450c978b9af3e6db055b37e5da},
      }

3258. T. Serrano-Gotarredona, A. Andreou, and B. Linares-Barranco, “Programmable 2D image filter for AER vision processing,” in ISCAS’99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 1999.
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      @inproceedings{21531828,
      title = {Programmable 2D image filter for AER vision processing},
      author = {{T. Serrano-Gotarredona} and {A. Andreou} and {B. Linares-Barranco}},
      year = 1999,
      month = {5},
      booktitle = {ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349)},
      url = {https://www.semanticscholar.org/paper/3dc72833b85cab838edaf843c82ec278cb4b3066},
      }

3259. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Bipolar/CMOS current-source flip-flop for application in neuro-fuzzy systems,” in Electronics Letters, 1999.
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      @inproceedings{110562712,
      title = {Bipolar/CMOS current-source flip-flop for application in neuro-fuzzy systems},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1999,
      month = {8},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/995bfe41821b641e8070f201d67d86f1014304b6},
      }

3260. T. Serrano-Gotarredona, A. Andreou, and B. Linares-Barranco, “A 2D image filtering architecture for real-time vision processing systems,” in Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems, 1999.
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      @inproceedings{62251046,
      title = {A 2D image filtering architecture for real-time vision processing systems},
      author = {{T. Serrano-Gotarredona} and {A. Andreou} and {B. Linares-Barranco}},
      year = 1999,
      month = {4},
      booktitle = {Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems},
      url = {https://www.semanticscholar.org/paper/d1e5d89a6540ae2e1d6f6070dd4c21708c205522},
      }

3261. E. Sánchez-Sinencio and A. Andreou, “HighEfficiency LowVoltage DCDC Conversion for Portable Applications.” 1999.
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      @inproceedings{108940837,
      title = {HighEfficiency LowVoltage DCDC Conversion for Portable Applications},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/5a8be5e7ca9621923e3a3596e766129f1797d26a},
      }

3262. E. Sánchez-Sinencio and A. Andreou, “Exploiting Device Physics in Circuit Design for Efficient Computational Functions in Analog VLSI.” 1999.
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      @inproceedings{30161288,
      title = {Exploiting Device Physics in Circuit Design for Efficient Computational Functions in Analog VLSI},
      author = {{E. Sánchez-Sinencio} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/2c6875c31ba1f64132534cba1885019be39c10b0},
      }

3263. Ashvin Kannan and S. Khudanpur, “Tree-structured models of parameter dependence for rapid adaptation in large vocabulary conversational speech recognition,” in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
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      @inproceedings{33767757,
      title = {Tree-structured models of parameter dependence for rapid adaptation in large vocabulary conversational speech recognition},
      author = {{Ashvin Kannan} and {S. Khudanpur}},
      year = 1999,
      month = {3},
      booktitle = {1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)},
      url = {https://www.semanticscholar.org/paper/67de97a84c74c5b2ab7abff0fa88e5197ef6124f},
      }

3264. F. Jelinek and Ciprian Chelba, “Putting language into language modeling,” in EUROSPEECH, 1999.
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      @inproceedings{6534066,
      title = {Putting language into language modeling},
      author = {{F. Jelinek} and {Ciprian Chelba}},
      year = 1999,
      month = {9},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/de92006681796ca5a0b5ed044cff47488e98be92},
      }

3265. S. Edgar and A. Andreou, “Low-Voltage/Low-Power Integrated Circuits and Systems.” 1999.
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      @inproceedings{57477236,
      title = {Low-Voltage/Low-Power Integrated Circuits and Systems},
      author = {{S. Edgar} and {A. Andreou}},
      year = 1999,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9e48131b4bdfcb5f748f72a54d418d86d566128c},
      }

3266. Nagendra Kumar and A. Andreou, “Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition,” in Speech Communication, 1998.
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      @inproceedings{28539506,
      title = {Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition},
      author = {{Nagendra Kumar} and {A. Andreou}},
      year = 1998,
      month = {12},
      booktitle = {Speech Communication},
      url = {https://www.semanticscholar.org/paper/3fd4b226ecf0465d952fac3cc7d161a583a7c10c},
      }

3267. Xiaoqiang Luo and F. Jelinek, “Nonreciprocal data sharing in estimating HMM parameters,” in ICSLP, 1998.
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      @inproceedings{1374153,
      title = {Nonreciprocal data sharing in estimating HMM parameters},
      author = {{Xiaoqiang Luo} and {F. Jelinek}},
      year = 1998,
      month = {11},
      booktitle = {ICSLP},
      url = {https://www.semanticscholar.org/paper/991706b299115ba8e36f8e08cb94ce472457363c},
      }

3268. C. Chelba and F. Jelinek, “Exploiting Syntactic Structure for Language Modeling,” in 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1, Montreal, Quebec, Canada, 1998, p. 225–231. doi:10.3115/980845.980882
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      @inproceedings{chelba-jelinek-1998-exploiting-syntactic,
      title = "Exploiting Syntactic Structure for Language Modeling",
      author = "Chelba, Ciprian and
      Jelinek, Frederick",
      booktitle = "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1",
      month = aug,
      year = "1998",
      address = "Montreal, Quebec, Canada",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P98-1035",
      doi = "10.3115/980845.980882",
      pages = "225--231",
      }

3269. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Analog Learning Fuzzy ART Chips.” 1998.
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      @inproceedings{108024869,
      title = {Analog Learning Fuzzy ART Chips},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/daaf3968e15e3c62c46a4cadc182169fcc40cf8d},
      }

3270. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “ART1 and ARTMAP VLSI Circuit Implementation.” 1998.
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      @inproceedings{62261210,
      title = {ART1 and ARTMAP VLSI Circuit Implementation},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0ecc08d2532f8eabcd7defa2f3a3d623519ebfeb},
      }

3271. G. Potamianos and F. Jelinek, “A study of n-gram and decision tree letter language modeling methods,” in Speech Communication, 1998.
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      @inproceedings{2793316,
      title = {A study of n-gram and decision tree letter language modeling methods},
      author = {{G. Potamianos} and {F. Jelinek}},
      year = 1998,
      month = {6},
      booktitle = {Speech Communication},
      url = {https://www.semanticscholar.org/paper/8dba93649dac30f00970cfbd706eac9ba794b836},
      }

3272. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Voltage clamping current mirrors with 13-decades gain adjustment range suitable for low power MOS/bipolar current mode signal processing circuits,” in ISCAS ’98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187), 1998.
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      @inproceedings{58249601,
      title = {Voltage clamping current mirrors with 13-decades gain adjustment range suitable for low power MOS/bipolar current mode signal processing circuits},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      month = {5},
      booktitle = {ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187)},
      url = {https://www.semanticscholar.org/paper/52200bc3603f73afc541eb32cd00dd5033f755ed},
      }

3273. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “An ART1/ARTMAP/Fuzzy-ART/Fuzzy-ARTMAP Chip.” 1998.
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      @inproceedings{59801958,
      title = {An ART1/ARTMAP/Fuzzy-ART/Fuzzy-ARTMAP Chip},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/61f9bda9b31d109fe219c0fd5bffaf0f12c1ce76},
      }

3274. P. Hasler, A. Andreou, C. Diorio, B. Minch, and C. Mead, “Impact Ionization and Hot-Electron Injection Derived Consistently from Boltzmann Transport,” in VLSI design (Print), 1998.
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      @inproceedings{17291975,
      title = {Impact Ionization and Hot-Electron Injection Derived Consistently from Boltzmann Transport},
      author = {{P. Hasler} and {A. Andreou} and {C. Diorio} and {B. Minch} and {C. Mead}},
      year = 1998,
      booktitle = {VLSI design (Print)},
      url = {https://www.semanticscholar.org/paper/ef96900a178f93b397d8a909ec585f09e8070d1c},
      }

3275. W. Byrne, S. Khudanpur, Eva M. Knodt, and J. Bernstein, “Is automatic speech recognition ready for non-native speech? A data collection effort and initial experiments in modelling conversational Hispanic English.” 1998.
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      @inproceedings{17724422,
      title = {Is automatic speech recognition ready for non-native speech? A data collection effort and initial experiments in modelling conversational Hispanic English},
      author = {{W. Byrne} and {S. Khudanpur} and {Eva M. Knodt} and {J. Bernstein}},
      year = 1998,
      month = {9},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a62f97c0f74b5729c7c2841fca3e81abc2769980},
      }

3276. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Adaptive Resonance Theory Algorithms.” 1998.
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      @inproceedings{59915686,
      title = {Adaptive Resonance Theory Algorithms},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a358defa85240dfa96355b668c430ccb990e189a},
      }

3277. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “MOS/bipolar active input current mirrors with 13–decades gain adjustment range,” in Proceedings of the 24th European Solid-State Circuits Conference, 1998.
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      @inproceedings{8970312,
      title = {MOS/bipolar active input current mirrors with 13–decades gain adjustment range},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {Proceedings of the 24th European Solid-State Circuits Conference},
      url = {https://www.semanticscholar.org/paper/364bea1db70b368ece8b17543bc582c543583851},
      }

3278. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “A VLSI-Friendly ART1 Algorithm.” 1998.
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      @inproceedings{58801370,
      title = {A VLSI-Friendly ART1 Algorithm},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fbd4fb462d699ad043d8cc9a4c723ae1b3c9564d},
      }

3279. Radu Florian and David Yarowsky, “Exploiting Nonlo al and Synta ti Word Relationships inLanguage ModelsRadu.” 1998.
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      @inproceedings{15785232,
      title = {Exploiting Nonlo al and Synta ti Word Relationships inLanguage ModelsRadu},
      author = {{Radu Florian} and {David Yarowsky}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/0ebbcc4aafbf4cfb7693c22490b9b58c26d5165c},
      }

3280. Vaibhava Goel, W. Byme, and S. Khudanpur, “RESCORING A DECISION.” 1998.
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      @inproceedings{61656041,
      title = {RESCORING A DECISION},
      author = {{Vaibhava Goel} and {W. Byme} and {S. Khudanpur}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/197e18f97c4fb350c5d15dd402c69e08484f7367},
      }

3281. W. Byrne, M. Finke, S. Khudanpur, J. McDonough, H. Nock, M. Riley, M. Saraçlar, Chuck Wooters, and G. Zavaliagkos, “Pronunciation modelling using a hand-labelled corpus for conversational speech recognition,” in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’98 (Cat. No.98CH36181), 1998.
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      @inproceedings{2080113,
      title = {Pronunciation modelling using a hand-labelled corpus for conversational speech recognition},
      author = {{W. Byrne} and {M. Finke} and {S. Khudanpur} and {J. McDonough} and {H. Nock} and {M. Riley} and {M. Saraçlar} and {Chuck Wooters} and {G. Zavaliagkos}},
      year = 1998,
      month = {5},
      booktitle = {Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)},
      url = {https://www.semanticscholar.org/paper/2bb1cf68d852e733233050d320776ef1068d40d1},
      }

3282. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, “Some Potential Applications For ART Microchips.” 1998.
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      @inproceedings{59708259,
      title = {Some Potential Applications For ART Microchips},
      author = {{T. Serrano-Gotarredona} and {B. Linares-Barranco} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/89ff3049b4e1687f73cfb98e16e0f4b7256d7ac5},
      }

3283. Z. Kalayjian and A. Andreou, “Integrated High Resolution Focal-Plane Polarization Imager.” 1998.
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      @inproceedings{17495926,
      title = {Integrated High Resolution Focal-Plane Polarization Imager},
      author = {{Z. Kalayjian} and {A. Andreou}},
      year = 1998,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fd35e758c56b85343fd8f68e96d7787c2f3078e7},
      }

3284. Vaibhava Goel, W. Byrne, and S. Khudanpur, “LVCSR rescoring with modified loss functions: a decision theoretic perspective,” in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’98 (Cat. No.98CH36181), 1998.
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      @inproceedings{8778283,
      title = {LVCSR rescoring with modified loss functions: a decision theoretic perspective},
      author = {{Vaibhava Goel} and {W. Byrne} and {S. Khudanpur}},
      year = 1998,
      month = {5},
      booktitle = {Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)},
      url = {https://www.semanticscholar.org/paper/609a22c85938bb8233deeb3a8058b5862afaedbc},
      }

3285. C. Chelba and F. Jelinek, “Exploiting Syntactic Structure for Language Modeling,” in COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics, 1998.
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      @inproceedings{chelba-jelinek-1998-exploiting,
      title = "Exploiting Syntactic Structure for Language Modeling",
      author = "Chelba, Ciprian and
      Jelinek, Frederick",
      booktitle = "{COLING} 1998 Volume 1: The 17th International Conference on Computational Linguistics",
      year = "1998",
      url = "https://aclanthology.org/C98-1035",
      }

3286. J. Eisner, “\sc FootForm Decomposed: Using Primitive Constraints in OT,” in Proceedings of SCIL VIII, Cambridge, MA, 1998, p. 115–143.
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      @InProceedings{eisner-1997-scil,
      author = "Jason Eisner",
      title = "{\sc FootForm} Decomposed: Using Primitive Constraints
      in {OT}",
      booktitle = "Proceedings of SCIL VIII",
      series = "MIT Working Papers in Linguistics",
      number = "31",
      pages = "115--143",
      editor = "Benjamin Bruening",
      year = "1998",
      address = "Cambridge, MA",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1997-scil",
      }

3287. B. Byrne, M. Finke, S. Khudanpur, J. McDonough, H. Nock, M. Riley, M. Saraçlar, Chuck Wooters, and G. Zavaliagkos, “Pronunciation modelling for conversational speech recognition: a status report from WS97,” in 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, 1997.
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      @inproceedings{12782687,
      title = {Pronunciation modelling for conversational speech recognition: a status report from WS97},
      author = {{B. Byrne} and {M. Finke} and {S. Khudanpur} and {J. McDonough} and {H. Nock} and {M. Riley} and {M. Saraçlar} and {Chuck Wooters} and {G. Zavaliagkos}},
      year = 1997,
      month = {12},
      booktitle = {1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings},
      url = {https://www.semanticscholar.org/paper/b064262efe27ffa46a6a4d206086ec0b0acb5f9d},
      }

3288. J. Eisner, “Bilexical Grammars and a Cubic-Time Probabilistic Parser,” in Proceedings of the 5th International Workshop on Parsing Technologies (IWPT), MIT, Cambridge, MA, 1997, p. 54–65.
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      @InProceedings{eisner-1997-iwpt,
      author = "Jason Eisner",
      title = "Bilexical Grammars and a Cubic-Time Probabilistic
      Parser",
      booktitle = "Proceedings of the 5th International Workshop on
      Parsing Technologies (IWPT)",
      pages = "54--65",
      year = "1997",
      month = sep,
      address = "MIT, Cambridge, MA",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1997-iwpt",
      }

3289. J. Eisner, “Efficient Generation in Primitive Optimality Theory,” in Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL), Madrid, 1997, p. 313–320.
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      @InProceedings{eisner-1997-acl,
      aclid = "P97-1040",
      author = "Jason Eisner",
      title = "Efficient Generation in Primitive {O}ptimality
      {T}heory",
      booktitle = "Proceedings of the 35th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "313--320",
      year = "1997",
      month = jul,
      address = "Madrid",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1997-acl",
      }

3290. Ciprian Chelba, David Engle, F. Jelinek, Víctor M. Jiménez, S. Khudanpur, L. Mangu, H. Printz, E. Ristad, R. Rosenfeld, A. Stolcke, and Dekai Wu, “Structure and performance of a dependency language model,” in EUROSPEECH, 1997.
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      @inproceedings{14933205,
      title = {Structure and performance of a dependency language model},
      author = {{Ciprian Chelba} and {David Engle} and {F. Jelinek} and {Víctor M. Jiménez} and {S. Khudanpur} and {L. Mangu} and {H. Printz} and {E. Ristad} and {R. Rosenfeld} and {A. Stolcke} and {Dekai Wu}},
      year = 1997,
      month = {9},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/d58244ed9b86e9ad7f90cb302d32e5f96a72d040},
      }

3291. Nagendra Kumar, W. Himmelbauer, G. Cauwenberghs, and A. Andreou, “An analog VLSI architecture for auditory based feature extraction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997.
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      @inproceedings{681336,
      title = {An analog VLSI architecture for auditory based feature extraction},
      author = {{Nagendra Kumar} and {W. Himmelbauer} and {G. Cauwenberghs} and {A. Andreou}},
      year = 1997,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/ba63dbbe9f16fd6d8ec1e56f8af524e44aa64b61},
      }

3292. David Yarowsky, “Homograph Disambiguation in Text-to-Speech Synthesis.” 1997.
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      @inproceedings{14648973,
      title = {Homograph Disambiguation in Text-to-Speech Synthesis},
      author = {{David Yarowsky}},
      year = 1997,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3d0f86a73f5c1b0373d25a201f0557fe8855f36d},
      }

3293. A. Andreou and N. Kumar, “Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition.” 1997.
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      @inproceedings{61052539,
      title = {Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition},
      author = {{A. Andreou} and {N. Kumar}},
      year = 1997,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/d7f5f913fcb96d8c351a36d4b1cf5833f7880691},
      }

3294. N. Kumar, G. Cauwenberghs, and A. Andreou, “Auditory feature extraction using self-timed, continuous-time discrete-signal processing circuits,” in IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1997.
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      @inproceedings{10372060,
      title = {Auditory feature extraction using self-timed, continuous-time discrete-signal processing circuits},
      author = {{N. Kumar} and {G. Cauwenberghs} and {A. Andreou}},
      year = 1997,
      month = {9},
      booktitle = {IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing},
      url = {https://www.semanticscholar.org/paper/de3a95bde46e390c7c141ee6113222bff39351ea},
      }

3295. N. Kumar, W. Himmelbauer, G. Cauwenberghs, and A. Andreou, “An analog VLSI front-end for auditory signal analysis,” in International Conference on Neural Networks, 1997.
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      @inproceedings{61931137,
      title = {An analog VLSI front-end for auditory signal analysis},
      author = {{N. Kumar} and {W. Himmelbauer} and {G. Cauwenberghs} and {A. Andreou}},
      year = 1997,
      month = {6},
      booktitle = {International Conference on Neural Networks},
      url = {https://www.semanticscholar.org/paper/7a791e3a7e1491f69c884172a39528920cd9fd28},
      }

3296. Z. Kalayjian and A. Andreou, “Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration,” in Analog Integrated Circuits and Signal Processing, 1997.
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      @inproceedings{56915443,
      title = {Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration},
      author = {{Z. Kalayjian} and {A. Andreou}},
      year = 1997,
      month = {5},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/57831a73adb42bcfc25542965c77f1f17749ce05},
      }

3297. P. Pouliquen, A. Andreou, and K. Strohbehn, “Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer,” in Analog Integrated Circuits and Signal Processing, 1997.
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      @inproceedings{61004814,
      title = {Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer},
      author = {{P. Pouliquen} and {A. Andreou} and {K. Strohbehn}},
      year = 1997,
      month = {5},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/3a59a5c9a44fc98db8d789b0a057c239c5a8834f},
      }

3298. A. Obeidat, Z. Kalayjian, A. Andreou, and J. Khurgin, “A model for visible photon emission from reverse-biased silicon p-n junctions,” in Applied Physics Letters, 1997.
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      @inproceedings{123193697,
      title = {A model for visible photon emission from reverse-biased silicon p-n junctions},
      author = {{A. Obeidat} and {Z. Kalayjian} and {A. Andreou} and {J. Khurgin}},
      year = 1997,
      month = {1},
      booktitle = {Applied Physics Letters},
      url = {https://www.semanticscholar.org/paper/b3ab53c5eaeb09d962bb102483b61a1950639f8a},
      }

3299. N. Kumar, W. Himmelbauer, Gert Cauwenberghs, and A. Andreou, “An analog VLSI chip with asynchronous interface for auditory feature extraction,” in Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS ’97, 1997.
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      @inproceedings{16138143,
      title = {An analog VLSI chip with asynchronous interface for auditory feature extraction},
      author = {{N. Kumar} and {W. Himmelbauer} and {Gert Cauwenberghs} and {A. Andreou}},
      year = 1997,
      month = {6},
      booktitle = {Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97},
      url = {https://www.semanticscholar.org/paper/c5cd5019321623a0b01399efedd50282cd337d68},
      }

3300. P. Furth and A. Andreou, “On fault probabilities and yield models for VLSI neural networks,” in IEEE J. Solid State Circuits, 1997.
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      @inproceedings{17770135,
      title = {On fault probabilities and yield models for VLSI neural networks},
      author = {{P. Furth} and {A. Andreou}},
      year = 1997,
      month = {8},
      booktitle = {IEEE J. Solid State Circuits},
      url = {https://www.semanticscholar.org/paper/653c2e248ee2f23b49f5d1d40398c69173ab7be0},
      }

3301. F. Jelinek, “Statistical methods for speech recognition.” 1997.
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      @inproceedings{12495425,
      title = {Statistical methods for speech recognition},
      author = {{F. Jelinek}},
      year = 1997,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/231f6de83cfa4d641da1681e97a11b689a48e3aa},
      }

3302. J. Eisner, “Three New Probabilistic Models for Dependency Parsing: An Exploration,” in Proceedings of the 16th International Conference on Computational Linguistics (COLING-96), Copenhagen, 1996, p. 340–345.
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      @InProceedings{eisner-1996-coling,
      aclid = "C96-1058",
      author = "Jason Eisner",
      title = "Three New Probabilistic Models for Dependency Parsing:
      An Exploration",
      booktitle = "Proceedings of the 16th International Conference on
      Computational Linguistics (COLING-96)",
      pages = "340--345",
      year = "1996",
      month = aug,
      address = "Copenhagen",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1996-coling",
      }

3303. J. Eisner, “Efficient Normal-Form Parsing for Combinatory Categorial Grammar,” in Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics (ACL), Santa Cruz, 1996, p. 79–86.
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      @InProceedings{eisner-1996-acl,
      aclid = "P96-1011",
      author = "Jason Eisner",
      title = "Efficient Normal-Form Parsing for Combinatory
      Categorial Grammar",
      booktitle = "Proceedings of the 34th Annual Meeting of the
      Association for Computational Linguistics (ACL)",
      pages = "79--86",
      year = "1996",
      month = jun,
      address = "Santa Cruz",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1996-acl",
      }

3304. David Yarowsky and Mitchell P. Marcus, “Three machine learning algorithms for lexical ambiguity resolution.” 1996.
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      @inproceedings{57083455,
      title = {Three machine learning algorithms for lexical ambiguity resolution},
      author = {{David Yarowsky} and {Mitchell P. Marcus}},
      year = 1996,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3121f48d51ba29c979e65567414ec984f15e7872},
      }

3305. N. Kumar and A. Andreou, “On Generalizations of Linear Discriminant Analysis.” 1996.
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      @inproceedings{16081514,
      title = {On Generalizations of Linear Discriminant Analysis},
      author = {{N. Kumar} and {A. Andreou}},
      year = 1996,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/db2605efc076059d07b6a7484e7ad247054b6ea7},
      }

3306. F. Jelinek, “Five speculations (and a divertimento) on the themes of H. Bourlard, H. Hermansky, and N. Morgan,” in Speech Communication, 1996.
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      @inproceedings{40300334,
      title = {Five speculations (and a divertimento) on the themes of H. Bourlard, H. Hermansky, and N. Morgan},
      author = {{F. Jelinek}},
      year = 1996,
      month = {5},
      booktitle = {Speech Communication},
      url = {https://www.semanticscholar.org/paper/22c231d427b68c4fa0e3ff4abbe0765451f8ede8},
      }

3307. F. Jelinek, “Direct Parsing of Text.” 1996.
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      @inproceedings{57864679,
      title = {Direct Parsing of Text},
      author = {{F. Jelinek}},
      year = 1996,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/f5457b1c3dc7c9ada6dcc8de37fbb052aa950b6d},
      }

3308. M.N. Martin, P. Pouliquen, A. Andreou, and M.E. Fraeman, “Current-mode differential logic circuits for low power digital systems,” in Proceedings of the 39th Midwest Symposium on Circuits and Systems, 1996.
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      @inproceedings{108433087,
      title = {Current-mode differential logic circuits for low power digital systems},
      author = {{M.N. Martin} and {P. Pouliquen} and {A. Andreou} and {M.E. Fraeman}},
      year = 1996,
      month = {8},
      booktitle = {Proceedings of the 39th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/2800ed5c41d8bd76719344c05304c1b5991c6c3b},
      }

3309. A. Andreou and K. Boahen, “Translinear circuits in subthreshold MOS,” in Analog Integrated Circuits and Signal Processing, 1996.
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      @inproceedings{62169532,
      title = {Translinear circuits in subthreshold MOS},
      author = {{A. Andreou} and {K. Boahen}},
      year = 1996,
      month = {3},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/c0b254eec92da2c17b53dd05060e92f7d8f7a14e},
      }

3310. Z. Kalayjian, A. Andreou, L. Wolff, and Norman Sheppard, “A Polarization Contrast Retina That Uses Patterned Iodine-Doped PVA Film.” 1996.
      [BibTeX] [Link]

      @inproceedings{17673412,
      title = {A Polarization Contrast Retina That Uses Patterned Iodine-Doped PVA Film},
      author = {{Z. Kalayjian} and {A. Andreou} and {L. Wolff} and {Norman Sheppard}},
      year = 1996,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/46756dd3cc254c772b212678d3b65acecec36148},
      }

3311. R. C. Meitzler and A. Andreou, “Modeling nonuniform doping in subthreshold MOSFETs,” in Proceedings of the 39th Midwest Symposium on Circuits and Systems, 1996.
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      @inproceedings{56842686,
      title = {Modeling nonuniform doping in subthreshold MOSFETs},
      author = {{R. C. Meitzler} and {A. Andreou}},
      year = 1996,
      month = {8},
      booktitle = {Proceedings of the 39th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/da80aa60b81a75e26d17c6d1855afd28d1b90799},
      }

3312. FRAMEWORKNagendra, Kumar, A. Andreou, Johns Hopkins, UniversityNagendra, B. Hall, and N. C. St, “A GENERALIZATION OF LINEAR DISCRIMINANT ANALYSIS INMAXIMUM LIKELIHOOD.” 1996.
      [BibTeX] [Link]

      @inproceedings{6568086,
      title = {A GENERALIZATION OF LINEAR DISCRIMINANT ANALYSIS INMAXIMUM LIKELIHOOD},
      author = {{FRAMEWORKNagendra} and {Kumar} and {A. Andreou} and {Johns Hopkins} and {UniversityNagendra} and {B. Hall} and {N. C. St}},
      year = 1996,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/553cf797ae70839b28afce91e86ab6f50b4e01e3},
      }

3313. Z. Kalayjian, A. Andreou, L. Wolff, and Norman Sheppard, “A Polarization Contrast Retina Using Patterned Iodine-doped PVA Film,” in European Solid-State Circuits Conference, 1996.
      [BibTeX] [Link]

      @inproceedings{26235255,
      title = {A Polarization Contrast Retina Using Patterned Iodine-doped PVA Film},
      author = {{Z. Kalayjian} and {A. Andreou} and {L. Wolff} and {Norman Sheppard}},
      year = 1996,
      month = {9},
      booktitle = {European Solid-State Circuits Conference},
      url = {https://www.semanticscholar.org/paper/8ba80257f2e9573a83d85e4b4128a3b5a0f44c84},
      }

3314. Z. Kalayjian, J. Waskiewicz, D. Yochelson, and A. Andreou, “Asynchronous sampling of 2D arrays using winner-takes-all arbitration,” in 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96, 1996.
      [BibTeX] [Link]

      @inproceedings{62313979,
      title = {Asynchronous sampling of 2D arrays using winner-takes-all arbitration},
      author = {{Z. Kalayjian} and {J. Waskiewicz} and {D. Yochelson} and {A. Andreou}},
      year = 1996,
      month = {5},
      booktitle = {1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96},
      url = {https://www.semanticscholar.org/paper/d45acfe1547afd403023008e6eb43822a7201235},
      }

3315. A. Andreou and K. Boahen, “Analog Integrated Circuits and Signal Processing,” in Analog Integrated Circuits and Signal Processing, 1996.
      [BibTeX] [Link]

      @inproceedings{16993216,
      title = {Analog Integrated Circuits and Signal Processing},
      author = {{A. Andreou} and {K. Boahen}},
      year = 1996,
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/4c756a4824b0b605b021c5f4fa4d716857c362a1},
      }

3316. N. Kumar, G. Cauwenberghs, and A. Andreou, “A circuit model of hair-cell transduction for temporal processing and auditory feature extraction,” in 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96, 1996.
      [BibTeX] [Link]

      @inproceedings{62657012,
      title = {A circuit model of hair-cell transduction for temporal processing and auditory feature extraction},
      author = {{N. Kumar} and {G. Cauwenberghs} and {A. Andreou}},
      year = 1996,
      month = {5},
      booktitle = {1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96},
      url = {https://www.semanticscholar.org/paper/ea12c40b0ed6fcbb9d8e116df16bb0f5ba4ce624},
      }

3317. P. Furth and A. Andreou, “Translinear transconductor design for cochlear filter banks,” in Proceedings of the 39th Midwest Symposium on Circuits and Systems, 1996.
      [BibTeX] [Link]

      @inproceedings{15900907,
      title = {Translinear transconductor design for cochlear filter banks},
      author = {{P. Furth} and {A. Andreou}},
      year = 1996,
      month = {8},
      booktitle = {Proceedings of the 39th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/598c6d9959085d8faf2dda342443fc11e64b2ffd},
      }

3318. A. Andreou, R. C. Meitzler, K. Strohbehn, and K. Boahen, “Analog VLSI neuromorphic image acquisition and pre-processing systems,” in Neural Networks, 1995.
      [BibTeX] [Link]

      @inproceedings{29109920,
      title = {Analog VLSI neuromorphic image acquisition and pre-processing systems},
      author = {{A. Andreou} and {R. C. Meitzler} and {K. Strohbehn} and {K. Boahen}},
      year = 1995,
      month = {12},
      booktitle = {Neural Networks},
      url = {https://www.semanticscholar.org/paper/e114041a1e42fe305ceb050d8a372073c3766d80},
      }

3319. P. Furth and A. Andreou, “A design framework for low power analog filter banks,” in IEEE Transactions on Circuits and Systems I-regular Papers, 1995.
      [BibTeX] [Link]

      @inproceedings{18736374,
      title = {A design framework for low power analog filter banks},
      author = {{P. Furth} and {A. Andreou}},
      year = 1995,
      month = {11},
      booktitle = {IEEE Transactions on Circuits and Systems I-regular Papers},
      url = {https://www.semanticscholar.org/paper/82b451ba770771fff2ad318f017540f78b15e6f5},
      }

3320. B. Baldwin, J. Reynar, M. Collins, J. Eisner, A. Ratnaparkhi, Joseph Rosenzweig, A. Sarkar, and Srinivas, “Description of the University of Pennsylvania Entry in the MUC-6 Competition,” in Proceedings of the Sixth Message Understanding Conference, Maryland, 1995, p. 177–191.
      [BibTeX] [Link]

      @InProceedings{baldwin-et-al-1995,
      aclid = "M95-1015",
      author = "Breck Baldwin and Jeff Reynar and Mike Collins and
      Jason Eisner and Adwait Ratnaparkhi and Joseph
      Rosenzweig and Anoop Sarkar and Srinivas",
      title = "Description of the {U}niversity of {P}ennsylvania
      Entry in the {MUC}-6 Competition",
      booktitle = "Proceedings of the Sixth Message Understanding
      Conference",
      pages = "177--191",
      year = "1995",
      month = oct,
      address = "Maryland",
      URL = "http://cs.jhu.edu/~jason/papers/#baldwin-et-al-1995",
      }

3321. R. C. Meitzler, K. Strohbehn, and A. Andreou, “A silicon retina for 2-D position and motion computation,” in International Symposium on Circuits and Systems, 1995.
      [BibTeX] [Link]

      @inproceedings{59161903,
      title = {A silicon retina for 2-D position and motion computation},
      author = {{R. C. Meitzler} and {K. Strohbehn} and {A. Andreou}},
      year = 1995,
      month = {4},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/f7b2f5ec2182dc260e3ae826f3a37e1ae82602f9},
      }

3322. L. B. Wolff and A. Andreou, “Polarization camera sensors,” in Image and Vision Computing, 1995.
      [BibTeX] [Link]

      @inproceedings{11362831,
      title = {Polarization camera sensors},
      author = {{L. B. Wolff} and {A. Andreou}},
      year = 1995,
      month = {8},
      booktitle = {Image and Vision Computing},
      url = {https://www.semanticscholar.org/paper/df7df9009491654012075c869e309ef2efef4127},
      }

3323. P. Furth and A. Andreou, “Linearised differential transconductors in subthreshold CMOS,” in Electronics Letters, 1995.
      [BibTeX] [Link]

      @inproceedings{18903153,
      title = {Linearised differential transconductors in subthreshold CMOS},
      author = {{P. Furth} and {A. Andreou}},
      year = 1995,
      month = {3},
      booktitle = {Electronics Letters},
      url = {https://www.semanticscholar.org/paper/47d4f6710d52d4864a7a5ff7b8e4d91ada7f6923},
      }

3324. F. Jelinek, “Two New Approaches to Language Modeling: A Tutorial.” 1995.
      [BibTeX] [Link]

      @inproceedings{60229418,
      title = {Two New Approaches to Language Modeling: A Tutorial},
      author = {{F. Jelinek}},
      year = 1995,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/bba743c9b1b64f7f787a603069c5d1c47b9fe84c},
      }

3325. Jordan Cohen, T. Kamm, and A. Andreou, “Vocal tract normalization in speech recognition: Compensating for systematic speaker variability,” in Journal of the Acoustical Society of America, 1995.
      [BibTeX] [Link]

      @inproceedings{120356311,
      title = {Vocal tract normalization in speech recognition: Compensating for systematic speaker variability},
      author = {{Jordan Cohen} and {T. Kamm} and {A. Andreou}},
      year = 1995,
      month = {5},
      booktitle = {Journal of the Acoustical Society of America},
      url = {https://www.semanticscholar.org/paper/b32cf0f63de63b339a0ed24a5b8788f8cbe86827},
      }

3326. A. Andreou and K. Boahen, “A 590,000 transistor 48,000 pixel, contrast sensitive, edge enhancing, CMOS imager-silicon retina,” in Proceedings Sixteenth Conference on Advanced Research in VLSI, 1995.
      [BibTeX] [Link]

      @inproceedings{6791864,
      title = {A 590,000 transistor 48,000 pixel, contrast sensitive, edge enhancing, CMOS imager-silicon retina},
      author = {{A. Andreou} and {K. Boahen}},
      year = 1995,
      month = {3},
      booktitle = {Proceedings Sixteenth Conference on Advanced Research in VLSI},
      url = {https://www.semanticscholar.org/paper/ddd0f73681f6c08f081577056b05ef783f1792d8},
      }

3327. N. Kumar, G. Cauwenberghs, and A. Andreou, “Level crossing time interval circuit for micro-power analog VLSI auditory processing,” in Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing, 1995.
      [BibTeX] [Link]

      @inproceedings{63990645,
      title = {Level crossing time interval circuit for micro-power analog VLSI auditory processing},
      author = {{N. Kumar} and {G. Cauwenberghs} and {A. Andreou}},
      year = 1995,
      month = {8},
      booktitle = {Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing},
      url = {https://www.semanticscholar.org/paper/7dd477a54aaf4110b504ad1e76eed4b045f7aed5},
      }

3328. M. Cohen and A. Andreou, “Analog CMOS integration and experimentation with an autoadaptive independent component analyzer.” 1995.
      [BibTeX] [Link]

      @inproceedings{60858644,
      title = {Analog CMOS integration and experimentation with an autoadaptive independent component analyzer},
      author = {{M. Cohen} and {A. Andreou}},
      year = 1995,
      month = {2},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/1483ad8f59ebd5f7cf4f97f859a50b544b842078},
      }

3329. N. Kumar, C. Neti, and A. Andreou, “Application of Discriminant Analysis to Speech Recognition with Auditory Features.” 1995.
      [BibTeX] [Link]

      @inproceedings{16849251,
      title = {Application of Discriminant Analysis to Speech Recognition with Auditory Features},
      author = {{N. Kumar} and {C. Neti} and {A. Andreou}},
      year = 1995,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/99db8a4f8c13a4b9cb4336b556c87e9cf9d3cb50},
      }

3330. A. Andreou, “Book Review: “Cellular Neural Networks”, by T. Roska and J. Vandewalle,” in International Journal of Neural Systems, 1995.
      [BibTeX] [Link]

      @inproceedings{39415148,
      title = {Book Review: "Cellular Neural Networks", by T. Roska and J. Vandewalle},
      author = {{A. Andreou}},
      year = 1995,
      month = {6},
      booktitle = {International Journal of Neural Systems},
      url = {https://www.semanticscholar.org/paper/67c8ee8ca7f3ef8eba7e0c15dcece598e9e43e03},
      }

3331. R. C. Meitzler, K. Strohbehn, and A. Andreou, “A Silicon Retina for 2-D Position and 2-D Motion Computation,” in International Symposium on Circuits and Systems, 1995.
      [BibTeX] [Link]

      @inproceedings{702299,
      title = {A Silicon Retina for 2-D Position and 2-D Motion Computation},
      author = {{R. C. Meitzler} and {K. Strohbehn} and {A. Andreou}},
      year = 1995,
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/ce43cbad87d2327b136f643a4e6b5cc15b8939eb},
      }

3332. P. Furth and A. Andreou, “Transconductors in Subthreshold CMOS.” 1995.
      [BibTeX] [Link]

      @inproceedings{10065245,
      title = {Transconductors in Subthreshold CMOS},
      author = {{P. Furth} and {A. Andreou}},
      year = 1995,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/f75689e3827e1901153d2d65c083f4b10ad8035e},
      }

3333. J. Eisner, “$\forall$-less in Wonderland? Revisiting \em any,” in Proceedings of ESCOL 11 (October 1994), Ithaca, NY, 1995, p. 92–103.
      [BibTeX] [Link]

      @InProceedings{eisner-1995,
      author = "Jason Eisner",
      title = "{$\forall$}-less in {W}onderland? {R}evisiting {\em
      any}",
      booktitle = "Proceedings of ESCOL 11 (October 1994)",
      pages = "92--103",
      year = "1995",
      editor = "Janet Fuller and Ho Han and David Parkinson",
      address = "Ithaca, NY",
      publisher = "DMLL Publications",
      URL = "http://cs.jhu.edu/~jason/papers/#eisner-1995",
      }

3334. A. Andreou, “On physical models of neural computation and their analog VLSI implementation,” in Proceedings Workshop on Physics and Computation. PhysComp ’94, 1994.
      [BibTeX] [Link]

      @inproceedings{61603974,
      title = {On physical models of neural computation and their analog VLSI implementation},
      author = {{A. Andreou}},
      year = 1994,
      month = {11},
      booktitle = {Proceedings Workshop on Physics and Computation. PhysComp '94},
      url = {https://www.semanticscholar.org/paper/99f36b2afc82c8aa67647f1cba9139d823141508},
      }

3335. F. Jelinek, “Training and search methods for speech recognition.,” in Proceedings of the National Academy of Sciences of the United States of America, 1994.
      [BibTeX] [Link]

      @inproceedings{25101454,
      title = {Training and search methods for speech recognition.},
      author = {{F. Jelinek}},
      year = 1994,
      month = {11},
      booktitle = {Proceedings of the National Academy of Sciences of the United States of America},
      url = {https://www.semanticscholar.org/paper/78c0eeb3e5e122e79ddd9527e50ff4d966a3ee52},
      }

3336. Kewei Yang and A. Andreou, “A multiple input differential amplifier based on charge sharing on a floating-gate MOSFET,” in Analog Integrated Circuits and Signal Processing, 1994.
      [BibTeX] [Link]

      @inproceedings{60906457,
      title = {A multiple input differential amplifier based on charge sharing on a floating-gate MOSFET},
      author = {{Kewei Yang} and {A. Andreou}},
      year = 1994,
      month = {11},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/adae53898fcd2fa4cacd560feab4de72e24da9a5},
      }

3337. F. Jelinek, “Session 8 &: 9: Statistical and Learning Methods,” in Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994, 1994.
      [BibTeX] [Link]

      @inproceedings{jelinek-1994-session,
      title = "Session 8 {\&}: 9: Statistical and Learning Methods",
      author = "Jelinek, Frederick",
      booktitle = "{H}uman {L}anguage {T}echnology: Proceedings of a Workshop held at {P}lainsboro, {N}ew {J}ersey, {M}arch 8-11, 1994",
      year = "1994",
      url = "https://aclanthology.org/H94-1045",
      }

3338. F. Pineda and A. Andreou, “ANALOG NEUROMORPHIC COMPUTATION: AN APPLICATION TO COMPRESSION.” 1994.
      [BibTeX] [Link]

      @inproceedings{1371034,
      title = {ANALOG NEUROMORPHIC COMPUTATION: AN APPLICATION TO COMPRESSION},
      author = {{F. Pineda} and {A. Andreou}},
      year = 1994,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/f3d1bcf2698bb24b90e8b790f4cb1e48aa7d5d9e},
      }

3339. P. Furth, N. Goel, A. Andreou, and M. Goldstein, “Experiments with the Hopkins Electronic EAR.” 1994.
      [BibTeX] [Link]

      @inproceedings{17321155,
      title = {Experiments with the Hopkins Electronic EAR},
      author = {{P. Furth} and {N. Goel} and {A. Andreou} and {M. Goldstein}},
      year = 1994,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b22b8ff72e1533baf00d444535abd13a88950b32},
      }

3340. Kewei Yang, R. C. Meitzler, and A. Andreou, “A model for MOS effective channel mobility with emphasis in the subthreshold and transition region,” in International Symposium on Circuits and Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{26284191,
      title = {A model for MOS effective channel mobility with emphasis in the subthreshold and transition region},
      author = {{Kewei Yang} and {R. C. Meitzler} and {A. Andreou}},
      year = 1994,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/81f137f92bf5763516657b1a0667801947c75870},
      }

3341. H. Miwa, Kewei Yang, P. Pouliquen, Nagendra Kumar, and A. Andreou, “Storage enhancement techniques for digital memory based, analog computational engines,” in International Symposium on Circuits and Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{20077335,
      title = {Storage enhancement techniques for digital memory based, analog computational engines},
      author = {{H. Miwa} and {Kewei Yang} and {P. Pouliquen} and {Nagendra Kumar} and {A. Andreou}},
      year = 1994,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/754447bd13cbf3819163211f14e3fa4dca4ff850},
      }

3342. V. Kantabutra and A. Andreou, “Approach to Asynchronous Circuit Design.” 1994.
      [BibTeX] [Link]

      @inproceedings{62948433,
      title = {Approach to Asynchronous Circuit Design},
      author = {{V. Kantabutra} and {A. Andreou}},
      year = 1994,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/ca22db4a450c6832f63a1987fd960deea87979ef},
      }

3343. V. Kantabutra and A. Andreou, “A State Assignment Approach to Asynchronous CMOS Circuit Design,” in IEEE Trans. Computers, 1994.
      [BibTeX] [Link]

      @inproceedings{7235015,
      title = {A State Assignment Approach to Asynchronous CMOS Circuit Design},
      author = {{V. Kantabutra} and {A. Andreou}},
      year = 1994,
      month = {4},
      booktitle = {IEEE Trans. Computers},
      url = {https://www.semanticscholar.org/paper/fdce6dff1bcad588391f08e6097a3d4c0653c907},
      }

3344. A. Pavasovic, A. Andreou, and C. Westgate, “Characterization of subthreshold MOS mismatch in transistors for VLSI systems,” in J. VLSI Signal Process., 1994.
      [BibTeX] [Link]

      @inproceedings{31862699,
      title = {Characterization of subthreshold MOS mismatch in transistors for VLSI systems},
      author = {{A. Pavasovic} and {A. Andreou} and {C. Westgate}},
      year = 1994,
      booktitle = {J. VLSI Signal Process.},
      url = {https://www.semanticscholar.org/paper/471dd4a784a2c07ab31172741081bd8e895d30ac},
      }

3345. A. Murray, I. Aleksander, A. Andreou, and M. Mahowald, “Analogue and Digital Neural VLSI: Duet or Duel?,” in International Symposium on Circuits and Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{41409703,
      title = {Analogue and Digital Neural VLSI: Duet or Duel?},
      author = {{A. Murray} and {I. Aleksander} and {A. Andreou} and {M. Mahowald}},
      year = 1994,
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/7a9eed09e013a7c5b54d1382a7dfab72f7cb068b},
      }

3346. V. Kantabutra and A. Andreou, “A State Assignment Approach to Asynchronous.” 1994.
      [BibTeX] [Link]

      @inproceedings{62261327,
      title = {A State Assignment Approach to Asynchronous},
      author = {{V. Kantabutra} and {A. Andreou}},
      year = 1994,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/7cc8b60de98e319109120df599c62ff0217b5bd6},
      }

3347. In Alex Waibel, Kai-Fu Lee, Morgan, F. Jelinek, R. Mercer, Salim, D. Lenat, R. Guha, K. Pittman, D. Pratt, Massada Brown, J. Cocke, S. Della, Pietra, V. Della, F. Jelinek, R. Mercer, P. Brown, S. Della, V. Della, and R. Mercer, “Self-organized Language Modeling for Speech Recognition. Estimation of Probabilities from Sparse Data for the Lan- Guage Model Component of a Speech Recognizer.” 1994.
      [BibTeX] [Link]

      @inproceedings{11994791,
      title = {Self-organized Language Modeling for Speech Recognition. Estimation of Probabilities from Sparse Data for the Lan- Guage Model Component of a Speech Recognizer},
      author = {{In Alex Waibel} and {Kai-Fu Lee} and {Morgan} and {F. Jelinek} and {R. Mercer} and {Salim} and {D. Lenat} and {R. Guha} and {K. Pittman} and {D. Pratt} and {Massada Brown} and {J. Cocke} and {S. Della} and {Pietra} and {V. Della} and {F. Jelinek} and {R. Mercer} and {P. Brown} and {S. Della} and {V. Della} and {R. Mercer}},
      year = 1994,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/612681ed5ea5251a1fe12523712088c5f72e6f20},
      }

3348. A. Andreou and K. Boahen, “A 48,000 pixel, 590,000 transistor silicon retina in current-mode subthreshold CMOS,” in Proceedings of 1994 37th Midwest Symposium on Circuits and Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{60882320,
      title = {A 48,000 pixel, 590,000 transistor silicon retina in current-mode subthreshold CMOS},
      author = {{A. Andreou} and {K. Boahen}},
      year = 1994,
      month = {8},
      booktitle = {Proceedings of 1994 37th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/bcae76b0358316e78160918962ae3c5b7f7d70d0},
      }

3349. Kewei Yang and A. Andreou, “The multiple input floating gate MOS differential amplifier: an analog computational building-block,” in International Symposium on Circuits and Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{40552257,
      title = {The multiple input floating gate MOS differential amplifier: an analog computational building-block},
      author = {{Kewei Yang} and {A. Andreou}},
      year = 1994,
      month = {5},
      booktitle = {International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/9d6c3340c013774534b96aa1b7c9e274b137060e},
      }

3350. F. Pineda and A. Andreou, “An Analog Neural Network Inspired by Fractal Block Coding,” in Neural Information Processing Systems, 1994.
      [BibTeX] [Link]

      @inproceedings{5788151,
      title = {An Analog Neural Network Inspired by Fractal Block Coding},
      author = {{F. Pineda} and {A. Andreou}},
      year = 1994,
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/072444b064842560a91f06e06157d9812a2c6c9a},
      }

3351. A. Pavasovic, A. Andreou, and C. Westgate, “Characterization of subthreshold MOS mismatch in transistors for VLSI systems,” in Analog Integrated Circuits and Signal Processing, 1994.
      [BibTeX] [Link]

      @inproceedings{62242197,
      title = {Characterization of subthreshold MOS mismatch in transistors for VLSI systems},
      author = {{A. Pavasovic} and {A. Andreou} and {C. Westgate}},
      year = 1994,
      month = {7},
      booktitle = {Analog Integrated Circuits and Signal Processing},
      url = {https://www.semanticscholar.org/paper/09644bd9d093ecb64d3ad0026c953efd3300a87d},
      }

3352. A. Andreou and T. Edwards, “Analog VLSI neuromorphic processing: case study of a multiple-target-tracking system,” in International Conference on Neural Networks, 1994.
      [BibTeX] [Link]

      @inproceedings{60712536,
      title = {Analog VLSI neuromorphic processing: case study of a multiple-target-tracking system},
      author = {{A. Andreou} and {T. Edwards}},
      year = 1994,
      month = {6},
      booktitle = {International Conference on Neural Networks},
      url = {https://www.semanticscholar.org/paper/31d62a1fe435c188a80f08c819684e131b321be0},
      }

3353. F. Jelinek, J. Lafferty, D. Magerman, R. Mercer, A. Ratnaparkhi, and S. Roukos, “Decision Tree Parsing using a Hidden Derivation Model,” in Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994, 1994.
      [BibTeX] [Link]

      @inproceedings{jelinek-etal-1994-decision,
      title = "Decision Tree Parsing using a Hidden Derivation Model",
      author = "Jelinek, F. and
      Lafferty, J. and
      Magerman, D. and
      Mercer, R. and
      Ratnaparkhi, A. and
      Roukos, S.",
      booktitle = "{H}uman {L}anguage {T}echnology: Proceedings of a Workshop held at {P}lainsboro, {N}ew {J}ersey, {M}arch 8-11, 1994",
      year = "1994",
      url = "https://aclanthology.org/H94-1052",
      }

3354. K. Strohbehm, David Rust, A. Andreou, and R. E. Jenkins, “A Biologically-Inspired Image Position Sensor.” 1993.
      [BibTeX] [Link]

      @inproceedings{117220967,
      title = {A Biologically-Inspired Image Position Sensor},
      author = {{K. Strohbehm} and {David Rust} and {A. Andreou} and {R. E. Jenkins}},
      year = 1993,
      month = {12},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/06879508b31d1f7eec29b3d9b4499de273c5f991},
      }

3355. A. Andreou and T. Edwards, “VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems,” in Neural Information Processing Systems, 1993.
      [BibTeX] [Link]

      @inproceedings{14436183,
      title = {VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems},
      author = {{A. Andreou} and {T. Edwards}},
      year = 1993,
      month = {11},
      booktitle = {Neural Information Processing Systems},
      url = {https://www.semanticscholar.org/paper/d90b8d0d100f1903d97f5ab58a75a6e093e3f192},
      }

3356. N. Paschalidis, A. Andreou, E. Sarris, and S. Krimigis, “Application Specific Integrated Circuits (ASICs) for Particle Measurements in Space Using Solid State Detectors.” 1993.
      [BibTeX] [Link]

      @inproceedings{107779550,
      title = {Application Specific Integrated Circuits (ASICs) for Particle Measurements in Space Using Solid State Detectors},
      author = {{N. Paschalidis} and {A. Andreou} and {E. Sarris} and {S. Krimigis}},
      year = 1993,
      month = {11},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/b07876c0240b87c8ddaa094b134d4acf3787b56b},
      }

3357. E. Black, F. Jelinek, J. Lafrerty, D. M. Magerman, R. Mercer, and S. Roukos, “Towards History-based Grammars: Using Richer Models for Probabilistic Parsing,” in 31st Annual Meeting of the Association for Computational Linguistics, Columbus, Ohio, USA, 1993, p. 31–37. doi:10.3115/981574.981579
      [BibTeX] [Link]

      @inproceedings{black-etal-1993-towards,
      title = "Towards History-based Grammars: Using Richer Models for Probabilistic Parsing",
      author = "Black, Ezra and
      Jelinek, Fred and
      Lafrerty, John and
      Magerman, David M. and
      Mercer, Robert and
      Roukos, Salim",
      booktitle = "31st Annual Meeting of the Association for Computational Linguistics",
      month = jun,
      year = "1993",
      address = "Columbus, Ohio, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/P93-1005",
      doi = "10.3115/981574.981579",
      pages = "31--37",
      }

3358. N. Kumar, P. Pouliquen, and A. Andreou, “Device mismatch limitations on the performance of an associative memory system,” in Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993.
      [BibTeX] [Link]

      @inproceedings{62679226,
      title = {Device mismatch limitations on the performance of an associative memory system},
      author = {{N. Kumar} and {P. Pouliquen} and {A. Andreou}},
      year = 1993,
      month = {8},
      booktitle = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/147f85bea71d8dfc311c912b6f1f40e2270e18c9},
      }

3359. P. Pouliquen, A. Andreou, K. Strohbehn, and R. E. Jenkins, “An associative memory integrated system for character recognition,” in Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993.
      [BibTeX] [Link]

      @inproceedings{61021358,
      title = {An associative memory integrated system for character recognition},
      author = {{P. Pouliquen} and {A. Andreou} and {K. Strohbehn} and {R. E. Jenkins}},
      year = 1993,
      month = {8},
      booktitle = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/420f65dc0310a79a940a73688cdbbc512b844645},
      }

3360. P. Furth and A. Andreou, “A high-drive low-power BiCMOS buffer using compound PMOS/NPN transistors,” in Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993.
      [BibTeX] [Link]

      @inproceedings{15574609,
      title = {A high-drive low-power BiCMOS buffer using compound PMOS/NPN transistors},
      author = {{P. Furth} and {A. Andreou}},
      year = 1993,
      month = {8},
      booktitle = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/0934f27065b1871fd0185124e4f6339c5e1526e1},
      }

3361. K. Boahen and A. Andreou, “Design of a bidirectional associative memory chip.” 1993.
      [BibTeX] [Link]

      @inproceedings{63103715,
      title = {Design of a bidirectional associative memory chip},
      author = {{K. Boahen} and {A. Andreou}},
      year = 1993,
      month = {8},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/14b1998ddcd4a0356c57c5e93884f5d85156cdd4},
      }

3362. N. Paschalidis, A. Andreou, and E. Sarris, “A CMOS analog-digital integrated circuit for charged particle spectrum measurements,” in IEEE Transactions on Nuclear Science, 1993.
      [BibTeX] [Link]

      @inproceedings{53286050,
      title = {A CMOS analog-digital integrated circuit for charged particle spectrum measurements},
      author = {{N. Paschalidis} and {A. Andreou} and {E. Sarris}},
      year = 1993,
      booktitle = {IEEE Transactions on Nuclear Science},
      url = {https://www.semanticscholar.org/paper/2a815fa1111b36651908f6da3d037715b0d121fe},
      }

3363. A. Andreou, “Analog VLSI neuromorphic systems,” in 1993 IEEE International Symposium on Circuits and Systems, 1993.
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      @inproceedings{10684271,
      title = {Analog VLSI neuromorphic systems},
      author = {{A. Andreou}},
      year = 1993,
      month = {5},
      booktitle = {1993 IEEE International Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/87d8eb1b50654db206f34f5bdc9ec2f855c2ba8a},
      }

3364. N. Kumar, P. Pouliquen, and A. Andreou, “Device Mismatch Limitations on the Performance of a Hamming Distance Classifier,” in IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, 1993.
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      @inproceedings{18121497,
      title = {Device Mismatch Limitations on the Performance of a Hamming Distance Classifier},
      author = {{N. Kumar} and {P. Pouliquen} and {A. Andreou}},
      year = 1993,
      booktitle = {IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems},
      url = {https://www.semanticscholar.org/paper/b887f679508e579911529f9312db3e87ef2df7d0},
      }

3365. Kewei Yang and A. Andreou, “Multiple input floating-gate MOS differential amplifiers and applications for analog computation,” in Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993.
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      @inproceedings{60557239,
      title = {Multiple input floating-gate MOS differential amplifiers and applications for analog computation},
      author = {{Kewei Yang} and {A. Andreou}},
      year = 1993,
      month = {8},
      booktitle = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/4328fd8a6e1067a6484653fddadcc752a6e5c0c7},
      }

3366. R. C. Meitzler and A. Andreou, “On the simulation of analog VLSI systems operating in the subthreshold and transition regions,” in [1993] Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium, 1993.
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      @inproceedings{109401790,
      title = {On the simulation of analog VLSI systems operating in the subthreshold and transition regions},
      author = {{R. C. Meitzler} and {A. Andreou}},
      year = 1993,
      month = {5},
      booktitle = {[1993] Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium},
      url = {https://www.semanticscholar.org/paper/c894aeec7bb30084cc9d6e2de07be3884f379165},
      }

3367. K. Yang and A. Andreou, “Subthreshold analysis of floating-gate MOSFET’s,” in [1993] Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium, 1993.
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      @inproceedings{110032741,
      title = {Subthreshold analysis of floating-gate MOSFET's},
      author = {{K. Yang} and {A. Andreou}},
      year = 1993,
      month = {5},
      booktitle = {[1993] Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium},
      url = {https://www.semanticscholar.org/paper/2207ad1cd67668a7a79a52f03d2e8cac0bd289e3},
      }

3368. P. Brown, PietraStephen A. Della, PietraVincent J. Della, R. Mercer, and F. Jelinek, “Speech recognition system for lifelike Language Translation.” 1993.
      [BibTeX] [Link]

      @inproceedings{64784176,
      title = {Speech recognition system for lifelike Language Translation},
      author = {{P. Brown} and {PietraStephen A. Della} and {PietraVincent J. Della} and {R. Mercer} and {F. Jelinek}},
      year = 1993,
      month = {1},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a15cfa1b2626f11cbd65777cf34b4fd57bf5eb3f},
      }

3369. K. Strohbehn, R. E. Jenkins, X. Sun, and A. Andreou, “Silicon retina for optical tracking systems.” 1993.
      [BibTeX] [Link]

      @inproceedings{109568542,
      title = {Silicon retina for optical tracking systems},
      author = {{K. Strohbehn} and {R. E. Jenkins} and {X. Sun} and {A. Andreou}},
      year = 1993,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/7f414c46e2bd7b886370826940b7057708604627},
      }

3370. R. C. Meitzler, A. Andreou, K. Strohbehn, and R. E. Jenkins, “A sampled-data motion chip,” in Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993.
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      @inproceedings{58781669,
      title = {A sampled-data motion chip},
      author = {{R. C. Meitzler} and {A. Andreou} and {K. Strohbehn} and {R. E. Jenkins}},
      year = 1993,
      month = {8},
      booktitle = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
      url = {https://www.semanticscholar.org/paper/5fc45836b809fc7c80d51286afac7b6718013bcd},
      }

3371. P. Brown, F. Jelinek, J. Lafferty, R. Mercer, and S. Roukos, “Acquisition of language models from text.” 1992.
      [BibTeX] [Link]

      @inproceedings{121671042,
      title = {Acquisition of language models from text},
      author = {{P. Brown} and {F. Jelinek} and {J. Lafferty} and {R. Mercer} and {S. Roukos}},
      year = 1992,
      month = {10},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/9d2fe71078fbe45f17d87a18dc16cc9a0dd5f298},
      }

3372. M. A. Jones and J. M. Eisner, “A Probabilistic Parser Applied to Software Testing Documents,” in Proceedings of National Conference on Artificial Intelligence (AAAI-92), San Jose, 1992, p. 322–328.
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      @InProceedings{jones-eisner-1992-aaai,
      author = "Mark A. Jones and Jason M. Eisner",
      title = "A Probabilistic Parser Applied to Software Testing
      Documents",
      booktitle = "Proceedings of National Conference on Artificial
      Intelligence (AAAI-92)",
      pages = "322--328",
      year = "1992",
      month = jul,
      address = "San Jose",
      URL = "http://cs.jhu.edu/~jason/papers/#jones-eisner-1992-aaai",
      }

3373. M. A. Jones and J. M. Eisner, “A Probabilistic Parser and Its Application,” in Statistically-Based Natural Language Processing Techniques: Papers from the 1992 Workshop, 1992, p. 20–27.
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      @InProceedings{jones-eisner-1992-workshop,
      author = "Mark A. Jones and Jason M. Eisner",
      title = "A Probabilistic Parser and Its Application",
      booktitle = "Statistically-Based Natural Language Processing
      Techniques: Papers from the 1992 Workshop",
      editor = "Carl Weir",
      pages = "20--27",
      year = "1992",
      month = jul,
      publisher = "Menlo Park: AAAI Press",
      note = "Technical Report WS-92-01",
      URL = "http://cs.jhu.edu/~jason/papers/#jones-eisner-1992-workshop",
      }

3374. E. Black, F. Jelinek, J. Lafferty, R. Mercer, and S. Roukos, “Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech,” in Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992, 1992.
      [BibTeX] [Link]

      @inproceedings{black-etal-1992-decision,
      title = "Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech",
      author = "Black, Ezra and
      Jelinek, Fred and
      Lafferty, John and
      Mercer, Robert and
      Roukos, Salim",
      booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, {F}ebruary 23-26, 1992",
      year = "1992",
      url = "https://aclanthology.org/H92-1023",
      }

3375. P. Brown, J. Cocke, PietraStephen A. Della, PietraVincent J. Della, F. Jelinek, J. Lai, and R. Mercer, “Method and system for language translation.” 1992.
      [BibTeX] [Link]

      @inproceedings{195968166,
      title = {Method and system for language translation},
      author = {{P. Brown} and {J. Cocke} and {PietraStephen A. Della} and {PietraVincent J. Della} and {F. Jelinek} and {J. Lai} and {R. Mercer}},
      year = 1992,
      month = {7},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/fef0e9b11ffc7a3034e3f5fda84f32b1728e8b19},
      }

3376. F. Jelinek, J. Lafferty, and R. Mercer, “Basic Methods of Probabilistic Context Free Grammars.” 1992.
      [BibTeX] [Link]

      @inproceedings{62304080,
      title = {Basic Methods of Probabilistic Context Free Grammars},
      author = {{F. Jelinek} and {J. Lafferty} and {R. Mercer}},
      year = 1992,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/617241818e8ddd6edcb4ee7682992673c18c6f3d},
      }

3377. F. Jelinek, B. Merialdo, S. Roukos, and M. Strauss, “A Dynamic Language Model for Speech Recognition,” in Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991, 1991.
      [BibTeX] [Link]

      @inproceedings{jelinek-etal-1991-dynamic,
      title = "A Dynamic Language Model for Speech Recognition",
      author = "Jelinek, F. and
      Merialdo, B. and
      Roukos, S. and
      Strauss, M.",
      booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, {F}ebruary 19-22, 1991",
      year = "1991",
      url = "https://aclanthology.org/H91-1057",
      }

3378. E. Black, S. Abney, D. Flickenger, C. Gdaniec, R. Grishman, P. Harrison, D. Hindle, R. Ingria, F. Jelinek, J. Klavans, M. Liberman, M. Marcus, S. Roukos, B. Santorini, and T. Strzalkowski, “A Procedure for Quantitatively Comparing the Syntactic Coverage of English Grammars,” in Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991, 1991.
      [BibTeX] [Link]

      @inproceedings{black-etal-1991-procedure,
      title = "A Procedure for Quantitatively Comparing the Syntactic Coverage of {E}nglish Grammars",
      author = "Black, E. and
      Abney, S. and
      Flickenger, D. and
      Gdaniec, C. and
      Grishman, R. and
      Harrison, P. and
      Hindle, D. and
      Ingria, R. and
      Jelinek, F. and
      Klavans, J. and
      Liberman, M. and
      Marcus, M. and
      Roukos, S. and
      Santorini, B. and
      Strzalkowski, T.",
      booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, {F}ebruary 19-22, 1991",
      year = "1991",
      url = "https://aclanthology.org/H91-1060",
      }

3379. R. R. Bahl, F. Jelinek, R. Mercer, A. Maximumlike, L. Rabiner, B. Juang, L. Bahl, S. V. Gennaro, P. Gopalakrishnan, R. Bakis, V. De Souza, R. L. Mercer øb, D. Kanevsky, and D. Na, “Results Hamoo \matrix Fast Match: a Fast Method for Iden- Tifying a Short List of Candidate Words for Decod- Ing”.” 1991.
      [BibTeX] [Link]

      @inproceedings{14918522,
      title = {Results Hamoo \matrix Fast Match: a Fast Method for Iden- Tifying a Short List of Candidate Words for Decod- Ing"},
      author = {{R. R. Bahl} and {F. Jelinek} and {R. Mercer} and {A. Maximumlike} and {L. Rabiner} and {B. Juang} and {L. Bahl} and {S. V. Gennaro} and {P. Gopalakrishnan} and {R. Bakis} and {V. De Souza} and {R. L. Mercer \ob} and {D. Kanevsky} and {D. Na}},
      year = 1991,
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/3d916cfd48d997e35518fd2e2f9c3a63f5d78a59},
      }

3380. F. Jelinek, “Up from trigrams! – the struggle for improved language models,” in EUROSPEECH, 1991.
      [BibTeX] [Link]

      @inproceedings{40671443,
      title = {Up from trigrams! - the struggle for improved language models},
      author = {{F. Jelinek}},
      year = 1991,
      month = {9},
      booktitle = {EUROSPEECH},
      url = {https://www.semanticscholar.org/paper/b336f9a030d0fb2983b34182b7333115c27b7712},
      }

3381. F. Jelinek, R. Mercer, and S. Roukos, “Classifying words for improved statistical language models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 1990.
      [BibTeX] [Link]

      @inproceedings{62221785,
      title = {Classifying words for improved statistical language models},
      author = {{F. Jelinek} and {R. Mercer} and {S. Roukos}},
      year = 1990,
      month = {4},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/e5cc136b70f61a199bc6886012f1a099590561a4},
      }

3382. F. Jelinek, “Self-organized language modeling for speech recognition.” 1990.
      [BibTeX] [Link]

      @inproceedings{59710768,
      title = {Self-organized language modeling for speech recognition},
      author = {{F. Jelinek}},
      year = 1990,
      month = {5},
      booktitle = {},
      url = {https://www.semanticscholar.org/paper/a6d096d6fa1b39aeeca0a9114b3b3ecdeb960a38},
      }

3383. T. Fujisaki, F. Jelinek, J. Cocke, E. Black, and T. Nishino, “Probabilistic Parsing Method for Sentence Disambiguation,” in Proceedings of the First International Workshop on Parsing Technologies, Pittsburgh, Pennsylvania, USA, 1989, p. 85–94.
      [BibTeX] [Link]

      @inproceedings{fujisaki-etal-1989-probabilistic,
      title = "Probabilistic Parsing Method for Sentence Disambiguation",
      author = "Fujisaki, T. and
      Jelinek, F. and
      Cocke, J. and
      Black, E. and
      Nishino, T.",
      editor = "Tomita, Masaru",
      booktitle = "Proceedings of the First International Workshop on Parsing Technologies",
      month = aug,
      year = "1989",
      address = "Pittsburgh, Pennsylvania, USA",
      publisher = "Carnegy Mellon University",
      url = "https://aclanthology.org/W89-0209",
      pages = "85--94",
      abstract = "",
      }

3384. J. Makhoul, F. Jelinek, L. Rabiner, C. Weinstein, and V. Zue, “White Paper on Spoken Language Systems,” in Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989, 1989.
      [BibTeX] [Link]

      @inproceedings{makhoul-etal-1989-white,
      title = "White Paper on Spoken Language Systems",
      author = "Makhoul, John and
      Jelinek, Fred and
      Rabiner, Larry and
      Weinstein, Clifford and
      Zue, Victor",
      booktitle = "Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989",
      year = "1989",
      url = "https://aclanthology.org/H89-2077",
      }

3385. L. Bahl, R. Bakis, J. Bellegarda, P. Brown, D. Burshtein, S. Das, P. D. Souza, P. Gopalakrishnan, F. Jelinek, D. Kanevsky, R. Mercer, A. Nádas, D. Nahamoo, and M. Picheny, “Large vocabulary natural language continuous speech recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 1989.
      [BibTeX] [Link]

      @inproceedings{10037547,
      title = {Large vocabulary natural language continuous speech recognition},
      author = {{L. Bahl} and {R. Bakis} and {J. Bellegarda} and {P. Brown} and {D. Burshtein} and {S. Das} and {P. D. Souza} and {P. Gopalakrishnan} and {F. Jelinek} and {D. Kanevsky} and {R. Mercer} and {A. Nádas} and {D. Nahamoo} and {M. Picheny}},
      year = 1989,
      month = {5},
      booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
      url = {https://www.semanticscholar.org/paper/709bab106c728c2567525374346e38fe5e1ebe7b},
      }