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

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

2. 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",
   }

3. 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",
   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.",
   }

4. 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",
   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.",
   }

5. 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",
   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.",
   }

6. 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",
   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.",
   }

7. 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",
   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.",
   }

8. 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",
   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.",
   }

9. 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",
   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.",
   }

10. 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",
    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.",
    }

11. 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},
    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.",
    }

12. 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",
    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.",
    }

13. 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",
    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.",
    }

14. 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",
    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.",
    }

15. 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",
    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.",
    }

16. 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",
    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.",
    }

17. 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",
    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.",
    }

18. 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",
    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.",
    }

19. 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",
    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.",
    }

20. 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",
    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.",
    }

21. 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",
    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.",
    }

22. 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",
    }

23. 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",
    }

24. 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",
    }

25. 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",
    }

26. 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",
    }

27. 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",
    }

28. 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",
    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.",
    }

29. 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",
    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.",
    }

30. 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",
    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.",
    }

31. Matt Post and Marcin Junczys-Dowmunt, “Escaping the sentence-level paradigm in machine translation,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{258309151,
    title = {Escaping the sentence-level paradigm in machine translation},
    author = {{Matt Post} and {Marcin Junczys-Dowmunt}},
    year = 2023,
    month = {4},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/59e2cfbb1395a4a02e9efecadd4f4005af462c1b},
    }

32. Enayat Ullah and R. Arora, “Generalization bounds for Kernel Canonical Correlation Analysis,” in Trans. Mach. Learn. Res., 2023.
    [BibTeX] [Link]

    @inproceedings{258766137,
    title = {Generalization bounds for Kernel Canonical Correlation Analysis},
    author = {{Enayat Ullah} and {R. Arora}},
    year = 2023,
    booktitle = {Trans. Mach. Learn. Res.},
    url = {https://www.semanticscholar.org/paper/4a55079d0145870461cbe2a48f53e40e64b7db3d},
    }

33. 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.” 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 = {},
    url = {https://www.semanticscholar.org/paper/fa5ebb425c57f6c4f1c36a7200ef1da867346e8c},
    }

34. 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},
    }

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

    @inproceedings{257735707,
    title = {Reference free auscultation quality metric and its trends},
    author = {{A. Kala} and {E. McCollum} and {Mounya Elhilali}},
    year = 2023,
    month = {8},
    booktitle = {Biomedical Signal Processing and Control},
    url = {https://www.semanticscholar.org/paper/4276e26be8c196ba4b496b4a0acc4102d32c0bd8},
    }

36. Tianyu Cao, L. Moro-Velázquez, Piotr Żelasko, J. Villalba, and N. Dehak, “Vsameter: Evaluation of a New Open-Source Tool to Measure Vowel Space Area and Related Metrics,” in Spoken Language Technology Workshop, 2023.
    [BibTeX] [Link]

    @inproceedings{256356339,
    title = {Vsameter: Evaluation of a New Open-Source Tool to Measure Vowel Space Area and Related Metrics},
    author = {{Tianyu Cao} and {L. Moro-Velázquez} and {Piotr Żelasko} and {J. Villalba} and {N. Dehak}},
    year = 2023,
    month = {1},
    booktitle = {Spoken Language Technology Workshop},
    url = {https://www.semanticscholar.org/paper/dd3d00bf410d95d15569443387082da13a2462c4},
    }

37. Yining Lu, Haoping Yu, and Daniel Khashabi, “GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{259950912,
    title = {GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution},
    author = {{Yining Lu} and {Haoping Yu} and {Daniel Khashabi}},
    year = 2023,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/3bd83ff979f3c0e9470f23c360a18333593dc5a1},
    }

38. 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.
    [BibTeX] [Link]

    @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},
    }

39. A. Hussein, Brian Yan, Antonios Anastasopoulos, Shinji Watanabe, and S. Khudanpur, “Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization.” 2023.
    [BibTeX] [Link]

    @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 = {},
    url = {https://www.semanticscholar.org/paper/c2745e86ecc9bec372690cced53ccfdf44f407f8},
    }

40. Deepti Hegde, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{257632366,
    title = {CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition},
    author = {{Deepti Hegde} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
    year = 2023,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/b460a263abec8b1aaa039963be9b371a581e7b21},
    }

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

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

42. Suzanna Sia and Kevin Duh, “In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{258547233,
    title = {In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models},
    author = {{Suzanna Sia} and {Kevin Duh}},
    year = 2023,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/91bc42852997bc774467e9ef8cda19a85507f663},
    }

43. Elias Stengel-Eskin and Benjamin Van Durme, “Did You Mean…? Confidence-based Trade-offs in Semantic Parsing,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{257805227,
    title = {Did You Mean...? Confidence-based Trade-offs in Semantic Parsing},
    author = {{Elias Stengel-Eskin} and {Benjamin Van Durme}},
    year = 2023,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/8aff5530f92684578f7d5a89e5fad7922f04b1e5},
    }

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

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

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

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

46. 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.
    [BibTeX] [Link]

    @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},
    }

47. Paul McNamee and Kevin Duh, “An Extensive Exploration of Back-Translation in 60 Languages,” in Annual Meeting of the Association for Computational Linguistics, 2023.
    [BibTeX] [Link]

    @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},
    }

48. Y. Ranasinghe, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{257663507,
    title = {Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models},
    author = {{Y. Ranasinghe} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
    year = 2023,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/851a11cfb1a5832f81717ee9a5b03901fe4f39e9},
    }

49. A. Favaro, L. Moro-Velázquez, A. Butala, C. Motley, Tianyu Cao, R. Stevens, J. Villalba, and N. Dehak, “Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson’s disease,” in Frontiers in Neurology, 2023.
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    @inproceedings{257323163,
    title = {Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease},
    author = {{A. Favaro} and {L. Moro-Velázquez} and {A. Butala} and {C. Motley} and {Tianyu Cao} and {R. Stevens} and {J. Villalba} and {N. Dehak}},
    year = 2023,
    month = {3},
    booktitle = {Frontiers in Neurology},
    url = {https://www.semanticscholar.org/paper/3ed2d557a323c9fc39dbdd64e0ffab064b35a7f9},
    }

50. Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Benjamin Van Durme, “Natural Language Decomposition and Interpretation of Complex Utterances,” in arXiv.org, 2023.
    [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 = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/68040213e9a83408cdc491ed3e235b52b537eed1},
    }

51. Elizabeth Salesky, Neha Verma, Philipp Koehn, and Matt Post, “Pixel Representations for Multilingual Translation and Data-efficient Cross-lingual Transfer,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{258841479,
    title = {Pixel Representations for Multilingual Translation and Data-efficient Cross-lingual Transfer},
    author = {{Elizabeth Salesky} and {Neha Verma} and {Philipp Koehn} and {Matt Post}},
    year = 2023,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/6237a49297f45ebfdeabbbf67d06de323d1fccd4},
    }

52. 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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    @inproceedings{256353599,
    title = {A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders},
    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}},
    year = 2023,
    month = {1},
    booktitle = {Spoken Language Technology Workshop},
    url = {https://www.semanticscholar.org/paper/40eb935374d67b7b9979e0c9333c291d188c472b},
    }

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

54. 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/0e632453c885422df2cc9f9075340f9e89308ae1},
    }

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

56. 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},
    }

57. 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.
    [BibTeX] [Link]

    @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,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/21510551199cc8ebfba5252575133de607448fa9},
    }

58. Liangyu Chen, A. Yuille, and Zongwei Zhou, “Making Your First Choice: To Address Cold Start Problem in Medical Active 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 = {},
    url = {https://www.semanticscholar.org/paper/251516c1549fc4566b801788c932ef1f18f343b3},
    }

59. Samuele Cornell, Matthew Wiesner, Shinji Watanabe, Desh Raj, Xuankai Chang, Paola García, Yoshiki Masuyama, Zhongqiu Wang, S. Squartini, and S. Khudanpur, “The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{259251557,
    title = {The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios},
    author = {{Samuele Cornell} and {Matthew Wiesner} and {Shinji Watanabe} and {Desh Raj} and {Xuankai Chang} and {Paola García} and {Yoshiki Masuyama} and {Zhongqiu Wang} and {S. Squartini} and {S. Khudanpur}},
    year = 2023,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/d4d5fe4a35e9de845877015075f727415e83d18f},
    }

60. J. Villalba, Jonas Borgstrom, Maliha Jahan, Saurabh Kataria, Leibny Paola Garcia, 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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    @inproceedings{260918551,
    title = {Advances in Language Recognition in Low Resource African Languages: The JHU-MIT Submission for NIST LRE22},
    author = {{J. Villalba} and {Jonas Borgstrom} and {Maliha Jahan} and {Saurabh Kataria} and {Leibny Paola Garcia} and {P. Torres-Carrasquillo} and {N. Dehak}},
    year = 2023,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/51aca07d500c44ebde896b8df3b0388dd3ade489},
    }

61. Zihao Xiao, A. Yuille, and Yi-Ting Chen, “Learning Road Scene-level Representations via Semantic Region Prediction,” in Conference on Robot Learning, 2023.
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    @inproceedings{255372874,
    title = {Learning Road Scene-level Representations via Semantic Region Prediction},
    author = {{Zihao Xiao} and {A. Yuille} and {Yi-Ting Chen}},
    year = 2023,
    month = {1},
    booktitle = {Conference on Robot Learning},
    url = {https://www.semanticscholar.org/paper/11b29ca1a235d80a2e55f6eb7711d2aa5785bb8c},
    }

62. 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.
    [BibTeX] [Link]

    @inproceedings{260681299,
    title = {Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis},
    author = {{Bowen Li} and {Yu-Cheng Chou} and {Shuwen Sun} and {Hualin Qiao} and {A. Yuille} and {Zongwei Zhou}},
    year = 2023,
    month = {8},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/d556aaf4ac55b6cebb9b889ec7b89b086cb41bff},
    }

63. 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",
    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}",
    }

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

65. A. Favaro, Yi-Ting Tsai, A. Butala, Thomas Thebaud, J. Villalba, N. Dehak, Laureano Moro-Vel´azquez, American English, Castilian Spanish, and Italian Colombian Spanish, “Interpretable Speech Features vs. DNN Embeddings: What to Use in the Automatic Assessment of Parkinson’s Disease in Multi-lingual Scenarios,” in medRxiv, 2023.
    [BibTeX] [Link]

    @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 {Laureano Moro-Vel´azquez} and {American English} and {Castilian Spanish} and {Italian Colombian Spanish}},
    year = 2023,
    month = {6},
    booktitle = {medRxiv},
    url = {https://www.semanticscholar.org/paper/8d18efe22ad66b53a0a13fc71c9b57c41b7790d0},
    }

66. Alexandra DeLucia, Mark Dredze, and A. Buczak, “A Multi-instance Learning Approach to Civil Unrest Event Detection using Twitter.” 2023.
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    @inproceedings{261665526,
    title = {A Multi-instance Learning Approach to Civil Unrest Event Detection using Twitter},
    author = {{Alexandra DeLucia} and {Mark Dredze} and {A. Buczak}},
    year = 2023,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/7eb89a6be7172b3fbcbaa3c10dcbd255760f02d9},
    }

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

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

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

69. Daniel R. Mendat, Jonah P. Sengupta, Gaspar Tognetti, M. Villemur, P. Pouliquen, Sergio Montano, Kayode A. Sanni, J. Molin, Nishant Zachariah, I. Doxas, and A. Andreou, “A RISC-V Neuromorphic Micro-Controller Unit (vMCU) with Event-Based Physical Interface and Computational Memory for Low-Latency Machine Perception and Intelligence at the Edge,” in International Symposium on Circuits and Systems, 2023.
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    @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},
    }

70. 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},
    }

71. Elias Stengel-Eskin, Kyle Rawlins, and Benjamin Van Durme, “Zero and Few-shot Semantic Parsing with Ambiguous Inputs,” in arXiv.org, 2023.
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    @inproceedings{258999294,
    title = {Zero and Few-shot Semantic Parsing with Ambiguous Inputs},
    author = {{Elias Stengel-Eskin} and {Kyle Rawlins} and {Benjamin Van Durme}},
    year = 2023,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/b8e49068441a43aaf039527c6063a033368dd357},
    }

72. 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},
    }

73. 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 arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{260125020,
    title = {SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation},
    author = {{Yiqing Wang} and {Zihan Li} and {Jieru Mei} and {Zi-Ying Wei} and {Li Liu} and {Chen Wang} and {Shengtian Sang} and {A. Yuille} and {Cihang Xie} and {Yuyin Zhou}},
    year = 2023,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/c84e03c8ba7feaa2679a90b1637f3b079be15aa9},
    }

74. Enayat Ullah and R. Arora, “From Adaptive Query Release to Machine Unlearning,” in International Conference on Machine Learning, 2023.
    [BibTeX] [Link]

    @inproceedings{260091661,
    title = {From Adaptive Query Release to Machine Unlearning},
    author = {{Enayat Ullah} and {R. Arora}},
    year = 2023,
    month = {7},
    booktitle = {International Conference on Machine Learning},
    url = {https://www.semanticscholar.org/paper/4164c47975d9576878ff2740d663fc74968c2e0c},
    }

75. 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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    @inproceedings{258383693,
    title = {Leveraging synthetic data for robust gesture recognition},
    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}},
    year = 2023,
    month = {6},
    booktitle = {Defense + Commercial Sensing},
    url = {https://www.semanticscholar.org/paper/922198774621861436721bd923dc0f0028872a84},
    }

76. 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, “Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{256389683,
    title = {Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans},
    author = {{Jieneng Chen} and {Yingda Xia} and {Jiawen Yao} and {K. Yan} and {Jianpeng Zhang} and {Le Lu} and {Fakai Wang} and {Bo Zhou} and {Mingyan Qiu} and {Qihang Yu} and {Ming Yuan} and {Wei Fang} and {Yuxing Tang} and {Minfeng Xu} and {Jian Zhou} and {Yuqian Zhao} and {Qifeng Wang} and {X. Ye} and {Xiaoli Yin} and {Yu Shi} and {Xin Chen} and {Jingren Zhou} and {A. Yuille} and {Zai-De Liu} and {Ling Zhang}},
    year = 2023,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/c12d7ef434266e4df411623c910e8d9bdf7d0b74},
    }

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

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

78. Dongji Gao, Matthew Wiesner, Hainan Xu, Leibny Paola Garcia, Daniel Povey, and S. Khudanpur, “Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts,” in Interspeech, 2023.
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    @inproceedings{259063779,
    title = {Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts},
    author = {{Dongji Gao} and {Matthew Wiesner} and {Hainan Xu} and {Leibny Paola Garcia} and {Daniel Povey} and {S. Khudanpur}},
    year = 2023,
    month = {6},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/b620d46668d62930e41393168434118bb9a2bfcb},
    }

79. Qihao Liu, Adam Kortylewski, Yutong Bai, Song Bai, and A. Yuille, “Intriguing Properties of Text-guided Diffusion Models,” in arXiv.org, 2023.
    [BibTeX] [Link]

    @inproceedings{258999203,
    title = {Intriguing Properties of Text-guided Diffusion Models},
    author = {{Qihao Liu} and {Adam Kortylewski} and {Yutong Bai} and {Song Bai} and {A. Yuille}},
    year = 2023,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/5a7a7655a7cc25e78a20892315902dceaaba3bf4},
    }

80. Alicia M. Braxton, A. Kiemen, Mia P. Grahn, André Forjaz, Jaanvi Mahesh Babu, Lily Zheng, Li-yu Jiang, H. Cheng, Q. Song, Rebecca Reichel, Sarah Graham, A. Damanakis, Catherine G. Fischer, Stephanie Mou, Cameron Metz, Julie Granger, Xiao-ding Liu, N. Bachmann, Cristina Almagro-Pérez, A. C. Jiang, Jeonghyun Yoo, Bridgette Kim, Scott Du, Eli Foster, Jocelyn Y Hsu, P.A. Rivera, L. Chu, Fengze Liu, N. Niknafs, E. Fishman, A. Yuille, Nicholas J. Roberts, E. Thompson, R. Scharpf, T. Cornish, Y. Jiao, R. Karchin, R. Hruban, Pei-Hsun Wu, D. Wirtz, and L. Wood, “Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions,” in bioRxiv, 2023.
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    @inproceedings{256391481,
    title = {Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions},
    author = {{Alicia M. Braxton} and {A. Kiemen} and {Mia P. Grahn} and {André Forjaz} and {Jaanvi Mahesh Babu} and {Lily Zheng} and {Li-yu Jiang} and {H. Cheng} and {Q. Song} and {Rebecca Reichel} and {Sarah Graham} and {A. Damanakis} and {Catherine G. Fischer} and {Stephanie Mou} and {Cameron Metz} and {Julie Granger} and {Xiao-ding Liu} and {N. Bachmann} and {Cristina Almagro-Pérez} and {A. C. Jiang} and {Jeonghyun Yoo} and {Bridgette Kim} and {Scott Du} and {Eli Foster} and {Jocelyn Y Hsu} and {P.A. Rivera} and {L. Chu} and {Fengze Liu} and {N. Niknafs} and {E. Fishman} and {A. Yuille} and {Nicholas J. Roberts} and {E. Thompson} and {R. Scharpf} and {T. Cornish} and {Y. Jiao} and {R. Karchin} and {R. Hruban} and {Pei-Hsun Wu} and {D. Wirtz} and {L. Wood}},
    year = 2023,
    month = {1},
    booktitle = {bioRxiv},
    url = {https://www.semanticscholar.org/paper/8d98352a5fd535de9be9e83fb00ee8ba32fd2761},
    }

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

82. Angtian Wang, Wufei Ma, A. Yuille, and Adam Kortylewski, “Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis,” in arXiv.org, 2023.
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    @inproceedings{258999635,
    title = {Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis},
    author = {{Angtian Wang} and {Wufei Ma} and {A. Yuille} and {Adam Kortylewski}},
    year = 2023,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/7752104e311751cc786a677f050e2815281039f8},
    }

83. Lingfeng Shen, Weiting Tan, Boyuan Zheng, and Daniel Khashabi, “Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,” in arXiv.org, 2023.
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    @inproceedings{258762744,
    title = {Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency},
    author = {{Lingfeng Shen} and {Weiting Tan} and {Boyuan Zheng} and {Daniel Khashabi}},
    year = 2023,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/f92dd36f1e08b9a8fc68896a3d72166d602280e2},
    }

84. Orion Weller, Kyle Lo, David Wadden, Dawn J Lawrie, Benjamin Van Durme, Arman Cohan, and Luca Soldaini, “When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets,” in arXiv.org, 2023.
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    @inproceedings{262012661,
    title = {When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets},
    author = {{Orion Weller} and {Kyle Lo} and {David Wadden} and {Dawn J Lawrie} and {Benjamin Van Durme} and {Arman Cohan} and {Luca Soldaini}},
    year = 2023,
    month = {9},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/dcba37400ad86aaf0d47e5cdfa2fcfa98f089401},
    }

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

86. 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,
    title = {MegaWika: Millions of reports and their sources across 50 diverse languages},
    author = {{Samuel Barham} and {Orion Weller} and {Michelle Yuan} and {Kenton Murray} and {M. Yarmohammadi} and {Zhengping Jiang} and {Siddharth Vashishtha} and {Alexander Martin} and {Anqi Liu} and {Aaron Steven White} and {Jordan L. Boyd-Graber} and {Benjamin Van Durme}},
    year = 2023,
    month = {7},
    booktitle = {arXiv.org},
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88. Orion Weller, Dawn J Lawrie, and Benjamin Van Durme, “NevIR: Negation in Neural Information Retrieval,” in arXiv.org, 2023.
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89. 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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90. Artur Jesslen, Guofeng Zhang, Angtian Wang, A. Yuille, and Adam Kortylewski, “Robust 3D-aware Object Classification via Discriminative Render-and-Compare,” in arXiv.org, 2023.
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91. 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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92. Denis Newman-Griffis, Bart Desmet, Ayah Zirikly, Suzanne Tamang, and Chih-Hung Chang, “Editorial: Artificial intelligence for human function and disability,” in Frontiers in Digital Health, 2023.
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93. 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.
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94. Huali Xu, S. 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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95. 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 arXiv.org, 2023.
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96. 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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97. Yoav Wald and S. Saria, “Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection,” in Conference on Uncertainty in Artificial Intelligence, 2023.
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98. Qihao Liu, Junfeng Wu, Yi Jiang, Xiang Bai, A. Yuille, and S. Bai, “InstMove: Instance Motion for Object-centric Video Segmentation,” in Computer Vision and Pattern Recognition, 2023.
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99. Hexin Liu, Leibny Paola Garcia, Xiangyu Zhang, Andy W. H. Khong, and S. Khudanpur, “Enhancing Code-switching Speech Recognition with Interactive Language Biases.” 2023.
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100. Margaret A. McMullin, N. 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.
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102. Anjalie Field, Prateek Verma, Nay San, J. Eberhardt, and Dan Jurafsky, “Developing Speech Processing Pipelines for Police Accountability,” in Interspeech, 2023.
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103. 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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105. Chenglin Yang, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, A. Yuille, Hartwig Adam, and Liang-Chieh Chen, “ATTENTION BRINGS STRONG VISION MODELS.” 2023.
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107. Malsha V. Perera and Vishal M. Patel, “Analyzing Bias in Diffusion-based Face Generation Models,” in arXiv.org, 2023.
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108. 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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109. 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.” 2023.
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110. Miguel Angrick, Shiyu Luo, Qinwan Rabbani, Daniel Candrea, Samyak Shah, Griffin W. Milsap, W. Anderson, C. Gordon, Kathryn R Rosenblatt, L. Clawson, N. Maragakis, F. Tenore, M. Fifer, H. Hermansky, N. Ramsey, and N. Crone, “Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS,” in medRxiv, 2023.
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111. 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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112. 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, K. 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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113. Manoj Jain, Salil Bhargava, R. Arora, Rajendra 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.
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114. V. Rennoll, I. 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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115. Yixiong Chen, A. Yuille, and Zongwei Zhou, “Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks,” in International Conference on Learning Representations, 2023.
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116. Dongji Gao, Hainan Xu, Desh Raj, Leibny Paola Garcia Perera, Daniel Povey, and S. Khudanpur, “Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition.” 2023.
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117. Marc Marone and Benjamin Van Durme, “Data Portraits: Recording Foundation Model Training Data,” in arXiv.org, 2023.
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118. Yixiao Zhang, Xinyi Li, Huimiao Chen, A. Yuille, Yaoyao Liu, and Zongwei Zhou, “Continual Learning for Abdominal Multi-Organ and Tumor Segmentation,” in arXiv.org, 2023.
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119. Samik Sadhu and H. Hermansky, “Self-supervised Learning with Speech Modulation Dropout,” in arXiv.org, 2023.
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     url = {https://www.semanticscholar.org/paper/eb6358eca5f4ee632f929cb384d07b6a5f04e0ef},
     }

120. J. Brugts, S. Radhoe, P. R. Clephas, Dilan Aydin, M. Gent, M. Szymanski, M. Rienstra, Mieke H van den Heuvel, Carlos A da Fonseca, G. Linssen, C. Borleffs, E. Boersma, F. Asselbergs, A. Mosterd, H. Rocca, R. A. Boer, M. Emans, S. Beeres, L. Heerebeek, C. Kirchhof, J. Ramshorst, R. Spee, T. Smilde, M. V. Eck, E. Kaplan, R. Hazeleger, R. Tukkie, M. Feenema, W. Kok, V. V. Halm, M. L. Handoko, R. Kimmenade, Matt Post, N. V. Mieghem, and O. Manintveld, “Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial,” in The Lancet, 2023.
     [BibTeX] [Link]

     @inproceedings{258808787,
     title = {Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial},
     author = {{J. Brugts} and {S. Radhoe} and {P. R. Clephas} and {Dilan Aydin} and {M. Gent} and {M. Szymanski} and {M. Rienstra} and {Mieke H van den Heuvel} and {Carlos A da Fonseca} and {G. Linssen} and {C. Borleffs} and {E. Boersma} and {F. Asselbergs} and {A. Mosterd} and {H. Rocca} and {R. A. Boer} and {M. Emans} and {S. Beeres} and {L. Heerebeek} and {C. Kirchhof} and {J. Ramshorst} and {R. Spee} and {T. Smilde} and {M. V. Eck} and {E. Kaplan} and {R. Hazeleger} and {R. Tukkie} and {M. Feenema} and {W. Kok} and {V. V. Halm} and {M. L. Handoko} and {R. Kimmenade} and {Matt Post} and {N. V. Mieghem} and {O. Manintveld}},
     year = 2023,
     month = {5},
     booktitle = {The Lancet},
     url = {https://www.semanticscholar.org/paper/80da514a8c411b22bd786a69f3ca62ae1d323ff1},
     }

121. Thanh Nguyen-Tang and R. Arora, “VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation,” in International Conference on Learning Representations, 2023.
     [BibTeX] [Link]

     @inproceedings{257366012,
     title = {VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation},
     author = {{Thanh Nguyen-Tang} and {R. Arora}},
     year = 2023,
     month = {2},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/7d200b868cb92657a68ac64c112a2cd0a4045f87},
     }

122. Chun-Chieh Chang, Leibny Paola García-Perera, and S. Khudanpur, “Crosslingual Handwritten Text Generation Using GANs,” in ICDAR Workshops, 2023.
     [BibTeX] [Link]

     @inproceedings{261065732,
     title = {Crosslingual Handwritten Text Generation Using GANs},
     author = {{Chun-Chieh Chang} and {Leibny Paola García-Perera} and {S. Khudanpur}},
     year = 2023,
     booktitle = {ICDAR Workshops},
     url = {https://www.semanticscholar.org/paper/48abc94b0eaf32dac2573dc5cdbe5dcfa897b7f0},
     }

123. 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},
     }

124. 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},
     }

125. 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 N. Schoenborn, “The promise of AI and technology to improve quality of life and care for older adults,” in Nature Aging, 2023.
     [BibTeX] [Link]

     @inproceedings{258909084,
     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 {N. Schoenborn}},
     year = 2023,
     month = {5},
     booktitle = {Nature Aging},
     url = {https://www.semanticscholar.org/paper/24eafaf005bd6d73870b66525e8978b760e7b3ad},
     }

126. Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, Thomas Thebaud, and N. Dehak, “Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition,” in Interspeech, 2023.
     [BibTeX] [Link]

     @inproceedings{257378503,
     title = {Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition},
     author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {Thomas Thebaud} and {N. Dehak}},
     year = 2023,
     month = {3},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/03e266795339008e9366daabfd2a2db2fbd51151},
     }

127. 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",
     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.",
     }

128. 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 arXiv.org, 2023.
     [BibTeX] [Link]

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

129. Jiacong Xu, Yi Zhang, Jia-Xiong Peng, Wufei Ma, Artur Jesslen, Pengliang Ji, Qixing Hu, Jiehua Zhang, Qihao Liu, Jiahao Wang, Wei Ji, Chen Wang, Xiaoding Yuan, Prakhar Kaushik, Guofeng Zhang, Jie Liu, Yushan Xie, Yawen Cui, A. Yuille, and Adam Kortylewski, “Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{261076475,
     title = {Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape},
     author = {{Jiacong Xu} and {Yi Zhang} and {Jia-Xiong Peng} and {Wufei Ma} and {Artur Jesslen} and {Pengliang Ji} and {Qixing Hu} and {Jiehua Zhang} and {Qihao Liu} and {Jiahao Wang} and {Wei Ji} and {Chen Wang} and {Xiaoding Yuan} and {Prakhar Kaushik} and {Guofeng Zhang} and {Jie Liu} and {Yushan Xie} and {Yawen Cui} and {A. Yuille} and {Adam Kortylewski}},
     year = 2023,
     month = {8},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/cf6f0b77e006083e74d5f08bae59bd207d0e4ac6},
     }

130. Wufei Ma, Qihao Liu, Jiahao Wang, Angtian Wang, Yaoyao Liu, Adam Kortylewski, and A. Yuille, “Adding 3D Geometry Control to Diffusion Models,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{259164709,
     title = {Adding 3D Geometry Control to Diffusion Models},
     author = {{Wufei Ma} and {Qihao Liu} and {Jiahao Wang} and {Angtian Wang} and {Yaoyao Liu} and {Adam Kortylewski} and {A. Yuille}},
     year = 2023,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/a2e36f1478cecebf2001d269f1ca92bbd674b1dc},
     }

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

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

132. 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},
     }

133. Victoria Y. H. Chua, Hexin Liu, Leibny Paola Garcia Perera, Fei Ting Woon, Jinyi Wong, Xiangyu Zhang, S. Khudanpur, Andy W. H. Khong, J. Dauwels, and S. Styles, “MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization,” in Interspeech, 2023.
     [BibTeX] [Link]

     @inproceedings{258967838,
     title = {MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization},
     author = {{Victoria Y. H. Chua} and {Hexin Liu} and {Leibny Paola Garcia Perera} and {Fei Ting Woon} and {Jinyi Wong} and {Xiangyu Zhang} and {S. Khudanpur} and {Andy W. H. Khong} and {J. Dauwels} and {S. Styles}},
     year = 2023,
     month = {5},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/ea701359c6e2b2ba5c36c4849d4144d318171418},
     }

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

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

135. Thanh Nguyen-Tang and R. Arora, “Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{257206027,
     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},
     }

136. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Regularizing Contrastive Predictive Coding for Speech Applications.” 2023.
     [BibTeX] [Link]

     @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 = {},
     url = {https://www.semanticscholar.org/paper/47ac48e7ee37e7cf4d3bb183477e42d6c5632b64},
     }

137. Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, A. Yuille, and Zongwei Zhou, “Annotating 8, 000 Abdominal CT Volumes for Multi-Organ Segmentation in Three Weeks,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258714648,
     title = {Annotating 8, 000 Abdominal 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}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/39a915bd2a67b0a81996e65a74a2896c757fe10b},
     }

138. Chen Wei, K. Mangalam, Po-Yao (Bernie) Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, A. Yuille, and Christoph Feichtenhofer, “Diffusion Models as Masked Autoencoders,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{257985028,
     title = {Diffusion Models as Masked Autoencoders},
     author = {{Chen Wei} and {K. Mangalam} and {Po-Yao (Bernie) Huang} and {Yanghao Li} and {Haoqi Fan} and {Hu Xu} and {Huiyu Wang} and {Cihang Xie} and {A. Yuille} and {Christoph Feichtenhofer}},
     year = 2023,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/b032f324a0d4a24fd917551345bd100dc368e41a},
     }

139. S. Styles, Victoria Y. H. Chua, Fei Ting Woon, Hexin Liu, Leibny Paola Garcia Perera, S. Khudanpur, Andy W. H. Khong, and J. Dauwels, “Investigating model performance in language identification: beyond simple error statistics,” in Interspeech, 2023.
     [BibTeX] [Link]

     @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 Garcia 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},
     }

140. Helin Wang, Thomas Thebaud, J. Villalba, Myra Sydnor, Becky Lammers, N. Dehak, and L. Moro-Velázquez, “DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model,” in Interspeech, 2023.
     [BibTeX] [Link]

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

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

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

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

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

143. Vikas Raunak, Tom Kocmi, and Matt Post, “SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{262043615,
     title = {SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window},
     author = {{Vikas Raunak} and {Tom Kocmi} and {Matt Post}},
     year = 2023,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/eea9f4ad5af30667535df7aa954c63f7cbc9f9da},
     }

144. 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, K. 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.
     [BibTeX] [Link]

     @inproceedings{257698009,
     title = {Biomonitoring and precision health in deep space supported by artificial intelligence},
     author = {{Ryan T. Scott} and {Lauren M. Sanders} and {E. Antonsen} and {Jaden J. A. Hastings} and {Seung-min Park} and {Graham Mackintosh} and {R. Reynolds} and {A. Hoarfrost} and {A. Sawyer} and {Casey S. Greene} and {Benjamin S. Glicksberg} and {C. Theriot} and {D. Berrios} and {Jack Miller} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {Stuart Chalk} and {Guillermo M. Delgado-Aparicio} and {Melissa Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {John Kalantari} and {K. Khezeli} and {Svetlana V. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Héctor García Martín} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {P. Parsons-Wingerter} and {R. K. Prabhu} and {A. Qutub} and {J. Rask} and {Amanda M. Saravia-Butler} and {S. Saria} and {N. Singh} and {M. Snyder} and {Frank Soboczenski} and {Karthik Soman} and {David Van Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {Jason H. Yang} and {M. Zitnik} and {S. Costes}},
     year = 2023,
     month = {3},
     booktitle = {Nature Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/275a42c374d6381406a5da16dfa52fa939817a15},
     }

145. Chen Wang, Angtian Wang, Junbo Li, A. Yuille, and Cihang Xie, “Benchmarking Robustness in Neural Radiance Fields,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{255569926,
     title = {Benchmarking Robustness in Neural Radiance Fields},
     author = {{Chen Wang} and {Angtian Wang} and {Junbo Li} and {A. Yuille} and {Cihang Xie}},
     year = 2023,
     month = {1},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/eaf0c04e9784d6efc8f9ce16d1d9c3ae43506ad9},
     }

146. Pengfei Yu, Heng Ji, Shih-Fu Chang, and Kevin Duh, “MULTIMEDIA CURRICULUM LEARNING FOR LANGUAGE ACQUISITION.” 2023.
     [BibTeX] [Link]

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

147. Ashwin Bellur, Karan Thakkar, and Mounya Elhilali, “Explicit-memory multiresolution adaptive framework for speech and music separation,” in EURASIP Journal on Audio, Speech, and Music Processing, 2023.
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     @inproceedings{258570384,
     title = {Explicit-memory multiresolution adaptive framework for speech and music separation},
     author = {{Ashwin Bellur} and {Karan Thakkar} and {Mounya Elhilali}},
     year = 2023,
     month = {5},
     booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
     url = {https://www.semanticscholar.org/paper/237ea0d3b14b924f12693a29de6fa903a3ae54ed},
     }

148. Matt Post, Thamme Gowda, Roman Grundkiewicz, Huda Khayrallah, Rohit Jain, and Marcin Junczys-Dowmunt, “SOTASTREAM: A Streaming Approach to Machine Translation Training,” in arXiv.org, 2023.
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     @inproceedings{260900231,
     title = {SOTASTREAM: A Streaming Approach to Machine Translation Training},
     author = {{Matt Post} and {Thamme Gowda} and {Roman Grundkiewicz} and {Huda Khayrallah} and {Rohit Jain} and {Marcin Junczys-Dowmunt}},
     year = 2023,
     month = {8},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ea6792cd3acbe38954ead38f222448457db19347},
     }

149. Jeya Maria Jose Valanarasu, Rahul Garg, Andeep S. Toor, Xin Tong, Weijuan Xi, Andreas Lugmayr, Vishal M. Patel, and A. Menini, “ReBotNet: Fast Real-time Video Enhancement,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{257687311,
     title = {ReBotNet: Fast Real-time Video Enhancement},
     author = {{Jeya Maria Jose Valanarasu} and {Rahul Garg} and {Andeep S. Toor} and {Xin Tong} and {Weijuan Xi} and {Andreas Lugmayr} and {Vishal M. Patel} and {A. Menini}},
     year = 2023,
     month = {3},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/15c2b3ecdf1b9af2f94a2b106fddcfc89cb336cb},
     }

150. 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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     @inproceedings{258766141,
     title = {Clustering using Approximate Nearest Neighbour Oracles},
     author = {{Enayat Ullah} and {Harry Lang} and {R. Arora} and {V. Braverman}},
     year = 2023,
     booktitle = {Trans. Mach. Learn. Res.},
     url = {https://www.semanticscholar.org/paper/2e864475d80f551d97232f9a6cba079dd128c54d},
     }

151. Orion Weller, Marc Marone, Nathaniel Weir, Dawn J Lawrie, Daniel Khashabi, and Benjamin Van Durme, “”According to …” Prompting Language Models Improves Quoting from Pre-Training Data,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258832937,
     title = {"According to ..." Prompting Language Models Improves Quoting from Pre-Training Data},
     author = {{Orion Weller} and {Marc Marone} and {Nathaniel Weir} and {Dawn J Lawrie} and {Daniel Khashabi} and {Benjamin Van Durme}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/3cf26008c7d425b8e9c33dec7fd633ec8c87bef6},
     }

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

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

153. 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.
     [BibTeX] [Link]

     @inproceedings{259300311,
     title = {JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift},
     author = {{Drew Prinster} and {S. Saria} and {Anqi Liu}},
     year = 2023,
     booktitle = {International Conference on Machine Learning},
     url = {https://www.semanticscholar.org/paper/6faf60347b9ec3672a4d191cfe9fe0076191e9a0},
     }

154. Anjalie Field, Amanda Coston, Nupoor Gandhi, A. Chouldechova, Emily Putnam-Hornstein, David Steier, and Yulia Tsvetkov, “Examining risks of racial biases in NLP tools for child protective services,” in Conference on Fairness, Accountability and Transparency, 2023.
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     @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},
     }

155. 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,
     booktitle = {International Conference on Machine Learning},
     url = {https://www.semanticscholar.org/paper/531b37c44c7e39539f617fb1a4149ef8cce8f4ec},
     }

156. 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},
     }

157. Sai Saketh Rambhatla, Ishan Misra, R. Chellappa, and Abhinav Shrivastava, “MOST: Multiple Object localization with Self-supervised Transformers for object discovery,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258060050,
     title = {MOST: Multiple Object localization with Self-supervised Transformers for object discovery},
     author = {{Sai Saketh Rambhatla} and {Ishan Misra} and {R. Chellappa} and {Abhinav Shrivastava}},
     year = 2023,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/3be837073f08eecc01e1bc742c541c5f0e644946},
     }

158. Haoran Xu, Maha Elbayad, Kenton Murray, Jean Maillard, and Vedanuj Goswami, “Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258460982,
     title = {Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity},
     author = {{Haoran Xu} and {Maha Elbayad} and {Kenton Murray} and {Jean Maillard} and {Vedanuj Goswami}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/29312bc12a22c423dd0968a18cd9e422881e29c6},
     }

159. 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 arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{261048725,
     title = {3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation},
     author = {{Yi Zhang} and {Pengliang Ji} and {Angtian Wang} and {Jieru Mei} and {Adam Kortylewski} and {A. Yuille}},
     year = 2023,
     month = {8},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/8a46058e5353ae5cc66dd8fc67e635aaad3a5f17},
     }

160. 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},
     }

161. Jay N. Paranjape, S. Sikder, Vishal M. Patel, and S. Vedula, “Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{260704213,
     title = {Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos},
     author = {{Jay N. Paranjape} and {S. Sikder} and {Vishal M. Patel} and {S. Vedula}},
     year = 2023,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/648a8bc5f5e6354aa56a899c327715bfaa80944d},
     }

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

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

163. 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},
     }

164. D. Li, A. Butala, T. Meyer, E. Oh, C. Motley, L. Moro-Velázquez, and N. Dehak, “Automating analysis of eye movement and feature extraction for different neurodegenerative disorders,” in medRxiv, 2023.
     [BibTeX] [Link]

     @inproceedings{258997975,
     title = {Automating analysis of eye movement and feature extraction for different neurodegenerative disorders},
     author = {{D. Li} and {A. Butala} and {T. Meyer} and {E. Oh} and {C. Motley} and {L. Moro-Velázquez} and {N. Dehak}},
     year = 2023,
     month = {6},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/2b5f1cfc2b507561bd463b0a5ac14fd92d75dc50},
     }

165. Neha Verma, Kenton Murray, and Kevin Duh, “Exploring Representational Disparities Between Multilingual and Bilingual Translation Models,” in arXiv.org, 2023.
     [BibTeX] [Link]

     @inproceedings{258841100,
     title = {Exploring Representational Disparities Between Multilingual and Bilingual Translation Models},
     author = {{Neha Verma} and {Kenton Murray} and {Kevin Duh}},
     year = 2023,
     month = {5},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/6321d7eec951dd1c6cea44a45f425b774d1b6b26},
     }

166. 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},
     }

167. Trevor Meyer, L. Moro-Velázquez, Seneca Motley, A. Butala, Ashley M Paul, Quincy M. Samus, Pedro 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 Irazoqui} and {N. Dehak} and {Esther S. Oh}},
     year = 2022,
     month = {12},
     booktitle = {Alzheimer's & Dementia},
     url = {https://www.semanticscholar.org/paper/e5a0988cdd73b981611be9fe06e0b7328ff1c0d0},
     }

168. 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},
     }

169. 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",
     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.",
     }

170. Orion Weller, Aleem Khan, Nathaniel Weir, Dawn J Lawrie, and Benjamin Van Durme, “Defending Against Misinformation Attacks in Open-Domain Question Answering.” 2022.
     [BibTeX] [Link]

     @inproceedings{258685264,
     title = {Defending Against Misinformation 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,
     month = {12},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/55dca1a431f3de1fc3abceb6d5ff1d424936dd6c},
     }

171. 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},
     }

172. 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},
     }

173. 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",
     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.",
     }

174. 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",
     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.",
     }

175. 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",
     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.",
     }

176. 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.
     [BibTeX] [Link]

     @inproceedings{254125621,
     title = {Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?},
     author = {{Zhuowan Li} and {Cihang Xie} and {Benjamin Van Durme} and {Alan Yuille Johns Hopkins University} and {U. California} and {Santa Cruz}},
     year = 2022,
     month = {12},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/970a8ed9de244b080aa69dbf5996a37057909ca6},
     }

177. 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},
     }

178. 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",
     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.",
     }

179. 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},
     }

180. 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},
     }

181. 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},
     }

182. 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",
     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.",
     }

183. 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},
     }

184. 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",
     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.",
     }

185. Kangda Wei, Dawn J Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller, “When Do Decompositions Help for Machine Reading?,” in arXiv.org, 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 = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/624ea7bdaf7e8e3f7bd76f72aa665b562f0dd70a},
     }

186. 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",
     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.",
     }

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

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

188. 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},
     }

189. 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",
     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.",
     }

190. 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",
     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.",
     }

191. E. Schumacher, J. Mayfield, and M. Dredze, “Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models,” in Proceedings of the 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",
     booktitle = "Proceedings of the 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.",
     }

192. 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},
     }

193. 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},
     }

194. 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",
     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.",
     }

195. 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},
     }

196. 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},
     }

197. 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},
     }

198. 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 \url{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",
     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 \url{https://github.com/kellymarchisio/isovec}.",
     }

199. 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",
     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.",
     }

200. 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",
     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.",
     }

201. 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 \url{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",
     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 \url{https://github.com/castorini/africlirmatrix}.",
     }

202. 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",
     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.",
     }

203. 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",
     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).",
     }

204. 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",
     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.",
     }

205. 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",
     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.",
     }

206. 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",
     }

207. 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",
     }

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

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

209. V. Rennoll, I. 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 {I. 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},
     }

210. 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},
     }

211. 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},
     }

212. Yuanze Lin, Chen Wei, Huiyu Wang, A. Yuille, and Cihang Xie, “SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training,” in arXiv.org, 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 = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/210f6ffbed4bf3a0f020cfcb48dab9d6a9939cdb},
     }

213. 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},
     }

214. 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},
     }

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

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

216. 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},
     }

217. 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},
     }

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

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

219. 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},
     }

220. 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},
     }

221. 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},
     }

222. 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",
     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.",
     }

223. 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},
     }

224. 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},
     }

225. 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",
     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.",
     }

226. S. 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 = {{S. 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},
     }

227. Shuyue Stella Li, Xiangyu Zhang, Shu Zhou, Hongchao Shu, Ruixing Liang, Hexin Liu, and Leibny Paola García-Perera, “PQLM – Multilingual Decentralized Portable Quantum Language Model for Privacy Protection,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @inproceedings{252968383,
     title = {PQLM - Multilingual Decentralized Portable Quantum Language Model for Privacy Protection},
     author = {{Shuyue Stella Li} and {Xiangyu Zhang} and {Shu Zhou} and {Hongchao Shu} and {Ruixing Liang} and {Hexin Liu} and {Leibny Paola García-Perera}},
     year = 2022,
     month = {10},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/747d3a8d6c7beff00377795c696f198b2c12ecff},
     }

228. 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},
     }

229. 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},
     }

230. 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},
     }

231. 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},
     }

232. 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},
     }

233. 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},
     }

234. Hexin Liu, Haihua Xu, Leibny Paola Garcia, 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 Garcia} 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},
     }

235. 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},
     }

236. 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},
     }

237. 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},
     }

238. 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",
     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.",
     }

239. 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},
     }

240. 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",
     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.",
     }

241. 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",
     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.",
     }

242. 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",
     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.",
     }

243. 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",
     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.",
     }

244. 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",
     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.",
     }

245. 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",
     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.",
     }

246. 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",
     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).",
     }

247. 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",
     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.",
     }

248. 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",
     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.",
     }

249. 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",
     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.",
     }

250. 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",
     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.",
     }

251. 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",
     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.",
     }

252. 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",
     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.",
     }

253. 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",
     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.",
     }

254. 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",
     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.",
     }

255. 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",
     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.",
     }

256. 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",
     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.",
     }

257. 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",
     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.",
     }

258. 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",
     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.",
     }

259. 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},
     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.",
     }

260. 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",
     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.",
     }

261. 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",
     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.",
     }

262. 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",
     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.",
     }

263. 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",
     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.",
     }

264. 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",
     }

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

     @InProceedings{cotterell-eisner-2022,
     author = "Ryan Cotterell and Jason Eisner",
     title = "A Functionalist Account of Vowel System Typology",
     booktitle = "Proceedings of the Association for Computational
     Linguistics (ACL)",
     year = "2022",
     month = may,
     address = "Dublin",
     URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2022",
     }

266. 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.
     [BibTeX] [Link]

     @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",
     }

267. 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.
     [BibTeX] [Link]

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

268. 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.
     [BibTeX] [Link]

     @inproceedings{249926634,
     title = {BenchCLAMP: A Benchmark for Evaluating Language Models on Semantic Parsing},
     author = {{Subhro Roy} and {Sam Thomson} and {Tongfei Chen} and {Richard Shin} and {Adam Pauls} and {J. Eisner} and {Benjamin Van Durme}},
     year = 2022,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/132751a80632e80a90d7c3d3cd8a361f48fdb9b4},
     }

269. 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, Haizhi Gary 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, S. 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.
     [BibTeX] [Link]

     @inproceedings{248227391,
     title = {Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks},
     author = {{Yizhong Wang} and {Swaroop Mishra} and {Pegah Alipoormolabashi} and {Yeganeh Kordi} and {Amirreza Mirzaei} and {Anjana Arunkumar} and {Arjun Ashok} and {Arut Selvan Dhanasekaran} and {Atharva Naik} and {David Stap} and {Eshaan Pathak} and {Giannis Karamanolakis} and {Haizhi Gary Lai} and {I. Purohit} and {Ishani Mondal} and {Jacob Anderson} and {Kirby Kuznia} and {Krima Doshi} and {Maitreya Patel} and {Kuntal Kumar Pal} and {M. Moradshahi} and {Mihir Parmar} and {Mirali Purohit} and {Neeraj Varshney} and {Phani Rohitha Kaza} and {Pulkit Verma} and {Ravsehaj Singh Puri} and {Rushang Karia} and {Shailaja Keyur Sampat} and {Savan Doshi} and {S. Mishra} and {Sujan Reddy} and {Sumanta Patro} and {Tanay Dixit} and {Xudong Shen} and {Chitta Baral} and {Yejin Choi} and {Hannaneh Hajishirzi} and {Noah A. Smith} and {Daniel Khashabi}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/ec64e324ce1210fe5245dfd0fb5a92058732e5b9},
     }

270. 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.",
     }

271. 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",
     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.",
     }

272. Yutong Bai, Xinlei Chen, Alexander Kirillov, A. Yuille, and A. Berg, “Point-Level Region Contrast for Object Detection Pre-Training,” in Computer Vision and Pattern Recognition, 2022.
     [BibTeX] [Link]

     @inproceedings{246680149,
     title = {Point-Level Region Contrast for Object Detection Pre-Training},
     author = {{Yutong Bai} and {Xinlei Chen} and {Alexander Kirillov} and {A. Yuille} and {A. Berg}},
     year = 2022,
     month = {2},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/7d692139562f9679a3694e1d408b00bd8259b6f1},
     }

273. 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.
     [BibTeX] [Link]

     @inproceedings{251710281,
     title = {A Risk-Sensitive Approach to Policy Optimization},
     author = {{Jared Markowitz} and {Ryan W. Gardner} and {Ashley J. Llorens} and {R. Arora} and {I-J. Wang}},
     year = 2022,
     month = {8},
     booktitle = {AAAI Conference on Artificial Intelligence},
     url = {https://www.semanticscholar.org/paper/d37ca9aa15d6f34d942180752552132c51fe27e5},
     }

274. 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.
     [BibTeX] [Link]

     @inproceedings{247244749,
     title = {HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening},
     author = {{W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2022,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/0477780c61e668c47630ae1cd74cee55c2493b5f},
     }

275. Shao-Yuan Lo and Vishal M. Patel, “Exploring Adversarially Robust Training for Unsupervised Domain Adaptation,” in Asian Conference on Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{246996539,
     title = {Exploring Adversarially Robust Training for Unsupervised Domain Adaptation},
     author = {{Shao-Yuan Lo} and {Vishal M. Patel}},
     year = 2022,
     month = {2},
     booktitle = {Asian Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/1329a9e14f6454227dfb584a57a910ef168f6a7d},
     }

276. 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.
     [BibTeX] [Link]

     @inproceedings{249827199,
     title = {Advances in Cross-Lingual and Cross-Source Audio-Visual Speaker Recognition: The JHU-MIT System for NIST SRE21},
     author = {{J. Villalba} and {B. J. Borgstrom} and {Saurabh Kataria} and {Magdalena Rybicka} and {C. Castillo} and {Jaejin Cho} and {Leibny Paola García-Perera} and {P. Torres-Carrasquillo} and {N. Dehak}},
     year = 2022,
     month = {6},
     booktitle = {The Speaker and Language Recognition Workshop},
     url = {https://www.semanticscholar.org/paper/9d9b5b782cbaf98bfb198b120c343d813c99ecf5},
     }

277. 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",
     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.",
     }

278. Hexin Liu, Leibny Paola Garcia Perera, Andy W. H. Khong, J. Dauwels, S. Styles, and S. Khudanpur, “Enhance Language Identification using Dual-mode Model with Knowledge Distillation,” in The Speaker and Language Recognition Workshop, 2022.
     [BibTeX] [Link]

     @inproceedings{247291930,
     title = {Enhance Language Identification using Dual-mode Model with Knowledge Distillation},
     author = {{Hexin Liu} and {Leibny Paola Garcia Perera} and {Andy W. H. Khong} and {J. Dauwels} and {S. Styles} and {S. Khudanpur}},
     year = 2022,
     month = {3},
     booktitle = {The Speaker and Language Recognition Workshop},
     url = {https://www.semanticscholar.org/paper/237833ac8dcdb5f472cfe662fd8593c2e11fca8d},
     }

279. 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.
     [BibTeX] [Link]

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

280. A. Hussein, S. A. Chowdhury, Ahmed Abdelali, N. Dehak, Ahmed M. Ali, and S. Khudanpur, “Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition,” in Spoken Language Technology Workshop, 2022.
     [BibTeX] [Link]

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

281. 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, Juliet O. 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.
     [BibTeX] [Link]

     @inproceedings{248832550,
     title = {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},
     author = {{Daniel E Park} and {Nora L. Watson} and {Christopher Focht} and {D. Feikin} and {Laura L Hammit} and {W. A. Brooks} and {S. Howie} and {K. Kotloff} and {O. Levine} and {S. Madhi} and {D. Murdoch} and {K. O'Brien} and {J. Scott} and {D. Thea} and {Tussanee Amorninthapichet} and {Juliet O. Awori} and {C. Bunthi} and {B. Ebruke} and {Mounya Elhilali} and {Melissa M Higdon} and {L. Hossain} and {Y. Jahan} and {D. Moore} and {J. Mulindwa} and {L. Mwananyanda} and {Sathapana Naorat} and {Christine Prosperi} and {S. Thamthitiwat} and {C. Verwey} and {K. Jablonski} and {M. Power} and {H. Young} and {M. Deloria Knoll} and {E. McCollum}},
     year = 2022,
     month = {5},
     booktitle = {BMJ Open Respiratory Research},
     url = {https://www.semanticscholar.org/paper/dfedb313d8718de8aa162813060af3e24e8cbe28},
     }

282. 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.
     [BibTeX] [Link]

     @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},
     }

283. 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.
     [BibTeX] [Link]

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

284. Daniel Khashabi, Yeganeh Kordi, and Hannaneh Hajishirzi, “UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training,” in arXiv.org, 2022.
     [BibTeX] [Link]

     @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},
     }

285. 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.
     [BibTeX] [Link]

     @inproceedings{251224367,
     title = {Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation},
     author = {{Shao-Yuan Lo} and {Wei Wang} and {Jim Thomas} and {Jingjing Zheng} and {Vishal M. Patel} and {Cheng-Hao Kuo}},
     year = 2022,
     month = {7},
     booktitle = {IEEE/RJS International Conference on Intelligent RObots and Systems},
     url = {https://www.semanticscholar.org/paper/c28582e042a0bb482517ef844d5a3a6794f994f6},
     }

286. Kangfu Mei, Vishal M. Patel, and Rui Huang, “Deep Semantic Statistics Matching (D2SM) Denoising Network,” in European Conference on Computer Vision, 2022.
     [BibTeX] [Link]

     @inproceedings{250644264,
     title = {Deep Semantic Statistics Matching (D2SM) Denoising Network},
     author = {{Kangfu Mei} and {Vishal M. Patel} and {Rui Huang}},
     year = 2022,
     month = {7},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/19f83c24c56904754be700247b416cee704d5738},
     }

287. Alex Mallen, Akari Asai, Victor Zhong, R. Das, Hanna 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 Mallen} and {Akari Asai} and {Victor Zhong} and {R. Das} and {Hanna Hajishirzi} and {Daniel Khashabi}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7b0f98f51040700aae3cd9f0e3432dedcd69fb30},
     }

288. 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.
     [BibTeX] [Link]

     @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},
     }

289. Yu Zeng, Zhe Lin, and Vishal M. Patel, “Shape-guided Object Inpainting,” in arXiv.org, 2022.
     [BibTeX] [Link]

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

290. Yunjuan Wang, Enayat Ullah, Poorya Mianjy, and R. Arora, “Adversarial Robustness is at Odds with Lazy Training,” in Neural Information Processing Systems, 2022.
     [BibTeX] [Link]

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

291. 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.
     [BibTeX] [Link]

     @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},
     }

292. 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.
     [BibTeX] [Link]

     @inproceedings{253513495,
     title = {Appendix for 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,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/46bfaa37e8b95f6bff810e5563d67e3404e78225},
     }

293. 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.
     [BibTeX] [Link]

     @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},
     }

294. 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",
     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.",
     }

295. 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.
     [BibTeX] [Link]

     @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},
     }

296. Anastasia Razdaibiedina, A. Khetan, Zohar S. Karnin, Daniel Khashabi, Vishaal Kapoor, and V. Madan, “Representation Projection Invariance Mitigates Representation Collapse.” 2022.
     [BibTeX] [Link]

     @inproceedings{258588228,
     title = {Representation Projection Invariance Mitigates Representation Collapse},
     author = {{Anastasia Razdaibiedina} and {A. Khetan} and {Zohar S. Karnin} and {Daniel Khashabi} and {Vishaal Kapoor} and {V. Madan}},
     year = 2022,
     month = {5},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/3746b0e7370784d5242dc9d3fc3fd3853a34409b},
     }

297. 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.
     [BibTeX] [Link]

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

298. 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.
     [BibTeX] [Link]

     @inproceedings{249538421,
     title = {SAR Despeckling Using a Denoising Diffusion Probabilistic Model},
     author = {{Malsha V. Perera} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
     year = 2022,
     month = {6},
     booktitle = {IEEE Geoscience and Remote Sensing Letters},
     url = {https://www.semanticscholar.org/paper/946e217f0e734561ac0acd8b58063ca882c963df},
     }

299. Samik Sadhu and H. Hermansky, “Complex Frequency Domain Linear Prediction: A Tool to Compute Modulation Spectrum of Speech,” in Interspeech, 2022.
     [BibTeX] [Link]

     @inproceedings{247628260,
     title = {Complex Frequency Domain Linear Prediction: A Tool to Compute Modulation Spectrum of Speech},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2022,
     month = {3},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/35a36559a133981c17759aa573afea646abe40f6},
     }

300. 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.
     [BibTeX] [Link]

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

301. 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.
     [BibTeX] [Link]

     @inproceedings{253107926,
     title = {Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media},
     author = {{Chan Young Park} and {Julia Mendelsohn} and {Anjalie Field} and {Yulia Tsvetkov}},
     year = 2022,
     month = {5},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/b616154578751e156b21561e1a5d5ed833a3506f},
     }

302. 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/6daa8592e9c895acd78ce0d798f5327add2902a4},
     }

303. 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},
     }

304. 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, A. Hussain, Amanda Askell, Amanda Dsouza, A. Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmuller, Andrew M. Dai, Andrew D. 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. 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     @inproceedings{249538544,
     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 {A. Hussain} and {Amanda Askell} and {Amanda Dsouza} and {A. Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Santilli} and {Andreas Stuhlmuller} and {Andrew M. Dai} and {Andrew D. La} and {Andrew Kyle 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 {Arfa Tabassum} and {Arul Menezes} and {Arun Kirubarajan} and {A. 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346. H. Goldman, Julia Porcino, G. Divita, Ayah Zirikly, Bart Desmet, Maryanne Sacco, E. Marfeo, Christine 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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347. S. Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi, “G ENERATING S EQUENCES BY L EARNING TO [S ELF -]C ORRECT.” 2022.
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348. 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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349. 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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350. 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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351. 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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352. 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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353. 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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354. 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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355. 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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356. 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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357. 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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358. Sai Saketh Rambhatla, Saksham Suri, R. Chellappa, and Abhinav Shrivastava, “SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining.” 2022.
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359. 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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360. Jaejin Cho, R. Pappagari, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations,” in Interspeech, 2022.
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361. 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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362. 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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363. 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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364. 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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365. Xiang Xiang, Yuwen Tan, Qian Wan, Jing Ma, A. Yuille, and Gregory D. Hager, “Coarse-To-Fine Incremental Few-Shot Learning – Appendix.” 2022.
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366. 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, “Publisher Correction: 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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367. 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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368. Cheng Peng and R. Chellappa, “PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images,” in AAAI Conference on Artificial Intelligence, 2022.
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369. 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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370. 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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371. 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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372. Malsha V. Perera, W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SAR Despeckling Using Overcomplete Convolutional Networks,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
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373. 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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374. 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 B 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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375. 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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     month = {9},
     booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
     url = {https://www.semanticscholar.org/paper/fc49634e80ab31929799786a97b7ea63834bbdb1},
     }

376. 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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     @inproceedings{247922750,
     title = {Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2022,
     month = {3},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/3ce501d4d81d9a78c2e506df7f6de0d79ca91a5b},
     }

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

378. 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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     @inproceedings{248218560,
     title = {Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking},
     author = {{Pirazh Khorramshahi} and {Vineet Shenoy} and {M. Pack} and {R. Chellappa}},
     year = 2022,
     month = {4},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/0babd241088a1d84dec824c9749c93a3e20fd583},
     }

379. Weiting Tan and Philipp Koehn, “Bitext Mining for Low-Resource Languages via Contrastive Learning,” in arXiv.org, 2022.
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     @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},
     }

380. 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},
     }

381. 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",
     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.",
     }

382. Samik Sadhu and H. Hermansky, “Blind Signal Dereverberation for Machine Speech Recognition,” in arXiv.org, 2022.
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     @inproceedings{252683281,
     title = {Blind Signal Dereverberation for Machine Speech Recognition},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2022,
     month = {9},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/d2fda509740eede59e46892958531088f3f25aed},
     }

383. Cheng Peng, Pengfei Guo, S. K. Zhou, Vishal M. Patel, and Ramalingam Chellappa, “Towards performant and reliable undersampled MR reconstruction via diffusion model sampling,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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     @inproceedings{247318941,
     title = {Towards performant and reliable undersampled MR reconstruction via diffusion model sampling},
     author = {{Cheng Peng} and {Pengfei Guo} and {S. K. Zhou} and {Vishal M. Patel} and {Ramalingam Chellappa}},
     year = 2022,
     month = {3},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/6a0bc6e194608b9e41cbf69794343888edb54378},
     }

384. 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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     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},
     }

385. 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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     @inproceedings{249877931,
     title = {Supplementary Materials: ”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 {Song Bai}},
     year = 2022,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/d378dc21ab5cfbde24b295ab759c9947f820bc94},
     }

386. 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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     year = 2022,
     month = {5},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/31e79b62a9483dcdf2575603469e6ff888e7f234},
     }

387. 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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     @inproceedings{246595318,
     title = {Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach},
     author = {{Amir Alipour-Fanid} and {Monireh Dabaghchian} and {R. Arora} and {K. Zeng}},
     year = 2022,
     month = {6},
     booktitle = {IEEE Transactions on Cognitive Communications and Networking},
     url = {https://www.semanticscholar.org/paper/ad0c8cc0a80c5873591e62ca9f47fa21b631c35f},
     }

388. 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},
     }

389. 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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     @inproceedings{251762249,
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     author = {{M. Villemur} and {Jonah P. Sengupta} and {P. Julián} and {A. Andreou}},
     year = 2022,
     month = {6},
     booktitle = {International Conference on Event-Based Control, Communication, and Signal Processing},
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     }

390. 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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     @inproceedings{252211999,
     title = {Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features},
     author = {{Wufei Ma} and {Angtian Wang} and {A. Yuille} and {Adam Kortylewski}},
     year = 2022,
     month = {9},
     booktitle = {European Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/efa699cba13396c1b6d05a0dea9840020d29ae57},
     }

391. 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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     @inproceedings{252355543,
     title = {Dynamic Generation of Interpretable Inference Rules in a Neuro-Symbolic Expert System},
     author = {{Nathaniel Weir} and {Benjamin Van Durme}},
     year = 2022,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/fa46f4ddd5c6e793a47c61db9c1ecde7ea1c82bc},
     }

392. 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},
     }

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

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

395. 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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     @inproceedings{247958394,
     title = {SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering},
     author = {{Vipul Gupta} and {Zhuowan Li} and {Adam Kortylewski} and {Chenyu Zhang} and {Yingwei Li} and {A. Yuille}},
     year = 2022,
     month = {4},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/0d2f848fff121133b3b77c7e691c6a2ba502be47},
     }

396. Yingwei Li, A. 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 {A. 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},
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     }

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

398. Nathaniel Weir and Benjamin Van Durme, “Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System.” 2022.
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     title = {Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System},
     author = {{Nathaniel Weir} and {Benjamin Van Durme}},
     year = 2022,
     month = {9},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/767c1338c001fdadb5bf4eed530fc78ede49e9ae},
     }

399. 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/92a574d34837b970e6c0610226362e801ca83442},
     }

400. Nllb team, M. Costa-jussà, James Cross, Onur cCelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Alison Youngblood, Bapi Akula, Loïc Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, S. Spruit, C. Tran, Pierre Yves Andrews, N. F. Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzm’an, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang, “No Language Left Behind: Scaling Human-Centered Machine Translation,” in arXiv.org, 2022.
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     year = 2022,
     month = {7},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/0e638ce20f3e9b4dd1c10c32a29495c798425e63},
     }

401. 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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     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},
     }

402. 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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     author = {{Shota Horiguchi} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Yuki Takashima} and {Y. Kawaguchi}},
     year = 2022,
     month = {6},
     booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
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     }

403. 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.” 2022.
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404. Yiqun Mei, Pengfei Guo, and Vishal M. Patel, “Supplementary File: Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination.” 2022.
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     booktitle = {},
     url = {https://www.semanticscholar.org/paper/33e69c1a173789d721185c07510af20013a509bf},
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405. Lianhui Qin, S. 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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     year = 2022,
     month = {2},
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     }

406. W. G. C. Bandara, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models,” in arXiv.org, 2022.
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     @inproceedings{249953553,
     title = {DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models},
     author = {{W. G. C. Bandara} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
     year = 2022,
     month = {6},
     booktitle = {arXiv.org},
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407. 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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408. Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser,” in Interspeech, 2022.
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409. 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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410. 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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411. 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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412. 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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413. 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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414. 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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415. 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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417. R. Arora, Raef Bassily, Crist’obal Guzm’an, Michael Menart, and Enayat Ullah, “Differentially Private Generalized Linear Models Revisited,” in Neural Information Processing Systems, 2022.
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418. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in arXiv.org, 2022.
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419. 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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420. 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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421. Jeya Maria Jose Valanarasu, Pengfei Guo, VS Vibashan, and Vishal M. Patel, “On-the-Fly Test-time Adaptation for Medical Image Segmentation,” in arXiv.org, 2022.
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422. 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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423. 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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424. 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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Biochemistry} and {Molecular Genetics} and {University of Maryland School of 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 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 Astrophysical Sciences} and {Mayo Clinic} and {Rochester} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {M. 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 Computational 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 Electrical Engineering} and {U. Texas} and {S. Antonio} and {UT Health Sciences} and {Office of the Director} and {Logyx} and {Computer Science} and {Statistics} and {H. Policy} and {J. University} and {Baltimore.} and {Md.} and {Ml} and {Ai} and {Healthcare Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {J. P. Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {Department of Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {D. Science} and {U. Alabama} and {Birmingham} and {Al} and {Center for Emerging} and {Re-Emerging Pathogens} and {Biochemistry} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Department of Biomedical Informatics} and {H. School} and {Harvard Data Science} and {Broad Institute of Mit} and {Harvard} and {Harvard University} and {Boston} and {Ma.}},
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     url = {https://www.semanticscholar.org/paper/0d6d142dc49cf7537ece045d8d469fd014a5d3b6},
     }

427. Lauren M. Sanders, Jason H. Yang, Ryan T. Scott, A. Qutub, H. Martín, D. Berrios, Jaden J. A. Hastings, J. Rask, Graham Mackintosh, A. Hoarfrost, Stuart Chalk, John Kalantari, K. Khezeli, E. Antonsen, Joel Babdor, Richard Barker, S. Baranzini, A. Beheshti, Guillermo M. Delgado-Aparicio, B. Glicksberg, C. Greene, M. Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, S. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Christopher E. Mason, Mona Matar, G. Mias, Jack M. Miller, J. Myers, Charlotte A. Nelson, Jonathan Oribello, Seung-min Park, P. Parsons-Wingerter, R. Prabhu, R. Reynolds, Amanda M. Saravia-Butler, S. Saria, A. Sawyer, N. Singh, Frank Soboczenski, Michael Snyder, Karthik Soman, C. Theriot, D. V. Valen, K. Venkateswaran, L. Warren, Liz Worthey, M. Zitnik, Sylvain V. Costes Blue Marble Space Institute of Science, Space Biosciences Division, N. R. Center, M. Field, Ca, USA., Center for Emerging, Re-Emerging Pathogens, D. Microbiology, Biochemistry, Molecular Genetics, Rutgers New Jersey Medical School, Newark, Nj, Kbr, AI Matrix Consortium, Department of Electrical Engineering, U. Texas, San Antonio, UT Health Sciences, Tx, Biological Systems, Engineering Division, Lawrence Berkeley National Lab., Berkeley, Doe Agile BioFoundry, Emeryville, Joint BioEnergy Institute, D. Physiology, Biophysics, Weill Cornell Medicine, N. York, Ny, Office of the Director, Bay Area Environmental Research Institute, Universities Space Research Association, D. Chemistry, U. Florida, Jacksonville, Fl, Center for Individualized Medicine, D. Surgery, Department of Astrophysical Sciences, Mayo Clinic, Rochester, Mn, Department of Preventive Medicine, Center for Individualized Medicine, Baylor College of Medicine, Houston, Immunology, Department of Otolaryngology, Head, N. Surgery, University of San Francisco, San Francisco, The Gilroy AstroBiology Research Group, Theodore Madison, Madison, Wi, Weill Institute for Neurosciences, D. Neurology, D. Analytics, G. I. O. Technology, Lima, Perú, Hasso Plattner Institute for Digital Health at Mount Sinai, Department of Genetics, Genomic Sciences, I. A. Sinai, AI CenterforHealth, D. Biochemistry, University of Maryland School of Medicine, Anschutz Medical Campus, Aurora, Co, Department of Neuroscience, U. Minnesota, Minneapolis, Department of Materials Science, College of Materials Science, San Diego State University, San José, Biorelate, Manchester, United Kingdom., UC Space Health, D. Surgery, U. California, Faculty of Veterinary Medicine, Oral Health Sciences, M. University, Montreal., Quebec., Canada., Faculty of Veterinary Medicine, Dept of Surgery, Cancer, I. -. London, London, SymbioSeq Llc, NASAMarshall Space Flight Center, Ashburn, Va, Center for Data Driven Discovery, California Institute of Technology., Pasadena, Waitt Advanced Biophotonics Center, Chan-Zuckerberg Imaging Scientist Fellow, Salk Institute for Biological Studies, La Jolla, Human Research Program Cross-cutting Computational Model Project, N. R. Center, Cleveland, Oh, Institute for Computational Science, Engineering, M. Biology, M. University, E. Lansing., Mi, D. Urology, D. Radiology, S. Medicine, Stanford, Low Exploration Gravity Technology, Human Research Program Cross-cutting Computational Model Project, Mortality ResearchConsulting, Inc., Logyx, Computer Science, Statistics, H. Policy, J. University, Baltimore., Md., Ml, Ai, Healthcare Lab, B. Health, Biotechnology, Planetary Protection Group, J. P. Laboratory, Sphes, Medical Faculty, King’s College London, S. Medicine, C. Usa, Department of Preventive Medicine, C. Health, Utmb, Galveston, Tx Usa, Human Health, Performance Directorate, Department of Biology, Iss National Laboratory, Center for Space, Melbourne, Uab Center for Computational Biology, D. Science, U. Alabama, Birmingham, Al, Department of Biomedical Informatics, H. School, Harvard Data Science, Broad Institute of Mit, Harvard, Harvard University, Boston, and Ma., “Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{245424654,
     title = {Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs},
     author = {{Lauren M. Sanders} and {Jason H. Yang} and {Ryan T. Scott} and {A. Qutub} and {H. Martín} and {D. Berrios} and {Jaden J. A. Hastings} and {J. Rask} and {Graham Mackintosh} and {A. Hoarfrost} and {Stuart Chalk} and {John Kalantari} and {K. Khezeli} and {E. Antonsen} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {Guillermo M. Delgado-Aparicio} and {B. Glicksberg} and {C. Greene} and {M. Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {S. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {Jack M. Miller} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {Seung-min Park} and {P. Parsons-Wingerter} and {R. Prabhu} and {R. Reynolds} and {Amanda M. Saravia-Butler} and {S. Saria} and {A. Sawyer} and {N. Singh} and {Frank Soboczenski} and {Michael Snyder} and {Karthik Soman} and {C. Theriot} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {Sylvain V. Costes Blue Marble Space Institute of Science} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Center for Emerging} and {Re-Emerging Pathogens} and {D. Microbiology} and {Biochemistry} and {Molecular Genetics} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Kbr} and {AI Matrix Consortium} and {Department of Electrical Engineering} and {U. Texas} and {San Antonio} and {UT Health Sciences} and {Tx} and {Biological Systems} and {Engineering Division} and {Lawrence Berkeley National Lab.} and {Berkeley} and {Doe Agile BioFoundry} and {Emeryville} and {Joint BioEnergy Institute} and {D. Physiology} and {Biophysics} and {Weill Cornell Medicine} and {N. York} and {Ny} and {Office of the Director} and {Bay Area Environmental Research Institute} and {Universities Space Research Association} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {Center for Individualized Medicine} and {D. Surgery} and {Department of Astrophysical Sciences} and {Mayo Clinic} and {Rochester} and {Mn} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of San Francisco} and {San Francisco} and {The Gilroy AstroBiology Research Group} and {Theodore Madison} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Perú} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {AI CenterforHealth} and {D. Biochemistry} and {University of Maryland School of Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {UC Space Health} and {D. Surgery} and {U. California} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {M. University} and {Montreal.} and {Quebec.} and {Canada.} and {Faculty of Veterinary Medicine} and {Dept of Surgery} and {Cancer} and {I. -. London} and {London} and {SymbioSeq Llc} and {NASAMarshall Space Flight Center} and {Ashburn} and {Va} and {Center for Data Driven Discovery} and {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 {Human Research Program Cross-cutting Computational Model Project} and {N. R. Center} and {Cleveland} and {Oh} and {Institute for Computational Science} and {Engineering} and {M. Biology} and {M. University} and {E. Lansing.} and {Mi} and {D. Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Low Exploration Gravity Technology} and {Human Research Program Cross-cutting Computational Model Project} and {Mortality ResearchConsulting} and {Inc.} and {Logyx} and {Computer Science} and {Statistics} and {H. Policy} and {J. University} and {Baltimore.} and {Md.} and {Ml} and {Ai} and {Healthcare Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {J. P. Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {C. Usa} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Tx Usa} and {Human Health} and {Performance Directorate} and {Department of Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {D. Science} and {U. Alabama} and {Birmingham} and {Al} 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/0c7240e91af2778ab1e65cc79c20704aa61e106b},
     }

428. 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},
     }

429. 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},
     }

430. 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.
     [BibTeX] [Link]

     @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},
     }

431. 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},
     }

432. 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},
     }

433. 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},
     }

434. 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},
     }

435. 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},
     }

436. 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",
     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.",
     }

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

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

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

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

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

439. Pengfei Guo and Vishal M. Patel, “Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme,” in arXiv.org, 2021.
     [BibTeX] [Link]

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

440. Yutong Bai, Jieru Mei, A. Yuille, and Cihang Xie, “Are Transformers More Robust Than CNNs?,” in Neural Information Processing Systems, 2021.
     [BibTeX] [Link]

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

441. 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",
     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.",
     }

442. 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",
     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.",
     }

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

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

444. 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",
     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.",
     }

445. 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},
     }

446. 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",
     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.",
     }

447. 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",
     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.",
     }

448. 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},
     }

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

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

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

450. 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",
     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.",
     }

451. 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",
     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.",
     }

452. 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",
     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.",
     }

453. 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},
     }

454. 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},
     }

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

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

     @inproceedings{ding-etal-2021-jhu,
     title = "The {JHU}-{M}icrosoft Submission for {WMT}21 Quality Estimation Shared Task",
     author = "Ding, Shuoyang and
     Junczys-Dowmunt, Marcin and
     Post, Matt and
     Federmann, Christian and
     Koehn, Philipp",
     booktitle = "Proceedings of the Sixth Conference on Machine Translation",
     month = nov,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.wmt-1.94",
     pages = "904--910",
     abstract = "This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.",
     }

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

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

457. 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},
     }

458. 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",
     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.",
     }

459. 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},
     }

460. 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},
     }

461. Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, S. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge,” in NeurIPS Datasets and Benchmarks, 2021.
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     @inproceedings{244117621,
     title = {Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge},
     author = {{Jiyang Qi} and {Yan Gao} and {Yao Hu} and {Xinggang Wang} and {Xiaoyu Liu} and {Xiang Bai} and {S. 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},
     }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

484. W. Wu, K. Duh, and D. Yarowsky, “Sequence Models for Computational Etymology of Borrowings,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, 2021, p. 4032–4037. doi:10.18653/v1/2021.findings-acl.353
     [BibTeX] [Link]

     @inproceedings{wu-etal-2021-sequence,
     title = "Sequence Models for Computational Etymology of Borrowings",
     author = "Wu, Winston and
     Duh, Kevin and
     Yarowsky, David",
     booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.findings-acl.353",
     doi = "10.18653/v1/2021.findings-acl.353",
     pages = "4032--4037",
     }

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

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

     @inproceedings{marchisio-etal-2021-alignment,
     title = "An Alignment-Based Approach to Semi-Supervised Bilingual Lexicon Induction with Small Parallel Corpora",
     author = "Marchisio, Kelly and
     Koehn, Philipp and
     Xiong, Conghao",
     booktitle = "Proceedings of Machine Translation Summit XVIII: Research Track",
     month = aug,
     year = "2021",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2021.mtsummit-research.24",
     pages = "293--304",
     abstract = "Aimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While interesting and fully unsupervised settings are unrealistic; small amounts of bilingual data are usually available due to the existence of massively multilingual parallel corpora and or linguists can create small amounts of parallel data. In this work and we demonstrate an effective bootstrapping approach for semi-supervised bilingual lexicon induction that capitalizes upon the complementary strengths of two disparate methods for inducing bilingual lexicons. Whereas statistical methods are highly effective at inducing correct translation pairs for words frequently occurring in a parallel corpus and monolingual embedding spaces have the advantage of having been trained on large amounts of data and and therefore may induce accurate translations for words absent from the small corpus. By combining these relative strengths and our method achieves state-of-the-art results on 3 of 4 language pairs in the challenging VecMap test set using minimal amounts of parallel data and without the need for a translation dictionary. We release our implementation at www.blind-review.code.",
     }

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

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

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

487. J. Ou, N. Weir, A. Belyy, F. Yu, and B. Van Durme, “InFillmore: Frame-Guided Language Generation with Bidirectional Context,” in Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, Online, 2021, p. 129–142. doi:10.18653/v1/2021.starsem-1.12
     [BibTeX] [Abstract] [Link]
     

     We propose a structured extension to bidirectional-context conditional language generation, or {“}infilling,{”} inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.

     @inproceedings{ou-etal-2021-infillmore,
     title = "{I}n{F}illmore: Frame-Guided Language Generation with Bidirectional Context",
     author = "Ou, Jiefu and
     Weir, Nathaniel and
     Belyy, Anton and
     Yu, Felix and
     Van Durme, Benjamin",
     booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.starsem-1.12",
     doi = "10.18653/v1/2021.starsem-1.12",
     pages = "129--142",
     abstract = "We propose a structured extension to bidirectional-context conditional language generation, or {``}infilling,{''} inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.",
     }

488. X. Zhang and K. Duh, “Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task,” in Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL), Virtual, 2021, p. 60–70.
     [BibTeX] [Abstract] [Link]
     

     A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.

     @inproceedings{zhang-duh-2021-approaching,
     title = "Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task",
     author = "Zhang, Xuan and
     Duh, Kevin",
     booktitle = "Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)",
     month = aug,
     year = "2021",
     address = "Virtual",
     publisher = "Association for Machine Translation in the Americas",
     url = "https://aclanthology.org/2021.mtsummit-at4ssl.7",
     pages = "60--70",
     abstract = "A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.",
     }

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

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

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

490. L. Zhou, L. Ding, K. Duh, S. Watanabe, R. Sasano, and K. Takeda, “Self-Guided Curriculum Learning for Neural Machine Translation,” in Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand (online), 2021, p. 206–214. doi:10.18653/v1/2021.iwslt-1.25
     [BibTeX] [Abstract] [Link]
     

     In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model {“}knows{”} how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.

     @inproceedings{zhou-etal-2021-self,
     title = "Self-Guided Curriculum Learning for Neural Machine Translation",
     author = "Zhou, Lei and
     Ding, Liang and
     Duh, Kevin and
     Watanabe, Shinji and
     Sasano, Ryohei and
     Takeda, Koichi",
     booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
     month = aug,
     year = "2021",
     address = "Bangkok, Thailand (online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.iwslt-1.25",
     doi = "10.18653/v1/2021.iwslt-1.25",
     pages = "206--214",
     abstract = "In supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. Inspired by this, we formulate a difficulty criterion based on the recovery degrees of training examples. Motivated by the intuition that after skimming through the training corpus, the neural machine translation (NMT) model {``}knows{''} how to schedule a suitable curriculum according to learning difficulty, we propose a self-guided curriculum learning strategy that encourages the NMT model to learn from easy to hard on the basis of recovery degrees. Specifically, we adopt sentence-level BLEU score as the proxy of recovery degree. Experimental results on translation benchmarks including WMT14 English-German and WMT17 Chinese-English demonstrate that our proposed method considerably improves the recovery degree, thus consistently improving the translation performance.",
     }

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

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

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

492. N. Holzenberger and B. Van Durme, “Factoring Statutory Reasoning as Language Understanding Challenges,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, 2021, p. 2742–2758. doi:10.18653/v1/2021.acl-long.213
     [BibTeX] [Abstract] [Link]
     

     Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.

     @inproceedings{holzenberger-van-durme-2021-factoring,
     title = "Factoring Statutory Reasoning as Language Understanding Challenges",
     author = "Holzenberger, Nils and
     Van Durme, Benjamin",
     booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.acl-long.213",
     doi = "10.18653/v1/2021.acl-long.213",
     pages = "2742--2758",
     abstract = "Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.",
     }

493. H. Inaguma, B. Yan, S. Dalmia, P. Guo, J. Shi, K. Duh, and S. Watanabe, “ESPnet-ST IWSLT 2021 Offline Speech Translation System,” in Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand (online), 2021, p. 100–109. doi:10.18653/v1/2021.iwslt-1.10
     [BibTeX] [Abstract] [Link]
     

     This paper describes the ESPnet-ST group{‘}s IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audio segmentation. On the data side, we investigated sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech translation. Specifically, we used multi-referenced SeqKD from multiple teachers trained on different amounts of bitext. On the architecture side, we adopted the Conformer encoder and the Multi-Decoder architecture, which equips dedicated decoders for speech recognition and translation tasks in a unified encoder-decoder model and enables search in both source and target language spaces during inference. We also significantly improved audio segmentation by using the pyannote.audio toolkit and merging multiple short segments for long context modeling. Experimental evaluations showed that each of them contributed to large improvements in translation performance. Our best E2E system combined all the above techniques with model ensembling and achieved 31.4 BLEU on the 2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of tst2021.

     @inproceedings{inaguma-etal-2021-espnet,
     title = "{ESP}net-{ST} {IWSLT} 2021 Offline Speech Translation System",
     author = "Inaguma, Hirofumi and
     Yan, Brian and
     Dalmia, Siddharth and
     Guo, Pengcheng and
     Shi, Jiatong and
     Duh, Kevin and
     Watanabe, Shinji",
     booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
     month = aug,
     year = "2021",
     address = "Bangkok, Thailand (online)",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.iwslt-1.10",
     doi = "10.18653/v1/2021.iwslt-1.10",
     pages = "100--109",
     abstract = "This paper describes the ESPnet-ST group{'}s IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audio segmentation. On the data side, we investigated sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech translation. Specifically, we used multi-referenced SeqKD from multiple teachers trained on different amounts of bitext. On the architecture side, we adopted the Conformer encoder and the Multi-Decoder architecture, which equips dedicated decoders for speech recognition and translation tasks in a unified encoder-decoder model and enables search in both source and target language spaces during inference. We also significantly improved audio segmentation by using the pyannote.audio toolkit and merging multiple short segments for long context modeling. Experimental evaluations showed that each of them contributed to large improvements in translation performance. Our best E2E system combined all the above techniques with model ensembling and achieved 31.4 BLEU on the 2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of tst2021.",
     }

494. E. Schumacher, J. Mayfield, and M. Dredze, “Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, 2021, p. 583–595. doi:10.18653/v1/2021.findings-acl.52
     [BibTeX] [Link]

     @inproceedings{schumacher-etal-2021-cross,
     title = "Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking",
     author = "Schumacher, Elliot and
     Mayfield, James and
     Dredze, Mark",
     booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.findings-acl.52",
     doi = "10.18653/v1/2021.findings-acl.52",
     pages = "583--595",
     }

495. G. Coppersmith, A. Fine, P. Crutchley, and J. Carroll, “Individual Differences in the Movement-Mood Relationship in Digital Life Data,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 25–31. doi:10.18653/v1/2021.clpsych-1.3
     [BibTeX] [Abstract] [Link]
     

     Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood, and wellbeing. Such {“}digital life data{”} provides crucial insight into the lives of patients outside the healthcare setting that has long been lacking, from a better understanding of mundane patterns of exercise and sleep routines to harbingers of emotional crisis. Moreover, information about individual differences and personalities is encoded in digital life data. In this paper we examine the relationship between mood and movement using linguistic and biometric data, respectively. Does increased physical activity (movement) have an effect on a person{‘}s mood (or vice-versa)? We find that weak group-level relationships between movement and mood mask interesting and often strong relationships between the two for individuals within the group. We describe these individual differences, and argue that individual variability in the relationship between movement and mood is one of many such factors that ought be taken into account in wellbeing-focused apps and AI systems.

     @inproceedings{coppersmith-etal-2021-individual,
     title = "Individual Differences in the Movement-Mood Relationship in Digital Life Data",
     author = "Coppersmith, Glen and
     Fine, Alex and
     Crutchley, Patrick and
     Carroll, Joshua",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.3",
     doi = "10.18653/v1/2021.clpsych-1.3",
     pages = "25--31",
     abstract = "Our increasingly digitized lives generate troves of data that reflect our behavior, beliefs, mood, and wellbeing. Such {``}digital life data{''} provides crucial insight into the lives of patients outside the healthcare setting that has long been lacking, from a better understanding of mundane patterns of exercise and sleep routines to harbingers of emotional crisis. Moreover, information about individual differences and personalities is encoded in digital life data. In this paper we examine the relationship between mood and movement using linguistic and biometric data, respectively. Does increased physical activity (movement) have an effect on a person{'}s mood (or vice-versa)? We find that weak group-level relationships between movement and mood mask interesting and often strong relationships between the two for individuals within the group. We describe these individual differences, and argue that individual variability in the relationship between movement and mood is one of many such factors that ought be taken into account in wellbeing-focused apps and AI systems.",
     }

496. T. Lippincott and B. Van Durme, “Active learning and negative evidence for language identification,” in Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, Online, 2021, p. 47–51. doi:10.18653/v1/2021.dash-1.8
     [BibTeX] [Abstract] [Link]
     

     Language identification (LID), the task of determining the natural language of a given text, is an essential first step in most NLP pipelines. While generally a solved problem for documents of sufficient length and languages with ample training data, the proliferation of microblogs and other social media has made it increasingly common to encounter use-cases that *don{‘}t* satisfy these conditions. In these situations, the fundamental difficulty is the lack of, and cost of gathering, labeled data: unlike some annotation tasks, no single {“}expert{”} can quickly and reliably identify more than a handful of languages. This leads to a natural question: can we gain useful information when annotators are only able to *rule out* languages for a given document, rather than supply a positive label? What are the optimal choices for gathering and representing such *negative evidence* as a model is trained? In this paper, we demonstrate that using negative evidence can improve the performance of a simple neural LID model. This improvement is sensitive to policies of how the evidence is represented in the loss function, and for deciding which annotators to employ given the instance and model state. We consider simple policies and report experimental results that indicate the optimal choices for this task. We conclude with a discussion of future work to determine if and how the results generalize to other classification tasks.

     @inproceedings{lippincott-van-durme-2021-active,
     title = "Active learning and negative evidence for language identification",
     author = "Lippincott, Thomas and
     Van Durme, Ben",
     booktitle = "Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.dash-1.8",
     doi = "10.18653/v1/2021.dash-1.8",
     pages = "47--51",
     abstract = "Language identification (LID), the task of determining the natural language of a given text, is an essential first step in most NLP pipelines. While generally a solved problem for documents of sufficient length and languages with ample training data, the proliferation of microblogs and other social media has made it increasingly common to encounter use-cases that *don{'}t* satisfy these conditions. In these situations, the fundamental difficulty is the lack of, and cost of gathering, labeled data: unlike some annotation tasks, no single {``}expert{''} can quickly and reliably identify more than a handful of languages. This leads to a natural question: can we gain useful information when annotators are only able to *rule out* languages for a given document, rather than supply a positive label? What are the optimal choices for gathering and representing such *negative evidence* as a model is trained? In this paper, we demonstrate that using negative evidence can improve the performance of a simple neural LID model. This improvement is sensitive to policies of how the evidence is represented in the loss function, and for deciding which annotators to employ given the instance and model state. We consider simple policies and report experimental results that indicate the optimal choices for this task. We conclude with a discussion of future work to determine if and how the results generalize to other classification tasks.",
     }

497. S. MacAvaney, A. Mittu, G. Coppersmith, J. Leintz, and P. Resnik, “Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 70–80. doi:10.18653/v1/2021.clpsych-1.7
     [BibTeX] [Abstract] [Link]
     

     Progress on NLP for mental health {–-} indeed, for healthcare in general {–-} is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.

     @inproceedings{macavaney-etal-2021-community,
     title = "Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the {CLP}sych 2021 Shared Task",
     author = "MacAvaney, Sean and
     Mittu, Anjali and
     Coppersmith, Glen and
     Leintz, Jeff and
     Resnik, Philip",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.7",
     doi = "10.18653/v1/2021.clpsych-1.7",
     pages = "70--80",
     abstract = "Progress on NLP for mental health {---} indeed, for healthcare in general {---} is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.",
     }

498. K. Harrigian, C. Aguirre, and M. Dredze, “On the State of Social Media Data for Mental Health Research,” in Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, Online, 2021, p. 15–24. doi:10.18653/v1/2021.clpsych-1.2
     [BibTeX] [Abstract] [Link]
     

     Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.

     @inproceedings{harrigian-etal-2021-state,
     title = "On the State of Social Media Data for Mental Health Research",
     author = "Harrigian, Keith and
     Aguirre, Carlos and
     Dredze, Mark",
     booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.clpsych-1.2",
     doi = "10.18653/v1/2021.clpsych-1.2",
     pages = "15--24",
     abstract = "Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.",
     }

499. Z. Wood-Doughty, P. Xu, X. Liu, and M. Dredze, “Using Noisy Self-Reports to Predict Twitter User Demographics,” in Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, Online, 2021, p. 123–137. doi:10.18653/v1/2021.socialnlp-1.11
     [BibTeX] [Abstract] [Link]
     

     Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.

     @inproceedings{wood-doughty-etal-2021-using,
     title = "Using Noisy Self-Reports to Predict {T}witter User Demographics",
     author = "Wood-Doughty, Zach and
     Xu, Paiheng and
     Liu, Xiao and
     Dredze, Mark",
     booktitle = "Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media",
     month = jun,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.socialnlp-1.11",
     doi = "10.18653/v1/2021.socialnlp-1.11",
     pages = "123--137",
     abstract = "Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.",
     }

500. C. Lin, A. Jaech, X. Li, Matt Gormley, and J. Eisner, “Limitations of Autoregressive Models and Their Alternatives,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Online, 2021, p. 5147–5173. doi:10.18653/v1/2021.naacl-main.405
     [BibTeX] [Link]

     @InProceedings{lin-et-al-2021-naacl,
     aclid = "2021.naacl-main.405",
     doi = "10.18653/v1/2021.naacl-main.405",
     author = "Chu-Cheng Lin and Aaron Jaech and Xin Li and Matt
     Gormley and Jason Eisner",
     title = "Limitations of Autoregressive Models and Their
     Alternatives",
     booktitle = "Proceedings of the 2021 Conference of the North
     American Chapter of the Association for Computational
     Linguistics: Human Language Technologies (NAACL-HLT)",
     pages = "5147--5173",
     year = "2021",
     month = jun,
     address = "Online",
     URL = "http://cs.jhu.edu/~jason/papers/#lin-et-al-2021-naacl",
     }

501. G. Qin and J. Eisner, “Learning How To Ask: Querying LMs with Mixtures of Soft Prompts,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Online, 2021, p. 5203–5212. doi:10.18653/v1/2021.naacl-main.410
     [BibTeX] [Link]

     @InProceedings{qin-eisner-2021,
     aclid = "2021.naacl-main.410",
     doi = "10.18653/v1/2021.naacl-main.410",
     author = "Guanghui Qin and Jason Eisner",
     title = "Learning How To Ask: Querying {LM}s with Mixtures of
     Soft Prompts",
     booktitle = "Proceedings of the 2021 Conference of the North
     American Chapter of the Association for Computational
     Linguistics: Human Language Technologies (NAACL-HLT)",
     pages = "5203--5212",
     year = "2021",
     month = jun,
     address = "Online",
     note = "Best Short Paper Award.",
     URL = "http://cs.jhu.edu/~jason/papers/#qin-eisner-2021",
     }

502. H. Xu, S. Ebner, M. Yarmohammadi, A. S. White, B. Van Durme, and K. Murray, “Gradual Fine-Tuning for Low-Resource Domain Adaptation,” in Proceedings of the Second Workshop on Domain Adaptation for NLP, Kyiv, Ukraine, 2021, p. 214–221.
     [BibTeX] [Abstract] [Link]
     

     Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.

     @inproceedings{xu-etal-2021-gradual,
     title = "Gradual Fine-Tuning for Low-Resource Domain Adaptation",
     author = "Xu, Haoran and
     Ebner, Seth and
     Yarmohammadi, Mahsa and
     White, Aaron Steven and
     Van Durme, Benjamin and
     Murray, Kenton",
     booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
     month = apr,
     year = "2021",
     address = "Kyiv, Ukraine",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.adaptnlp-1.22",
     pages = "214--221",
     abstract = "Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.",
     }

503. M. Martindale, K. Duh, and M. Carpuat, “Machine Translation Believability,” in Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing, Online, 2021, p. 88–95.
     [BibTeX] [Abstract] [Link]
     

     Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.

     @inproceedings{martindale-etal-2021-machine,
     title = "Machine Translation Believability",
     author = "Martindale, Marianna and
     Duh, Kevin and
     Carpuat, Marine",
     booktitle = "Proceedings of the First Workshop on Bridging Human{--}Computer Interaction and Natural Language Processing",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.hcinlp-1.14",
     pages = "88--95",
     abstract = "Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.",
     }

504. P. Xia, G. Qin, S. Vashishtha, Y. Chen, T. Chen, C. May, C. Harman, K. Rawlins, A. S. White, and B. Van Durme, “LOME: Large Ontology Multilingual Extraction,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, Online, 2021, p. 149–159. doi:10.18653/v1/2021.eacl-demos.19
     [BibTeX] [Abstract] [Link]
     

     We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.

     @inproceedings{xia-etal-2021-lome,
     title = "{LOME}: Large Ontology Multilingual Extraction",
     author = "Xia, Patrick and
     Qin, Guanghui and
     Vashishtha, Siddharth and
     Chen, Yunmo and
     Chen, Tongfei and
     May, Chandler and
     Harman, Craig and
     Rawlins, Kyle and
     White, Aaron Steven and
     Van Durme, Benjamin",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-demos.19",
     doi = "10.18653/v1/2021.eacl-demos.19",
     pages = "149--159",
     abstract = "We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relation prediction between events. By doing so, the system constructs an event and entity focused knowledge graph. We can further apply third-party modules for other types of annotation, like relation extraction. Our (multilingual) first-party modules either outperform or are competitive with the (monolingual) state-of-the-art. We achieve this through the use of multilingual encoders like XLM-R (Conneau et al., 2020) and leveraging multilingual training data. LOME is available as a Docker container on Docker Hub. In addition, a lightweight version of the system is accessible as a web demo.",
     }

505. S. Sia and K. Duh, “Adaptive Mixed Component LDA for Low Resource Topic Modeling,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, p. 2451–2469. doi:10.18653/v1/2021.eacl-main.209
     [BibTeX] [Abstract] [Link]
     

     Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods. In this challenging resource constrained setting, we explore mixture models which interpolate between the discrete and continuous topic-word distributions that utilise pre-trained embeddings to improve topic coherence. We introduce an automatic trade-off between the discrete and continuous representations via an adaptive mixture coefficient, which places greater weight on the discrete representation when the corpus statistics are more reliable. The adaptive mixture coefficient takes into account global corpus statistics, and the uncertainty in each topic{‘}s continuous distributions. Our approach outperforms the fully discrete, fully continuous, and static mixture model on topic coherence in low resource settings. We additionally demonstrate the generalisability of our method by extending it to handle multilingual document collections.

     @inproceedings{sia-duh-2021-adaptive,
     title = "Adaptive Mixed Component {LDA} for Low Resource Topic Modeling",
     author = "Sia, Suzanna and
     Duh, Kevin",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-main.209",
     doi = "10.18653/v1/2021.eacl-main.209",
     pages = "2451--2469",
     abstract = "Probabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining word or topic embeddings using neural methods. In this challenging resource constrained setting, we explore mixture models which interpolate between the discrete and continuous topic-word distributions that utilise pre-trained embeddings to improve topic coherence. We introduce an automatic trade-off between the discrete and continuous representations via an adaptive mixture coefficient, which places greater weight on the discrete representation when the corpus statistics are more reliable. The adaptive mixture coefficient takes into account global corpus statistics, and the uncertainty in each topic{'}s continuous distributions. Our approach outperforms the fully discrete, fully continuous, and static mixture model on topic coherence in low resource settings. We additionally demonstrate the generalisability of our method by extending it to handle multilingual document collections.",
     }

506. X. Huang, M. J. Paul, F. Dernoncourt, R. Burke, and M. Dredze, “User Factor Adaptation for User Embedding via Multitask Learning,” in Proceedings of the Second Workshop on Domain Adaptation for NLP, Kyiv, Ukraine, 2021, p. 172–182.
     [BibTeX] [Abstract] [Link]
     

     Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.

     @inproceedings{huang-etal-2021-user,
     title = "User Factor Adaptation for User Embedding via Multitask Learning",
     author = "Huang, Xiaolei and
     Paul, Michael J. and
     Dernoncourt, Franck and
     Burke, Robin and
     Dredze, Mark",
     booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
     month = apr,
     year = "2021",
     address = "Kyiv, Ukraine",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.adaptnlp-1.18",
     pages = "172--182",
     abstract = "Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing methods to train user embeddings ignore the variations across user interests, such as product and movie categories (e.g., drama vs. action). In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets. We then propose a user embedding model to account for the language variability of user interests via a multitask learning framework. The model learns user language and its variations without human supervision. While existing work mainly evaluated the user embedding by extrinsic tasks, we propose an intrinsic evaluation via clustering and evaluate user embeddings by an extrinsic task, text classification. The experiments on the three English-language social media datasets show that our proposed approach can generally outperform baselines via adapting the user factor.",
     }

507. R. Wicks and M. Post, “A unified approach to sentence segmentation of punctuated text in many languages,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, 2021, p. 3995–4007. doi:10.18653/v1/2021.acl-long.309
     [BibTeX] [Abstract] [Link]
     

     The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is \textit{sentence segmentation}. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.

     @inproceedings{wicks-post-2021-unified,
     title = "A unified approach to sentence segmentation of punctuated text in many languages",
     author = "Wicks, Rachel and
     Post, Matt",
     booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
     month = aug,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.acl-long.309",
     doi = "10.18653/v1/2021.acl-long.309",
     pages = "3995--4007",
     abstract = "The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is \textit{sentence segmentation}. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.",
     }

508. Jeya Maria Jose Valanarasu, Christina Chen, and Vishal M. Patel, “R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{237571374,
     title = {R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection},
     author = {{Jeya Maria Jose Valanarasu} and {Christina Chen} and {Vishal M. Patel}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/6336d19526c1028ec9d9317fdbec68c8ca901eaa},
     }

509. Nanxin Chen, Yu Zhang, H. Zen, Ron J. Weiss, Mohammad Norouzi, N. Dehak, and William Chan, “WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis,” in Interspeech, 2021.
     [BibTeX] [Link]

     @inproceedings{235458124,
     title = {WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis},
     author = {{Nanxin Chen} and {Yu Zhang} and {H. Zen} and {Ron J. Weiss} and {Mohammad Norouzi} and {N. Dehak} and {William Chan}},
     year = 2021,
     month = {6},
     booktitle = {Interspeech},
     url = {https://www.semanticscholar.org/paper/10ae9a3d1e0874a50820766bd414f98e095cdd8a},
     }

510. N. Mathioudakis, Mohammed S. Abusamaan, Ahmed F. Shakarchi, Sam Sokolinsky, Shamil Fayzullin, J. McGready, M. Zilbermint, S. Saria, and S. Golden, “Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients,” in JAMA Network Open, 2021.
     [BibTeX] [Link]

     @inproceedings{231194796,
     title = {Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients},
     author = {{N. Mathioudakis} and {Mohammed S. Abusamaan} and {Ahmed F. Shakarchi} and {Sam Sokolinsky} and {Shamil Fayzullin} and {J. McGready} and {M. Zilbermint} and {S. Saria} and {S. Golden}},
     year = 2021,
     month = {1},
     booktitle = {JAMA Network Open},
     url = {https://www.semanticscholar.org/paper/c172a8a4adad62a49167e9a76e6a4951565a4894},
     }

511. K. Henry, R. Adams, C. Parent, A. Sridharan, L. Johnson, D. Hager, S. Cosgrove, A. Markowski, E. Klein, E. Chen, M. Henley, S. Miranda, K. Houston, R. C. Linton, A. Ahluwalia, A. Wu, and S. Saria, “Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System,” in medRxiv, 2021.
     [BibTeX] [Link]

     @inproceedings{235752600,
     title = {Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System},
     author = {{K. Henry} and {R. Adams} and {C. Parent} and {A. Sridharan} and {L. Johnson} and {D. Hager} and {S. Cosgrove} and {A. Markowski} and {E. Klein} and {E. Chen} and {M. Henley} and {S. Miranda} and {K. Houston} and {R. C. Linton} and {A. Ahluwalia} and {A. Wu} and {S. Saria}},
     year = 2021,
     month = {7},
     booktitle = {medRxiv},
     url = {https://www.semanticscholar.org/paper/6cb36845038e3b299b1d9116a90c4fb0f52b657d},
     }

512. M. Landers, R. Dorsey, and S. Saria, “Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption,” in Digital Biomarkers, 2021.
     [BibTeX] [Link]

     @inproceedings{239134009,
     title = {Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption},
     author = {{M. Landers} and {R. Dorsey} and {S. Saria}},
     year = 2021,
     month = {9},
     booktitle = {Digital Biomarkers},
     url = {https://www.semanticscholar.org/paper/a79a3ea8141ac2d9f780abbcd1ee0b2bfbd78ead},
     }

513. Pramuditha Perera and Vishal M. Patel, “A Joint Representation Learning and Feature Modeling Approach for One-class Recognition,” in International Conference on Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{231698354,
     title = {A Joint Representation Learning and Feature Modeling Approach for One-class Recognition},
     author = {{Pramuditha Perera} and {Vishal M. Patel}},
     year = 2021,
     month = {1},
     booktitle = {International Conference on Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/370f4722d2fe2a3ea9ae9198ecaf5047685be904},
     }

514. L. Chu, Seyoun Park, S. Kawamoto, A. Yuille, R. Hruban, and E. Fishman, “Current Status of Radiomics and Deep Learning in Liver Imaging,” in Journal of computer assisted tomography, 2021.
     [BibTeX] [Link]

     @inproceedings{235128460,
     title = {Current Status of Radiomics and Deep Learning in Liver Imaging},
     author = {{L. Chu} and {Seyoun Park} and {S. Kawamoto} and {A. Yuille} and {R. Hruban} and {E. Fishman}},
     year = 2021,
     booktitle = {Journal of computer assisted tomography},
     url = {https://www.semanticscholar.org/paper/3ed6aa987299d52aad39a6e8339f57dc27c81980},
     }

515. A. Hussein, Shammur A. Chowdhury, N. Dehak, and Ahmed Ali, “Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition.” 2021.
     [BibTeX] [Link]

     @inproceedings{246863876,
     title = {Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition},
     author = {{A. Hussein} and {Shammur A. Chowdhury} and {N. Dehak} and {Ahmed Ali}},
     year = 2021,
     month = {6},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/4781f897c02809c1522a06668ae1f4fa0e68e5ac},
     }

516. Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “Over-and-Under Complete Convolutional RNN for MRI Reconstruction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
     [BibTeX] [Link]

     @inproceedings{235446931,
     title = {Over-and-Under Complete Convolutional RNN for MRI Reconstruction},
     author = {{Pengfei Guo} and {Jeya Maria Jose Valanarasu} and {Puyang Wang} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
     year = 2021,
     month = {6},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/4e5095ca6e280b068aa572c6d4afc32d6b246492},
     }

517. Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning,” in Computer Vision and Pattern Recognition, 2021.
     [BibTeX] [Link]

     @inproceedings{232104977,
     title = {Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning},
     author = {{Pengfei Guo} and {Puyang Wang} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
     year = 2021,
     month = {3},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/245df7d8e53b3860107edc76b467e055eb80744d},
     }

518. Shota Horiguchi, Nelson Yalta, Leibny Paola García-Perera, Yuki Takashima, Yawen Xue, Desh Raj, Zili Huang, Yusuke Fujita, Shinji Watanabe, and S. Khudanpur, “The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{231749872,
     title = {The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap},
     author = {{Shota Horiguchi} and {Nelson Yalta} and {Leibny Paola García-Perera} and {Yuki Takashima} and {Yawen Xue} and {Desh Raj} and {Zili Huang} and {Yusuke Fujita} and {Shinji Watanabe} and {S. Khudanpur}},
     year = 2021,
     month = {2},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/7a737872a6693ba3f0c99651191b93dad0dadcee},
     }

519. S. Saria, M. Ghassemi, Z. Obermeyer, Karandeep Singh, P. Hsueh, and E. Topol, “Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery,” in American Medical Informatics Association Annual Symposium, 2021.
     [BibTeX] [Link]

     @inproceedings{256549237,
     title = {Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery},
     author = {{S. Saria} and {M. Ghassemi} and {Z. Obermeyer} and {Karandeep Singh} and {P. Hsueh} and {E. Topol}},
     year = 2021,
     booktitle = {American Medical Informatics Association Annual Symposium},
     url = {https://www.semanticscholar.org/paper/d41dfad8ea7ae4ead9e38fcc425c02749a1a8d64},
     }

520. Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, D. Cremers, L. Leal-Taixé, A. Yuille, Florian Schroff, Hartwig Adam, and Liang-Chieh Chen, “DeepLab2: A TensorFlow Library for Deep Labeling,” in arXiv.org, 2021.
     [BibTeX] [Link]

     @inproceedings{235485458,
     title = {DeepLab2: A TensorFlow Library for Deep Labeling},
     author = {{Mark Weber} and {Huiyu Wang} and {Siyuan Qiao} and {Jun Xie} and {Maxwell D. Collins} and {Yukun Zhu} and {Liangzhe Yuan} and {Dahun Kim} and {Qihang Yu} and {D. Cremers} and {L. Leal-Taixé} and {A. Yuille} and {Florian Schroff} and {Hartwig Adam} and {Liang-Chieh Chen}},
     year = 2021,
     month = {6},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/21f6586c13403beb2fa383201719287869de6beb},
     }

521. Yingda Xia, Jiawen Yao, Le Lu, Lingyun Huang, G. Xie, Jing Xiao, A. Yuille, K. Cao, and Ling Zhang, “Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
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     @inproceedings{238207681,
     title = {Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers},
     author = {{Yingda Xia} and {Jiawen Yao} and {Le Lu} and {Lingyun Huang} and {G. Xie} and {Jing Xiao} and {A. Yuille} and {K. Cao} and {Ling Zhang}},
     year = 2021,
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/bd935c747f901482c291cbe769eb2ee81d568ef0},
     }

522. Xing Di, He Zhang, and Vishal M. Patel, “Thermal-to-Visible Face Synthesis and Recognition,” in Advances in Computer Vision and Pattern Recognition, 2021.
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     title = {Thermal-to-Visible Face Synthesis and Recognition},
     author = {{Xing Di} and {He Zhang} and {Vishal M. Patel}},
     year = 2021,
     booktitle = {Advances in Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/c02d3e2e51d8fcea46e36822b110b26c140d5d24},
     }

523. Hexin Liu, Leibny Paola Garcia Perera, Xinyi Zhang, J. Dauwels, Andy W. H. Khong, S. Khudanpur, and S. Styles, “End-to-End Language Diarization for Bilingual Code-Switching Speech,” in Interspeech, 2021.
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     year = 2021,
     month = {8},
     booktitle = {Interspeech},
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     }

524. Qing Liu, Vignesh Ramanathan, D. Mahajan, A. Yuille, and Zhenheng Yang, “Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency,” in Computer Vision and Pattern Recognition, 2021.
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     author = {{Qing Liu} and {Vignesh Ramanathan} and {D. Mahajan} and {A. Yuille} and {Zhenheng Yang}},
     year = 2021,
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525. Pirazh Khorramshahi, Sai Saketh Rambhatla, and R. Chellappa, “Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
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     year = 2021,
     month = {6},
     booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
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526. Nanxin Chen, Shinji Watanabe, J. Villalba, Piotr Żelasko, and N. Dehak, “Non-Autoregressive Transformer for Speech Recognition,” in IEEE Signal Processing Letters, 2021.
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     author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {Piotr Żelasko} and {N. Dehak}},
     year = 2021,
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527. Prithviraj Dhar, Joshua Gleason, A. Roy, C. Castillo, and R. Chellappa, “Supplementary Material-PASS.” 2021.
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     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/ee50fa46cd195e4b59330297d4285877906583b5},
     }

528. Xing Di and Vishal M. Patel, “Multimodal Face Synthesis From Visual Attributes,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2021.
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     @inproceedings{233204538,
     title = {Multimodal Face Synthesis From Visual Attributes},
     author = {{Xing Di} and {Vishal M. Patel}},
     year = 2021,
     month = {4},
     booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
     url = {https://www.semanticscholar.org/paper/b3b4be784e92a78ac4987faa0d9d39f113807efc},
     }

529. Kelly Marchisio, Youngser Park, Ali Saad-Eldin, A. Alyakin, Kevin Duh, C. Priebe, and Philipp Koehn, “An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces,” in Conference on Empirical Methods in Natural Language Processing, 2021.
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     @inproceedings{237941142,
     title = {An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces},
     author = {{Kelly Marchisio} and {Youngser Park} and {Ali Saad-Eldin} and {A. Alyakin} and {Kevin Duh} and {C. Priebe} and {Philipp Koehn}},
     year = 2021,
     month = {9},
     booktitle = {Conference on Empirical Methods in Natural Language Processing},
     url = {https://www.semanticscholar.org/paper/0a5fc6d1735dd2761fc31fad5a3b40a4fa06546b},
     }

530. Angtian Wang, Adam Kortylewski, and A. Yuille, “NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation,” in International Conference on Learning Representations, 2021.
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     title = {NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation},
     author = {{Angtian Wang} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     month = {1},
     booktitle = {International Conference on Learning Representations},
     url = {https://www.semanticscholar.org/paper/02200d454717ffea6c1daf64d635ab945d4fa140},
     }

531. Noah Weber, Anton Belyy, Nils Holzenberger, Rachel Rudinger, and Benjamin Van Durme, “Schema Curation via Causal Association Rule Mining,” in arXiv.org, 2021.
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     title = {Schema Curation via Causal Association Rule Mining},
     author = {{Noah Weber} and {Anton Belyy} and {Nils Holzenberger} and {Rachel Rudinger} and {Benjamin Van Durme}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/1da3dc6f997599a197b1510f2c2bdd86e2d86e49},
     }

532. Chenxu Luo, Xiaodong Yang, and A. Yuille, “Exploring Simple 3D Multi-Object Tracking for Autonomous Driving,” in IEEE International Conference on Computer Vision, 2021.
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     @inproceedings{237266533,
     title = {Exploring Simple 3D Multi-Object Tracking for Autonomous Driving},
     author = {{Chenxu Luo} and {Xiaodong Yang} and {A. Yuille}},
     year = 2021,
     month = {8},
     booktitle = {IEEE International Conference on Computer Vision},
     url = {https://www.semanticscholar.org/paper/31115520c75bb9eb11ff2aee37c7605684d039f5},
     }

533. Jonah P. Sengupta, M. Villemur, and A. Andreou, “Efficient, event-driven feature extraction and unsupervised object tracking for embedded applications,” in Annual Conference on Information Sciences and Systems, 2021.
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     @inproceedings{233333562,
     title = {Efficient, event-driven feature extraction and unsupervised object tracking for embedded applications},
     author = {{Jonah P. Sengupta} and {M. Villemur} and {A. Andreou}},
     year = 2021,
     month = {3},
     booktitle = {Annual Conference on Information Sciences and Systems},
     url = {https://www.semanticscholar.org/paper/b943079dc74c91a11ff4c7ccd9477775398edba2},
     }

534. Chenglin Yang, Siyuan Qiao, Adam Kortylewski, and A. Yuille, “Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms.” 2021.
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     @inproceedings{244728325,
     title = {Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms},
     author = {{Chenglin Yang} and {Siyuan Qiao} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     month = {7},
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/fc19e94109d4c2f05f3639a67327c708543def98},
     }

535. Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, S. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: A Benchmark,” in International Journal of Computer Vision, 2021.
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     @inproceedings{244117311,
     title = {Occluded Video Instance Segmentation: A Benchmark},
     author = {{Jiyang Qi} and {Yan Gao} and {Yao Hu} and {Xinggang Wang} and {Xiaoyu Liu} and {Xiang Bai} and {S. Belongie} and {A. Yuille} and {Philip H. S. Torr} and {S. Bai}},
     year = 2021,
     month = {2},
     booktitle = {International Journal of Computer Vision},
     url = {https://www.semanticscholar.org/paper/f0597d0543b0b315e9290ec49017424aeeb8d3e5},
     }

536. Ju He, Adam Kortylewski, and A. Yuille, “CORL: Compositional Representation Learning for Few-Shot Classification,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
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     @inproceedings{254823669,
     title = {CORL: Compositional Representation Learning for Few-Shot Classification},
     author = {{Ju He} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     month = {1},
     booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
     url = {https://www.semanticscholar.org/paper/0f10d0f5355a3f7ce371008e26419172d258bf77},
     }

537. Sangwook Park, Ashwin Bellur, D. Han, and Mounya Elhilali, “Self-Training for Sound Event Detection in Audio Mixtures,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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     @inproceedings{235400502,
     title = {Self-Training for Sound Event Detection in Audio Mixtures},
     author = {{Sangwook Park} and {Ashwin Bellur} and {D. Han} and {Mounya Elhilali}},
     year = 2021,
     month = {6},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/1c8465fc6210e5daeccad968e84259cff8185cb5},
     }

538. Prakhar Kaushik, Alex Gain, Adam Kortylewski, and A. Yuille, “Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping,” in arXiv.org, 2021.
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     @inproceedings{232013967,
     title = {Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping},
     author = {{Prakhar Kaushik} and {Alex Gain} and {Adam Kortylewski} and {A. Yuille}},
     year = 2021,
     month = {2},
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/be864a16ec597c76d1ab36453d01471723a37bac},
     }

539. Adam Kortylewski, Ju He, Qing Liu, Christian Cosgrove, Chenglin Yang, and A. Yuille, “Compositional Generative Networks and Robustness to Perceptible Image Changes,” in Annual Conference on Information Sciences and Systems, 2021.
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     @inproceedings{233333658,
     title = {Compositional Generative Networks and Robustness to Perceptible Image Changes},
     author = {{Adam Kortylewski} and {Ju He} and {Qing Liu} and {Christian Cosgrove} and {Chenglin Yang} and {A. Yuille}},
     year = 2021,
     month = {3},
     booktitle = {Annual Conference on Information Sciences and Systems},
     url = {https://www.semanticscholar.org/paper/90acfd3862d20103cd925d470ca7f6bf83e3514f},
     }

540. Matt Post, “S CIENTIFIC DISSEMINATION VIA COMIC STRIP : A CASE STUDY WITH SACREBLEU.” 2021.
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     @inproceedings{234491121,
     title = {S CIENTIFIC DISSEMINATION VIA COMIC STRIP : A CASE STUDY WITH SACREBLEU},
     author = {{Matt Post}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/a8d2b6e0d180f0b5f3d444c6ac7302f531dc90c2},
     }

541. Ke Li, Daniel Povey, and S. Khudanpur, “A Parallelizable Lattice Rescoring Strategy with Neural Language Models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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     @inproceedings{232168928,
     title = {A Parallelizable Lattice Rescoring Strategy with Neural Language Models},
     author = {{Ke Li} and {Daniel Povey} and {S. Khudanpur}},
     year = 2021,
     month = {3},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
     url = {https://www.semanticscholar.org/paper/736f0404a5352ea100d9a81f4b0b3af10a14b4fe},
     }

542. Hao Ding, Siyuan Qiao, A. Yuille, and Wei Shen, “Deeply Shape-guided Cascade for Instance Segmentation,” in Computer Vision and Pattern Recognition, 2021.
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     @inproceedings{235726458,
     title = {Deeply Shape-guided Cascade for Instance Segmentation},
     author = {{Hao Ding} and {Siyuan Qiao} and {A. Yuille} and {Wei Shen}},
     year = 2021,
     month = {6},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/c14e9e74519a2a791f99e2dd8723b9b4f6bfef0e},
     }

543. Faisal Rahman, N. Finkelstein, A. Alyakin, N. Gilotra, Jeff Trost, S. Schulman, and S. Saria, “Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure,” in Journal of the Society for Cardiovascular Angiography & Interventions, 2021.
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     @inproceedings{236623062,
     title = {Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure},
     author = {{Faisal Rahman} and {N. Finkelstein} and {A. Alyakin} and {N. Gilotra} and {Jeff Trost} and {S. Schulman} and {S. Saria}},
     year = 2021,
     month = {4},
     booktitle = {Journal of the Society for Cardiovascular Angiography & Interventions},
     url = {https://www.semanticscholar.org/paper/d3d1d3e4e14e7810e94de4c11eda135bc17bd41f},
     }

544. Hui Che, J. Radbel, J. Sunderram, J. Nosher, Vishal M. Patel, and I. Hacihaliloglu, “Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021.
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     @inproceedings{232035634,
     title = {Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data},
     author = {{Hui Che} and {J. Radbel} and {J. Sunderram} and {J. Nosher} and {Vishal M. Patel} and {I. Hacihaliloglu}},
     year = 2021,
     month = {2},
     booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
     url = {https://www.semanticscholar.org/paper/09efab3f011c4738b16fed6edd67a77c9a7b13c2},
     }

545. Shao-Yuan Lo, Poojan Oza, and Vishal M. Patel, “Adversarially Robust One-Class Novelty Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
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     @inproceedings{237291787,
     title = {Adversarially Robust One-Class Novelty Detection},
     author = {{Shao-Yuan Lo} and {Poojan Oza} and {Vishal M. Patel}},
     year = 2021,
     month = {8},
     booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     url = {https://www.semanticscholar.org/paper/4093d9e59f0be07b709d1157aab7fa2d0e41689b},
     }

546. Samik Sadhu and H. Hermansky, “FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition.” 2021.
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     @inproceedings{232380365,
     title = {FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition},
     author = {{Samik Sadhu} and {H. Hermansky}},
     year = 2021,
     booktitle = {},
     url = {https://www.semanticscholar.org/paper/e0a963ee0038b6cbe3e2aa90770080056d8555e6},
     }

547. J. Shi, J. D. Amith, R. Castillo Garc{‘i}a, E. Guadalupe Sierra, K. Duh, and S. Watanabe, “Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, p. 1134–1145. doi:10.18653/v1/2021.eacl-main.96
     [BibTeX] [Abstract] [Link]
     

     {“}Transcription bottlenecks{”}, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yoloxóchitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.

     @inproceedings{shi-etal-2021-leveraging,
     title = "Leveraging End-to-End {ASR} for Endangered Language Documentation: An Empirical Study on Yol{\'o}xochitl {M}ixtec",
     author = "Shi, Jiatong and
     Amith, Jonathan D. and
     Castillo Garc{\'\i}a, Rey and
     Guadalupe Sierra, Esteban and
     Duh, Kevin and
     Watanabe, Shinji",
     booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
     month = apr,
     year = "2021",
     address = "Online",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2021.eacl-main.96",
     doi = "10.18653/v1/2021.eacl-main.96",
     pages = "1134--1145",
     abstract = "{``}Transcription bottlenecks{''}, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yolox{\'o}chitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.",
     }

548. Nataniel Ruiz, Adam Kortylewski, Weichao Qiu, Cihang Xie, Sarah Adel Bargal, A. Yuille, and S. Sclaroff, “Simulated Adversarial Testing of Face Recognition Models,” in Computer Vision and Pattern Recognition, 2021.
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     @inproceedings{235367664,
     title = {Simulated Adversarial Testing of Face Recognition Models},
     author = {{Nataniel Ruiz} and {Adam Kortylewski} and {Weichao Qiu} and {Cihang Xie} and {Sarah Adel Bargal} and {A. Yuille} and {S. Sclaroff}},
     year = 2021,
     month = {6},
     booktitle = {Computer Vision and Pattern Recognition},
     url = {https://www.semanticscholar.org/paper/1f5ec5c5bc69ee850d31d70281322e8026c5bd52},
     }

549. Tiange Xiang, Yongyi Lu, A. Yuille, Chaoyi Zhang, Weidong (Tom) Cai, and Zongwei Zhou, “In-painting Radiography Images for Unsupervised Anomaly Detection,” in arXiv.org, 2021.
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     @inproceedings{244709610,
     title = {In-painting Radiography Images for Unsupervised Anomaly Detection},
     author = {{Tiange Xiang} and {Yongyi Lu} and {A. Yuille} and {Chaoyi Zhang} and {Weidong (Tom) Cai} and {Zongwei Zhou}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/0a0adec915e0a0165e9d048df46dc11404d8f9ca},
     }

550. L. Moro-Velázquez, Jorge Andrés Gómez García, J. D. Arias-Londoño, N. Dehak, and J. Godino-Llorente, “Advances in Parkinson’s Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects,” in Biomedical Signal Processing and Control, 2021.
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     @inproceedings{233261144,
     title = {Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects},
     author = {{L. Moro-Velázquez} and {Jorge Andrés Gómez García} and {J. D. Arias-Londoño} and {N. Dehak} and {J. Godino-Llorente}},
     year = 2021,
     booktitle = {Biomedical Signal Processing and Control},
     url = {https://www.semanticscholar.org/paper/e05b3799939621e0dd12cfe2a10f21788c6f4293},
     }

551. V. Rennoll, I. McLane, Adebayo A. Eisape, Mounya Elhilali, and James E. West, “Evaluating the impact of acoustic impedance matching on the airborne noise rejection and sensitivity of an electrostatic transducer,” in Journal of the Acoustical Society of America, 2021.
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     @inproceedings{236219196,
     title = {Evaluating the impact of acoustic impedance matching on the airborne noise rejection and sensitivity of an electrostatic transducer},
     author = {{V. Rennoll} and {I. McLane} and {Adebayo A. Eisape} and {Mounya Elhilali} and {James E. West}},
     year = 2021,
     month = {6},
     booktitle = {Journal of the Acoustical Society of America},
     url = {https://www.semanticscholar.org/paper/ea047ae6955b4f0343c48e3b9066efbc9d5e7d20},
     }

552. Junfei Xiao, Lequan Yu, Lei Xing, A. Yuille, and Yuyin Zhou, “DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation,” in arXiv.org, 2021.
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     @inproceedings{232417893,
     title = {DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation},
     author = {{Junfei Xiao} and {Lequan Yu} and {Lei Xing} and {A. Yuille} and {Yuyin Zhou}},
     year = 2021,
     booktitle = {arXiv.org},
     url = {https://www.semanticscholar.org/paper/c624668efef8a707b2e7122f6cad648296b254a8},
     }

553. Jeya Maria Jose Valanarasu, Poojan Oza, I. Hacihaliloglu, and Vishal M. Patel, “Medical Transformer: Gated Axial-Attention for Medical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
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     @inproceedings{231986084,
     title = {Medical Transformer: Gated Axial-Attention for Medical Image Segmentation},
     author = {{Jeya Maria Jose Valanarasu} and {Poojan Oza} and {I. Hacihaliloglu} and {Vishal M. Patel}},
     year = 2021,
     month = {2},
     booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
     url = {https://www.semanticscholar.org/paper/1e5e8106700c8dbdfa036a5a9be5e61e06c0ed02},
     }

554. Jejin Cho, J. Villalba, and N. Dehak, “The JHU submission to VoxSRC-21: Track 3,” in arXiv.org, 2021.
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555. Chen Wei, Huiyu Wang, Wei Shen, and A. Yuille, “CO 2 : C ONSISTENT C ONTRAST FOR U NSUPERVISED V ISUAL R EPRESENTATION L EARNING.” 2021.
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556. Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, A. Yuille, and Wei Shen, “Glance-and-Gaze Vision Transformer,” in Neural Information Processing Systems, 2021.
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557. Haoran Xu and Philipp Koehn, “Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions,” in arXiv.org, 2021.
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558. Jiefu Ou, Nathaniel Weir, Anton Belyy, Felix X. Yu, and Benjamin Van Durme, “InFillmore: Neural Frame Lexicalization for Narrative Text Infilling,” in arXiv.org, 2021.
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559. Nanxin Chen, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition,” in Interspeech, 2021.
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560. Sangwook Park, Woohyun Choi, and Mounya Elhilali, “Sound Event Detection with Cross-Referencing Self-Training.” 2021.
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561. L. Moro-Velázquez, J. Gómez-García, N. Dehak, and J. Godino-Llorente, “New tools for the differential evaluation of Parkinson’s disease using voice and speech processing,” in IberSPEECH Conference, 2021.
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562. Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, and Benjamin Van Durme, “Joint Universal Syntactic and Semantic Parsing,” in Transactions of the Association for Computational Linguistics, 2021.
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563. Sangwook Park, Angeles Salles, K. Allen, C. Moss, and Mounya Elhilali, “Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks,” in eNeuro, 2021.
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564. Sonal Joshi, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Adversarial Attacks and Defenses for Speaker Identification Systems,” in arXiv.org, 2021.
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565. Yao Sun, R. Kaur, Shubham Gupta, Rahul Paul, Ritu Das, S. Cho, Saket Anand, J. Boutilier, S. Saria, J. Palma, S. Saluja, R. McAdams, A. Kaur, Gautam Yadav, and Harpreet Singh, “Development and validation of high definition phenotype-based mortality prediction in critical care units,” in JAMIA Open, 2021.
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566. Seyoun Park, E. Fishman, and A. Yuille, “Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations,” in arXiv.org, 2021.
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567. Velat Kilic, Deepti Hegde, Vishwanath A. Sindagi, A. Cooper, M. Foster, and Vishal M. Patel, “Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection,” in arXiv.org, 2021.
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568. Chun Pong Lau, Amit Kumar, and R. Chellappa, “Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images,” in IEEE Journal on Selected Topics in Signal Processing, 2021.
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569. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation,” in Interspeech, 2021.
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570. Sangwook Park, D. Han, and Mounya Elhilali, “Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures,” in IEEE transactions on multimedia, 2021.
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571. Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, and A. Yuille, “CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models,” in arXiv.org, 2021.
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572. W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving,” in IEEE International Conference on Robotics and Automation, 2021.
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573. S. G. Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, I. Kohane, and S. Saria, “The Clinician and Dataset Shift in Artificial Intelligence.,” in New England Journal of Medicine, 2021.
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574. E. Stengel-Eskin, J. Guallar-Blasco, and B. Van Durme, “Human-Model Divergence in the Handling of Vagueness,” in Proceedings of the Society for Computation in Linguistics 2021, Online, 2021, p. 390–393.
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575. Nicholas Ichien, Qing Liu, Shuhao Fu, K. Holyoak, A. Yuille, and Hongjing Lu, “Visual analogy: Deep learning versus compositional models,” in arXiv.org, 2021.
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576. Aviad Shtrosberg, J. Villalba, N. Dehak, Azaria Cohen, and Bar Ben-Yair, “Invariant Representation Learning for Robust Far-Field Speaker Recognition,” in International Conference on Statistical Language and Speech Processing, 2021.
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577. Michelle Yuan, Patrick Xia, Benjamin Van Durme, and Jordan L. Boyd-Graber, “Adaptive Active Learning for Coreference Resolution,” in arXiv.org, 2021.
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578. Jiteng Mu, Weichao Qiu, Adam Kortylewski, A. Yuille, N. Vasconcelos, and Xiaolong Wang, “A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation,” in IEEE International Conference on Computer Vision, 2021.
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579. Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractors for End-to-End Neural Diarization,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2021.
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580. Patricia M. Johnson, Geunu Jeong, K. Hammernik, Jo Schlemper, C. Qin, J. Duan, D. Rueckert, Jingu Lee, Nicola Pezzotti, E. Weerdt, Sahar Yousefi, M. Elmahdy, J. V. Gemert, C. Schülke, M. Doneva, T. Nielsen, Sergey Kastryulin, B. Lelieveldt, M. Osch, M. Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, Nikita Miriakov, B. Bakker, M. Caan, Max Welling, Matthew Muckley, and F. Knoll, “Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge,” in MLMIR@MICCAI, 2021.
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581. Magdalena Rybicka, J. Villalba, Piotr Żelasko, N. Dehak, and K. Kowalczyk, “Spine2Net: SpineNet with Res2Net and Time-Squeeze-and-Excitation Blocks for Speaker Recognition,” in Interspeech, 2021.
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582. R. Pappagari, Jaejin Cho, Sonal Joshi, L. Moro-Velázquez, Piotr Żelasko, J. Villalba, and N. Dehak, “Automatic Detection and Assessment of Alzheimer Disease Using Speech and Language Technologies in Low-Resource Scenarios,” in Interspeech, 2021.
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583. Samik Sadhu and H. Hermansky, “Radically Old Way of Computing Spectra: Applications in End-to-End ASR,” in Interspeech, 2021.
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584. Matthew Maciejewski, Shinji Watanabe, and S. Khudanpur, “Speaker Verification-Based Evaluation of Single-Channel Speech Separation,” in Interspeech, 2021.
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585. Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Leibny Paola García-Perera, and Kenji Nagamatsu, “Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers,” in Interspeech, 2021.
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586. R. Yasarla, Federico Perazzi, and Vishal M. Patel, “Deblurring Face Images Using Deep Networks,” in Advances in Computer Vision and Pattern Recognition, 2021.
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587. Zefan Li, Chenxin Liu, A. Yuille, Bingbing Ni, Wenjun Zhang, and Wen Gao, “Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement,” in Computer Vision and Pattern Recognition, 2021.
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591. S. Saria, Peter F. Schulam, Brian J. Yeh, Daniel Burke, S. Mooney, C. Fong, Jacob E. Sunshine, Dustin R. Long, and V. O’Reilly-Shah, “Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients,” in Critical Care Explorations, 2021.
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595. Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization,” in arXiv.org, 2021.
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602. Poojan Oza and Vishal M. Patel, “Federated Learning-based Active Authentication on Mobile Devices,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
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605. Hui Che, S. Ramanathan, D. Foran, J. Nosher, Vishal M. Patel, and I. Hacihaliloglu, “Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease,” in ASMUS@MICCAI, 2021.
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606. Kevin Duh and Francisco Guzmán, “Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track),” in Machine Translation Summit, 2021.
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607. P. Resnik, M. de Choudhury, Katherine Musacchio Schafer, and Glen A. Coppersmith, “Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”,” in Journal of Medical Internet Research, 2021.
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608. Nithin Gopalakrishnan Nair and Vishal M. Patel, “Confidence Guided Network For Atmospheric Turbulence Mitigation,” in International Conference on Information Photonics, 2021.
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611. Guoguo Chen, Shuzhou Chai, Guan-Bo Wang, Jiayu Du, Weiqiang Zhang, Chao Weng, Dan Su, Daniel Povey, J. Trmal, Junbo Zhang, Mingjie Jin, S. Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, and Zhiyong Yan, “GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio,” in Interspeech, 2021.
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612. Pramuditha Perera, Poojan Oza, and Vishal M. Patel, “One-Class Classification: A Survey,” in arXiv.org, 2021.