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. doi:10.18653/v1/2024.insights-1.9
   [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",
   doi = "10.18653/v1/2024.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. 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. doi:10.18653/v1/2024.naacl-long.170
   [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",
   doi = "10.18653/v1/2024.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.",
   }

9. 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. doi:10.18653/v1/2024.naacl-long.457
   [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",
   doi = "10.18653/v1/2024.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.",
   }

10. 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. doi:10.18653/v1/2024.naacl-long.226
    [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",
    doi = "10.18653/v1/2024.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.",
    }

11. 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. doi:10.18653/v1/2024.naacl-short.18
    [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",
    doi = "10.18653/v1/2024.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.",
    }

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. 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.",
    }

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, 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.",
    }

19. 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.",
    }

20. 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.",
    }

21. 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.",
    }

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. 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},
    }

25. Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, and Tianxing He, “k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{267750325,
    title = {k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text},
    author = {{Abe Bohan Hou} and {Jingyu Zhang} and {Yichen Wang} and {Daniel Khashabi} and {Tianxing He}},
    year = 2024,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/94c28609614719c68469081ed99315f54cb1fb6a},
    }

26. 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.",
    }

27. 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},
    }

28. 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},
    }

29. Jeffrey Cheng, Marc Marone, Orion Weller, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme, “Dated Data: Tracing Knowledge Cutoffs in Large Language Models,” in arXiv.org, 2024.
    [BibTeX] [Link]

    @inproceedings{268531479,
    title = {Dated Data: Tracing Knowledge Cutoffs in Large Language Models},
    author = {{Jeffrey Cheng} and {Marc Marone} and {Orion Weller} and {Dawn Lawrie} and {Daniel Khashabi} and {Benjamin Van Durme}},
    year = 2024,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/5ee0c8975b965a413b27332b5cbfb2745251dc52},
    }

30. Niyati Bafna, Kenton Murray, and David Yarowsky, “Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization,” in arXiv.org, 2024.
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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},
    }

31. 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.
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    @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}},
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    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/5c24dd41f46fd7107997b0a46e1207e0fed63b34},
    }

32. 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.
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    @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},
    }

33. Kate Sanders, Nathaniel Weir, and Benjamin Van Durme, “TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning,” in arXiv.org, 2024.
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    @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},
    }

34. Peter M Abadir, Esther S Oh, Rama Chellappa, N. Choudhry, George Demiris, Deepak Ganesan, Jason Karlawish, Benjamin M. Marlin, Rose M Li, N. Dehak, Alicia Arbaje, Mathias Unberath, Thomas Cudjoe, Christopher Chute, Jason H Moore, Phillip Phan, Quincy M. Samus, Nancy L. Schoenborn, Alexis Battle, and Jeremy D Walston, “Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer’s care,” in Alzheimer’s & Dementia, 2024.
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    author = {{Peter M Abadir} and {Esther S Oh} and {Rama Chellappa} and {N. Choudhry} and {George Demiris} and {Deepak Ganesan} and {Jason Karlawish} and {Benjamin M. Marlin} and {Rose M Li} and {N. Dehak} and {Alicia Arbaje} and {Mathias Unberath} and {Thomas Cudjoe} and {Christopher Chute} and {Jason H Moore} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn} and {Alexis Battle} and {Jeremy D Walston}},
    year = 2024,
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35. 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. doi:10.18653/v1/2024.naacl-long.431
    [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",
    doi = "10.18653/v1/2024.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",
    }

36. 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,” in arXiv.org, 2024.
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    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 = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/80461ace9ab0565aeef74ad0fbbe3ed8e0be5438},
    }

37. Neha Joshi, Yu Ng, Karan Thakkkar, Daniel Duque, Pingbo Yin, Jonathan Fritz, Mounya Elhilali, and S. Shamma, “Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party,” in bioRxiv, 2024.
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    }

38. Zhengping Jiang, Yining Lu, Hanjie Chen, Daniel Khashabi, Benjamin Van Durme, and Anqi Liu, “RORA: Robust Free-Text Rationale Evaluation,” in arXiv.org, 2024.
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    @inproceedings{268063486,
    title = {RORA: Robust Free-Text Rationale Evaluation},
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    }

39. 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,” in arXiv.org, 2024.
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    title = {Core: Robust Factual Precision Scoring with Informative Sub-Claim Identification},
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40. 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.
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41. 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.
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    booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
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42. Margaret A. McMullin, Rohit Kumar, Nathan C. Higgins, Brian Gygi, Mounya Elhilali, and J. Snyder, “Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception,” in Open Mind, 2024.
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43. Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, and S. Saria, “FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion,” in arXiv.org, 2024.
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44. Ruizhe Huang, M. Yarmohammadi, S. Khudanpur, and Dan Povey, “Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation.” 2024.
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45. 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.
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    }

46. 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.
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    }

47. 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.
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    author = {{Drew Prinster} and {Samuel Stanton} and {Anqi Liu} and {S. Saria}},
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    }

48. 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.
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    author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {J. Villalba} and {Esther S Oh} and {L. Moro-Velázquez}},
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    }

49. 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.
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    }

50. 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.
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    @inproceedings{268987627,
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    author = {{Jingyu Zhang} and {Marc Marone} and {Tianjian Li} and {Benjamin Van Durme} and {Daniel Khashabi}},
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    }

51. 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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    }

52. H. E. 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.
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53. 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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54. 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.
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55. 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.
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56. 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.
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57. 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.
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58. 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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73. 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.
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    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",
    }

74. 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},
    }

75. Magdalena Rybicka, J. Villalba, Thomas Thebaud, N. Dehak, and Konrad Kowalczyk, “End-to-End Neural Speaker Diarization with Non-Autoregressive Attractors,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2024.
    [BibTeX] [Link]

    @inproceedings{271776874,
    title = {End-to-End Neural Speaker Diarization with Non-Autoregressive Attractors},
    author = {{Magdalena Rybicka} and {J. Villalba} and {Thomas Thebaud} and {N. Dehak} and {Konrad Kowalczyk}},
    year = 2024,
    booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
    url = {https://www.semanticscholar.org/paper/3eb78f245370801e1ed5a80972c8d88d275b0852},
    }

76. 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},
    }

77. 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},
    }

78. 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},
    }

79. 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},
    }

80. 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},
    }

81. 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},
    }

82. 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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    @inproceedings{267044002,
    title = {Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis},
    author = {{Jiarui Hai} and {Yu-Jeh Liu} and {Mounya Elhilali}},
    year = 2023,
    month = {12},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/053dc825f19474a6b8b49239a7c0290aecb57771},
    }

83. 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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    @inproceedings{266602177,
    title = {Binocular Discoordination Kinetic Features: A Novel Approach to Evaluate Neurodegenerative Diseases},
    author = {{Y. Wang} and {L. Moro-Velázquez} and {A. Favaro} and {D. Li} and {E. Oh} and {A. Butala} and {J. Villalba} and {N. Dehak}},
    year = 2023,
    month = {12},
    booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
    url = {https://www.semanticscholar.org/paper/306f3684946774ed21ddba490c0f120f02a5421a},
    }

84. 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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    @inproceedings{267043424,
    title = {Model-Based Fairness Metric for Speaker Verification},
    author = {{Maliha Jahan} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak} and {J. Villalba}},
    year = 2023,
    month = {12},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/f308fc3883c5a18c050b44ec932b59067dfd83f3},
    }

85. 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.
    [BibTeX] [Link]

    @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},
    }

86. 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},
    }

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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    @inproceedings{266574417,
    title = {Decoding contextual influences on auditory perception from primary auditory cortex},
    author = {{B. Englitz} and {S. Akram} and {Mounya Elhilali} and {S. Shamma}},
    year = 2023,
    month = {12},
    booktitle = {bioRxiv},
    url = {https://www.semanticscholar.org/paper/2e536d19f547f000d99351659622ae8204f3467a},
    }

88. 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{266523924,
    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/b6ffb09dbe20a54ddb5f3e6f3a319f482bb3c0aa},
    }

89. 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,
    month = {12},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/1fd003bf9de393bcddbda63b738b71ced6203802},
    }

90. Nigam H Shah, John D Halamka, S. Saria, Michael J. 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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    @inproceedings{266373629,
    title = {A Nationwide Network of Health AI Assurance Laboratories.},
    author = {{Nigam H Shah} and {John D Halamka} and {S. Saria} and {Michael J. Pencina} and {Troy Tazbaz} and {Micky Tripathi} and {Alison Callahan} and {Hailey Hildahl} and {Brian Anderson}},
    year = 2023,
    month = {12},
    booktitle = {Journal of the American Medical Association (JAMA)},
    url = {https://www.semanticscholar.org/paper/194398467b7ae5074cbc626a859935ff2a790962},
    }

91. 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",
    }

92. 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",
    }

93. 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",
    }

94. 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},
    }

95. 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},
    }

96. 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},
    }

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. 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},
    }

99. 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 North American Chapter of the Association for Computational Linguistics, 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 = {North American Chapter of the Association for Computational Linguistics},
    url = {https://www.semanticscholar.org/paper/f2abeec1256f80970827d60f0151c7a19f2dbe7a},
    }

100. 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 North American Chapter of the Association for Computational Linguistics, 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 = {North American Chapter of the Association for Computational Linguistics},
     url = {https://www.semanticscholar.org/paper/bac133a9cb14eadea94c55ec15ea3bb866bf6c03},
     }

101. 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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     @inproceedings{265128667,
     title = {Time Scale Network: A Shallow Neural Network For Time Series Data},
     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},
     }

102. 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},
     }

103. 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.
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     @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},
     }

104. 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},
     }

105. 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},
     }

106. 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 International Conference on Learning Representations, 2023.
     [BibTeX] [Link]

     @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 = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/d15021d750dbe9cee120b562acea857ca02d9104},
     }

107. 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},
     }

108. 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},
     }

109. 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},
     }

110. Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Do pretrained Transformers Learn In-Context by Gradient Descent?.” 2023.
     [BibTeX] [Link]

     @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},
     }

111. 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},
     }

112. Guanghui Qin and Benjamin Van Durme, “Nugget: Neural Agglomerative Embeddings of Text,” in International Conference on Machine Learning, 2023.
     [BibTeX] [Link]

     @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},
     }

113. 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.
     [BibTeX] [Link]

     @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},
     }

114. 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.",
     }

115. 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",
     }

116. 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.",
     }

117. 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.",
     }

118. 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}",
     }

119. 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.",
     }

120. 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.",
     }

121. 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.",
     }

122. 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.",
     }

123. 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.",
     }

124. 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.",
     }

125. 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.",
     }

126. 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.",
     }

127. 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.",
     }

128. 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.",
     }

129. 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.",
     }

130. 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.",
     }

131. 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.",
     }

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. 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.",
     }

135. 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",
     }

136. 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",
     }

137. 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",
     }

138. 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",
     }

139. 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",
     }

140. 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",
     }

141. 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.",
     }

142. 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.",
     }

143. 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.",
     }

144. 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.",
     }

145. 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.",
     }

146. 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},
     }

147. 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},
     }

148. 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.
     [BibTeX] [Link]

     @inproceedings{257505032,
     title = {InstMove: Instance Motion for Object-centric Video Segmentation},
     author = {{Qihao Liu} and {Junfeng Wu} and {Yi Jiang} and {Xiang Bai} and {A. Yuille} and {S. Bai}},
     year = 2023,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/0e60c1229d7963b605b83cb10a90ed6a8cf79149},
     }

149. 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},
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150. 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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     title = {Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis},
     author = {{H. E. Wang} and {Jonathan P. Weiner} and {S. Saria} and {Hadi Kharrazi}},
     year = 2023,
     month = {3},
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     }

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

152. Yu Zeng, Mo Zhou, Yuan Xue, and Vishal M. Patel, “Securing Deep Generative Models with Universal Adversarial Signature,” in arXiv.org, 2023.
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     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},
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     }

153. 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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     author = {{A. Favaro} and {C. Motley} and {Tianyu Cao} and {Miguel Iglesias} and {A. Butala} and {E. Oh} and {R. Stevens} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
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     }

154. 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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     @inproceedings{260334568,
     title = {Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training},
     author = {{Jeya Maria Jose Valanarasu} and {Yucheng Tang} and {Dong Yang} and {Ziyue Xu} and {Can Zhao} and {Wenqi Li} and {Vishal M. Patel} and {Bennett A. Landman} and {Daguang Xu} and {Yufan He} and {V. Nath}},
     year = 2023,
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     }

155. 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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     @inproceedings{248397783,
     title = {Conference on Health, Inference, and Learning, CHIL 2023, Broad Institute of MIT and Harvard (Merkin Building), 415 Main Street, Cambridge, MA, USA},
     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 = 2023,
     booktitle = {ACM Conference on Health, Inference, and Learning},
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     }

156. 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,
     title = {PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation},
     author = {{Qihao Liu} and {Adam Kortylewski} and {A. Yuille}},
     year = 2023,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/85fcce7ef6f5eec2d5e5bce82fc7246e8a90696c},
     }

157. 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}},
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     month = {5},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/6616c330539e1f38b8d80d5aec6eaf0be98f9314},
     }

158. A. Favaro, Yi-Ting Tsai, A. Butala, Thomas Thebaud, J. Villalba, N. Dehak, and L. Moro-Velázquez, “Interpretable Speech Features vs. DNN Embeddings: What to Use in the Automatic Assessment of Parkinson’s Disease in Multi-lingual Scenarios,” in medRxiv, 2023.
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     @inproceedings{259047417,
     title = {Interpretable Speech Features vs. DNN Embeddings: What to Use in the Automatic Assessment of Parkinson's Disease in Multi-lingual Scenarios},
     author = {{A. Favaro} and {Yi-Ting Tsai} and {A. Butala} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
     year = 2023,
     month = {6},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/8d18efe22ad66b53a0a13fc71c9b57c41b7790d0},
     }

159. 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.
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     @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},
     }

160. 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.
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     @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},
     }

161. 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},
     }

162. 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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     @inproceedings{258967563,
     title = {Investigating model performance in language identification: beyond simple error statistics},
     author = {{S. Styles} and {Victoria Y. H. Chua} and {Fei Ting Woon} and {Hexin Liu} and {Leibny Paola García Perera} and {S. Khudanpur} and {Andy W. H. Khong} and {J. Dauwels}},
     year = 2023,
     month = {5},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/5550d0db7030aa77480bfb10c4ec00862bb233eb},
     }

163. Kangfu Mei, Mo Zhou, and Vishal M. Patel, “T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities,” in arXiv.org, 2023.
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     @inproceedings{258865247,
     title = {T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities},
     author = {{Kangfu Mei} and {Mo Zhou} and {Vishal M. Patel}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/95dd7685e9b4e6195d80b22167d980be4379da44},
     }

164. 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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     @inproceedings{259859140,
     title = {An Extensive Exploration of Back-Translation in 60 Languages},
     author = {{Paul McNamee} and {Kevin Duh}},
     year = 2023,
     booktitle = {Annual Meeting of the Association for Computational Linguistics},
     url = {https://www.semanticscholar.org/paper/3b1cea929fb0a44886ed654c9ca88a9df959f371},
     }

165. 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.
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     @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},
     }

166. 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},
     }

167. 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},
     }

168. 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.",
     }

169. 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/7f6b686b1a9ae3983dd4facfb23038d49f16dcc4},
     }

170. 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},
     url = {https://www.semanticscholar.org/paper/d6592cd383da0ebae9f4ff78a4df96ab635acfa2},
     }

171. 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.
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     @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},
     }

172. 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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     @inproceedings{259840157,
     title = {Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline},
     author = {{Wei Ji} and {Jingjing Li} and {Cheng Bian} and {Zongwei Zhou} and {Jiaying Zhao} and {A. Yuille} and {Li Cheng}},
     year = 2023,
     month = {6},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/fa0215c456d1957e131e43702e50e4c8f3e9477d},
     }

173. 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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     @inproceedings{259436318,
     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},
     url = {https://www.semanticscholar.org/paper/c061dd875146aa8d87b5bfe45eea73df8da3c373},
     }

174. 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.
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     @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},
     }

175. 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.
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     @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},
     }

176. 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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     @inproceedings{259993644,
     title = {Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.},
     author = {{V. Rennoll} and {Ian McLane} and {Adebayo A. Eisape} and {D. Grant} and {Helena Hahn} and {Mounya Elhilali} and {James E. West}},
     year = 2023,
     month = {7},
     booktitle = {ACS Applied Bio Materials},
     url = {https://www.semanticscholar.org/paper/bf5172b246adb601b731618108ba8ce5d1367177},
     }

177. 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.
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     @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},
     }

178. 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.
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     @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},
     }

179. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Regularizing Contrastive Predictive Coding for Speech Applications.” 2023.
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     @inproceedings{258079344,
     title = {Regularizing Contrastive Predictive Coding for Speech Applications},
     author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2023,
     month = {4},
     booktitle = {},
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185. 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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186. 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.
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187. Chun-Chieh Chang, Leibny Paola García-Perera, and S. Khudanpur, “Crosslingual Handwritten Text Generation Using GANs,” in ICDAR Workshops, 2023.
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189. 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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190. Matt Post and Marcin Junczys-Dowmunt, “Escaping the sentence-level paradigm in machine translation,” in arXiv.org, 2023.
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191. Yiqing Wang, Zihan Li, Jieru Mei, Zi-Ying Wei, Li Liu, Chen Wang, Shengtian Sang, A. Yuille, Cihang Xie, and Yuyin Zhou, “SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
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192. Elias Stengel-Eskin, Kyle Rawlins, and Benjamin Van Durme, “Zero and Few-shot Semantic Parsing with Ambiguous Inputs,” in International Conference on Learning Representations, 2023.
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193. 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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196. Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Yaoyao Liu, Adam Kortylewski, and A. Yuille, “Generating Images with 3D Annotations Using Diffusion Models,” in International Conference on Learning Representations, 2023.
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199. 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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200. 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.
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201. 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.
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214. 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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     author = {{Drew Prinster} and {S. Saria} and {Anqi Liu}},
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     url = {https://www.semanticscholar.org/paper/6faf60347b9ec3672a4d191cfe9fe0076191e9a0},
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215. 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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216. 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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217. 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.
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218. 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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219. 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.
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220. 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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221. 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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222. 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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223. 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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224. 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.
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225. 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.
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226. 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.
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227. 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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228. 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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229. 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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230. 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, Andrew K. 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 Ramirez, Clara E. 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 A. 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.
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     @inproceedings{259267429,
     title = {What are Adapters Really Efficient At?},
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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 {Andrew K. 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 Ramirez} and {Clara E. 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. 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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}},
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     booktitle = {},
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     }

231. 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.",
     }

232. 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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     @inproceedings{268042483,
     title = {MultiVENT: Multilingual Videos of Events and Aligned Natural Text},
     author = {{Kate Sanders} and {David Etter} and {Reno Kriz} and {Benjamin Van Durme}},
     year = 2023,
     booktitle = {Neural Information Processing Systems},
     url = {https://www.semanticscholar.org/paper/30e77a7464e9d62a4a9b075050d82ef85c4ea899},
     }

233. 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},
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     year = 2023,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/7c7d8f106f8cd1bdadfd3b46f6ebb1509cb1be42},
     }

234. Jonah P. Sengupta and A. Andreou, “Retinomorphic Channel Design and Considerations,” in Annual Conference on Information Sciences and Systems, 2023.
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     booktitle = {Annual Conference on Information Sciences and Systems},
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     }

235. 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.
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     title = {Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey},
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236. 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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     author = {{Kapil D. Katyal} and {R. Chellappa} and {Ketul Shah} and {Arun V. Reddy} and {Judy Hoffman} and {William Paul} and {Rohita Mocharla} and {D. Handelman} and {Celso de Melo}},
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     }

237. Y. Ranasinghe, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models.” 2023.
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     author = {{Y. Ranasinghe} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
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     }

238. Marc Marone and Benjamin Van Durme, “Data Portraits: Recording Foundation Model Training Data,” in Neural Information Processing Systems, 2023.
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     @inproceedings{257378087,
     title = {Data Portraits: Recording Foundation Model Training Data},
     author = {{Marc Marone} and {Benjamin Van Durme}},
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     booktitle = {Neural Information Processing Systems},
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239. Samuel Barham, Orion Weller, Michelle Yuan, Kenton Murray, M. Yarmohammadi, Zhengping Jiang, Siddharth Vashishtha, Alexander Martin, Anqi Liu, Aaron Steven White, Jordan L. Boyd-Graber, and Benjamin Van Durme, “MegaWika: Millions of reports and their sources across 50 diverse languages,” in arXiv.org, 2023.
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     @inproceedings{259924959,
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     }

240. Bowen Li, Yu-Cheng Chou, Shuwen Sun, Hualin Qiao, A. Yuille, and Zongwei Zhou, “Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis,” in arXiv.org, 2023.
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     @inproceedings{260681299,
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     author = {{Bowen Li} and {Yu-Cheng Chou} and {Shuwen Sun} and {Hualin Qiao} and {A. Yuille} and {Zongwei Zhou}},
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241. 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.
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     @inproceedings{258887517,
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     }

242. 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.
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     author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}},
     year = 2023,
     booktitle = {Neural Information Processing Systems},
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     }

243. Samik Sadhu and H. Hermansky, “Self-supervised Learning with Speech Modulation Dropout,” in arXiv.org, 2023.
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     @inproceedings{257687723,
     title = {Self-supervised Learning with Speech Modulation Dropout},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
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     }

244. 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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     @inproceedings{258714648,
     title = {AbdomenAtlas-8K: Annotating 8, 000 CT Volumes for Multi-Organ Segmentation in Three Weeks},
     author = {{Chongyu Qu} and {Tiezheng Zhang} and {Hualin Qiao} and {Jie Liu} and {Yucheng Tang} and {A. Yuille} and {Zongwei Zhou}},
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     }

245. 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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     title = {3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation},
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     year = 2023,
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     }

246. W. G. C. Bandara and Vishal M. Patel, “Deep Metric Learning for Unsupervised Remote Sensing Change Detection,” in arXiv.org, 2023.
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     @inproceedings{257557771,
     title = {Deep Metric Learning for Unsupervised Remote Sensing Change Detection},
     author = {{W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2023,
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     booktitle = {arXiv.org},
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     }

247. P. Abadir, Ramalingam Chellappa, N. Choudhry, G. Demiris, Deepak Ganesan, Jason Karlawish, Rose M Li, Jason H. Moore, J. Walston, Benjamin Najim Alicia I. Mathias Thomas K. M. Suchi Esther Marlin Dehak Arbaje Unberath Cudjoe Saria Oh Lunde, Benjamin M Marlin, N. Dehak, A. Arbaje, M. Unberath, T. Cudjoe, S. Saria, Esther Oh, N. Lundebjerg, C. Chute, Phillip Phan, Quincy M. Samus, and Nancy L. Schoenborn, “The promise of AI and technology to improve quality of life and care for older adults,” in Nature Aging, 2023.
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     title = {The promise of AI and technology to improve quality of life and care for older adults},
     author = {{P. Abadir} and {Ramalingam Chellappa} and {N. Choudhry} and {G. Demiris} and {Deepak Ganesan} and {Jason Karlawish} and {Rose M Li} and {Jason H. Moore} and {J. Walston} and {Benjamin Najim Alicia I. Mathias Thomas K. M. Suchi Esther Marlin Dehak Arbaje Unberath Cudjoe Saria Oh Lunde} and {Benjamin M Marlin} and {N. Dehak} and {A. Arbaje} and {M. Unberath} and {T. Cudjoe} and {S. Saria} and {Esther Oh} and {N. Lundebjerg} and {C. Chute} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn}},
     year = 2023,
     month = {5},
     booktitle = {Nature Aging},
     url = {https://www.semanticscholar.org/paper/24eafaf005bd6d73870b66525e8978b760e7b3ad},
     }

248. 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},
     }

249. 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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     @inproceedings{258841086,
     title = {Condensing Multilingual Knowledge with Lightweight Language-Specific Modules},
     author = {{Haoran Xu} and {Weiting Tan} and {Shuyue Stella Li} and {Yunmo Chen} and {Benjamin Van Durme} and {Philipp Koehn} and {Kenton Murray}},
     year = 2023,
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     booktitle = {Conference on Empirical Methods in Natural Language Processing},
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     }

250. Samuele Cornell, Matthew Wiesner, Shinji Watanabe, Desh Raj, Xuankai Chang, Paola García, Yoshiki Masuyama, Zhong-Qiu Wang, S. Squartini, and S. Khudanpur, “The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios,” in 7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023), 2023.
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     author = {{Samuele Cornell} and {Matthew Wiesner} and {Shinji Watanabe} and {Desh Raj} and {Xuankai Chang} and {Paola García} and {Yoshiki Masuyama} and {Zhong-Qiu Wang} and {S. Squartini} and {S. Khudanpur}},
     year = 2023,
     month = {6},
     booktitle = {7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023)},
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251. 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.
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     @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},
     }

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     @inproceedings{271601672,
     title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
     author = {{Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Abu Awal Md Shoeb} and {Abubakar Abid} and {Adam Fisch} and {Adam R. Brown} and {Adam Santoro} and {Aditya Gupta} and {Adrià Garriga-Alonso} and {Agnieszka 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 Parrish} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Amanda Dsouza} and {Ambrose Slone} and {Ameet Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmüller} and {Andrew M. Dai} and {Andrew La} and {Andrew K. Lampinen} and {Andy Zou} and {Angela Jiang} and {Angelica Chen} and {Anh Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {A. Tabassum} and {Arul Menezes} and {Arun Kirubarajan} and {A. Mullokandov} and {Ashish Sabharwal} and {Austin Herrick} and {Avia Efrat} and {Aykut Erdem} and {Ayla Karakas} and {B. R. Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Özyurt} and {Behnam Hedayatnia} and {Behnam Neyshabur} and {Benjamin Inden} and {Benno Stein} and {Berk Ekmekci} and {Bill Yuchen Lin} and {B. Howald} and {Bryan Orinion} and {Cameron Diao} and {Cameron Dour} and {Catherine Stinson} and {Cedrick Argueta} and {Cèsar Ferri Ramírez} and {Chandan Singh} and {Charles Rathkopf} and {Chenlin Meng} and {Chitta Baral} and {Chiyu Wu} and {Christopher Callison-Burch} and {Christian Waites} and {Christian Voigt} and {Christopher D. Manning} and {Christopher Potts} and {Cindy Ramirez} and {Clara E. Rivera} and {Clemencia Siro} and {Colin Raffel} and {Courtney Ashcraft} and {Cristina Garbacea} and {Damien Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {D. 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     url = {https://www.semanticscholar.org/paper/9dfe937f405b95bfc5e8e3679329a1f16b37e276},
     }

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     url = {https://www.semanticscholar.org/paper/09d3a354d22d75deddb0dcc99870f307aae2fd3c},
     }

260. 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.
     [BibTeX] [Link]

     @inproceedings{257404851,
     title = {Stabilized training of joint energy-based models and their practical applications},
     author = {{Martin Sustek} and {Samik Sadhu} and {L. Burget} and {H. Hermansky} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/46fd16213979b00e741b926539ad4ba7a1acd1cf},
     }

261. Margaret A. McMullin, Nathan C Higgins, Brian Gygi, Rohit Kumar, Mounya Elhilali, and J. Snyder, “Perception of global properties, objects, and settings in natural auditory scenes,” in Journal of the Acoustical Society of America, 2023.
     [BibTeX] [Link]

     @inproceedings{258374319,
     title = {Perception of global properties, objects, and settings in natural auditory scenes},
     author = {{Margaret A. McMullin} and {Nathan C Higgins} and {Brian Gygi} and {Rohit Kumar} and {Mounya Elhilali} and {J. Snyder}},
     year = 2023,
     month = {3},
     booktitle = {Journal of the Acoustical Society of America},
     url = {https://www.semanticscholar.org/paper/8890c3afc837ab0871d11f6ed17bfb6109943bff},
     }

262. 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.
     [BibTeX] [Link]

     @inproceedings{257833842,
     title = {BloombergGPT: A Large Language Model for Finance},
     author = {{Shijie Wu} and {Ozan Irsoy} and {Steven Lu} and {Vadim Dabravolski} and {Mark Dredze} and {Sebastian Gehrmann} and {P. Kambadur} and {D. Rosenberg} and {Gideon Mann}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/83edcfbb206ddad38a971d605da09390604248ea},
     }

263. 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.
     [BibTeX] [Link]

     @inproceedings{259893511,
     title = {Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era},
     author = {{M. Marín-Jiménez} and {Shiqi Yu} and {Yasushi Makihara} and {Vishal M. Patel} and {Maneet Singh} and {M. De Marsico}},
     year = 2023,
     month = {5},
     booktitle = {IEEE Journal on Selected Topics in Signal Processing},
     url = {https://www.semanticscholar.org/paper/df958104e921170b592e27798e18de9b9c892cbd},
     }

264. 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.
     [BibTeX] [Link]

     @inproceedings{259302532,
     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 {W. Anderson} and {C. Gordon} and {Kathryn R Rosenblatt} and {L. Clawson} and {N. Maragakis} and {F. Tenore} and {M. Fifer} and {H. Hermansky} and {N. Ramsey} and {N. Crone}},
     year = 2023,
     month = {7},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/3991788eee23646956065f50303a62379c1150dd},
     }

265. 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},
     }

266. 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},
     }

267. Rachael E. Jack, Vishal M. Patel, P. Turaga, Mayank Vatsa, Ramalingam Chellappa, A. Pentland, and Richa Singh, “Best Paper Section IEEE International Conference on Automatic Face and Gesture Recognition 2021,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2023.
     [BibTeX] [Link]

     @inproceedings{260715129,
     title = {Best Paper Section IEEE International Conference on Automatic Face and Gesture Recognition 2021},
     author = {{Rachael E. Jack} and {Vishal M. Patel} and {P. Turaga} and {Mayank Vatsa} and {Ramalingam Chellappa} and {A. Pentland} and {Richa Singh}},
     year = 2023,
     month = {7},
     booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
     url = {https://www.semanticscholar.org/paper/51bdfb0e1834a150abd52ad73b63e80ad690aa80},
     }

268. 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.
     [BibTeX] [Link]

     @inproceedings{263152081,
     title = {Speech Collage: Code-Switched Audio Generation by Collaging Monolingual Corpora},
     author = {{A. Hussein} and {Dorsa Zeinali} and {Ondrej Klejch} and {Matthew Wiesner} and {Brian Yan} and {Shammur A. Chowdhury} and {Ahmed Ali} 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/fa5ebb425c57f6c4f1c36a7200ef1da867346e8c},
     }

269. 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},
     }

270. 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.
     [BibTeX] [Link]

     @inproceedings{258987867,
     title = {Examining risks of racial biases in NLP tools for child protective services},
     author = {{Anjalie Field} and {Amanda Coston} and {Nupoor Gandhi} and {A. Chouldechova} and {Emily Putnam-Hornstein} and {David Steier} and {Yulia Tsvetkov}},
     year = 2023,
     month = {5},
     booktitle = {Conference on Fairness, Accountability and Transparency},
     url = {https://www.semanticscholar.org/paper/346e4f35a5a81ef893792133ec1fec18f23c1768},
     }

271. 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.
     [BibTeX] [Link]

     @inproceedings{260003158,
     title = {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},
     author = {{Daniel R. Mendat} and {Jonah P. Sengupta} and {Gaspar Tognetti} and {M. Villemur} and {P. Pouliquen} and {Sergio Montano} and {Kayode A. Sanni} and {J. Molin} and {Nishant Zachariah} and {I. Doxas} and {A. Andreou}},
     year = 2023,
     month = {5},
     booktitle = {International Symposium on Circuits and Systems},
     url = {https://www.semanticscholar.org/paper/0d2f0f6eb40d3be7b97a19315439721cf7ae8469},
     }

272. Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Benjamin Van Durme, “Natural Language Decomposition and Interpretation of Complex Utterances,” in Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, 2023.
     [BibTeX] [Link]

     @inproceedings{258686491,
     title = {Natural Language Decomposition and Interpretation of Complex Utterances},
     author = {{Harsh Jhamtani} and {Hao Fang} and {Patrick Xia} and {Eran Levy} and {Jacob Andreas} and {Benjamin Van Durme}},
     year = 2023,
     month = {5},
     booktitle = {Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/68040213e9a83408cdc491ed3e235b52b537eed1},
     }

273. Anjalie Field, Prateek Verma, Nay San, J. Eberhardt, and Dan Jurafsky, “Developing Speech Processing Pipelines for Police Accountability,” in Interspeech, 2023.
     [BibTeX] [Link]

     @inproceedings{259129355,
     title = {Developing Speech Processing Pipelines for Police Accountability},
     author = {{Anjalie Field} and {Prateek Verma} and {Nay San} and {J. Eberhardt} and {Dan Jurafsky}},
     year = 2023,
     month = {6},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/38c2e4e54a50ea5027c7a06ab325c22ece7b6c40},
     }

274. 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},
     }

275. 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},
     }

276. 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},
     }

277. 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.",
     }

278. 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.",
     }

279. 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 Trans. Mach. Learn. Res., 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 = {Trans. Mach. Learn. Res.},
     url = {https://www.semanticscholar.org/paper/7786825fd653b398c3975c3ff876459307d871f4},
     }

280. 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},
     }

281. 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.",
     }

282. 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.",
     }

283. 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.",
     }

284. Kangfu Mei, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{254636282,
     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},
     }

285. 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},
     }

286. 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.",
     }

287. Desh Raj, Daniel Povey, and S. Khudanpur, “GPU-accelerated Guided Source Separation for Meeting Transcription,” in Interspeech, 2022.
     [BibTeX] [Link]

     @inproceedings{254563914,
     title = {GPU-accelerated Guided Source Separation for Meeting Transcription},
     author = {{Desh Raj} and {Daniel Povey} and {S. Khudanpur}},
     year = 2022,
     month = {12},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/ad9199a35c5ce4738c39bb4af8abf7caee418365},
     }

288. 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.",
     }

289. 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.
     [BibTeX] [Link]

     @inproceedings{254825419,
     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},
     }

290. 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},
     }

291. 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.",
     }

292. 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},
     }

293. 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.",
     }

294. 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.
     [BibTeX] [Link]

     @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},
     }

295. 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},
     }

296. 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.",
     }

297. 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.
     [BibTeX] [Link]

     @inproceedings{254926993,
     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},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/68f1b94bbc900d2b5c60192a7e9eea4b046dd18a},
     }

298. 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.
     [BibTeX] [Link]

     @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},
     }

299. 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.",
     }

300. 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},
     }

301. 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},
     }

302. 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.",
     }

303. 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.
     [BibTeX] [Link]

     @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},
     booktitle = {Alzheimer's & Dementia},
     url = {https://www.semanticscholar.org/paper/e8f74514d4b195230ddd7dd6b60cabbc7ed240b1},
     }

304. 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.",
     }

305. 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.",
     }

306. 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.",
     }

307. 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.",
     }

308. 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.",
     }

309. 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.
     [BibTeX] [Link]

     @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},
     }

310. 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.",
     }

311. 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.",
     }

312. 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.",
     }

313. 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).",
     }

314. 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},
     }

315. 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},
     }

316. 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",
     }

317. 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",
     }

318. 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.
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     @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},
     }

319. 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},
     }

320. 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.
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     @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},
     }

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.
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     @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. 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},
     }

323. 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},
     }

324. 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},
     }

325. 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},
     }

326. 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},
     }

327. 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},
     }

328. 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},
     }

329. 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},
     }

330. 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},
     }

331. 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.
     [BibTeX] [Link]

     @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},
     }

332. 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},
     }

333. 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},
     }

334. 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.
     [BibTeX] [Link]

     @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},
     }

335. 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},
     }

336. 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.
     [BibTeX] [Link]

     @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},
     }

337. 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.
     [BibTeX] [Abstract] [Link]
     

     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.",
     }

338. 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.",
     }

339. 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.",
     }

340. 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.
     [BibTeX] [Link]

     @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},
     }

341. 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.
     [BibTeX] [Link]

     @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},
     }

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. 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},
     }

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. 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},
     }

346. 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},
     }

347. 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},
     }

348. 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},
     }

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. 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.",
     }

351. 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.",
     }

352. 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.",
     }

353. 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).",
     }

354. 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.",
     }

355. 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.",
     }

356. 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.",
     }

357. 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.",
     }

358. 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.",
     }

359. 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.",
     }

360. 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.",
     }

361. 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.",
     }

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. 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.",
     }

364. 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.",
     }

365. 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.",
     }

366. 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.",
     }

367. 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.",
     }

368. 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.",
     }

369. 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.",
     }

370. 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.",
     }

371. 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.",
     }

372. 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.",
     }

373. 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.",
     }

374. 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.",
     }

375. 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",
     }

376. R. Cotterell and J. Eisner, “A Functionalist Account of Vowel System Typology,” in Proceedings of the Association for Computational Linguistics (ACL), Dublin, 2022.
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     @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",
     }

377. 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",
     }

378. 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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     @inproceedings{260444131,
     title = {Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution},
     author = {{Nupoor Gandhi} and {Anjalie Field} and {Emma Strubell}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/43f09be116b87046334d395a71919ab423b204a1},
     }

379. Nithin Gopalakrishnan Nair, R. Yasarla, and Vishal M. Patel, “NBD-GAP: Non-Blind Image Deblurring without Clean Target Images,” in International Conference on Information Photonics, 2022.
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     @inproceedings{252383306,
     title = {NBD-GAP: Non-Blind Image Deblurring without Clean Target Images},
     author = {{Nithin Gopalakrishnan Nair} and {R. Yasarla} and {Vishal M. Patel}},
     year = 2022,
     month = {9},
     booktitle = {International Conference on Information Photonics},
     url = {https://www.semanticscholar.org/paper/28a43c5d52c421b1ccc24d15f39b2cdb82ed84de},
     }

380. Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hatwig Adam, A. Yuille, and Liang-Chieh Chen, “k-means Mask Transformer,” in European Conference on Computer Vision, 2022.
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     @inproceedings{250408015,
     title = {k-means Mask Transformer},
     author = {{Qihang Yu} and {Huiyu Wang} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hatwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
     year = 2022,
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/f3b1dd33a2a8b533a0c08382b2a2bbf721beac21},
     }

381. 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},
     }

382. Mo Zhou and Vishal M. Patel, “Enhancing Adversarial Robustness for Deep Metric Learning,” in Computer Vision and Pattern Recognition, 2022.
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     @inproceedings{247223074,
     title = {Enhancing Adversarial Robustness for Deep Metric Learning},
     author = {{Mo Zhou} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/5bcdc704df91b425b76fc6b64f1582667505cfae},
     }

383. 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},
     }

384. 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},
     }

385. 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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     @inproceedings{253833423,
     title = {A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy},
     author = {{Emma Bigelow} and {S. Saria} and {B. Piening} and {B. Curti} and {A. Dowdell} and {R. Weerasinghe} and {C. Bifulco} and {W. Urba} and {N. Finkelstein} and {E. Fertig} and {A. Baras} and {N. Zaidi} and {E. Jaffee} and {M. Yarchoan}},
     year = 2022,
     month = {1},
     booktitle = {Cancer Informatics},
     url = {https://www.semanticscholar.org/paper/a407bd6bae19371a8d3c92da0981aaf1e80b382e},
     }

386. 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},
     }

387. 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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     @inproceedings{251800257,
     title = {Masked Autoencoders Enable Efficient Knowledge Distillers},
     author = {{Yutong Bai} and {Zeyu Wang} and {Junfei Xiao} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
     year = 2022,
     month = {8},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/a7cd547c539d69f99f17855242cb07bd80047f9a},
     }

388. Daniel Khashabi, Yeganeh Kordi, and Hannaneh Hajishirzi, “UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training,” in arXiv.org, 2022.
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     @inproceedings{247154787,
     title = {UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training},
     author = {{Daniel Khashabi} and {Yeganeh Kordi} and {Hannaneh Hajishirzi}},
     year = 2022,
     month = {2},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/5b44101b2372a33ec06e15ce4d20ad9a15518325},
     }

389. 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},
     }

390. 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},
     }

391. Jeya Maria Jose Valanarasu and Vishal M. Patel, “UNeXt: MLP-based Rapid Medical Image Segmentation Network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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     @inproceedings{247362702,
     title = {UNeXt: MLP-based Rapid Medical Image Segmentation Network},
     author = {{Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/ccb5a70f8a6f7b7fc923b9d4c18488b2837daa6f},
     }

392. 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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     @inproceedings{252333098,
     title = {Addressing the 'coin flip model' and the role of 'process of care' variables in the analysis of TREWS},
     author = {{R. Adams} and {K. Henry} and {S. Saria}},
     year = 2022,
     month = {9},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/39383cc7a62fdd63e05873096d7283d5f1b90d59},
     }

393. 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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     @inproceedings{251765371,
     title = {AT-DDPM: Restoring Faces Degraded by Atmospheric Turbulence Using Denoising Diffusion Probabilistic Models},
     author = {{Nithin Gopalakrishnan Nair} and {Kangfu Mei} and {Vishal M. Patel}},
     year = 2022,
     month = {8},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/dad4a46e1fe0e8317bd6734ffdf5609d1f577559},
     }

394. 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},
     }

395. 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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     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}},
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     url = {https://www.semanticscholar.org/paper/6f85ad4e04fc157ed5b499e348972f188a39cd10},
     }

396. 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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     @inproceedings{247778672,
     title = {Target and Task specific Source-Free Domain Adaptive Image Segmentation},
     author = {{VS Vibashan} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/db37fdfed1260f94ffb08a174e3e19f28dd8835e},
     }

397. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmuller, Andrew M. Dai, Andrew La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, A. Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakacs, B. R. Roberts, B. S. Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Ozyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, B. Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, C’esar Ferri Ram’irez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Daniel H Garrette, Dan Hendrycks, D. Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, D. Gonz’alez, Danielle R. Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. 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Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jaehoon Lee, J. Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Koco’n, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Narain Sohl-Dickstein, Jason Phang, Jason Wei, J. Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Oluwadara Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Jane W Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jorg Frohberg, Jos Rozen, J. Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, K. Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, K. Mathewson, Kristen Chiafullo, Ksenia Shkaruta, K. Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Luca Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Col’on, Luke Metz, Lutfi Kerem cSenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ram’irez Quintana, M. Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, M. Schubert, Medina Baitemirova, Melody Arnaud, M. McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, M. Strube, Michal Swkedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Monica Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, T. MukundVarma, Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, N. Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, P. Doshi, Pascale Fung, P. Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, P. Eckersley, Phu Mon Htut, P. Hwang, P. Milkowski, P. Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphael Milliere, Rhythm Garg, Richard Barnes, R. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, R. Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi S. Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, S. Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Debnath, Siamak Shakeri, Simon Thormeyer, S. Melzi, Siva Reddy, S. Makini, Soo-Hwan Lee, Spencer Bradley Torene, Sriharsha Hatwar, S. Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T Piantadosi, Stuart M. Shieber, Summer Misherghi, S. Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsunori Hashimoto, Te-Lin Wu, T. Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, T. Kornev, T. Tunduny, Tobias Gerstenberg, T. Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, W. Fedus, W. Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yu Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, and Ziyi Wu, “Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,” in arXiv.org, 2022.
     [BibTeX] [Link]

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     }

414. 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.
     [BibTeX] [Link]

     @inproceedings{252533374,
     title = {Cross-Lingual Speaker Identification from Weak Local Evidence},
     author = {{Thomas Wolf} and {Lysandre Debut} and {Julien Victor Sanh} and {Clement Chaumond} and {Anthony Delangue} and {Pier-339 Moi} and {Clara ric Cistac} and {Yacine Ma} and {Julien Jernite} and {Plu} and {Teven Xu} and {Sylvain Le Scao} and {Gugger} and {Mariama} and {Quentin Drame} and {M. LhoestAlexander} and {Rush} and {Michael Miller Yoder} and {Sopan Khosla} and {Qinlan Shen} and {Ben Zhou} and {Qiang Ning} and {Daniel Khashabi} and {Kyle Richardson} and {Tushar Khot}},
     year = 2022,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/385b71fb56b54b019b855ff0265bbdbb01ad01ea},
     }

415. 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.
     [BibTeX] [Link]

     @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},
     }

416. 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.
     [BibTeX] [Link]

     @inproceedings{251253396,
     title = {Multilingual Coreference Resolution in Multiparty Dialogue},
     author = {{Boyuan Zheng} and {Patrick Xia} and {M. Yarmohammadi} and {Benjamin Van Durme}},
     year = 2022,
     month = {8},
     booktitle = {Transactions of the Association for Computational Linguistics},
     url = {https://www.semanticscholar.org/paper/ced26dee12fe5fa2f666bcc2ba5b0a1969240887},
     }

417. 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.",
     }

418. 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.
     [BibTeX] [Link]

     @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},
     }

419. 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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     @inproceedings{251539930,
     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},
     }

420. 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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     @inproceedings{252090174,
     title = {Time-Domain Speech Super-Resolution With GAN Based Modeling for Telephony Speaker Verification},
     author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {Piotr Żelasko} and {N. Dehak}},
     year = 2022,
     month = {9},
     booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
     url = {https://www.semanticscholar.org/paper/312a44c9d2d2719ca8d3eb22539edd215415229e},
     }

421. 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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     @inproceedings{249063004,
     title = {VoynaSlov: A Data Set of Russian Social Media Activity during the 2022 Ukraine-Russia War},
     author = {{Chan Young Park} and {Julia Mendelsohn} and {Anjalie Field} and {Yulia Tsvetkov}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/215a6f2b4c206975f59d81c0c9f45cfe935a85e9},
     }

422. 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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     @inproceedings{249889093,
     title = {Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency},
     author = {{Aimon Rahman} and {Jeya Maria Jose Valanarasu} and {I. Hacihaliloglu} and {Vishal M. Patel}},
     year = 2022,
     month = {6},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/706ae2328d0207f956b7fd644b1bb64b130950e5},
     }

423. 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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     @inproceedings{247476162,
     title = {DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection},
     author = {{Yingwei Li} and {Adams Wei Yu} and {Tianjian Meng} and {Benjamin Caine} and {Jiquan Ngiam} and {Daiyi Peng} and {Junyang Shen} and {Bo-Xun Wu} and {Yifeng Lu} and {Denny Zhou} and {Quoc V. Le} and {A. Yuille} and {Mingxing Tan}},
     year = 2022,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/5ffca96f4becdab649f085699594caa7c5c03e86},
     }

424. 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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     @inproceedings{248832471,
     title = {A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models},
     author = {{H. E. Echo Wang} and {M. Landers} and {R. Adams} and {Adarsh Subbaswamy} and {Hadi Kharrazi} and {D. Gaskin} and {S. Saria}},
     year = 2022,
     month = {5},
     booktitle = {J. Am. Medical Informatics Assoc.},
     url = {https://www.semanticscholar.org/paper/cdb65bc7700f365cf5ff152b6f3cb7434d9ad7e8},
     }

425. Y. Xia, Q. Yu, L. Chu, S. Kawamoto, S. Park, F. Liu, J. Chen, Z. Zhu, B. Li, Z. Zhou, Y. Lu, Y. Wang, W. Shen, L. Xie, Y. Zhou, elliot k fishman, A. Javed, D. Fouladi, S. Shayesteh, J. Graves, A. Blanco, E. Zinreich, B. Kinny-Koster, K. Kinzler, R. Hruban, B. Vogelstein, A. Yuille, and E. Fishman, “The FELIX Project: Deep Networks To Detect Pancreatic Neoplasms,” in medRxiv, 2022.
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     @inproceedings{252519938,
     title = {The FELIX Project: Deep Networks To Detect Pancreatic Neoplasms},
     author = {{Y. Xia} and {Q. Yu} and {L. Chu} and {S. Kawamoto} and {S. Park} and {F. Liu} and {J. Chen} and {Z. Zhu} and {B. Li} and {Z. Zhou} and {Y. Lu} and {Y. Wang} and {W. Shen} and {L. Xie} and {Y. Zhou} and {elliot k fishman} and {A. Javed} and {D. Fouladi} and {S. Shayesteh} and {J. Graves} and {A. Blanco} and {E. Zinreich} and {B. Kinny-Koster} and {K. Kinzler} and {R. Hruban} and {B. Vogelstein} and {A. Yuille} and {E. Fishman}},
     year = 2022,
     month = {9},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/3167cedfe031711fa832f5ba48519357923ac0c7},
     }

426. 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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     @inproceedings{259692914,
     title = {kMaX-DeepLab: k-means Mask Transformer},
     author = {{Qihang Yu} and {Huiyu Wang} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
     year = 2022,
     month = {7},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/5f3c2a31fc84d13a72008f70106163bd92f2f9d0},
     }

427. 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.
     [BibTeX] [Link]

     @inproceedings{260443047,
     title = {When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories},
     author = {{Alex Troy Mallen} and {Akari Asai} and {Victor Zhong} and {Rajarshi Das} and {Hannaneh Hajishirzi} and {Daniel Khashabi}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7b0f98f51040700aae3cd9f0e3432dedcd69fb30},
     }

428. 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.
     [BibTeX] [Link]

     @inproceedings{248300043,
     title = {Fast AdvProp},
     author = {{Jieru Mei} and {Yucheng Han} and {Yutong Bai} and {Yixiao Zhang} and {Yingwei Li} and {Xianhang Li} and {A. Yuille} and {Cihang Xie}},
     year = 2022,
     month = {4},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/f2eaaf8afc89a86035fd7127305f2bb9d2169495},
     }

429. 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.
     [BibTeX] [Link]

     @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},
     }

430. 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.
     [BibTeX] [Link]

     @inproceedings{247618802,
     title = {PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification},
     author = {{Hexin Liu} and {Leibny Paola García Perera} and {Andy W. H. Khong} and {S. Styles} and {S. Khudanpur}},
     year = 2022,
     month = {3},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/3f7542a6f77db123632ac723ab49f5a62f6184e3},
     }

431. Sai Saketh Rambhatla, Saksham Suri, R. Chellappa, and Abhinav Shrivastava, “SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining,” in IEEE International Conference on Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{245877805,
     title = {SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining},
     author = {{Sai Saketh Rambhatla} and {Saksham Suri} and {R. Chellappa} and {Abhinav Shrivastava}},
     year = 2022,
     month = {1},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/7f71d5804fe434168643babc616a76eb65d5882e},
     }

432. 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},
     }

433. 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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     @inproceedings{253461961,
     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},
     }

434. 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},
     }

435. 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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     @inproceedings{246823296,
     title = {Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning},
     author = {{Rui Shao} and {Pramuditha Perera} and {P. Yuen} and {Vishal M. Patel}},
     year = 2022,
     month = {2},
     booktitle = {International Journal of Computer Vision},
     url = {https://www.semanticscholar.org/paper/bce77cb22110eaf52438cf03b8668b875c699c46},
     }

436. Weiting Tan and Philipp Koehn, “Bitext Mining for Low-Resource Languages via Contrastive Learning,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{251765426,
     title = {Bitext Mining for Low-Resource Languages via Contrastive Learning},
     author = {{Weiting Tan} and {Philipp Koehn}},
     year = 2022,
     month = {8},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/767853fdd964e043c485ebb92afdcdf3ee8457e8},
     }

437. 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},
     }

438. Mo Zhou and Vishal M. Patel, “On Trace of PGD-Like Adversarial Attacks,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @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},
     }

439. 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.
     [BibTeX] [Link]

     @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},
     }

440. 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.",
     }

441. 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.
     [BibTeX] [Link]

     @inproceedings{248069341,
     title = {Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint 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 = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/49011d1b139bbb65fe273fd9e4b2197cee237385},
     }

442. 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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443. 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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444. 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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445. 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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446. 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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447. 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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448. 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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449. 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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450. 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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451. Tae Soo Kim, Bohoon Shim, Michael Peven, Weichao Qiu, A. Yuille, and Gregory Hager, “Learning from Synthetic Vehicles,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2022.
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452. 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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453. 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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454. 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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455. Feng Wang, Huiyu Wang, Chen Wei, A. Yuille, and Wei Shen, “CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation,” in European Conference on Computer Vision, 2022.
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456. 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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457. 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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458. Malsha V. Perera, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “SAR Despeckling Using a Denoising Diffusion Probabilistic Model,” in IEEE Geoscience and Remote Sensing Letters, 2022.
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459. 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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460. 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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461. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in arXiv.org, 2022.
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462. 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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463. 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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464. 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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465. 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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466. 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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467. 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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468. 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.
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469. 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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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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471. 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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472. 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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473. 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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474. 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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475. 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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476. Xiaolei Huang, Franck Dernoncourt, and Mark Dredze, “Enriching Unsupervised User Embedding via Medical Concepts,” in ACM Conference on Health, Inference, and Learning, 2022.
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477. 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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478. Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “Appendix for k -means Mask Transformer.” 2022.
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479. 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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480. 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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481. Chan Young Park, Julia Mendelsohn, Anjalie Field, and Yulia Tsvetkov, “Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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482. Yiqun Mei, Pengfei Guo, and Vishal M. Patel, “Supplementary File: Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination.” 2022.
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483. 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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484. 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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485. 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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486. 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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487. 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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488. Amir Alipour-Fanid, Monireh Dabaghchian, R. Arora, and K. Zeng, “Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach,” in IEEE Transactions on Cognitive Communications and Networking, 2022.
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489. 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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490. 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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491. Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté, Matthew J. Hausknecht, R. Laroche, I. Momennejad, H. V. Seijen, and Benjamin Van Durme, “One-Shot Learning from a Demonstration with Hierarchical Latent Language,” in Adaptive Agents and Multi-Agent Systems, 2022.
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492. 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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493. Zili Huang, Shinji Watanabe, Shu-Wen Yang, Leibny Paola García-Perera, and S. Khudanpur, “Investigating Self-Supervised Learning for Speech Enhancement and Separation,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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494. 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 British medical journal, 2022.
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495. 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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496. Kangfu Mei, Vishal M. Patel, and Rui Huang, “Deep Semantic Statistics Matching (D2SM) Denoising Network,” in European Conference on Computer Vision, 2022.
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497. 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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498. 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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499. 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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500. 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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501. 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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502. 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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503. 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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504. Yu Zeng, Zhe Lin, and Vishal M. Patel, “Shape-guided Object Inpainting,” in arXiv.org, 2022.
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505. 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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506. M. Villemur, Jonah P. Sengupta, P. Julián, and A. Andreou, “Morphological, Object Detection Framework for Embedded, Event-based Sensing,” in International Conference on Event-Based Control, Communication, and Signal Processing, 2022.
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507. 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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508. 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.
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509. 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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510. 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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511. Sangwook Park and Mounya Elhilali, “Time-Balanced Focal Loss for Audio Event Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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512. 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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513. 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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515. 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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516. 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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517. 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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518. 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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519. 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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520. 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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521. 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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522. 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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523. Xianhang Li, Huiyu Wang, Chen Wei, Jieru Mei, A. Yuille, Yuyin Zhou, and Cihang Xie, “In Defense of Image Pre-Training for Spatiotemporal Recognition,” in European Conference on Computer Vision, 2022.
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524. Nathaniel Weir and Benjamin Van Durme, “NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning,” in Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, 2022.
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525. 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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526. 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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527. 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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528. 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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529. 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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530. 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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532. 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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533. 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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534. 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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535. 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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536. 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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537. 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},
     }

538. 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, California Institute of 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.
     [BibTeX] [Link]

     @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 {California Institute of 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.}},
     year = 2021,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/0d6d142dc49cf7537ece045d8d469fd014a5d3b6},
     }

539. 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.
     [BibTeX] [Link]

     @inproceedings{244896502,
     title = {MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification},
     author = {{Jingye Chen} and {Jieneng Chen} and {Zongwei Zhou} and {Bin Li} and {A. Yuille} and {Yongyi Lu}},
     year = 2021,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/aca5ae479a48aaee0b02cf07b55e909abee51ebb},
     }

540. 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.
     [BibTeX] [Link]

     @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},
     }

541. 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.
     [BibTeX] [Link]

     @inproceedings{245353696,
     title = {Lite Vision Transformer with Enhanced Self-Attention},
     author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zijun Wei} and {Zhe L. Lin} and {A. Yuille}},
     year = 2021,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/72e81bc41ffae1d414836169107910025aaacb75},
     }

542. Kangfu Mei and Vishal M. Patel, “LTT-GAN: Looking Through Turbulence by Inverting GANs,” in IEEE Journal on Selected Topics in Signal Processing, 2021.
     [BibTeX] [Link]

     @inproceedings{244909001,
     title = {LTT-GAN: Looking Through Turbulence by Inverting GANs},
     author = {{Kangfu Mei} and {Vishal M. Patel}},
     year = 2021,
     month = {12},
     booktitle = {IEEE Journal on Selected Topics in Signal Processing},
     url = {https://www.semanticscholar.org/paper/49b7c72a65263f7f0f1eaa4fd23ef3b73c013de1},
     }

543. 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.
     [BibTeX] [Link]

     @inproceedings{245594119,
     title = {External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data},
     author = {{Yuyin Zhou} and {D. Dreizin} and {Yan Wang} and {Fengze Liu} and {Wei-lei Shen} and {A. Yuille}},
     year = 2021,
     month = {12},
     booktitle = {IEEE Transactions on Medical Imaging},
     url = {https://www.semanticscholar.org/paper/286f82f75ac6a7baa342217296d68eff30c07af6},
     }

544. 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.
     [BibTeX] [Link]

     @inproceedings{244798652,
     title = {PartImageNet: A Large, High-Quality Dataset of Parts},
     author = {{Ju He} and {Shuo Yang} and {Shaokang Yang} and {Adam Kortylewski} and {Xiaoding Yuan} and {Jieneng Chen} and {Shuai Liu} and {Cheng Yang} and {A. Yuille}},
     year = 2021,
     month = {12},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/5c1dd63a45dc56009d1d499c8c2f4d7b9953a507},
     }

545. Zach Wood-Doughty, Isabel Cachola, and Mark Dredze, “Proxy Model Explanations for Time Series RNNs,” in International Conference on Machine Learning and Applications, 2021.
     [BibTeX] [Link]

     @inproceedings{246291268,
     title = {Proxy Model Explanations for Time Series RNNs},
     author = {{Zach Wood-Doughty} and {Isabel Cachola} and {Mark Dredze}},
     year = 2021,
     month = {12},
     booktitle = {International Conference on Machine Learning and Applications},
     url = {https://www.semanticscholar.org/paper/9e031c15797f9e41598a6c7ebe583e3bb72dceb0},
     }

546. 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.
     [BibTeX] [Link]

     @inproceedings{245218767,
     title = {Masked Feature Prediction for Self-Supervised Visual Pre-Training},
     author = {{Chen Wei} and {Haoqi Fan} and {Saining Xie} and {Chaoxia Wu} and {A. Yuille} and {Christoph Feichtenhofer}},
     year = 2021,
     month = {12},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/008a428e049003fe768068a0f1fa1416af5c4982},
     }

547. 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.",
     }

548. 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.",
     }

549. 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},
     }

550. 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.
     [BibTeX] [Link]

     @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},
     }

551. 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.",
     }

552. 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.",
     }

553. 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.",
     }

554. 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.
     [BibTeX] [Link]

     @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},
     }

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. 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},
     }

557. 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.
     [BibTeX] [Link]

     @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},
     }

558. 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},
     }

559. 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.",
     }

560. 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.",
     }

561. 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},
     }

562. 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.
     [BibTeX] [Link]

     @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},
     }

563. 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.",
     }

564. 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.",
     }

565. 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 Mac