Publications
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].
- Yen-Ju Lu, Jing Liu, Thomas Thebaud, L. Moro-Velázquez, A. Rastrow, N. Dehak, and J. Villalba, “CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing.” 2024.
[BibTeX] [Link]@inproceedings{274515167, title = {CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing}, author = {{Yen-Ju Lu} and {Jing Liu} and {Thomas Thebaud} and {L. Moro-Velázquez} and {A. Rastrow} and {N. Dehak} and {J. Villalba}}, year = 2024, month = {12}, booktitle = {}, url = {https://www.semanticscholar.org/paper/daf0aba5a88ce8461062165249baba14f723c151}, } - Yahan Li, Keith Harrigian, Ayah Zirikly, and Mark Dredze, “Are Clinical T5 Models Better for Clinical Text?.” 2024.
[BibTeX] [Link]@inproceedings{274598076, title = {Are Clinical T5 Models Better for Clinical Text?}, author = {{Yahan Li} and {Keith Harrigian} and {Ayah Zirikly} and {Mark Dredze}}, year = 2024, month = {12}, booktitle = {}, url = {https://www.semanticscholar.org/paper/88d0b0189e6d33ce0a4b9315bd8fe82bad326824}, } - B. Englitz, S. Akram, Mounya Elhilali, and S. Shamma, “Decoding contextual influences on auditory perception from primary auditory cortex,” in eLife, 2024.
[BibTeX] [Link]@inproceedings{274606149, title = {Decoding contextual influences on auditory perception from primary auditory cortex}, author = {{B. Englitz} and {S. Akram} and {Mounya Elhilali} and {S. Shamma}}, year = 2024, month = {12}, booktitle = {eLife}, url = {https://www.semanticscholar.org/paper/67535ffb91c1f43a979f686f66551000013f83de}, } - Sijia Zhao, Benjamin Skerritt-Davis, Mounya Elhilali, Frederic Dick, and M. Chait, “Sustained EEG responses to rapidly unfolding stochastic sounds reflect Bayesian inferred reliability tracking,” in Progress in neurobiology, 2024.
[BibTeX] [Link]@inproceedings{274529945, title = {Sustained EEG responses to rapidly unfolding stochastic sounds reflect Bayesian inferred reliability tracking}, author = {{Sijia Zhao} and {Benjamin Skerritt-Davis} and {Mounya Elhilali} and {Frederic Dick} and {M. Chait}}, year = 2024, month = {12}, booktitle = {Progress in neurobiology}, url = {https://www.semanticscholar.org/paper/b7f0613a7bbcb22fc658764f2269d91ecdedd542}, } - Thomas Thebaud, A. Favaro, Yaohan Guan, Yuchen Yang, Prabhav Singh, J. Villalba, Laureano Mono-Velazquez, and N. Dehak, “Multimodal Emotion Recognition Harnessing the Complementarity of Speech, Language, and Vision,” in International Conference on Multimodel Interaction, 2024.
[BibTeX] [Link]@inproceedings{273745499, title = {Multimodal Emotion Recognition Harnessing the Complementarity of Speech, Language, and Vision}, author = {{Thomas Thebaud} and {A. Favaro} and {Yaohan Guan} and {Yuchen Yang} and {Prabhav Singh} and {J. Villalba} and {Laureano Mono-Velazquez} and {N. Dehak}}, year = 2024, month = {11}, booktitle = {International Conference on Multimodel Interaction}, url = {https://www.semanticscholar.org/paper/acf86833b29d724bf3a2134f60a2cf4497e013e0}, } - Drew Prinster, Amama Mahmood, S. Saria, Jean Jeudy, Cheng Ting Lin, Paul H Yi, and Chien-Ming Huang, “Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.,” in Radiology, 2024.
[BibTeX] [Link]@inproceedings{274139049, title = {Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.}, author = {{Drew Prinster} and {Amama Mahmood} and {S. Saria} and {Jean Jeudy} and {Cheng Ting Lin} and {Paul H Yi} and {Chien-Ming Huang}}, year = 2024, month = {11}, booktitle = {Radiology}, url = {https://www.semanticscholar.org/paper/71e974f5a49bcdfa72aad21b64158a9f5c176b2e}, } - Adarsh Subbaswamy, B. Sahiner, Nicholas Petrick, Vinay Pai, Roy Adams, Matthew C. Diamond, and S. Saria, “A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform,” in npj Digital Medicine, 2024.
[BibTeX] [Link]@inproceedings{274178323, title = {A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform}, author = {{Adarsh Subbaswamy} and {B. Sahiner} and {Nicholas Petrick} and {Vinay Pai} and {Roy Adams} and {Matthew C. Diamond} and {S. Saria}}, year = 2024, month = {11}, booktitle = {npj Digital Medicine}, url = {https://www.semanticscholar.org/paper/54ec15ff209d42b8540da5786b4262f74e869151}, } - Taiming Lu, Tianmin Shu, Alan Yuille, Daniel Khashabi, and Jieneng Chen, “Generative World Explorer.” 2024.
[BibTeX] [Link]@inproceedings{274131239, title = {Generative World Explorer}, author = {{Taiming Lu} and {Tianmin Shu} and {Alan Yuille} and {Daniel Khashabi} and {Jieneng Chen}}, year = 2024, month = {11}, booktitle = {}, url = {https://www.semanticscholar.org/paper/13279a99e7a2b6c8aaac9026cef53034e6cfa1de}, } - K. Sanders, N. Weir, and B. Van Durme, “TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 19009–19028. doi:10.18653/v1/2024.emnlp-main.1059
[BibTeX] [Abstract] [Link]
It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by searching for trees of entailment relationships between simple text-video evidence and higher-level conclusions that prove question-answer pairs. We also introduce the task of multimodal entailment tree generation to evaluate reasoning quality. Our method{‘}s performance on the challenging TVQA benchmark demonstrates interpretable, state-of-the-art zero-shot performance on full clips, illustrating that multimodal entailment tree generation can be a best-of-both-worlds alternative to black-box systems.
@inproceedings{sanders-etal-2024-tv, title = "{TV}-{TREES}: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning", author = "Sanders, Kate and Weir, Nathaniel and Van Durme, Benjamin", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1059", doi = "10.18653/v1/2024.emnlp-main.1059", pages = "19009--19028", abstract = "It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by searching for trees of entailment relationships between simple text-video evidence and higher-level conclusions that prove question-answer pairs. We also introduce the task of multimodal entailment tree generation to evaluate reasoning quality. Our method{'}s performance on the challenging TVQA benchmark demonstrates interpretable, state-of-the-art zero-shot performance on full clips, illustrating that multimodal entailment tree generation can be a best-of-both-worlds alternative to black-box systems.", } - X. Ye, A. Wang, J. Choi, Y. Lu, S. Sharma, L. Shen, V. M. Tiyyala, N. Andrews, and D. Khashabi, “AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 13060–13082. doi:10.18653/v1/2024.emnlp-main.725
[BibTeX] [Abstract] [Link]
Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We collect a set of 340 high quality, human written analogies for use in our benchmark, which constitutes the largest such collection to date. We then test a broad collection of models consisting of 12 open source and 3 proprietary in various sizes and architectures. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field.
@inproceedings{ye-etal-2024-analobench, title = "{A}nalo{B}ench: Benchmarking the Identification of Abstract and Long-context Analogies", author = "Ye, Xiao and Wang, Andrew and Choi, Jacob and Lu, Yining and Sharma, Shreya and Shen, Lingfeng and Tiyyala, Vijay Murari and Andrews, Nicholas and Khashabi, Daniel", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.725", doi = "10.18653/v1/2024.emnlp-main.725", pages = "13060--13082", abstract = "Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We collect a set of 340 high quality, human written analogies for use in our benchmark, which constitutes the largest such collection to date. We then test a broad collection of models consisting of 12 open source and 3 proprietary in various sizes and architectures. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field.", } - F. Samir, C. Y. Park, A. Field, V. Shwartz, and Y. Tsvetkov, “Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 6747–6762. doi:10.18653/v1/2024.emnlp-main.384
[BibTeX] [Abstract] [Link]
To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method{–-}an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people{‘}s portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale.
@inproceedings{samir-etal-2024-locating, title = "Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of {LGBT} People Portrayals on {W}ikipedia", author = "Samir, Farhan and Park, Chan Young and Field, Anjalie and Shwartz, Vered and Tsvetkov, Yulia", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.384", doi = "10.18653/v1/2024.emnlp-main.384", pages = "6747--6762", abstract = "To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method{---}an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people{'}s portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale.", } - Natalie Wang, Sukrit Treewaree, Ayah Zirikly, Yuzhi L. Lu, Michelle H. Nguyen, Bhavik Agarwal, Jash Shah, J. M. Stevenson, and Casey Overby Taylor, “Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.,” in Journal of Biomedical Informatics, 2024.
[BibTeX] [Link]@inproceedings{274287296, title = {Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.}, author = {{Natalie Wang} and {Sukrit Treewaree} and {Ayah Zirikly} and {Yuzhi L. Lu} and {Michelle H. Nguyen} and {Bhavik Agarwal} and {Jash Shah} and {J. M. Stevenson} and {Casey Overby Taylor}}, year = 2024, month = {11}, booktitle = {Journal of Biomedical Informatics}, url = {https://www.semanticscholar.org/paper/aceb915df57e8f1708fa54cb7f828da00c76c42f}, } - Trevor Meyer, A. Favaro, Esther S. Oh, A. Butala, C. Motley, Pedro P. Irazoqui, N. Dehak, and L. Moro-Velázquez, “Deep Stroop: Integrating eye tracking and speech processing to characterize people with neurodegenerative disorders while performing neuropsychological tests.,” in Computers in Biology and Medicine, 2024.
[BibTeX] [Link]@inproceedings{274444639, title = {Deep Stroop: Integrating eye tracking and speech processing to characterize people with neurodegenerative disorders while performing neuropsychological tests.}, author = {{Trevor Meyer} and {A. Favaro} and {Esther S. Oh} and {A. Butala} and {C. Motley} and {Pedro P. Irazoqui} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2024, month = {11}, booktitle = {Computers in Biology and Medicine}, url = {https://www.semanticscholar.org/paper/528e2a1618feeb267fc7fc4873207266ef5c01a3}, } - N. Weir, K. Sanders, O. Weller, S. Sharma, D. Jiang, Z. Jiang, B. Dalvi Mishra, O. Tafjord, P. Jansen, P. Clark, and B. Van Durme, “Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 9458–9482. doi:10.18653/v1/2024.emnlp-main.531
[BibTeX] [Abstract] [Link]
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what {_}valid decompositional entailment{_} is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.
@inproceedings{weir-etal-2024-enhancing, title = "Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic", author = "Weir, Nathaniel and Sanders, Kate and Weller, Orion and Sharma, Shreya and Jiang, Dongwei and Jiang, Zhengping and Dalvi Mishra, Bhavana and Tafjord, Oyvind and Jansen, Peter and Clark, Peter and Van Durme, Benjamin", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.531", doi = "10.18653/v1/2024.emnlp-main.531", pages = "9458--9482", abstract = "Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what {\_}valid decompositional entailment{\_} is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.", } - A. Blair-Stanek, N. Holzenberger, and B. Van Durme, “BLT: Can Large Language Models Handle Basic Legal Text?,” in Proceedings of the Natural Legal Language Processing Workshop 2024, Miami, FL, USA, 2024, p. 216–232. doi:10.18653/v1/2024.nllp-1.18
[BibTeX] [Abstract] [Link]
We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of examples that lawyers and paralegals would expect LLMs to handle zero-shot, such as looking up the text at a line of a witness deposition or at a subsection of a contract. LLMs{‘} poor performance on this benchmark casts into doubt their reliability as-is for legal practice. However, fine-tuning on our training set brings even a small model to near-perfect performance. This benchmark will be useful for fine-tuning LLMs for downstream legal tasks, as well as for tracking LLMs{‘} reliability as-is for basic legal tasks.
@inproceedings{blair-stanek-etal-2024-blt, title = "{BLT}: Can Large Language Models Handle Basic Legal Text?", author = "Blair-Stanek, Andrew and Holzenberger, Nils and Van Durme, Benjamin", editor = "Aletras, Nikolaos and Chalkidis, Ilias and Barrett, Leslie and Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and Preo{\textcommabelow{t}}iuc-Pietro, Daniel and Spanakis, Gerasimos", booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024", month = nov, year = "2024", address = "Miami, FL, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.nllp-1.18", doi = "10.18653/v1/2024.nllp-1.18", pages = "216--232", abstract = "We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of examples that lawyers and paralegals would expect LLMs to handle zero-shot, such as looking up the text at a line of a witness deposition or at a subsection of a contract. LLMs{'} poor performance on this benchmark casts into doubt their reliability as-is for legal practice. However, fine-tuning on our training set brings even a small model to near-perfect performance. This benchmark will be useful for fine-tuning LLMs for downstream legal tasks, as well as for tracking LLMs{'} reliability as-is for basic legal tasks.", } - N. Weir, R. Thomas, R. d{‘}Amore, K. Hill, B. Van Durme, and H. Jhamtani, “Ontologically Faithful Generation of Non-Player Character Dialogues,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 9212–9242. doi:10.18653/v1/2024.emnlp-main.520
[BibTeX] [Abstract] [Link]
We introduce a language generation dataset grounded in a popular video game. KNUDGE (**KN**owledge Constrained **U**ser-NPC **D**ialogue **GE**neration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment{‘}s {_}The Outer Worlds{_}, leading to real-world complexities in generation: (1) utterances must remain faithful to the game lore, including character personas and backstories; (2) a dialogue must accurately reveal new quest details to the human player; and (3) dialogues are large trees as opposed to linear chains of utterances. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.
@inproceedings{weir-etal-2024-ontologically, title = "Ontologically Faithful Generation of Non-Player Character Dialogues", author = "Weir, Nathaniel and Thomas, Ryan and d{'}Amore, Randolph and Hill, Kellie and Van Durme, Benjamin and Jhamtani, Harsh", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.520", doi = "10.18653/v1/2024.emnlp-main.520", pages = "9212--9242", abstract = "We introduce a language generation dataset grounded in a popular video game. KNUDGE (**KN**owledge Constrained **U**ser-NPC **D**ialogue **GE**neration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment{'}s {\_}The Outer Worlds{\_}, leading to real-world complexities in generation: (1) utterances must remain faithful to the game lore, including character personas and backstories; (2) a dialogue must accurately reveal new quest details to the human player; and (3) dialogues are large trees as opposed to linear chains of utterances. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.", } - Y. Chen, T. Chen, H. Jhamtani, P. Xia, R. Shin, J. Eisner, and B. V. Durme, “Learning to Retrieve Iteratively for In-Context Learning,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024, p. 7156–7168.
[BibTeX] [Link]@InProceedings{chen-et-al-2024, aclid = "2024.emnlp-main.406", author = "Yunmo Chen and Tongfei Chen and Harsh Jhamtani and Patrick Xia and Richard Shin and Jason Eisner and Benjamin Van Durme", title = "Learning to Retrieve Iteratively for In-Context Learning", booktitle = "Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)", pages = "7156--7168", year = "2024", month = nov, URL = "http://cs.jhu.edu/~jason/papers/#chen-et-al-2024", } - K. Bostrom, H. Jhamtani, H. Fang, Sam Thomson, R. Shin, P. Xia, B. Durme, J. Eisner, and J. Andreas, “Language-to-Code Translation with a Single Labeled Example,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024, p. 8101–8112.
[BibTeX] [Link]@InProceedings{bostrom-et-al-2024, aclid = "2024.emnlp-main.462", author = "Kaj Bostrom and Harsh Jhamtani and Hao Fang and Sam Thomson and Richard Shin and Patrick Xia and Benjamin Van Durme and Jason Eisner and Jacob Andreas", title = "Language-to-Code Translation with a Single Labeled Example", booktitle = "Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)", pages = "8101--8112", year = "2024", month = nov, URL = "http://cs.jhu.edu/~jason/papers/#bostrom-et-al-2024", } - Huy Nguyen, Xing Han, C. Harris, S. Saria, and Nhat Ho, “On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{273163058, title = {On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions}, author = {{Huy Nguyen} and {Xing Han} and {C. Harris} and {S. Saria} and {Nhat Ho}}, year = 2024, month = {10}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/8fa041c2762bd3ad22dc673d89059e27daf01881}, } - Neha Joshi, Wing Yiu Ng, Karan Thakkar, Daniel Duque, Pingbo Yin, Jonathan Fritz, Mounya Elhilali, and S. Shamma, “Temporal coherence shapes cortical responses to speech mixtures in a ferret cocktail party,” in Communications Biology, 2024.
[BibTeX] [Link]@inproceedings{273636715, title = {Temporal coherence shapes cortical responses to speech mixtures in a ferret cocktail party}, author = {{Neha Joshi} and {Wing Yiu Ng} and {Karan Thakkar} and {Daniel Duque} and {Pingbo Yin} and {Jonathan Fritz} and {Mounya Elhilali} and {S. Shamma}}, year = 2024, month = {10}, booktitle = {Communications Biology}, url = {https://www.semanticscholar.org/paper/8afe9f8e4a9485e20070c362067d3a06f3653311}, } - A. Favaro, A. Butala, Thomas Thebaud, J. Villalba, N. Dehak, and L. Moro-Velázquez, “Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings,” in npj Parkinson’s Disease, 2024.
[BibTeX] [Link]@inproceedings{273648380, title = {Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings}, author = {{A. Favaro} and {A. Butala} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2024, month = {10}, booktitle = {npj Parkinson's Disease}, url = {https://www.semanticscholar.org/paper/4daac9614c0979ec790583db1a2c86c07456f32a}, } - E. Hatef, C. Kitchen, Geoffrey M Gray, Ayah Zirikly, Thomas M Richards, Luis M Ahumada, and Jonathan P. Weiner, “Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes,” in JAMIA Open, 2024.
[BibTeX] [Link]@inproceedings{273691413, title = {Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes}, author = {{E. Hatef} and {C. Kitchen} and {Geoffrey M Gray} and {Ayah Zirikly} and {Thomas M Richards} and {Luis M Ahumada} and {Jonathan P. Weiner}}, year = 2024, month = {10}, booktitle = {JAMIA Open}, url = {https://www.semanticscholar.org/paper/d5013b3593f9a1e25a3b0bb84150158911d752f0}, } - Krithika Ramesh, Nupoor Gandhi, Pulkit Madaan, Lisa Bauer, Charith Peris, and Anjalie Field, “Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains,” in Conference on Empirical Methods in Natural Language Processing, 2024.
[BibTeX] [Link]@inproceedings{273323322, title = {Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains}, author = {{Krithika Ramesh} and {Nupoor Gandhi} and {Pulkit Madaan} and {Lisa Bauer} and {Charith Peris} and {Anjalie Field}}, year = 2024, month = {10}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/d1bc6e53f2cc088406ab8bb4d8b63d26944c15c8}, } - H. Hashemi, C. Rosset, B. V. Durme, J. Eisner, and C. Kedzie, “\textscLLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024, p. 13806–13834.
[BibTeX] [Link]@InProceedings{hashemi-et-al-2024, aclid = "2024.acl-long.745", author = "Helia Hashemi and Corby Rosset and Benjamin Van Durme and Jason Eisner and Chris Kedzie", title = "\textsc{LLM-Rubric}: {A} Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts", booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)", pages = "13806--13834", year = "2024", month = aug, URL = "http://cs.jhu.edu/~jason/papers/#hashemi-et-al-2024", } - B. Wang, H. Fang, J. Eisner, B. Durme, and Y. Su, “LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024, p. 10583–10604.
[BibTeX] [Link]@InProceedings{wang-et-al-2024-tools, aclid = "2024.acl-long.570", author = "Boshi Wang and Hao Fang and Jason Eisner and Benjamin Van Durme and Yu Su", title = "{LLMs} in the {I}maginarium: Tool Learning through Simulated Trial and Error", booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)", pages = "10583--10604", year = "2024", month = aug, URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-tools", } - S. CH-Wang, B. V. Durme, J. Eisner, and C. Kedzie, “Do Androids Know They’re Only Dreaming of Electric Sheep?,” in Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024, p. 4401–4420.
[BibTeX] [Link]@InProceedings{wang-et-al-2024-hallucination, aclid = "2024.findings-acl.260", author = "Sky CH-Wang and Benjamin Van Durme and Jason Eisner and Chris Kedzie", title = "Do Androids Know They’re Only Dreaming of Electric Sheep?", booktitle = "Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)", pages = "4401--4420", year = "2024", month = aug, URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-hallucination", } - G. Monea, M. Peyrard, M. Josifoski, V. Chaudhary, J. Eisner, K{\i}c. i, H. Palangi, B. Patra, and R. West, “A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024, p. 6828–6844.
[BibTeX] [Link]@InProceedings{monea-et-al-2024, aclid = "2024.acl-long.369", author = "Giovanni Monea and Maxime Peyrard and Martin Josifoski and Vishrav Chaudhary and Jason Eisner and Emre K{\i}c{\i}man and Hamid Palangi and Barun Patra and Robert West", title = "A Glitch in the {M}atrix? {L}ocating and Detecting Language Model Grounding with {F}akepedia", booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)", pages = "6828--6844", year = "2024", month = aug, URL = "http://cs.jhu.edu/~jason/papers/#monea-et-al-2024", } - L. Du, J. Eisner, H. Lee, and Ryan Cotterell, “When is a Language Process a Language Model?,” in Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024, p. 11083–11094.
[BibTeX] [Link]@InProceedings{du-et-al-2024-tight, aclid = "2024.findings-acl.659", author = "Li Du and Jason Eisner and Holden Lee and Ryan Cotterell", title = "When is a Language Process a Language Model?", booktitle = "Findings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL)", pages = "11083--11094", year = "2024", month = aug, URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-tight", } - L. Du, A. Amini, L. T. Hennigen, X. V. Yu, H. Lee, J. Eisner, and R. Cotterell, “Principled Gradient-Based MCMC for Conditional Sampling of Text,” in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
[BibTeX] [Link]@InProceedings{du-et-al-2024-mcmc, author = "Li Du and Afra Amini and Lucas Torroba Hennigen and Xinyan Velocity Yu and Holden Lee and Jason Eisner and Ryan Cotterell", title = "Principled Gradient-Based {MCMC} for Conditional Sampling of Text", booktitle = "Proceedings of the 41st International Conference on Machine Learning (ICML)", year = "2024", month = jul, URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-mcmc", } - A. Hou, J. Zhang, T. He, Y. Wang, Y. Chuang, H. Wang, L. Shen, B. Van Durme, D. Khashabi, and Y. Tsvetkov, “SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 4067–4082. doi:10.18653/v1/2024.naacl-long.226
[BibTeX] [Abstract] [Link]
Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a {“}bigram paraphrase{”} attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation.
@inproceedings{hou-etal-2024-semstamp, title = "{S}em{S}tamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation", author = "Hou, Abe and Zhang, Jingyu and He, Tianxing and Wang, Yichen and Chuang, Yung-Sung and Wang, Hongwei and Shen, Lingfeng and Van Durme, Benjamin and Khashabi, Daniel and Tsvetkov, Yulia", editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven", booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.naacl-long.226", doi = "10.18653/v1/2024.naacl-long.226", pages = "4067--4082", abstract = "Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a {``}bigram paraphrase{''} attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation.", } - J. Su, M. Ahmed, B. Wen, L. Ao, M. Zhu, and Y. Liu, “Naive Bayes-based Context Extension for Large Language Models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 7791–7807. doi:10.18653/v1/2024.naacl-long.431
[BibTeX] [Abstract] [Link]
Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM{‘}s maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes{‘} theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master
@inproceedings{su-etal-2024-naive, title = "Naive {B}ayes-based Context Extension for Large Language Models", author = "Su, Jianlin and Ahmed, Murtadha and Wen, Bo and Ao, Luo and Zhu, Mingren and Liu, Yunfeng", editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven", booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.naacl-long.431", doi = "10.18653/v1/2024.naacl-long.431", pages = "7791--7807", abstract = "Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM{'}s maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes{'} theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master", } - N. Bafna, P. Koehn, and D. Yarowsky, “Pointer-Generator Networks for Low-Resource Machine Translation: Don’t Copy That!,” in Proceedings of the Fifth Workshop on Insights from Negative Results in NLP, Mexico City, Mexico, 2024, p. 60–72. doi:10.18653/v1/2024.insights-1.9
[BibTeX] [Abstract] [Link]
While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural {“}shortcuts{”}, such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.
@inproceedings{bafna-etal-2024-pointer, title = "Pointer-Generator Networks for Low-Resource Machine Translation: Don{'}t Copy That!", author = "Bafna, Niyati and Koehn, Philipp and Yarowsky, David", editor = "Tafreshi, Shabnam and Akula, Arjun and Sedoc, Jo{\~a}o and Drozd, Aleksandr and Rogers, Anna and Rumshisky, Anna", booktitle = "Proceedings of the Fifth Workshop on Insights from Negative Results in NLP", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.insights-1.9", doi = "10.18653/v1/2024.insights-1.9", pages = "60--72", abstract = "While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural {``}shortcuts{''}, such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.", } - S. Vashishtha, A. Martin, W. Gantt, B. Van Durme, and A. White, “FAMuS: Frames Across Multiple Sources,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 8250–8273. doi:10.18653/v1/2024.naacl-long.457
[BibTeX] [Abstract] [Link]
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{–-}determining whether a document is a valid source for a target report event{–-}and cross-document argument extraction{–-}full-document argument extraction for a target event from both its report and the correct source article.
@inproceedings{vashishtha-etal-2024-famus, title = "{FAM}u{S}: Frames Across Multiple Sources", author = "Vashishtha, Siddharth and Martin, Alexander and Gantt, William and Van Durme, Benjamin and White, Aaron", editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven", booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.naacl-long.457", doi = "10.18653/v1/2024.naacl-long.457", pages = "8250--8273", abstract = "Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{---}determining whether a document is a valid source for a target report event{---}and cross-document argument extraction{---}full-document argument extraction for a target event from both its report and the correct source article.", } - V. Raunak, T. Kocmi, and M. Post, “SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), Mexico City, Mexico, 2024, p. 205–211. doi:10.18653/v1/2024.naacl-short.18
[BibTeX] [Abstract] [Link]
Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations.
@inproceedings{raunak-etal-2024-slide, title = "{SLIDE}: Reference-free Evaluation for Machine Translation using a Sliding Document Window", author = "Raunak, Vikas and Kocmi, Tom and Post, Matt", editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven", booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.naacl-short.18", doi = "10.18653/v1/2024.naacl-short.18", pages = "205--211", abstract = "Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations.", } - N. Moghe, P. Xia, J. Andreas, J. Eisner, B. V. Durme, and Harsh Jhamtani, “Interpreting User Requests in the Context of Natural Language Standing Instructions,” in Findings of the North American Conference on Cmputational Linguistics (NAACL), 2024, p. 4043–4060.
[BibTeX] [Link]@InProceedings{moghe-et-al-2024, aclid = "2024.findings-naacl.255", author = "Nikita Moghe and Patrick Xia and Jacob Andreas and Jason Eisner and Benjamin Van Durme and Harsh Jhamtani", title = "Interpreting User Requests in the Context of Natural Language Standing Instructions", booktitle = "Findings of the North American Conference on Cmputational Linguistics (NAACL)", volume = "arXiv:2311.09796", pages = "4043--4060", year = "2024", month = jun, URL = "http://cs.jhu.edu/~jason/papers/#moghe-et-al-2024", } - M. Jahan, H. Wang, T. Thebaud, Y. Sun, G. H. Le, Z. Fagyal, O. Scharenborg, M. Hasegawa-Johnson, L. Moro Velazquez, and N. Dehak, “Finding Spoken Identifications: Using GPT-4 Annotation for an Efficient and Fast Dataset Creation Pipeline,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 7296–7306.
[BibTeX] [Abstract] [Link]
The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI{‘}s GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4{‘}s performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93{\%}. GPT-4{‘}s tagging performance showed a trade-off between precision and recall, where the latter got as high as 97{\%}, but precision never exceeded 45{\%}. Our approach reduces the time required for the filtering and tagging tasks by 95{\%} and 80{\%}, respectively. We also present an in-depth error analysis of GPT-4{‘}s performance.
@inproceedings{jahan-etal-2024-finding, title = "Finding Spoken Identifications: Using {GPT}-4 Annotation for an Efficient and Fast Dataset Creation Pipeline", author = "Jahan, Maliha and Wang, Helin and Thebaud, Thomas and Sun, Yinglun and Le, Giang Ha and Fagyal, Zsuzsanna and Scharenborg, Odette and Hasegawa-Johnson, Mark and Moro Velazquez, Laureano and Dehak, Najim", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.641", pages = "7296--7306", abstract = "The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI{'}s GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4{'}s performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93{\%}. GPT-4{'}s tagging performance showed a trade-off between precision and recall, where the latter got as high as 97{\%}, but precision never exceeded 45{\%}. Our approach reduces the time required for the filtering and tagging tasks by 95{\%} and 80{\%}, respectively. We also present an in-depth error analysis of GPT-4{'}s performance.", } - N. Verma, K. Murray, and K. Duh, “Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 6909–6921.
[BibTeX] [Abstract] [Link]
Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity.
@inproceedings{verma-etal-2024-exploring, title = "Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models", author = "Verma, Neha and Murray, Kenton and Duh, Kevin", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.604", pages = "6909--6921", abstract = "Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity.", } - H. Sirin, S. Li, and T. Lippincott, “Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with Fourier Analysis,” in Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), St. Julians, Malta, 2024, p. 46–50.
[BibTeX] [Abstract] [Link]
In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.
@inproceedings{sirin-etal-2024-detecting, title = "Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with {F}ourier Analysis", author = "Sirin, Hale and Li, Sabrina and Lippincott, Thomas", editor = "Bizzoni, Yuri and Degaetano-Ortlieb, Stefania and Kazantseva, Anna and Szpakowicz, Stan", booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)", month = mar, year = "2024", address = "St. Julians, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.latechclfl-1.6", pages = "46--50", abstract = "In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.", } - H. Sirin and T. Lippincott, “Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses,” in Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), St. Julians, Malta, 2024, p. 231–236.
[BibTeX] [Abstract] [Link]
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.
@inproceedings{sirin-lippincott-2024-dynamic, title = "Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses", author = "Sirin, Hale and Lippincott, Thomas", editor = "Bizzoni, Yuri and Degaetano-Ortlieb, Stefania and Kazantseva, Anna and Szpakowicz, Stan", booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)", month = mar, year = "2024", address = "St. Julians, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.latechclfl-1.22", pages = "231--236", abstract = "We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.", } - J. Chim, A. Tsakalidis, D. Gkoumas, D. Atzil-Slonim, Y. Ophir, A. Zirikly, P. Resnik, and M. Liakata, “Overview of the CLPsych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts,” in Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), St. Julians, Malta, 2024, p. 177–190.
[BibTeX] [Abstract] [Link]
We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level ({`}Low{‘}, {`}Mod- erate{‘} or {`}High{‘}) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.
@inproceedings{chim-etal-2024-overview, title = "Overview of the {CLP}sych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts", author = "Chim, Jenny and Tsakalidis, Adam and Gkoumas, Dimitris and Atzil-Slonim, Dana and Ophir, Yaakov and Zirikly, Ayah and Resnik, Philip and Liakata, Maria", editor = "Yates, Andrew and Desmet, Bart and Prud{'}hommeaux, Emily and Zirikly, Ayah and Bedrick, Steven and MacAvaney, Sean and Bar, Kfir and Ireland, Molly and Ophir, Yaakov", booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)", month = mar, year = "2024", address = "St. Julians, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.clpsych-1.15", pages = "177--190", abstract = "We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level ({`}Low{'}, {`}Mod- erate{'} or {`}High{'}) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.", } - O. Weller, M. Marone, N. Weir, D. Lawrie, D. Khashabi, and B. Van Durme, ““According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2288–2301.
[BibTeX] [Abstract] [Link]
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of {“}according to sources{”}, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.
@inproceedings{weller-etal-2024-according, title = "{``}According to . . . {''}: Prompting Language Models Improves Quoting from Pre-Training Data", author = "Weller, Orion and Marone, Marc and Weir, Nathaniel and Lawrie, Dawn and Khashabi, Daniel and Van Durme, Benjamin", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.140", pages = "2288--2301", abstract = "Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of {``}according to sources{''}, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.", } - O. Weller, K. Lo, D. Wadden, D. Lawrie, B. Van Durme, A. Cohan, and L. Soldaini, “When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets,” in Findings of the Association for Computational Linguistics: EACL 2024, St. Julian{‘}s, Malta, 2024, p. 1987–2003.
[BibTeX] [Abstract] [Link]
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.
@inproceedings{weller-etal-2024-generative, title = "When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets", author = "Weller, Orion and Lo, Kyle and Wadden, David and Lawrie, Dawn and Van Durme, Benjamin and Cohan, Arman and Soldaini, Luca", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-eacl.134", pages = "1987--2003", abstract = "Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.", } - Y. Lu, H. Yu, and D. Khashabi, “GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 112–138.
[BibTeX] [Abstract] [Link]
Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use.
@inproceedings{lu-etal-2024-gear, title = "{GEAR}: Augmenting Language Models with Generalizable and Efficient Tool Resolution", author = "Lu, Yining and Yu, Haoping and Khashabi, Daniel", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.7", pages = "112--138", abstract = "Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use.", } - O. Weller, D. Lawrie, and B. Van Durme, “NevIR: Negation in Neural Information Retrieval,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2274–2287.
[BibTeX] [Abstract] [Link]
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.
@inproceedings{weller-etal-2024-nevir, title = "{N}ev{IR}: Negation in Neural Information Retrieval", author = "Weller, Orion and Lawrie, Dawn and Van Durme, Benjamin", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.139", pages = "2274--2287", abstract = "Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.", } - Z. Li, C. Xie, B. Van Durme, and A. Yuille, “Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 2378–2390.
[BibTeX] [Abstract] [Link]
Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
@inproceedings{li-etal-2024-localization, title = "Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models", author = "Li, Zhuowan and Xie, Cihang and Van Durme, Benjamin and Yuille, Alan", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.146", pages = "2378--2390", abstract = "Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.", } - O. Weller, A. Khan, N. Weir, D. Lawrie, and B. Van Durme, “Defending Against Disinformation Attacks in Open-Domain Question Answering,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), St. Julian{‘}s, Malta, 2024, p. 402–417.
[BibTeX] [Abstract] [Link]
Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20{\%} exact match across varying levels of data poisoning/knowledge conflicts.
@inproceedings{weller-etal-2024-defending, title = "Defending Against Disinformation Attacks in Open-Domain Question Answering", author = "Weller, Orion and Khan, Aleem and Weir, Nathaniel and Lawrie, Dawn and Van Durme, Benjamin", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-short.35", pages = "402--417", abstract = "Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20{\%} exact match across varying levels of data poisoning/knowledge conflicts.", } - Jingyu (Jack) Zhang, Marc Marone, Tianjian Li, Benjamin Van Durme, and Daniel Khashabi, “Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{268987627, title = {Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data}, author = {{Jingyu (Jack) Zhang} and {Marc Marone} and {Tianjian Li} and {Benjamin Van Durme} and {Daniel Khashabi}}, year = 2024, month = {4}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/9689b5fdb0d3a1bad802d03d348bd32aa5a4c2df}, } - W. Gantt, S. Behzad, H. An, Y. Chen, A. White, B. Van Durme, and M. Yarmohammadi, “MultiMUC: Multilingual Template Filling on MUC-4,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{‘}s, Malta, 2024, p. 349–368.
[BibTeX] [Abstract] [Link]
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.
@inproceedings{gantt-etal-2024-multimuc, title = "{M}ulti{MUC}: Multilingual Template Filling on {MUC}-4", author = "Gantt, William and Behzad, Shabnam and An, Hannah and Chen, Yunmo and White, Aaron and Van Durme, Benjamin and Yarmohammadi, Mahsa", editor = "Graham, Yvette and Purver, Matthew", booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", month = mar, year = "2024", address = "St. Julian{'}s, Malta", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.eacl-long.21", pages = "349--368", abstract = "We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.", } - Thomas Thebaud, A. Favaro, Casey Chen, Gabrielle Chavez, L. Moro-Velázquez, A. Butala, and N. Dehak, “Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272654115, title = {Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis}, author = {{Thomas Thebaud} and {A. Favaro} and {Casey Chen} and {Gabrielle Chavez} and {L. Moro-Velázquez} and {A. Butala} and {N. Dehak}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/c8851ccf781b66eb487ca7a77784baf168d9ef23}, } - J. Villalba, Tiantian Feng, Thomas Thebaud, Jihwan Lee, Shrikanth S. Narayanan, and N. Dehak, “The SHADOW team submission to the ASVSpoof 2024 Challenge,” in The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024), 2024.
[BibTeX] [Link]@inproceedings{273770555, title = {The SHADOW team submission to the ASVSpoof 2024 Challenge}, author = {{J. Villalba} and {Tiantian Feng} and {Thomas Thebaud} and {Jihwan Lee} and {Shrikanth S. Narayanan} and {N. Dehak}}, year = 2024, month = {8}, booktitle = {The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024)}, url = {https://www.semanticscholar.org/paper/20ab192366ccf71e3d1feda7c8b040030d58266f}, } - Aamir Javaid, Sruthika Baviriseaty, Rehan Javaid, Ayah Zirikly, Harshita Kukreja, Chang H. Kim, Michael J. Blaha, Roger S. Blumenthal, Seth S. Martin, and F. Marvel, “Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence,” in JACC: Advances, 2024.
[BibTeX] [Link]@inproceedings{272236956, title = {Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence}, author = {{Aamir Javaid} and {Sruthika Baviriseaty} and {Rehan Javaid} and {Ayah Zirikly} and {Harshita Kukreja} and {Chang H. Kim} and {Michael J. Blaha} and {Roger S. Blumenthal} and {Seth S. Martin} and {F. Marvel}}, year = 2024, month = {8}, booktitle = {JACC: Advances}, url = {https://www.semanticscholar.org/paper/fa5702a10f5b9b5d53cd7e82e06658bfde95c77f}, } - Jiarui Hai, Yong Xu, Hao Zhang, Chenxing Li, Helin Wang, Mounya Elhilali, and Dong Yu, “EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272694526, title = {EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer}, author = {{Jiarui Hai} and {Yong Xu} and {Hao Zhang} and {Chenxing Li} and {Helin Wang} and {Mounya Elhilali} and {Dong Yu}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/8e146956594a7580148dad5ccdb2c0c791abd386}, } - Dongwei Jiang, Jingyu (Jack) Zhang, Orion Weller, Nathaniel Weir, Benjamin Van Durme, and Daniel Khashabi, “SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{269005582, title = {SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses}, author = {{Dongwei Jiang} and {Jingyu (Jack) Zhang} and {Orion Weller} and {Nathaniel Weir} and {Benjamin Van Durme} and {Daniel Khashabi}}, year = 2024, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/9e3d14e4697a325fcabc6d952232a3ad7e9fa809}, } - Zhengping Jiang, Yining Lu, Hanjie Chen, Daniel Khashabi, Benjamin Van Durme, and Anqi Liu, “RORA: Robust Free-Text Rationale Evaluation,” in Annual Meeting of the Association for Computational Linguistics, 2024.
[BibTeX] [Link]@inproceedings{268063486, title = {RORA: Robust Free-Text Rationale Evaluation}, author = {{Zhengping Jiang} and {Yining Lu} and {Hanjie Chen} and {Daniel Khashabi} and {Benjamin Van Durme} and {Anqi Liu}}, year = 2024, month = {2}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/17ba73a2a332a44bb1a00622beab96f33d4b1ba7}, } - Sangwook Park, Angeles Salles, Kathryne Allen, Cynthia Moss, and Mounya Elhilali, “Biomimetic Mappings for Active Sonar Object Recognition in Clutter,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2024.
[BibTeX] [Link]@inproceedings{268527006, title = {Biomimetic Mappings for Active Sonar Object Recognition in Clutter}, author = {{Sangwook Park} and {Angeles Salles} and {Kathryne Allen} and {Cynthia Moss} and {Mounya Elhilali}}, year = 2024, month = {4}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/aba6e97d493b8b593eac929b49ee8d1c7bf9c953}, } - Trevor Meyer, Camden Shultz, N. Dehak, L. Moro-Velázquez, and Pedro P. Irazoqui, “Time Scale Network: An Efficient Shallow Neural Network For Time Series Data in Biomedical Applications,” in IEEE Journal on Selected Topics in Signal Processing, 2024.
[BibTeX] [Link]@inproceedings{272087268, title = {Time Scale Network: An Efficient Shallow Neural Network For Time Series Data in Biomedical Applications}, author = {{Trevor Meyer} and {Camden Shultz} and {N. Dehak} and {L. Moro-Velázquez} and {Pedro P. Irazoqui}}, year = 2024, booktitle = {IEEE Journal on Selected Topics in Signal Processing}, url = {https://www.semanticscholar.org/paper/4e2f72bf6970fdbafb922f26d80b1d5626fa6939}, } - Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim, “Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation,” in International Conference on Machine Learning, 2024.
[BibTeX] [Link]@inproceedings{267028540, title = {Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}, author = {{Haoran Xu} and {Amr Sharaf} and {Yunmo Chen} and {Weiting Tan} and {Lingfeng Shen} and {Benjamin Van Durme} and {Kenton Murray} and {Young Jin Kim}}, year = 2024, month = {1}, booktitle = {International Conference on Machine Learning}, url = {https://www.semanticscholar.org/paper/ebd1c04c61f73f46def3305ca11d038c46665b65}, } - Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Nouha Dziri, Dan Jurafsky, Maarten Sap, Epistemic Marker, Sebastian Schuster, Judith Degen. 2019, Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal, Md. Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Ambrose Slone, Annasaheb Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, A. Santilli, Andreas Stuhlmuller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, A. Tabassum, Arul Menezes, Arun Kirubarajan, A. Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakacs, Ryan Roberts, B. S. Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Ozyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen, B. Lin, Bryan Howald, Orinion Cameron, Cameron Diao, Catherine Dour, Stinson Cedrick, Argueta, C’esar Ferri, C. Ram’irez, Charles Singh, Chenlin Rathkopf, Chitta Meng, Chiyu Baral, Chris Wu, Christopher Callison-Burch, Christian Waites, Christian Voigt, Manning Christopher, Cindy Potts, Clara Ramirez, Rivera Clemencia, Colin Siro, Courtney Raffel, Ashcraft Cristina, Damien Garbacea, Dan Sileo, Daniel H Garrette, Dan Hendrycks, D. Kilman, Roth Daniel, Daniel Freeman, Daniel Khashabi, Levy Daniel, Danielle R Mosegu’i Gonz’alez, Perszyk Danny, Danqi Hernandez, Daphne Chen, Ippolito Dar, D. Gilboa, David Dohan, D. Drakard, Debajyoti Jurgens, Deep Datta, Denis Ganguli, Denis Emelin, D. Kleyko, Derek Yuret, Chen Derek, Dieuwke Tam, Diganta Hupkes, Dilyar Misra, Dimitri Coelho Buzan, Diyi Mollo, Dong-Ho Yang, Dylan Lee, Ekaterina Schrader, Ekin Dogus Shutova, Elad Cubuk, Eleanor Segal, Elizabeth Hagerman, Elizabeth Barnes, E. Donoway, Pavlick Emanuele, E. Rodolà, Eric Lam, Eric Chu, Tang Erkut, Ernie Erdem, Ethan A Chang, Ethan A. Chi, J. DyerEthan, E. Jerzak, Eunice Engefu Kim, Manyasi Evgenii, Fanyue Zheltonozhskii, Fatemeh Xia, Siar Fernando, Francesca Mart’inez-Plumed, Happ’e François, Frieda Chollet, Gaurav Rong, Mishra, Genta Indra, Gerard Winata, Germán de Melo, Giambattista Kruszewski, Giorgio Parascandolo, Gloria Mariani, Gonzalo Wang, Jaimovitch-L’opez Gregor, Guy Betz, Hana Gur-Ari, Galijasevic Hannah, Hannah Kim, Hannaneh Rashkin, Hajishirzi Harsh, Hayden Mehta, Henry Bogar, Shevlin Hinrich, Hiromu Schutze, Hongming Yakura, Zhang Hugh Mee, Ian Wong, Isaac Ng, Jaap Noble, Jumelet Jack, Jack Geissinger, Jacob Kernion, Jaehoon Hilton, Jaime Fernández Lee, James B Fisac, James B. Simon, James Koppel, James Zheng, Jan Zou, Koco’n Jana, Janelle Thompson, Jared Wingfield, Kaplan Jarema, Radom, Jascha Narain, Jason Sohl-Dickstein, Jason Phang, Jason Wei, Jekaterina Yosinski, Jelle Novikova, Jennifer Bosscher, Jeremy Marsh, Jeroen Kim, Jesse Taal, Jesujoba Engel, Oluwadara, K. Kanclerz, Karl Livescu, Karthik Krauth, Katerina Gopalakrishnan, Katja Ignatyeva, D. MarkertKaustubh, Kevin Dhole, Kevin Gimpel, Omondi Kory Wallace, Kristen Mathewson, Ksenia Chiafullo, Kumar Shkaruta, Kyle Shridhar, Kyle McDonell, Laria Richardson, Leo Reynolds, Li Gao, Zhang Liam, Lianhui Dugan, Lidia Qin, Contreras-Ochando, Luke Metz, Lutfi Kerem, Maarten Sap Maartje ter Hoeve Maarten Bosma, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ram’irez Quintana, M. Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Medina M’aty’as Schubert, Melody Baitemirova, Melvin Andrew Arnaud, Michael A McElrath, Yee Michael, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, M. Strube, Michele Swkedrowski, Michihiro Bevilacqua, Mihir Yasunaga, Mike Kale, Mimee Cain, Xu Mirac, Mitch Suzgun, Monica Walker, Mohit Tiwari, Moin Bansal, Mor Aminnaseri, Mozhdeh Geva, T. Gheini, Nanyun MukundVarma, Nathan A Peng, Nayeon Chi, Neta Lee, Gur-Ari, Nicholas Krakover, Nicholas Cameron, Nicholas Roberts, Nicole Doiron, Nikita Martinez, Niklas Nangia, Niklas Deckers, Muennighoff, Nitish Shirish, Niveditha Keskar, Iyer Noah, Noah Constant, Nuan Fiedel, Oliver Wen, Omar Zhang, Omar Agha, Omer Elbaghdadi, Levy Owain, Pablo Evans, Antonio Moreno, Parth Casares, Pascale Doshi, Paul Pu Fung, P. Liang, Vicol Pegah, Peiyuan Alipoormolabashi, Percy Liao, Liang Peter, Peter Chang, Phu Mon Eckersley, Pi-Bei Htut, P. Hwang, Piyush S Milkowski, Pouya Patil, Priti Pezeshkpour, Qiaozhu Oli, Qing Mei, Lyu Qinlang, Rabin Chen, Rachel Etta Banjade, Rudolph Raefer, Rahel Gabriel, Ramon Habacker, Risco Raphael, Rhythm Milliere, Richard Garg, A. BarnesRif, Riku Saurous, Robbe Arakawa, Raymaekers Robert, Rohan Frank, Roman Sikand, Roman Novak, Ronan Sitelew, Rosanne Lebras, Rowan Liu, Jacobs Rui, Ruslan Zhang, Ryan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Rylan Teehan, Sahib Yang, Saif M Singh, Sajant Mohammad, Sam Anand, Sam Dillavou, Sam Shleifer, Samuel Wiseman, Samuel Gruetter, Sam Bowman, Schoenholz Sanghyun, Sanjeev Han, Sarah A Kwatra, Rous Sarik, Sayan Ghazarian, Sean Ghosh, Casey Sebastian, Sebastian Bischoff, Sebastian Gehrmann, Sepideh Schuster, Shadi S Sadeghi, and Hamdan, “Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{271093594, title = {Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance}, author = {{Kaitlyn Zhou} and {Jena D. Hwang} and {Xiang Ren} and {Nouha Dziri} and {Dan Jurafsky} and {Maarten Sap} and {Epistemic Marker} and {Sebastian Schuster} and {Judith Degen. 2019} and {Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Abu Awal} and {Md. Shoeb} and {Abubakar Abid} and {Adam Fisch} and {Adam R. Brown} and {Adam Santoro} and {Aditya Gupta} and {Adrià Garriga-Alonso} and {Agnieszka Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Parrish} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Amanda Dsouza} and {Ameet Ambrose Slone} and {Annasaheb Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmuller} and {Andrew M. Dai} and {Andrew La} and {Andrew K. Lampinen} and {Andy Zou} and {Angela Jiang} and {Angelica Chen} and {Anh Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {A. Tabassum} and {Arul Menezes} and {Arun Kirubarajan} and {A. Mullokandov} and {Ashish Sabharwal} and {Austin Herrick} and {Avia Efrat} and {Aykut Erdem} and {Ayla Karakacs} and {Ryan Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Ozyurt} and {Behnam Hedayatnia} and {Behnam Neyshabur} and {Benjamin Inden} and {Benno Stein} and {Berk Ekmekci} and {Bill Yuchen} and {B. Lin} and {Bryan Howald} and {Orinion Cameron} and {Cameron Diao} and {Catherine Dour} and {Stinson Cedrick} and {Argueta} and {C’esar Ferri} and {C. Ram’irez} and {Charles Singh} and {Chenlin Rathkopf} and {Chitta Meng} and {Chiyu Baral} and {Chris Wu} and {Christopher Callison-Burch} and {Christian Waites} and {Christian Voigt} and {Manning Christopher} and {Cindy Potts} and {Clara Ramirez} and {Rivera Clemencia} and {Colin Siro} and {Courtney Raffel} and {Ashcraft Cristina} and {Damien Garbacea} and {Dan Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {D. Kilman} and {Roth Daniel} and {Daniel Freeman} and {Daniel Khashabi} and {Levy Daniel} and {Danielle R Mosegu’i Gonz’alez} and {Perszyk Danny} and {Danqi Hernandez} and {Daphne Chen} and {Ippolito Dar} and {D. Gilboa} and {David Dohan} and {D. Drakard} and {Debajyoti Jurgens} and {Deep Datta} and {Denis Ganguli} and {Denis Emelin} and {D. Kleyko} and {Derek Yuret} and {Chen Derek} and {Dieuwke Tam} and {Diganta Hupkes} and {Dilyar Misra} and {Dimitri Coelho Buzan} and {Diyi Mollo} and {Dong-Ho Yang} and {Dylan Lee} and {Ekaterina Schrader} and {Ekin Dogus Shutova} and {Elad Cubuk} and {Eleanor Segal} and {Elizabeth Hagerman} and {Elizabeth Barnes} and {E. Donoway} and {Pavlick Emanuele} and {E. Rodolà} and {Eric Lam} and {Eric Chu} and {Tang Erkut} and {Ernie Erdem} and {Ethan A Chang} and {Ethan A. Chi} and {J. DyerEthan} and {E. Jerzak} and {Eunice Engefu Kim} and {Manyasi Evgenii} and {Fanyue Zheltonozhskii} and {Fatemeh Xia} and {Siar Fernando} and {Francesca Mart’inez-Plumed} and {Happ’e François} and {Frieda Chollet} and {Gaurav Rong} and {Mishra} and {Genta Indra} and {Gerard Winata} and {Germán de Melo} and {Giambattista Kruszewski} and {Giorgio Parascandolo} and {Gloria Mariani} and {Gonzalo Wang} and {Jaimovitch-L’opez Gregor} and {Guy Betz} and {Hana Gur-Ari} and {Galijasevic Hannah} and {Hannah Kim} and {Hannaneh Rashkin} and {Hajishirzi Harsh} and {Hayden Mehta} and {Henry Bogar} and {Shevlin Hinrich} and {Hiromu Schutze} and {Hongming Yakura} and {Zhang Hugh Mee} and {Ian Wong} and {Isaac Ng} and {Jaap Noble} and {Jumelet Jack} and {Jack Geissinger} and {Jacob Kernion} and {Jaehoon Hilton} and {Jaime Fernández Lee} and {James B Fisac} and {James B. Simon} and {James Koppel} and {James Zheng} and {Jan Zou} and {Koco’n Jana} and {Janelle Thompson} and {Jared Wingfield} and {Kaplan Jarema} and {Radom} and {Jascha Narain} and {Jason Sohl-Dickstein} and {Jason Phang} and {Jason Wei} and {Jekaterina Yosinski} and {Jelle Novikova} and {Jennifer Bosscher} and {Jeremy Marsh} and {Jeroen Kim} and {Jesse Taal} and {Jesujoba Engel} and {Oluwadara} and {K. Kanclerz} and {Karl Livescu} and {Karthik Krauth} and {Katerina Gopalakrishnan} and {Katja Ignatyeva} and {D. MarkertKaustubh} and {Kevin Dhole} and {Kevin Gimpel} and {Omondi Kory Wallace} and {Kristen Mathewson} and {Ksenia Chiafullo} and {Kumar Shkaruta} and {Kyle Shridhar} and {Kyle McDonell} and {Laria Richardson} and {Leo Reynolds} and {Li Gao} and {Zhang Liam} and {Lianhui Dugan} and {Lidia Qin} and {Contreras-Ochando} and {Luke Metz} and {Lutfi Kerem} and {Maarten Sap Maartje ter Hoeve Maarten Bosma} and {Maheen Farooqi} and {Manaal Faruqui} and {Mantas Mazeika} and {Marco Baturan} and {Marco Marelli} and {Marco Maru} and {Maria Jose Ram’irez Quintana} and {M. Tolkiehn} and {Mario Giulianelli} and {Martha Lewis} and {Martin Potthast} and {Matthew L. Leavitt} and {Matthias Hagen} and {Medina M’aty’as Schubert} and {Melody Baitemirova} and {Melvin Andrew Arnaud} and {Michael A McElrath} and {Yee Michael} and {Michael Cohen} and {Michael Gu} and {Michael Ivanitskiy} and {Michael Starritt} and {M. Strube} and {Michele Swkedrowski} and {Michihiro Bevilacqua} and {Mihir Yasunaga} and {Mike Kale} and {Mimee Cain} and {Xu Mirac} and {Mitch Suzgun} and {Monica Walker} and {Mohit Tiwari} and {Moin Bansal} and {Mor Aminnaseri} and {Mozhdeh Geva} and {T. Gheini} and {Nanyun MukundVarma} and {Nathan A Peng} and {Nayeon Chi} and {Neta Lee} and {Gur-Ari} and {Nicholas Krakover} and {Nicholas Cameron} and {Nicholas Roberts} and {Nicole Doiron} and {Nikita Martinez} and {Niklas Nangia} and {Niklas Deckers} and {Muennighoff} and {Nitish Shirish} and {Niveditha Keskar} and {Iyer Noah} and {Noah Constant} and {Nuan Fiedel} and {Oliver Wen} and {Omar Zhang} and {Omar Agha} and {Omer Elbaghdadi} and {Levy Owain} and {Pablo Evans} and {Antonio Moreno} and {Parth Casares} and {Pascale Doshi} and {Paul Pu Fung} and {P. Liang} and {Vicol Pegah} and {Peiyuan Alipoormolabashi} and {Percy Liao} and {Liang Peter} and {Peter Chang} and {Phu Mon Eckersley} and {Pi-Bei Htut} and {P. Hwang} and {Piyush S Milkowski} and {Pouya Patil} and {Priti Pezeshkpour} and {Qiaozhu Oli} and {Qing Mei} and {Lyu Qinlang} and {Rabin Chen} and {Rachel Etta Banjade} and {Rudolph Raefer} and {Rahel Gabriel} and {Ramon Habacker} and {Risco Raphael} and {Rhythm Milliere} and {Richard Garg} and {A. BarnesRif} and {Riku Saurous} and {Robbe Arakawa} and {Raymaekers Robert} and {Rohan Frank} and {Roman Sikand} and {Roman Novak} and {Ronan Sitelew} and {Rosanne Lebras} and {Rowan Liu} and {Jacobs Rui} and {Ruslan Zhang} and {Ryan Salakhutdinov} and {Ryan Chi} and {Ryan Lee} and {Ryan Stovall} and {Rylan Teehan} and {Sahib Yang} and {Saif M Singh} and {Sajant Mohammad} and {Sam Anand} and {Sam Dillavou} and {Sam Shleifer} and {Samuel Wiseman} and {Samuel Gruetter} and {Sam Bowman} and {Schoenholz Sanghyun} and {Sanjeev Han} and {Sarah A Kwatra} and {Rous Sarik} and {Sayan Ghazarian} and {Sean Ghosh} and {Casey Sebastian} and {Sebastian Bischoff} and {Sebastian Gehrmann} and {Sepideh Schuster} and {Shadi S Sadeghi} and {Hamdan}}, year = 2024, month = {7}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/1d370b8b475603a44b4babcee622362aa4579f87}, } - N. Romney Robinson, K. Sun, C. Xiao, N. Bafna, W. Tan, H. Xu, H. Li Xinyuan, A. Kejriwal, S. Khudanpur, K. Murray, and P. McNamee, “JHU IWSLT 2024 Dialectal and Low-resource System Description,” in Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), Bangkok, Thailand (in-person and online), 2024, p. 140–153. doi:10.18653/v1/2024.iwslt-1.19
[BibTeX] [Abstract] [Link]
Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.
@inproceedings{romney-robinson-etal-2024-jhu, title = "{JHU} {IWSLT} 2024 Dialectal and Low-resource System Description", author = "Romney Robinson, Nathaniel and Sun, Kaiser and Xiao, Cihan and Bafna, Niyati and Tan, Weiting and Xu, Haoran and Li Xinyuan, Henry and Kejriwal, Ankur and Khudanpur, Sanjeev and Murray, Kenton and McNamee, Paul", editor = "Salesky, Elizabeth and Federico, Marcello and Carpuat, Marine", booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)", month = aug, year = "2024", address = "Bangkok, Thailand (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.iwslt-1.19", doi = "10.18653/v1/2024.iwslt-1.19", pages = "140--153", abstract = "Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.", } - William V Padula, David G. Armstrong, Peter J Pronovost, and S. Saria, “Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study,” in BMJ Open, 2024.
[BibTeX] [Link]@inproceedings{269031228, title = {Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study}, author = {{William V Padula} and {David G. Armstrong} and {Peter J Pronovost} and {S. Saria}}, year = 2024, month = {4}, booktitle = {BMJ Open}, url = {https://www.semanticscholar.org/paper/d44b54dab53950fcd81592d3b9181c1aa8c4d73b}, } - Yuzhe Wang, A. Favaro, Thomas Thebaud, J. Villalba, N. Dehak, and L. Moro-Velázquez, “Exploring the Complementary Nature of Speech and Eye Movements for Profiling Neurological Disorders,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{272345914, title = {Exploring the Complementary Nature of Speech and Eye Movements for Profiling Neurological Disorders}, author = {{Yuzhe Wang} and {A. Favaro} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2024, month = {9}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/06c6bbd302ec67def5ccd2df13d8563e2d4a5e31}, } - Amalio Telenti, Michael Auli, B. Hie, Cyrus Maher, S. Saria, and J. P. Ioannidis, “Large language models for science and medicine,” in European Journal of Clinical Investigation, 2024.
[BibTeX] [Link]@inproceedings{267781862, title = {Large language models for science and medicine}, author = {{Amalio Telenti} and {Michael Auli} and {B. Hie} and {Cyrus Maher} and {S. Saria} and {J. P. Ioannidis}}, year = 2024, month = {2}, booktitle = {European Journal of Clinical Investigation}, url = {https://www.semanticscholar.org/paper/6dcb9cb40c948aa6033c0ae2228542c14acf0ff7}, } - Thomas Thebaud, Gabriel Hernández, Sarah Flora Samson Juan, and Marie Tahon, “A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients,” in The Speaker and Language Recognition Workshop, 2024.
[BibTeX] [Link]@inproceedings{271211023, title = {A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients}, author = {{Thomas Thebaud} and {Gabriel Hernández} and {Sarah Flora Samson Juan} and {Marie Tahon}}, year = 2024, month = {6}, booktitle = {The Speaker and Language Recognition Workshop}, url = {https://www.semanticscholar.org/paper/098e0d7c2f273bc052a74f1bd91db8cf2b57cd1f}, } - N. Robinson, R. Dabre, A. Shurtz, R. Dent, O. Onesi, C. Monroc, L. Grobol, H. Muhammad, A. Garg, N. Etori, V. M. Tiyyala, O. Samuel, M. Stutzman, B. Odoom, S. Khudanpur, S. Richardson, and K. Murray, “Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico, 2024, p. 3083–3110. doi:10.18653/v1/2024.naacl-long.170
[BibTeX] [Abstract] [Link]
A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations{–-}11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages{–-}the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.
@inproceedings{robinson-etal-2024-kreyol, title = "Krey{\`o}l-{MT}: Building {MT} for {L}atin {A}merican, {C}aribbean and Colonial {A}frican Creole Languages", author = {Robinson, Nathaniel and Dabre, Raj and Shurtz, Ammon and Dent, Rasul and Onesi, Onenamiyi and Monroc, Claire and Grobol, Lo{\"\i}c and Muhammad, Hasan and Garg, Ashi and Etori, Naome and Tiyyala, Vijay Murari and Samuel, Olanrewaju and Stutzman, Matthew and Odoom, Bismarck and Khudanpur, Sanjeev and Richardson, Stephen and Murray, Kenton}, editor = "Duh, Kevin and Gomez, Helena and Bethard, Steven", booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", month = jun, year = "2024", address = "Mexico City, Mexico", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.naacl-long.170", doi = "10.18653/v1/2024.naacl-long.170", pages = "3083--3110", abstract = "A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations{---}11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages{---}the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.", } - Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Slowness Regularized Contrastive Predictive Coding for Acoustic Unit Discovery,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2024.
[BibTeX] [Link]@inproceedings{267144330, title = {Slowness Regularized Contrastive Predictive Coding for Acoustic Unit Discovery}, author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}}, year = 2024, booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing}, url = {https://www.semanticscholar.org/paper/1748de2018438a1015f557ed72424602b144f5ba}, } - Boshi Wang, Hao Fang, Jason Eisner, Benjamin Van Durme, and Yu Su, “LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error,” in Annual Meeting of the Association for Computational Linguistics, 2024.
[BibTeX] [Link]@inproceedings{268264353, title = {LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error}, author = {{Boshi Wang} and {Hao Fang} and {Jason Eisner} and {Benjamin Van Durme} and {Yu Su}}, year = 2024, month = {3}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/ae4635297ad87fcb3ec4105a51b5cbcb4075e5e2}, } - Lingfeng Shen, Sihao Chen, Linfeng Song, Lifeng Jin, Baolin Peng, Haitao Mi, Daniel Khashabi, and Dong Yu, “The Trickle-down Impact of Reward Inconsistency on RLHF,” in International Conference on Learning Representations, 2024.
[BibTeX] [Link]@inproceedings{271745819, title = {The Trickle-down Impact of Reward Inconsistency on RLHF}, author = {{Lingfeng Shen} and {Sihao Chen} and {Linfeng Song} and {Lifeng Jin} and {Baolin Peng} and {Haitao Mi} and {Daniel Khashabi} and {Dong Yu}}, year = 2024, booktitle = {International Conference on Learning Representations}, url = {https://www.semanticscholar.org/paper/1eecfd21543286d45ca44e424a2e351d1ee6ab12}, } - Jeffrey Cheng, Marc Marone, Orion Weller, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme, “Dated Data: Tracing Knowledge Cutoffs in Large Language Models,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{268531479, title = {Dated Data: Tracing Knowledge Cutoffs in Large Language Models}, author = {{Jeffrey Cheng} and {Marc Marone} and {Orion Weller} and {Dawn Lawrie} and {Daniel Khashabi} and {Benjamin Van Durme}}, year = 2024, month = {3}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/5ee0c8975b965a413b27332b5cbfb2745251dc52}, } - Kartik Narayan, VS Vibashan, R. Chellappa, and Vishal M. Patel, “FaceXFormer: A Unified Transformer for Facial Analysis,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{268532373, title = {FaceXFormer: A Unified Transformer for Facial Analysis}, author = {{Kartik Narayan} and {VS Vibashan} and {R. Chellappa} and {Vishal M. Patel}}, year = 2024, month = {3}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/cf9ea0a2ae56bce6d2fdbc9f81633ef8ce9df59c}, } - Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu (Jack) Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, and Daniel Khashabi, “The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts,” in Annual Meeting of the Association for Computational Linguistics, 2024.
[BibTeX] [Link]@inproceedings{267200158, title = {The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts}, author = {{Lingfeng Shen} and {Weiting Tan} and {Sihao Chen} and {Yunmo Chen} and {Jingyu (Jack) Zhang} and {Haoran Xu} and {Boyuan Zheng} and {Philipp Koehn} and {Daniel Khashabi}}, year = 2024, month = {1}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/3cd81b0123b5f8477f6b5777681030ef6b05dd46}, } - Henry Li Xinyuan, Sonal Joshi, Thomas Thebaud, J. Villalba, N. Dehak, and S. Khudanpur, “Clean Label Attacks against SLU Systems,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272653876, title = {Clean Label Attacks against SLU Systems}, author = {{Henry Li Xinyuan} and {Sonal Joshi} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {S. Khudanpur}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/4196ee966cae649605415e0069dd9e830545f9fe}, } - Matthew Maciejewski, Dominik Klement, Ruizhe Huang, Matthew Wiesner, and S. Khudanpur, “Evaluating the Santa Barbara Corpus: Challenges of the Breadth of Conversational Spoken Language,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{272332095, title = {Evaluating the Santa Barbara Corpus: Challenges of the Breadth of Conversational Spoken Language}, author = {{Matthew Maciejewski} and {Dominik Klement} and {Ruizhe Huang} and {Matthew Wiesner} and {S. Khudanpur}}, year = 2024, month = {9}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/01da401eabaca52dccc642ec6d25fb5170e0538b}, } - Amir Hussein, Desh Raj, Matthew Wiesner, Dan Povey, Paola Garcia, and S. Khudanpur, “Enhancing Neural Transducer for Multilingual ASR with Synchronized Language Diarization,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{272357972, title = {Enhancing Neural Transducer for Multilingual ASR with Synchronized Language Diarization}, author = {{Amir Hussein} and {Desh Raj} and {Matthew Wiesner} and {Dan Povey} and {Paola Garcia} and {S. Khudanpur}}, year = 2024, month = {9}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/8f6b595c7e09a7bec11726e8e1520c3eb693fec4}, } - Yining Lu, Dixuan Wang, Tianjian Li, Dongwei Jiang, and Daniel Khashabi, “Benchmarking Language Model Creativity: A Case Study on Code Generation,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{271162279, title = {Benchmarking Language Model Creativity: A Case Study on Code Generation}, author = {{Yining Lu} and {Dixuan Wang} and {Tianjian Li} and {Dongwei Jiang} and {Daniel Khashabi}}, year = 2024, month = {7}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/20be3ba3f361d9630cc0c17442a0d0132873e63d}, } - N. Bafna, K. Murray, and D. Yarowsky, “Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 2024, p. 18742–18762. doi:10.18653/v1/2024.emnlp-main.1044
[BibTeX] [Abstract] [Link]
While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.
@inproceedings{bafna-etal-2024-evaluating, title = "Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization", author = "Bafna, Niyati and Murray, Kenton and Yarowsky, David", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.1044", doi = "10.18653/v1/2024.emnlp-main.1044", pages = "18742--18762", abstract = "While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.", } - Jieneng Chen, Luoxin Ye, Ju He, Zhao-Yang Wang, Daniel Khashabi, and Alan L. Yuille, “Efficient Large Multi-modal Models via Visual Context Compression.” 2024.
[BibTeX] [Link]@inproceedings{270845380, title = {Efficient Large Multi-modal Models via Visual Context Compression}, author = {{Jieneng Chen} and {Luoxin Ye} and {Ju He} and {Zhao-Yang Wang} and {Daniel Khashabi} and {Alan L. Yuille}}, year = 2024, month = {6}, booktitle = {}, url = {https://www.semanticscholar.org/paper/227c2f542f7848c16af712ea417ea2177a6450b9}, } - Xing Han, Huy Nguyen, C. Harris, Nhat Ho, and S. Saria, “FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{267413013, title = {FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion}, author = {{Xing Han} and {Huy Nguyen} and {C. Harris} and {Nhat Ho} and {S. Saria}}, year = 2024, month = {2}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/f71ea9673be9c7a76d0fa6695a5713f150b64304}, } - Taiming Lu, Muhan Gao, Kuai Yu, Adam Byerly, and Daniel Khashabi, “Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell,” in Conference on Empirical Methods in Natural Language Processing, 2024.
[BibTeX] [Link]@inproceedings{270688653, title = {Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell}, author = {{Taiming Lu} and {Muhan Gao} and {Kuai Yu} and {Adam Byerly} and {Daniel Khashabi}}, year = 2024, month = {6}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/2138841e6e5ecf59d881a0108e0d9551484cfe32}, } - Jiarui Hai, Karan Thakkar, Helin Wang, Zengyi Qin, and Mounya Elhilali, “DreamVoice: Text-Guided Voice Conversion,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{270702574, title = {DreamVoice: Text-Guided Voice Conversion}, author = {{Jiarui Hai} and {Karan Thakkar} and {Helin Wang} and {Zengyi Qin} and {Mounya Elhilali}}, year = 2024, month = {6}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/fe5cccd11f8cdcfa6584b25b3762d06253bdd813}, } - Peter M Abadir, Esther S Oh, Rama Chellappa, N. Choudhry, George Demiris, Deepak Ganesan, Jason Karlawish, Benjamin M. Marlin, Rose M Li, N. Dehak, Alicia Arbaje, Mathias Unberath, Thomas K. M. Cudjoe, Christopher Chute, Jason H Moore, Phillip Phan, Quincy M. Samus, Nancy L. Schoenborn, Alexis Battle, and Jeremy D Walston, “Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer’s care,” in Alzheimer’s & Dementia, 2024.
[BibTeX] [Link]@inproceedings{267522236, title = {Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care}, author = {{Peter M Abadir} and {Esther S Oh} and {Rama Chellappa} and {N. Choudhry} and {George Demiris} and {Deepak Ganesan} and {Jason Karlawish} and {Benjamin M. Marlin} and {Rose M Li} and {N. Dehak} and {Alicia Arbaje} and {Mathias Unberath} and {Thomas K. M. Cudjoe} and {Christopher Chute} and {Jason H Moore} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn} and {Alexis Battle} and {Jeremy D Walston}}, year = 2024, month = {2}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/893d9dc2d86b71e3ba67490decd96f91954e47ce}, } - Helin Wang, Meng Yu, Jiarui Hai, Chen Chen, Yuchen Hu, Rilin Chen, N. Dehak, and Dong Yu, “SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272600353, title = {SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis}, author = {{Helin Wang} and {Meng Yu} and {Jiarui Hai} and {Chen Chen} and {Yuchen Hu} and {Rilin Chen} and {N. Dehak} and {Dong Yu}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/a96b6a7d9d8146cb28643672a5013fec19ad4cbb}, } - Qiming Xie, Zengzhi Wang, Yihao Feng, Rui Xia, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Juraf-sky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Pang Omar Khattab, Wei Koh, M. Krass, Ranjay Krishna, Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. Henighan, R. Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma-teusz Litwin, Scott Gray, B. Chess, J. Clark, Christopher Berner, Sam McCandlish, Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Hyung Paul Barham, Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, M. Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghe-mawat, Sunipa Dev, H. Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fe-dus, Denny Zhou, Daphne Ippolito, D. Luan, Hyeontaek Lim, Barret Zoph, A. Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Díaz, Orhan Firat, M. Catasta, Jason Wei, K. Meier-Hellstern, K. Cobbe, Vineet Kosaraju, Mo Bavarian, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, L. D. Angelis, F. Baglivo, G. Arzilli, Gaetano Pierpaolo, P. Privitera, Alberto Eugenio Ferrag-ina, Tozzi Caterina, Rizzo, ChatGPT, Deep Ganguli, Liane Lovitt, John Kernion, Yuntao Bai, Saurav Kadavath, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Con-erly, Nova Dassarma, Dawn Drain, Sheer Nelson El-hage, El Showk, Stanislav Fort, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, J. Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Nicholas Joseph, C. Olah, Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant. 2021, Did Aristo-tle, Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Mohammad Hosseini, Catherine A Gao, David M. Liebovitz, Faraz Alexandre M Carvalho, S. Ahmad, Yuan Luo, N. MacDonald, Kristi L. Holmes, Abel Kho. 2023, An, Edward J. Hu, Yelong Shen, Zeyuan Phillip Wallis, Kevin B. Johnson, Wei-Qi Wei, D. Weeraratne, M. Frisse, K. Misulis, Kyu Rhee, Juan Zhao, Tom Conerly, Nelson Elhage, Tristan Hume, Kamal Ndousse, Stephanie Lin, Owain Evans. 2022, Yao Lu, Max Bartolo, Alastair Moore, Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Ouyang Long, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Katarina Slama, Alex Ray, John Schulman, Fraser Kelton, Luke Miller, Maddie Simens, P. Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe. 2022, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Sandipan Kundu, Saurav Kada-vath, Brian Israel, Bryan Seethor, C. McKinnon, Da Yan, D. Amodei, Dustin Li, Guro Khundadze, James Landis, Jamie Kerr, J. Mueller, Jeeyoon Hyun, Joshua Landau, Landon Goldberg, Martin Lucas, M. Sellitto, Miranda Zhang, Neerav Kingsland, Noem’i Mercado, Oliver Rausch, Robin Larson, Tamera Lanham, Timothy Telleen-Lawton, Roger Grosse, Evan Hubinger, Ansh Radhakrishnan, Carol Chen, Carson E. Denison, Esin Durmus, Newton Cheng, Sheer Sam McCan-dlish, Tamera Lanham, Tim Maxwell, and Venkatesa Chandrasekaran, “Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment,” in Volume 1, 2024.
[BibTeX] [Link]@inproceedings{271894946, title = {Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment}, author = {{Qiming Xie} and {Zengzhi Wang} and {Yihao Feng} and {Rui Xia} and {Neel Guha} and {Tatsunori Hashimoto} and {Peter Henderson} and {John Hewitt} and {Daniel E. Ho} and {Jenny Hong} and {Kyle Hsu} and {Jing Huang} and {Thomas Icard} and {Saahil Jain} and {Dan Juraf-sky} and {Pratyusha Kalluri} and {Siddharth Karamcheti} and {Geoff Keeling} and {Fereshte Khani} and {Pang Omar Khattab} and {Wei Koh} and {M. Krass} and {Ranjay Krishna} and {Tom Brown} and {Benjamin Mann} and {Nick Ryder} and {Melanie Subbiah} and {Jared Kaplan} and {Prafulla Dhariwal} and {Arvind Neelakantan} and {Pranav Shyam} and {Girish Sastry} and {Amanda Askell} and {Sandhini Agarwal} and {Ariel Herbert-Voss} and {Gretchen Krueger} and {T. Henighan} and {R. Child} and {Aditya Ramesh} and {Daniel M. Ziegler} and {Jeffrey Wu} and {Clemens Winter} and {Chris Hesse} and {Mark Chen} and {Eric Sigler} and {Ma-teusz Litwin} and {Scott Gray} and {B. Chess} and {J. Clark} and {Christopher Berner} and {Sam McCandlish} and {Wei-Lin Chiang} and {Zhuohan Li} and {Zi Lin} and {Ying Sheng} and {Zhanghao Wu} and {Hao Zhang} and {Lianmin Zheng} and {Siyuan Zhuang} and {Yonghao Zhuang} and {Joseph E. Gonzalez} and {Aakanksha Chowdhery} and {Sharan Narang} and {Jacob Devlin} and {Maarten Bosma} and {Gaurav Mishra} and {Adam Roberts} and {Hyung Paul Barham} and {Won Chung} and {Charles Sutton} and {Sebastian Gehrmann} and {Parker Schuh} and {Kensen Shi} and {Sasha Tsvyashchenko} and {Joshua Maynez} and {Abhishek Rao} and {Parker Barnes} and {Yi Tay} and {Noam M. Shazeer} and {Vinodkumar Prabhakaran} and {Emily Reif} and {Nan Du} and {Ben Hutchinson} and {Reiner Pope} and {James Bradbury} and {Jacob Austin} and {M. Isard} and {Guy Gur-Ari} and {Pengcheng Yin} and {Toju Duke} and {Anselm Levskaya} and {Sanjay Ghe-mawat} and {Sunipa Dev} and {H. Michalewski} and {Xavier Garcia} and {Vedant Misra} and {Kevin Robinson} and {Liam Fe-dus} and {Denny Zhou} and {Daphne Ippolito} and {D. Luan} and {Hyeontaek Lim} and {Barret Zoph} and {A. Spiridonov} and {Ryan Sepassi} and {David Dohan} and {Shivani Agrawal} and {Mark Omernick} and {Andrew M. Dai} and {Thanumalayan Sankaranarayana Pillai} and {Marie Pellat} and {Aitor Lewkowycz} and {Erica Moreira} and {Oleksandr Polozov} and {Katherine Lee} and {Zongwei Zhou} and {Xuezhi Wang} and {Brennan Saeta} and {Mark Díaz} and {Orhan Firat} and {M. Catasta} and {Jason Wei} and {K. Meier-Hellstern} and {K. Cobbe} and {Vineet Kosaraju} and {Mo Bavarian} and {Heewoo Jun} and {Lukasz Kaiser} and {Matthias Plappert} and {Jerry Tworek} and {Jacob Hilton} and {Reiichiro Nakano} and {L. D. Angelis} and {F. Baglivo} and {G. Arzilli} and {Gaetano Pierpaolo} and {P. Privitera} and {Alberto Eugenio Ferrag-ina} and {Tozzi Caterina} and {Rizzo} and {ChatGPT} and {Deep Ganguli} and {Liane Lovitt} and {John Kernion} and {Yuntao Bai} and {Saurav Kadavath} and {Ethan Perez} and {Nicholas Schiefer} and {Kamal Ndousse} and {Andy Jones} and {Sam Bowman} and {Anna Chen} and {Tom Con-erly} and {Nova Dassarma} and {Dawn Drain} and {Sheer Nelson El-hage} and {El Showk} and {Stanislav Fort} and {Zac Hatfield-Dodds} and {Danny Hernandez} and {Tristan Hume} and {J. Jacobson} and {Scott Johnston} and {Shauna Kravec} and {Catherine Olsson} and {Sam Ringer} and {Eli Tran-Johnson} and {Dario Amodei} and {Nicholas Joseph} and {C. Olah} and {Mor Geva} and {Daniel Khashabi} and {Elad Segal} and {Tushar Khot} and {Dan Roth} and {Jonathan Berant. 2021} and {Did Aristo-tle} and {Kai Greshake} and {Sahar Abdelnabi} and {Shailesh Mishra} and {Christoph Endres} and {Thorsten Holz} and {Dan Hendrycks} and {Collin Burns} and {Steven Basart} and {Andy Zou} and {Mantas Mazeika} and {Mohammad Hosseini} and {Catherine A Gao} and {David M. Liebovitz} and {Faraz Alexandre M Carvalho} and {S. Ahmad} and {Yuan Luo} and {N. MacDonald} and {Kristi L. Holmes} and {Abel Kho. 2023} and {An} and {Edward J. Hu} and {Yelong Shen} and {Zeyuan Phillip Wallis} and {Kevin B. Johnson} and {Wei-Qi Wei} and {D. Weeraratne} and {M. Frisse} and {K. Misulis} and {Kyu Rhee} and {Juan Zhao} and {Tom Conerly} and {Nelson Elhage} and {Tristan Hume} and {Kamal Ndousse} and {Stephanie Lin} and {Owain Evans. 2022} and {Yao Lu} and {Max Bartolo} and {Alastair Moore} and {Sewon Min} and {Xinxi Lyu} and {Ari Holtzman} and {Mikel Artetxe} and {Ouyang Long} and {Xu Jiang} and {Diogo Almeida} and {Carroll L. Wainwright} and {Pamela Mishkin} and {Chong Zhang} and {Katarina Slama} and {Alex Ray} and {John Schulman} and {Fraser Kelton} and {Luke Miller} and {Maddie Simens} and {P. Welinder} and {Paul F. Christiano} and {Jan Leike} and {Ryan Lowe. 2022} and {Kamilė Lukošiūtė} and {Karina Nguyen} and {Edwin Chen} and {Scott Heiner} and {Craig Pettit} and {Sandipan Kundu} and {Saurav Kada-vath} and {Brian Israel} and {Bryan Seethor} and {C. McKinnon} and {Da Yan} and {D. Amodei} and {Dustin Li} and {Guro Khundadze} and {James Landis} and {Jamie Kerr} and {J. Mueller} and {Jeeyoon Hyun} and {Joshua Landau} and {Landon Goldberg} and {Martin Lucas} and {M. Sellitto} and {Miranda Zhang} and {Neerav Kingsland} and {Noem'i Mercado} and {Oliver Rausch} and {Robin Larson} and {Tamera Lanham} and {Timothy Telleen-Lawton} and {Roger Grosse} and {Evan Hubinger} and {Ansh Radhakrishnan} and {Carol Chen} and {Carson E. Denison} and {Esin Durmus} and {Newton Cheng} and {Sheer Sam McCan-dlish} and {Tamera Lanham} and {Tim Maxwell} and {Venkatesa Chandrasekaran}}, year = 2024, booktitle = {Volume 1}, url = {https://www.semanticscholar.org/paper/7925f16e76cf274c257570ed3aa0ab97638be7d1}, } - Lucas Goncalves, Ali N. Salman, Abinay Reddy Naini, Laureano Moro Velázquez, Thomas Thebaud, Leibny Paola, Najim Garcia, Berrak Dehak, Carlos Sisman, and Busso, “Odyssey 2024 – Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results,” in The Speaker and Language Recognition Workshop, 2024.
[BibTeX] [Link]@inproceedings{269459473, title = {Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results}, author = {{Lucas Goncalves} and {Ali N. Salman} and {Abinay Reddy Naini} and {Laureano Moro Velázquez} and {Thomas Thebaud} and {Leibny Paola} and {Najim Garcia} and {Berrak Dehak} and {Carlos Sisman} and {Busso}}, year = 2024, month = {6}, booktitle = {The Speaker and Language Recognition Workshop}, url = {https://www.semanticscholar.org/paper/44a30157f437065fd0672b1327edaa32a9239ce5}, } - Sijia Zhao, Benjamin Skirritt-Davis, Mounya Elhilali, Fred Dick, and M. Chait, “Sustained EEG responses to rapidly unfolding stochastic sounds reflect precision tracking,” in bioRxiv, 2024.
[BibTeX] [Link]@inproceedings{266873790, title = {Sustained EEG responses to rapidly unfolding stochastic sounds reflect precision tracking}, author = {{Sijia Zhao} and {Benjamin Skirritt-Davis} and {Mounya Elhilali} and {Fred Dick} and {M. Chait}}, year = 2024, month = {1}, booktitle = {bioRxiv}, url = {https://www.semanticscholar.org/paper/e69427d53f37698a57706e91a275d57e582baba4}, } - Yiwen Shao, Shizhong Zhang, Yong Xu, Meng Yu, Dong Yu, Dan Povey, and S. Khudanpur, “Multi-Channel Multi-Speaker ASR Using Target Speaker’s Solo Segment,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{270521572, title = {Multi-Channel Multi-Speaker ASR Using Target Speaker’s Solo Segment}, author = {{Yiwen Shao} and {Shizhong Zhang} and {Yong Xu} and {Meng Yu} and {Dong Yu} and {Dan Povey} and {S. Khudanpur}}, year = 2024, month = {6}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/18eadf3b7cf2ceb7b3c4e034b948af90ceec7219}, } - Helin Wang, Jiarui Hai, Yen-Ju Lu, Karan Thakkar, Mounya Elhilali, and N. Dehak, “SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272653828, title = {SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer}, author = {{Helin Wang} and {Jiarui Hai} and {Yen-Ju Lu} and {Karan Thakkar} and {Mounya Elhilali} and {N. Dehak}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/7f38e38bcdf7312cad3534211d40f4a86431ff7d}, } - Magdalena Rybicka, J. Villalba, Thomas Thebaud, N. Dehak, and Konrad Kowalczyk, “End-to-End Neural Speaker Diarization With Non-Autoregressive Attractors,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2024.
[BibTeX] [Link]@inproceedings{271776874, title = {End-to-End Neural Speaker Diarization With Non-Autoregressive Attractors}, author = {{Magdalena Rybicka} and {J. Villalba} and {Thomas Thebaud} and {N. Dehak} and {Konrad Kowalczyk}}, year = 2024, booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing}, url = {https://www.semanticscholar.org/paper/3eb78f245370801e1ed5a80972c8d88d275b0852}, } - Henry Li Xinyuan, Zexin Cai, Ashi Garg, Kevin Duh, Leibny Paola Garc’ia-Perera, S. Khudanpur, Nicholas Andrews, and Matthew Wiesner, “HLTCOE JHU Submission to the Voice Privacy Challenge 2024,” in 4th Symposium on Security and Privacy in Speech Communication, 2024.
[BibTeX] [Link]@inproceedings{272653901, title = {HLTCOE JHU Submission to the Voice Privacy Challenge 2024}, author = {{Henry Li Xinyuan} and {Zexin Cai} and {Ashi Garg} and {Kevin Duh} and {Leibny Paola Garc'ia-Perera} and {S. Khudanpur} and {Nicholas Andrews} and {Matthew Wiesner}}, year = 2024, month = {9}, booktitle = {4th Symposium on Security and Privacy in Speech Communication}, url = {https://www.semanticscholar.org/paper/d20a9b6e43042214017716d06e7cd7f21da9371c}, } - Miguel Angrick, Shiyu Luo, Qinwan Rabbani, Daniel N Candrea, Samyak Shah, Griffin W. Milsap, William S Anderson, Chad R Gordon, Kathryn R Rosenblatt, Lora Clawson, Donna C. Tippett, Nicholas J Maragakis, F. Tenore, M. Fifer, H. Hermansky, Nick F Ramsey, and N. Crone, “Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS,” in Scientific Reports, 2024.
[BibTeX] [Link]@inproceedings{269408252, title = {Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS}, author = {{Miguel Angrick} and {Shiyu Luo} and {Qinwan Rabbani} and {Daniel N Candrea} and {Samyak Shah} and {Griffin W. Milsap} and {William S Anderson} and {Chad R Gordon} and {Kathryn R Rosenblatt} and {Lora Clawson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {F. Tenore} and {M. Fifer} and {H. Hermansky} and {Nick F Ramsey} and {N. Crone}}, year = 2024, month = {4}, booktitle = {Scientific Reports}, url = {https://www.semanticscholar.org/paper/fa58f21ecda2053d2c1c9360e682b1140b6ff4c1}, } - Kai Lu, Kelsey Dutta, Mounya Elhilali, and S. Shamma, “Temporal-Coherence Induces Binding of Responses to Sound Sequences in Ferret Auditory Cortex,” in bioRxiv, 2024.
[BibTeX] [Link]@inproceedings{269987122, title = {Temporal-Coherence Induces Binding of Responses to Sound Sequences in Ferret Auditory Cortex}, author = {{Kai Lu} and {Kelsey Dutta} and {Mounya Elhilali} and {S. Shamma}}, year = 2024, month = {5}, booktitle = {bioRxiv}, url = {https://www.semanticscholar.org/paper/ad2b79189ddfee6af017d1569bfebde37e587aea}, } - Qinwan Rabbani, Samyak Shah, Griffin W. Milsap, M. Fifer, H. Hermansky, and N. Crone, “Iterative alignment discovery of speech-associated neural activity,” in Journal of Neural Engineering, 2024.
[BibTeX] [Link]@inproceedings{271967099, title = {Iterative alignment discovery of speech-associated neural activity}, author = {{Qinwan Rabbani} and {Samyak Shah} and {Griffin W. Milsap} and {M. Fifer} and {H. Hermansky} and {N. Crone}}, year = 2024, month = {8}, booktitle = {Journal of Neural Engineering}, url = {https://www.semanticscholar.org/paper/d182dc65e550bb6cf2eb956e5c2278a45c983572}, } - Jiefu Ou, Arda Uzunouglu, Benjamin Van Durme, and Daniel Khashabi, “WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{271088562, title = {WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment}, author = {{Jiefu Ou} and {Arda Uzunouglu} and {Benjamin Van Durme} and {Daniel Khashabi}}, year = 2024, month = {7}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/143ff789f500307bab6725c466da97492cb5c771}, } - Kevin Xu, Yeganeh Kordi, Kate Sanders, Yizhong Wang, Adam Byerly, Jingyu (Jack) Zhang, Benjamin Van Durme, and Daniel Khashabi, “Tur[k]ingBench: A Challenge Benchmark for Web Agents,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{268531391, title = {Tur[k]ingBench: A Challenge Benchmark for Web Agents}, author = {{Kevin Xu} and {Yeganeh Kordi} and {Kate Sanders} and {Yizhong Wang} and {Adam Byerly} and {Jingyu (Jack) Zhang} and {Benjamin Van Durme} and {Daniel Khashabi}}, year = 2024, month = {3}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/341da3f8af6edd31edd8f5a3d9452957aeaaa744}, } - Alexander Polok, Dominik Klement, Matthew Wiesner, S. Khudanpur, J. Černocký, and L. Burget, “Target Speaker ASR with Whisper,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272689050, title = {Target Speaker ASR with Whisper}, author = {{Alexander Polok} and {Dominik Klement} and {Matthew Wiesner} and {S. Khudanpur} and {J. Černocký} and {L. Burget}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/9ff8924b37f827d695db1354ab15374e2076abc3}, } - Helin Wang, J. Villalba, L. Moro-Velázquez, Jiarui Hai, Thomas Thebaud, and N. Dehak, “Noise-robust Speech Separation with Fast Generative Correction,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{270380183, title = {Noise-robust Speech Separation with Fast Generative Correction}, author = {{Helin Wang} and {J. Villalba} and {L. Moro-Velázquez} and {Jiarui Hai} and {Thomas Thebaud} and {N. Dehak}}, year = 2024, month = {6}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/6983f5ef3a136df52cbaf0b623c16a7983e99d59}, } - H. E. Wang, Jonathan P. Weiner, S. Saria, Harold P. Lehmann, and Hadi Kharrazi, “Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models,” in Journal of Biomedical Informatics, 2024.
[BibTeX] [Link]@inproceedings{270746357, title = {Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models}, author = {{H. E. Wang} and {Jonathan P. Weiner} and {S. Saria} and {Harold P. Lehmann} and {Hadi Kharrazi}}, year = 2024, month = {6}, booktitle = {Journal of Biomedical Informatics}, url = {https://www.semanticscholar.org/paper/205acb6a954600dacbcb877a625605a16ca261f2}, } - R. Huang, M. Yarmohammadi, J. Trmal, J. Liu, D. Raj, L. P. Garcia, A. V. Ivanov, P. Ehlen, M. Yu, D. Povey, and S. Khudanpur, “ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 2024, p. 3700–3706.
[BibTeX] [Abstract] [Link]
Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {–-} such as the speaker{‘}s contacts or recent song playlists {–-} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({“}ConEC{”}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{‘} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology
@inproceedings{huang-etal-2024-conec, title = "{C}on{EC}: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition", author = "Huang, Ruizhe and Yarmohammadi, Mahsa and Trmal, Jan and Liu, Jing and Raj, Desh and Garcia, Leibny Paola and Ivanov, Alexei V. and Ehlen, Patrick and Yu, Mingzhi and Povey, Dan and Khudanpur, Sanjeev", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.328", pages = "3700--3706", abstract = "Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology", } - Xinrui Zou, Ming Zhang, Nathaniel Weir, Benjamin Van Durme, and Nils Holzenberger, “Reframing Tax Law Entailment as Analogical Reasoning,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{266977175, title = {Reframing Tax Law Entailment as Analogical Reasoning}, author = {{Xinrui Zou} and {Ming Zhang} and {Nathaniel Weir} and {Benjamin Van Durme} and {Nils Holzenberger}}, year = 2024, month = {1}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/e993e7971fa8b96514fd63699c12bec68e83b6d1}, } - A. Favaro, N. Dehak, Thomas Thebaud, J. Villalba, Esther S Oh, and L. Moro-Velázquez, “Discovering Invariant Patterns of Cognitive Decline Via an Automated Analysis of the Cookie Thief Picture Description Task,” in The Speaker and Language Recognition Workshop, 2024.
[BibTeX] [Link]@inproceedings{271207138, title = {Discovering Invariant Patterns of Cognitive Decline Via an Automated Analysis of the Cookie Thief Picture Description Task}, author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {J. Villalba} and {Esther S Oh} and {L. Moro-Velázquez}}, year = 2024, month = {6}, booktitle = {The Speaker and Language Recognition Workshop}, url = {https://www.semanticscholar.org/paper/99dec8ab1d7aa47117062e1daf36dcbcce4aece2}, } - Ruizhe Huang, M. Yarmohammadi, S. Khudanpur, and Dan Povey, “Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{271212774, title = {Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation}, author = {{Ruizhe Huang} and {M. Yarmohammadi} and {S. Khudanpur} and {Dan Povey}}, year = 2024, month = {7}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/6ebfef1daea743456536d620e894eee8992aa124}, } - Weiting Tan, Yunmo Chen, Tongfei Chen, Guanghui Qin, Haoran Xu, Heidi C. Zhang, Benjamin Van Durme, and Philipp Koehn, “Streaming Sequence Transduction through Dynamic Compression,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{267406764, title = {Streaming Sequence Transduction through Dynamic Compression}, author = {{Weiting Tan} and {Yunmo Chen} and {Tongfei Chen} and {Guanghui Qin} and {Haoran Xu} and {Heidi C. Zhang} and {Benjamin Van Durme} and {Philipp Koehn}}, year = 2024, month = {2}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/50b2a8c4c8a0d4b26621e84e3e0b5b300775e030}, } - Sonal Joshi, Thomas Thebaud, J. Villalba, and N. Dehak, “Unraveling Adversarial Examples against Speaker Identification – Techniques for Attack Detection and Victim Model Classification,” in The Speaker and Language Recognition Workshop, 2024.
[BibTeX] [Link]@inproceedings{268091248, title = {Unraveling Adversarial Examples against Speaker Identification - Techniques for Attack Detection and Victim Model Classification}, author = {{Sonal Joshi} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak}}, year = 2024, month = {2}, booktitle = {The Speaker and Language Recognition Workshop}, url = {https://www.semanticscholar.org/paper/af87c6786c1e7f8345f3c5768668617df6cc2771}, } - Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Position: Do pretrained Transformers Learn In-Context by Gradient Descent?,” in International Conference on Machine Learning, 2024.
[BibTeX] [Link]@inproceedings{272330336, title = {Position: Do pretrained Transformers Learn In-Context by Gradient Descent?}, author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}}, year = 2024, booktitle = {International Conference on Machine Learning}, url = {https://www.semanticscholar.org/paper/703ead78cdfa8e77b7800d29a9163d22891d4adc}, } - A. Hou, Jingyu (Jack) Zhang, Yichen Wang, Daniel Khashabi, and Tianxing He, “k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text,” in Annual Meeting of the Association for Computational Linguistics, 2024.
[BibTeX] [Link]@inproceedings{267750325, title = {k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text}, author = {{A. Hou} and {Jingyu (Jack) Zhang} and {Yichen Wang} and {Daniel Khashabi} and {Tianxing He}}, year = 2024, month = {2}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/94c28609614719c68469081ed99315f54cb1fb6a}, } - Weiting Tan, Jingyu (Jack) Zhang, Lingfeng Shen, Daniel Khashabi, and Philipp Koehn, “DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{269983348, title = {DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation}, author = {{Weiting Tan} and {Jingyu (Jack) Zhang} and {Lingfeng Shen} and {Daniel Khashabi} and {Philipp Koehn}}, year = 2024, month = {5}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/f2b48782b86f4f8bc0b537d1d91bae6cf642c8c4}, } - A. Favaro, Tianyu Cao, N. Dehak, and L. Moro-Velázquez, “Leveraging Universal Speech Representations for Detecting and Assessing the Severity of Mild Cognitive Impairment Across Languages,” in Interspeech, 2024.
[BibTeX] [Link]@inproceedings{272340732, title = {Leveraging Universal Speech Representations for Detecting and Assessing the Severity of Mild Cognitive Impairment Across Languages}, author = {{A. Favaro} and {Tianyu Cao} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2024, month = {9}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/a01c088d9cd7523e36c21353cc972690f0de307d}, } - Neha Joshi, Yu Ng, Karan Thakkkar, Daniel Duque, Pingbo Yin, Jonathan Fritz, Mounya Elhilali, and S. Shamma, “Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party,” in bioRxiv, 2024.
[BibTeX] [Link]@inproceedings{269987070, title = {Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party}, author = {{Neha Joshi} and {Yu Ng} and {Karan Thakkkar} and {Daniel Duque} and {Pingbo Yin} and {Jonathan Fritz} and {Mounya Elhilali} and {S. Shamma}}, year = 2024, month = {6}, booktitle = {bioRxiv}, url = {https://www.semanticscholar.org/paper/ba6ad577da0d2144dbd3313404ea580fc155ad03}, } - Drew Prinster, Samuel Stanton, Anqi Liu, and S. Saria, “Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them),” in International Conference on Machine Learning, 2024.
[BibTeX] [Link]@inproceedings{269741223, title = {Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)}, author = {{Drew Prinster} and {Samuel Stanton} and {Anqi Liu} and {S. Saria}}, year = 2024, month = {5}, booktitle = {International Conference on Machine Learning}, url = {https://www.semanticscholar.org/paper/bb86b50363e9d100606a534c6a877dacbf8b0e25}, } - Anthony J. Anderson, David Eguren, Michael A. Gonzalez, Naima Khan, Sophia Watkinson, Michael Caiola, Siegfried S Hirczy, Cyrus P. Zabetian, Kelly Mills, Emile Moukheiber, L. Moro-Velázquez, N. Dehak, Chelsie Motely, B. Muir, A. Butala, and Kimberly Kontson, “WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson’s Disease and Age-Matched Controls,” in medRxiv, 2024.
[BibTeX] [Link]@inproceedings{272593614, title = {WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson's Disease and Age-Matched Controls}, author = {{Anthony J. Anderson} and {David Eguren} and {Michael A. Gonzalez} and {Naima Khan} and {Sophia Watkinson} and {Michael Caiola} and {Siegfried S Hirczy} and {Cyrus P. Zabetian} and {Kelly Mills} and {Emile Moukheiber} and {L. Moro-Velázquez} and {N. Dehak} and {Chelsie Motely} and {B. Muir} and {A. Butala} and {Kimberly Kontson}}, year = 2024, month = {9}, booktitle = {medRxiv}, url = {https://www.semanticscholar.org/paper/74a0ee0f0a4fb6f63c903768d002413e58458269}, } - Desh Raj, Matthew Wiesner, Matthew Maciejewski, Leibny Paola García-Perera, Daniel Povey, and S. Khudanpur, “On Speaker Attribution with SURT,” in The Speaker and Language Recognition Workshop, 2024.
[BibTeX] [Link]@inproceedings{267311651, title = {On Speaker Attribution with SURT}, author = {{Desh Raj} and {Matthew Wiesner} and {Matthew Maciejewski} and {Leibny Paola García-Perera} and {Daniel Povey} and {S. Khudanpur}}, year = 2024, month = {1}, booktitle = {The Speaker and Language Recognition Workshop}, url = {https://www.semanticscholar.org/paper/924d189fbe7aa43ab9de9989ce45d4e4936f533d}, } - Zexin Cai, Henry Li Xinyuan, Ashi Garg, Leibny Paola Garc’ia-Perera, Kevin Duh, S. Khudanpur, Nicholas Andrews, and Matthew Wiesner, “Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization,” in arXiv.org, 2024.
[BibTeX] [Link]@inproceedings{272423555, title = {Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization}, author = {{Zexin Cai} and {Henry Li Xinyuan} and {Ashi Garg} and {Leibny Paola Garc'ia-Perera} and {Kevin Duh} and {S. Khudanpur} and {Nicholas Andrews} and {Matthew Wiesner}}, year = 2024, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/3c2d94cfa24b979eb13ca06d8a2bce79b5ab2321}, } - Michael R. Pinsky, Armando Bedoya, A. Bihorac, L. Celi, Matthew Churpek, Nicoleta J. Economou-Zavlanos, Paul Elbers, S. Saria, Vincent Liu, Patrick G. Lyons, B. Shickel, Patrick Toral, D. Tscholl, and Gilles Clermont, “Use of artificial intelligence in critical care: opportunities and obstacles,” in Critical Care, 2024.
[BibTeX] [Link]@inproceedings{269006160, title = {Use of artificial intelligence in critical care: opportunities and obstacles}, author = {{Michael R. Pinsky} and {Armando Bedoya} and {A. Bihorac} and {L. Celi} and {Matthew Churpek} and {Nicoleta J. Economou-Zavlanos} and {Paul Elbers} and {S. Saria} and {Vincent Liu} and {Patrick G. Lyons} and {B. Shickel} and {Patrick Toral} and {D. Tscholl} and {Gilles Clermont}}, year = 2024, month = {4}, booktitle = {Critical Care}, url = {https://www.semanticscholar.org/paper/a386424f2f61647ebec3dd27e33c6db92c1c07ac}, } - Margaret A. McMullin, Rohit Kumar, Nathan C. Higgins, Brian Gygi, Mounya Elhilali, and J. Snyder, “Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception,” in Open Mind, 2024.
[BibTeX] [Link]@inproceedings{268877286, title = {Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception}, author = {{Margaret A. McMullin} and {Rohit Kumar} and {Nathan C. Higgins} and {Brian Gygi} and {Mounya Elhilali} and {J. Snyder}}, year = 2024, month = {3}, booktitle = {Open Mind}, url = {https://www.semanticscholar.org/paper/e46542680689e1358d6ed072560f1ec7eefce069}, } - Sandeep Reddy Kothinti and Mounya Elhilali, “Multi-rate modulation encoding via unsupervised learning for audio event detection,” in EURASIP Journal on Audio, Speech, and Music Processing, 2024.
[BibTeX] [Link]@inproceedings{268863052, title = {Multi-rate modulation encoding via unsupervised learning for audio event detection}, author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}}, year = 2024, month = {4}, booktitle = {EURASIP Journal on Audio, Speech, and Music Processing}, url = {https://www.semanticscholar.org/paper/3cc427a861147fc147b316bfd73da03761e41a4d}, } - Deming Li, A. Butala, L. Moro-Velázquez, Trevor Meyer, Esther S. Oh, Chelsey Motley, J. Villalba, and N. Dehak, “Automating the analysis of eye movement for different neurodegenerative disorders,” in Comput. Biol. Medicine, 2024.
[BibTeX] [Link]@inproceedings{266798865, title = {Automating the analysis of eye movement for different neurodegenerative disorders}, author = {{Deming Li} and {A. Butala} and {L. Moro-Velázquez} and {Trevor Meyer} and {Esther S. Oh} and {Chelsey Motley} and {J. Villalba} and {N. Dehak}}, year = 2024, month = {1}, booktitle = {Comput. Biol. Medicine}, url = {https://www.semanticscholar.org/paper/f375c9d0a595152ff21f96a0a5606c7d033548f3}, } - Ruizhe Huang, Xiaohui Zhang, Zhaoheng Ni, Li Sun, Moto Hira, Jeff Hwang, Vimal Manohar, Vineel Pratap, Matthew Wiesner, Shinji Watanabe, Daniel Povey, and S. Khudanpur, “Less Peaky and More Accurate CTC Forced Alignment by Label Priors,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2024.
[BibTeX] [Link]@inproceedings{268577202, title = {Less Peaky and More Accurate CTC Forced Alignment by Label Priors}, author = {{Ruizhe Huang} and {Xiaohui Zhang} and {Zhaoheng Ni} and {Li Sun} and {Moto Hira} and {Jeff Hwang} and {Vimal Manohar} and {Vineel Pratap} and {Matthew Wiesner} and {Shinji Watanabe} and {Daniel Povey} and {S. Khudanpur}}, year = 2024, month = {4}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/ca6c9789f4dd10e1908aaeaa6a37bd682af4f86a}, } - Zhengping Jiang, Jingyu (Jack) Zhang, Nathaniel Weir, Seth Ebner, Miriam Wanner, Kate Sanders, Daniel Khashabi, Anqi Liu, and Benjamin Van Durme, “Core: Robust Factual Precision with Informative Sub-Claim Identification.” 2024.
[BibTeX] [Link]@inproceedings{271039322, title = {Core: Robust Factual Precision with Informative Sub-Claim Identification}, author = {{Zhengping Jiang} and {Jingyu (Jack) Zhang} and {Nathaniel Weir} and {Seth Ebner} and {Miriam Wanner} and {Kate Sanders} and {Daniel Khashabi} and {Anqi Liu} and {Benjamin Van Durme}}, year = 2024, month = {7}, booktitle = {}, url = {https://www.semanticscholar.org/paper/1a37246f60f0c47cd7d3f9d91b09d7be970850a6}, } - Maliha Jahan, L. Moro-Velázquez, Thomas Thebaud, N. Dehak, and J. Villalba, “Model-Based Fairness Metric for Speaker Verification,” in Automatic Speech Recognition & Understanding, 2023.
[BibTeX] [Link]@inproceedings{267043424, title = {Model-Based Fairness Metric for Speaker Verification}, author = {{Maliha Jahan} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak} and {J. Villalba}}, year = 2023, month = {12}, booktitle = {Automatic Speech Recognition & Understanding}, url = {https://www.semanticscholar.org/paper/f308fc3883c5a18c050b44ec932b59067dfd83f3}, } - B. Englitz, S. Akram, Mounya Elhilali, and S. Shamma, “Decoding contextual influences on auditory perception from primary auditory cortex,” in bioRxiv, 2023.
[BibTeX] [Link]@inproceedings{266574417, title = {Decoding contextual influences on auditory perception from primary auditory cortex}, author = {{B. Englitz} and {S. Akram} and {Mounya Elhilali} and {S. Shamma}}, year = 2023, month = {12}, booktitle = {bioRxiv}, url = {https://www.semanticscholar.org/paper/2e536d19f547f000d99351659622ae8204f3467a}, } - Jiarui Hai, Yu-Jeh Liu, and Mounya Elhilali, “Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis,” in Automatic Speech Recognition & Understanding, 2023.
[BibTeX] [Link]@inproceedings{267044002, title = {Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis}, author = {{Jiarui Hai} and {Yu-Jeh Liu} and {Mounya Elhilali}}, year = 2023, month = {12}, booktitle = {Automatic Speech Recognition & Understanding}, url = {https://www.semanticscholar.org/paper/053dc825f19474a6b8b49239a7c0290aecb57771}, } - Nigam H. Shah, John D Halamka, S. Saria, Michael J. Pencina, Troy Tazbaz, Micky Tripathi, Alison Callahan, Hailey Hildahl, and Brian Anderson, “A Nationwide Network of Health AI Assurance Laboratories.,” in Journal of the American Medical Association (JAMA), 2023.
[BibTeX] [Link]@inproceedings{266373629, title = {A Nationwide Network of Health AI Assurance Laboratories.}, author = {{Nigam H. Shah} and {John D Halamka} and {S. Saria} and {Michael J. Pencina} and {Troy Tazbaz} and {Micky Tripathi} and {Alison Callahan} and {Hailey Hildahl} and {Brian Anderson}}, year = 2023, month = {12}, booktitle = {Journal of the American Medical Association (JAMA)}, url = {https://www.semanticscholar.org/paper/194398467b7ae5074cbc626a859935ff2a790962}, } - Deming Li, Trevor Meyer, Esther S Oh, A. Butala, N. Dehak, and L. Moro-Velázquez, “Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266523855, title = {Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease}, author = {{Deming Li} and {Trevor Meyer} and {Esther S Oh} and {A. Butala} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/c2f99d03369b3583618c774b58c871c9707724bb}, } - Y. Wang, L. Moro-Velázquez, A. Favaro, D. Li, E. Oh, A. Butala, J. Villalba, and N. Dehak, “Binocular Discoordination Kinetic Features: A Novel Approach to Evaluate Neurodegenerative Diseases,” in IEEE Signal Processing in Medicine and Biology Symposium, 2023.
[BibTeX] [Link]@inproceedings{266602177, title = {Binocular Discoordination Kinetic Features: A Novel Approach to Evaluate Neurodegenerative Diseases}, author = {{Y. Wang} and {L. Moro-Velázquez} and {A. Favaro} and {D. Li} and {E. Oh} and {A. Butala} and {J. Villalba} and {N. Dehak}}, year = 2023, month = {12}, booktitle = {IEEE Signal Processing in Medicine and Biology Symposium}, url = {https://www.semanticscholar.org/paper/306f3684946774ed21ddba490c0f120f02a5421a}, } - A. Favaro, N. Dehak, Thomas Thebaud, Esther S Oh, and L. Moro-Velázquez, “Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266523081, title = {Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression}, author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {Esther S Oh} and {L. Moro-Velázquez}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/2f88f04aeb6eb8cac8c5706c294bcd3045faa966}, } - Thomas Thebaud, Casey Chen, L. Moro-Velázquez, N. Dehak, and Esther S Oh, “Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266523924, title = {Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment}, author = {{Thomas Thebaud} and {Casey Chen} and {L. Moro-Velázquez} and {N. Dehak} and {Esther S Oh}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/b6ffb09dbe20a54ddb5f3e6f3a319f482bb3c0aa}, } - Sky CH-Wang, Benjamin Van Durme, Jason Eisner, and Chris Kedzie, “Do Androids Know They’re Only Dreaming of Electric Sheep?,” in Annual Meeting of the Association for Computational Linguistics, 2023.
[BibTeX] [Link]@inproceedings{266573169, title = {Do Androids Know They're Only Dreaming of Electric Sheep?}, author = {{Sky CH-Wang} and {Benjamin Van Durme} and {Jason Eisner} and {Chris Kedzie}}, year = 2023, month = {12}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/6789e0a19f70e283b120fd6a3792162d01a021d5}, } - Thomas Thebaud, Casey Chen, L. Moro-Velázquez, N. Dehak, and Esther S Oh, “Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266522435, title = {Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment}, author = {{Thomas Thebaud} and {Casey Chen} and {L. Moro-Velázquez} and {N. Dehak} and {Esther S Oh}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/65b786c68cef24ed41374bd9f279617d694e2dd4}, } - Martin Sustek, Sonal Joshi, Henry Li, Thomas Thebaud, J. Villalba, S. Khudanpur, and N. Dehak, “Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks,” in Automatic Speech Recognition & Understanding, 2023.
[BibTeX] [Link]@inproceedings{267044159, title = {Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks}, author = {{Martin Sustek} and {Sonal Joshi} and {Henry Li} and {Thomas Thebaud} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}}, year = 2023, month = {12}, booktitle = {Automatic Speech Recognition & Understanding}, url = {https://www.semanticscholar.org/paper/1fd003bf9de393bcddbda63b738b71ced6203802}, } - A. Favaro, N. Dehak, Thomas Thebaud, Esther S Oh, and L. Moro-Velázquez, “Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266523165, title = {Evaluation of Interpretable Speech Biomarkers for Monitoring Alzheimer’s Disease and Mild Cognitive Impairment Progression}, author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {Esther S Oh} and {L. Moro-Velázquez}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/3434b5755b9c8bdb4250cabaabad655aee402440}, } - Thomas Thebaud, Sonal Joshi, Henry Li, Martin Sustek, J. Villalba, S. Khudanpur, and N. Dehak, “Clustering Unsupervised Representations as Defense Against Poisoning Attacks on Speech Commands Classification System,” in Automatic Speech Recognition & Understanding, 2023.
[BibTeX] [Link]@inproceedings{267043595, title = {Clustering Unsupervised Representations as Defense Against Poisoning Attacks on Speech Commands Classification System}, author = {{Thomas Thebaud} and {Sonal Joshi} and {Henry Li} and {Martin Sustek} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}}, year = 2023, month = {12}, booktitle = {Automatic Speech Recognition & Understanding}, url = {https://www.semanticscholar.org/paper/d59282b7adbdd1aef7754309aa72e98598059c1a}, } - Deming Li, Trevor Meyer, Esther S Oh, A. Butala, N. Dehak, and L. Moro-Velázquez, “Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2023.
[BibTeX] [Link]@inproceedings{266522904, title = {Multi‐task analysis of oculographic biomarkers to evaluate motoric and cognitive patterns in Alzheimer’s Disease}, author = {{Deming Li} and {Trevor Meyer} and {Esther S Oh} and {A. Butala} and {N. Dehak} and {L. Moro-Velázquez}}, year = 2023, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/d958ba9662d442878a1d1d11d4e0968e6df42e4d}, } - W. Tan, C. Lin, and J. Eisner, “Structure-Aware Path Inference for Neural Finite State Transducers,” in Proceedings of the NeurIPS 2023 Workshop “I Can’t Believe It’s Not Better: Failure Modes in the Age of Foundation Models”, 2023.
[BibTeX] [Link]@InProceedings{tan-et-al-2023, author = "Weiting Tan and Chu-Cheng Lin and Jason Eisner", title = "Structure-Aware Path Inference for Neural Finite State Transducers", booktitle = "Proceedings of the {NeurIPS} 2023 Workshop ``{I} Can’t Believe It’s Not Better: Failure Modes in the Age of Foundation Models''", year = "2023", month = dec, URL = "http://cs.jhu.edu/~jason/papers/#tan-et-al-2023", } - S. Roy, S. Thomson, T. Chen, R. Shin, A. Pauls, J. Eisner, and B. V. Durme, “BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing,” in Proceedings of the Thirty-Seventh Conference on Neural Information Processing Systems, 2023.
[BibTeX] [Link]@InProceedings{roy-et-al-2023, author = "Subhro Roy and Sam Thomson and Tongfei Chen and Richard Shin and Adam Pauls and Jason Eisner and Benjamin Van Durme", title = "{BenchCLAMP}: {A} Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing", booktitle = "Proceedings of the Thirty-Seventh Conference on Neural Information Processing Systems", note = "Datasets and Benchmarks Track", year = "2023", month = dec, URL = "http://cs.jhu.edu/~jason/papers/#roy-et-al-2023", } - R. Zhong, C. Snell, D. Klein, and Jason Eisner, “Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, p. 5126–5152.
[BibTeX] [Link]@InProceedings{zhong-et-al-2023, aclid = "2023.emnlp-main.312", author = "Ruiqi Zhong and Charlie Snell and Dan Klein and Jason Eisner", title = "Non-Programmers Can Label Programs Indirectly via Active Examples: {A} Case Study with Text-to-{SQL}", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", pages = "5126--5152", year = "2023", month = dec, URL = "http://cs.jhu.edu/~jason/papers/#zhong-et-al-2023", } - Helin Wang, Venkatesh Ravichandran, Milind Rao, Becky Lammers, Myra Sydnor, Nicholas J Maragakis, A. Butala, Jayne Zhang, Lora Clawson, Victoria Chovaz, and L. Moro-Velázquez, “Improving fairness for spoken language understanding in atypical speech with Text-to-Speech.” 2023.
[BibTeX] [Link]@inproceedings{265281100, title = {Improving fairness for spoken language understanding in atypical speech with Text-to-Speech}, author = {{Helin Wang} and {Venkatesh Ravichandran} and {Milind Rao} and {Becky Lammers} and {Myra Sydnor} and {Nicholas J Maragakis} and {A. Butala} and {Jayne Zhang} and {Lora Clawson} and {Victoria Chovaz} and {L. Moro-Velázquez}}, year = 2023, month = {11}, booktitle = {}, url = {https://www.semanticscholar.org/paper/45e3115df40de802f9c4095f329ea374aac56825}, } - Trevor Meyer, Camden Shultz, N. Dehak, L. Moro-Velázquez, and Pedro P. Irazoqui, “Time Scale Network: A Shallow Neural Network For Time Series Data,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{265128667, title = {Time Scale Network: A Shallow Neural Network For Time Series Data}, author = {{Trevor Meyer} and {Camden Shultz} and {N. Dehak} and {L. Moro-Velázquez} and {Pedro P. Irazoqui}}, year = 2023, month = {11}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/deacbb4906e1d2e597602a65b434a8132953ad8d}, } - Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, and Yunmo Chen, “Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles,” in NAACL-HLT, 2023.
[BibTeX] [Link]@inproceedings{265033117, title = {Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles}, author = {{Weiting Tan} and {Haoran Xu} and {Lingfeng Shen} and {Shuyue Stella Li} and {Kenton Murray} and {Philipp Koehn} and {Benjamin Van Durme} and {Yunmo Chen}}, year = 2023, month = {11}, booktitle = {NAACL-HLT}, url = {https://www.semanticscholar.org/paper/bac133a9cb14eadea94c55ec15ea3bb866bf6c03}, } - Sandeep Reddy Kothinti and Mounya Elhilali, “Are acoustics enough? Semantic effects on auditory salience in natural scenes,” in Frontiers in Psychology, 2023.
[BibTeX] [Link]@inproceedings{265539658, title = {Are acoustics enough? Semantic effects on auditory salience in natural scenes}, author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}}, year = 2023, month = {11}, booktitle = {Frontiers in Psychology}, url = {https://www.semanticscholar.org/paper/cdb491d1d121a7461fac00c4c71dfc45b9c8ae7a}, } - Karan Thakkar, Jiarui Hai, and Mounya Elhilali, “Investigating Self-Supervised Deep Representations for EEG-Based Auditory Attention Decoding,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
[BibTeX] [Link]@inproceedings{264935647, title = {Investigating Self-Supervised Deep Representations for EEG-Based Auditory Attention Decoding}, author = {{Karan Thakkar} and {Jiarui Hai} and {Mounya Elhilali}}, year = 2023, month = {11}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/57b903a709dc9ab64b3bd81378f33547eabb01bd}, } - William Fleshman and Benjamin Van Durme, “Toucan: Token-Aware Character Level Language Modeling,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{265213263, title = {Toucan: Token-Aware Character Level Language Modeling}, author = {{William Fleshman} and {Benjamin Van Durme}}, year = 2023, month = {11}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/28c75ab7f6951f1c2bb349b34abc3204ba8b9498}, } - Nikita Moghe, Patrick Xia, Jacob Andreas, J. Eisner, Benjamin Van Durme, and Harsh Jhamtani, “Interpreting User Requests in the Context of Natural Language Standing Instructions,” in NAACL-HLT, 2023.
[BibTeX] [Link]@inproceedings{265220995, title = {Interpreting User Requests in the Context of Natural Language Standing Instructions}, author = {{Nikita Moghe} and {Patrick Xia} and {Jacob Andreas} and {J. Eisner} and {Benjamin Van Durme} and {Harsh Jhamtani}}, year = 2023, month = {11}, booktitle = {NAACL-HLT}, url = {https://www.semanticscholar.org/paper/f2abeec1256f80970827d60f0151c7a19f2dbe7a}, } - Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, and E. Nouri, “InstructExcel: A Benchmark for Natural Language Instruction in Excel,” in Conference on Empirical Methods in Natural Language Processing, 2023.
[BibTeX] [Link]@inproceedings{264426857, title = {InstructExcel: A Benchmark for Natural Language Instruction in Excel}, author = {{Justin Payan} and {Swaroop Mishra} and {Mukul Singh} and {Carina Negreanu} and {Christian Poelitz} and {Chitta Baral} and {Subhro Roy} and {Rasika Chakravarthy} and {Benjamin Van Durme} and {E. Nouri}}, year = 2023, month = {10}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/706fc333fc951f48ea26169d88478b5e4c36e82c}, } - Tianjian Li, Haoran Xu, Philipp Koehn, Daniel Khashabi, and Kenton Murray, “Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models,” in International Conference on Learning Representations, 2023.
[BibTeX] [Link]@inproceedings{263605981, title = {Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models}, author = {{Tianjian Li} and {Haoran Xu} and {Philipp Koehn} and {Daniel Khashabi} and {Kenton Murray}}, year = 2023, month = {10}, booktitle = {International Conference on Learning Representations}, url = {https://www.semanticscholar.org/paper/d15021d750dbe9cee120b562acea857ca02d9104}, } - Guanghui Qin, Corby Rosset, Ethan C. Chau, Nikhil Rao, and Benjamin Van Durme, “Dodo: Dynamic Contextual Compression for Decoder-only LMs,” in Annual Meeting of the Association for Computational Linguistics, 2023.
[BibTeX] [Link]@inproceedings{263620438, title = {Dodo: Dynamic Contextual Compression for Decoder-only LMs}, author = {{Guanghui Qin} and {Corby Rosset} and {Ethan C. Chau} and {Nikhil Rao} and {Benjamin Van Durme}}, year = 2023, month = {10}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/883187d0bacf57238ac95e2749bcc601baf2c212}, } - Geoffrey M Gray, Ayah Zirikly, Luis M Ahumada, Masoud Rouhizadeh, Thomas M Richards, C. Kitchen, Iman Foroughmand, and E. Hatef, “Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system,” in JAMIA Open, 2023.
[BibTeX] [Link]@inproceedings{263669151, title = {Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system}, author = {{Geoffrey M Gray} and {Ayah Zirikly} and {Luis M Ahumada} and {Masoud Rouhizadeh} and {Thomas M Richards} and {C. Kitchen} and {Iman Foroughmand} and {E. Hatef}}, year = 2023, month = {10}, booktitle = {JAMIA Open}, url = {https://www.semanticscholar.org/paper/1be931a9ebfeaa018e47abc582b1a9760ced4710}, } - A. Hou, Jingyu (Jack) Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, and Yulia Tsvetkov, “SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{272144679, title = {SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation}, author = {{A. Hou} and {Jingyu (Jack) Zhang} and {Tianxing He} and {Yichen Wang} and {Yung-Sung Chuang} and {Hongwei Wang} and {Lingfeng Shen} and {Benjamin Van Durme} and {Daniel Khashabi} and {Yulia Tsvetkov}}, year = 2023, month = {10}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/f81faa4f130c287a293c9d21eb27b325be9b5ee9}, } - Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Do pretrained Transformers Learn In-Context by Gradient Descent?.” 2023.
[BibTeX] [Link]@inproceedings{263909464, title = {Do pretrained Transformers Learn In-Context by Gradient Descent?}, author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}}, year = 2023, month = {10}, booktitle = {}, url = {https://www.semanticscholar.org/paper/f30ddf0c7455f89f016c540564e235b191c503db}, } - 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}, } - Amir Feder, Yoav Wald, Claudia Shi, S. Saria, and David M. Blei, “Data Augmentations for Improved (Large) Language Model Generalization.” 2023.
[BibTeX] [Link]@inproceedings{264305897, title = {Data Augmentations for Improved (Large) Language Model Generalization}, author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}}, year = 2023, month = {10}, booktitle = {}, url = {https://www.semanticscholar.org/paper/23f96db82ae02c5c3c0a861571e7aa8d27c91bc9}, } - Shiyu Luo, Miguel Angrick, Christopher Coogan, Daniel N Candrea, Kimberley Wyse-Sookoo, Samyak Shah, Qinwan Rabbani, Griffin W. Milsap, Alexander R Weiss, William S Anderson, Donna C. Tippett, Nicholas J Maragakis, Lora Clawson, M. Vansteensel, Brock Andrew Wester, F. Tenore, H. Hermansky, M. Fifer, Nick F Ramsey, and N. Crone, “Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months,” in Advancement of science, 2023.
[BibTeX] [Link]@inproceedings{264448311, title = {Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months}, author = {{Shiyu Luo} and {Miguel Angrick} and {Christopher Coogan} and {Daniel N Candrea} and {Kimberley Wyse-Sookoo} and {Samyak Shah} and {Qinwan Rabbani} and {Griffin W. Milsap} and {Alexander R Weiss} and {William S Anderson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {Lora Clawson} and {M. Vansteensel} and {Brock Andrew Wester} and {F. Tenore} and {H. Hermansky} and {M. Fifer} and {Nick F Ramsey} and {N. Crone}}, year = 2023, month = {10}, booktitle = {Advancement of science}, url = {https://www.semanticscholar.org/paper/dee851d6c5652ee423118132e1483bc0af9f30fc}, } - Guanghui Qin and Benjamin Van Durme, “Nugget: Neural Agglomerative Embeddings of Text,” in International Conference on Machine Learning, 2023.
[BibTeX] [Link]@inproceedings{260957214, title = {Nugget: Neural Agglomerative Embeddings of Text}, author = {{Guanghui Qin} and {Benjamin Van Durme}}, year = 2023, month = {10}, booktitle = {International Conference on Machine Learning}, url = {https://www.semanticscholar.org/paper/531b37c44c7e39539f617fb1a4149ef8cce8f4ec}, } - Alex Walker, Ayah Zirikly, Melissa D. Stockbridge, and H. C. Wilcox, “A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.,” in Crisis, 2023.
[BibTeX] [Link]@inproceedings{263827534, title = {A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.}, author = {{Alex Walker} and {Ayah Zirikly} and {Melissa D. Stockbridge} and {H. C. Wilcox}}, year = 2023, month = {10}, booktitle = {Crisis}, url = {https://www.semanticscholar.org/paper/a0ee01acead1ccb6064f603f75186f8aa25d2562}, } - Yunmo Chen, William Gantt Walden, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme, “A Unified View of Evaluation Metrics for Structured Prediction,” in Conference on Empirical Methods in Natural Language Processing, 2023.
[BibTeX] [Link]@inproceedings{264426523, title = {A Unified View of Evaluation Metrics for Structured Prediction}, author = {{Yunmo Chen} and {William Gantt Walden} and {Tongfei Chen} and {Aaron Steven White} and {Benjamin Van Durme}}, year = 2023, month = {10}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/a0df8169889043dae6ac111136a61162a5185a77}, } - Jiarui Hai, Helin Wang, Dongchao Yang, Karan Thakkar, N. Dehak, and Mounya Elhilali, “DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
[BibTeX] [Link]@inproceedings{263830793, title = {DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction}, author = {{Jiarui Hai} and {Helin Wang} and {Dongchao Yang} and {Karan Thakkar} and {N. Dehak} and {Mounya Elhilali}}, year = 2023, month = {10}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/70aec6486668cc5ca25d45240c68de223a8deda7}, } - S. Sia and K. Duh, “In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models,” in Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, Macau SAR, China, 2023, p. 173–185.
[BibTeX] [Abstract] [Link]
The phenomena of in-context learning has typically been thought of as {“}learning from examples{”}. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation.
@inproceedings{sia-duh-2023-context, title = "In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models", author = "Sia, Suzanna and Duh, Kevin", editor = "Utiyama, Masao and Wang, Rui", booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track", month = sep, year = "2023", address = "Macau SAR, China", publisher = "Asia-Pacific Association for Machine Translation", url = "https://aclanthology.org/2023.mtsummit-research.15", pages = "173--185", abstract = "The phenomena of in-context learning has typically been thought of as {``}learning from examples{''}. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation.", } - 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", } - V. Raunak, A. Menezes, M. Post, and H. Hassan, “Do GPTs Produce Less Literal Translations?,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 1041–1050. doi:10.18653/v1/2023.acl-short.90
[BibTeX] [Abstract] [Link]
Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.
@inproceedings{raunak-etal-2023-gpts, title = "Do {GPT}s Produce Less Literal Translations?", author = "Raunak, Vikas and Menezes, Arul and Post, Matt and Hassan, Hany", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-short.90", doi = "10.18653/v1/2023.acl-short.90", pages = "1041--1050", abstract = "Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.", } - X. Zhang, K. Duh, and P. McNamee, “A Hyperparameter Optimization Toolkit for Neural Machine Translation Research,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Toronto, Canada, 2023, p. 161–168. doi:10.18653/v1/2023.acl-demo.15
[BibTeX] [Abstract] [Link]
Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}
@inproceedings{zhang-etal-2023-hyperparameter, title = "A Hyperparameter Optimization Toolkit for Neural Machine Translation Research", author = "Zhang, Xuan and Duh, Kevin and McNamee, Paul", editor = "Bollegala, Danushka and Huang, Ruihong and Ritter, Alan", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-demo.15", doi = "10.18653/v1/2023.acl-demo.15", pages = "161--168", abstract = "Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}", } - A. Hussein, C. Xiao, N. Verma, T. Thebaud, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2023 Dialect Speech Translation System Description,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 283–290. doi:10.18653/v1/2023.iwslt-1.26
[BibTeX] [Abstract] [Link]
This paper presents JHU{‘}s submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively.
@inproceedings{hussein-etal-2023-jhu, title = "{JHU} {IWSLT} 2023 Dialect Speech Translation System Description", author = "Hussein, Amir and Xiao, Cihan and Verma, Neha and Thebaud, Thomas and Wiesner, Matthew and Khudanpur, Sanjeev", editor = "Salesky, Elizabeth and Federico, Marcello and Carpuat, Marine", booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)", month = jul, year = "2023", address = "Toronto, Canada (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.iwslt-1.26", doi = "10.18653/v1/2023.iwslt-1.26", pages = "283--290", abstract = "This paper presents JHU{'}s submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively.", } - D. Verma, Y. K. Lal, S. Sinha, B. Van Durme, and A. Poliak, “Evaluating Paraphrastic Robustness in Textual Entailment Models,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 880–892. doi:10.18653/v1/2023.acl-short.76
[BibTeX] [Abstract] [Link]
We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models{‘} predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16{\%} of paraphrased examples, indicating that there is still room for improvement.
@inproceedings{verma-etal-2023-evaluating, title = "Evaluating Paraphrastic Robustness in Textual Entailment Models", author = "Verma, Dhruv and Lal, Yash Kumar and Sinha, Shreyashee and Van Durme, Benjamin and Poliak, Adam", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-short.76", doi = "10.18653/v1/2023.acl-short.76", pages = "880--892", abstract = "We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models{'} predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16{\%} of paraphrased examples, indicating that there is still room for improvement.", } - S. Behzad, S. Ebner, M. Marone, B. Van Durme, and M. Yarmohammadi, “The Effect of Alignment Correction on Cross-Lingual Annotation Projection,” in Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII), Toronto, Canada, 2023, p. 244–251. doi:10.18653/v1/2023.law-1.24
[BibTeX] [Abstract] [Link]
Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{–-}automatic, manual, and mixed{–-}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.
@inproceedings{behzad-etal-2023-effect, title = "The Effect of Alignment Correction on Cross-Lingual Annotation Projection", author = "Behzad, Shabnam and Ebner, Seth and Marone, Marc and Van Durme, Benjamin and Yarmohammadi, Mahsa", editor = "Prange, Jakob and Friedrich, Annemarie", booktitle = "Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.law-1.24", doi = "10.18653/v1/2023.law-1.24", pages = "244--251", abstract = "Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality{---}automatic, manual, and mixed{---}on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.", } - H. L. Xinyuan, N. Verma, B. Bamfo Odoom, U. Pradeep, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2023 Multilingual Speech Translation System Description,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 302–310. doi:10.18653/v1/2023.iwslt-1.28
[BibTeX] [Abstract] [Link]
We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.
@inproceedings{xinyuan-etal-2023-jhu, title = "{JHU} {IWSLT} 2023 Multilingual Speech Translation System Description", author = "Xinyuan, Henry Li and Verma, Neha and Bamfo Odoom, Bismarck and Pradeep, Ujvala and Wiesner, Matthew and Khudanpur, Sanjeev", editor = "Salesky, Elizabeth and Federico, Marcello and Carpuat, Marine", booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)", month = jul, year = "2023", address = "Toronto, Canada (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.iwslt-1.28", doi = "10.18653/v1/2023.iwslt-1.28", pages = "302--310", abstract = "We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.", } - M. Antoniak, A. Field, J. Mun, M. Walsh, L. Klein, and M. Sap, “Riveter: Measuring Power and Social Dynamics Between Entities,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Toronto, Canada, 2023, p. 377–388. doi:10.18653/v1/2023.acl-demo.36
[BibTeX] [Abstract] [Link]
Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.
@inproceedings{antoniak-etal-2023-riveter, title = "Riveter: Measuring Power and Social Dynamics Between Entities", author = "Antoniak, Maria and Field, Anjalie and Mun, Jimin and Walsh, Melanie and Klein, Lauren and Sap, Maarten", editor = "Bollegala, Danushka and Huang, Ruihong and Ritter, Alan", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-demo.36", doi = "10.18653/v1/2023.acl-demo.36", pages = "377--388", abstract = "Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.", } - M. Agarwal, S. Agrawal, A. Anastasopoulos, L. Bentivogli, O. Bojar, C. Borg, M. Carpuat, R. Cattoni, M. Cettolo, M. Chen, W. Chen, K. Choukri, A. Chronopoulou, A. Currey, T. Declerck, Q. Dong, K. Duh, Y. Estève, M. Federico, S. Gahbiche, B. Haddow, B. Hsu, P. Mon Htut, H. Inaguma, D. Javorský, J. Judge, Y. Kano, T. Ko, R. Kumar, P. Li, X. Ma, P. Mathur, E. Matusov, P. McNamee, J. P. McCrae, K. Murray, M. Nadejde, S. Nakamura, M. Negri, H. Nguyen, J. Niehues, X. Niu, A. Kr. Ojha, J. E. Ortega, P. Pal, J. Pino, L. van der Plas, P. Polák, E. Rippeth, E. Salesky, J. Shi, M. Sperber, S. Stüker, K. Sudoh, Y. Tang, B. Thompson, K. Tran, M. Turchi, A. Waibel, M. Wang, S. Watanabe, and R. Zevallos, “FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 1–61. doi:10.18653/v1/2023.iwslt-1.1
[BibTeX] [Abstract] [Link]
This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.
@inproceedings{agrawal-etal-2023-findings, title = "{FINDINGS} {OF} {THE} {IWSLT} 2023 {EVALUATION} {CAMPAIGN}", author = {Agarwal, Milind and Agrawal, Sweta and Anastasopoulos, Antonios and Bentivogli, Luisa and Bojar, Ond{\v{r}}ej and Borg, Claudia and Carpuat, Marine and Cattoni, Roldano and Cettolo, Mauro and Chen, Mingda and Chen, William and Choukri, Khalid and Chronopoulou, Alexandra and Currey, Anna and Declerck, Thierry and Dong, Qianqian and Duh, Kevin and Est{\`e}ve, Yannick and Federico, Marcello and Gahbiche, Souhir and Haddow, Barry and Hsu, Benjamin and Mon Htut, Phu and Inaguma, Hirofumi and Javorsk{\'y}, D{\'a}vid and Judge, John and Kano, Yasumasa and Ko, Tom and Kumar, Rishu and Li, Pengwei and Ma, Xutai and Mathur, Prashant and Matusov, Evgeny and McNamee, Paul and P. McCrae, John and Murray, Kenton and Nadejde, Maria and Nakamura, Satoshi and Negri, Matteo and Nguyen, Ha and Niehues, Jan and Niu, Xing and Kr. Ojha, Atul and E. Ortega, John and Pal, Proyag and Pino, Juan and van der Plas, Lonneke and Pol{\'a}k, Peter and Rippeth, Elijah and Salesky, Elizabeth and Shi, Jiatong and Sperber, Matthias and St{\"u}ker, Sebastian and Sudoh, Katsuhito and Tang, Yun and Thompson, Brian and Tran, Kevin and Turchi, Marco and Waibel, Alex and Wang, Mingxuan and Watanabe, Shinji and Zevallos, Rodolfo}, editor = "Salesky, Elizabeth and Federico, Marcello and Carpuat, Marine", booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)", month = jul, year = "2023", address = "Toronto, Canada (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.iwslt-1.1", doi = "10.18653/v1/2023.iwslt-1.1", pages = "1--61", abstract = "This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.", } - E. Spaulding, G. Kazantsev, and M. Dredze, “Joint End-to-end Semantic Proto-role Labeling,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 723–736. doi:10.18653/v1/2023.acl-short.63
[BibTeX] [Abstract] [Link]
Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.
@inproceedings{spaulding-etal-2023-joint, title = "Joint End-to-end Semantic Proto-role Labeling", author = "Spaulding, Elizabeth and Kazantsev, Gary and Dredze, Mark", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-short.63", doi = "10.18653/v1/2023.acl-short.63", pages = "723--736", abstract = "Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.", } - E. Schumacher, J. Mayfield, and M. Dredze, “On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking,” in Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023), Toronto, ON, Canada, 2023, p. 58–69. doi:10.18653/v1/2023.matching-1.6
[BibTeX] [Abstract] [Link]
Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.
@inproceedings{schumacher-etal-2023-surprising, title = "On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking", author = "Schumacher, Elliot and Mayfield, James and Dredze, Mark", editor = "Hruschka, Estevam and Mitchell, Tom and Rahman, Sajjadur and Mladeni{\'c}, Dunja and Grobelnik, Marko", booktitle = "Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)", month = jul, year = "2023", address = "Toronto, ON, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.matching-1.6", doi = "10.18653/v1/2023.matching-1.6", pages = "58--69", abstract = "Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.", } - S. Zhang, S. Wu, O. Irsoy, S. Lu, M. Bansal, M. Dredze, and D. Rosenberg, “MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 9027–9050. doi:10.18653/v1/2023.acl-long.502
[BibTeX] [Abstract] [Link]
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P {–} that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may {“}over-generalize{”}, in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies.
@inproceedings{zhang-etal-2023-mixce, title = "{M}ix{CE}: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies", author = "Zhang, Shiyue and Wu, Shijie and Irsoy, Ozan and Lu, Steven and Bansal, Mohit and Dredze, Mark and Rosenberg, David", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.502", doi = "10.18653/v1/2023.acl-long.502", pages = "9027--9050", abstract = "Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P {--} that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may {``}over-generalize{''}, in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies.", } - G. Portillo Wightman, A. Delucia, and M. Dredze, “Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement,” in Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023), Toronto, Canada, 2023, p. 326–362. doi:10.18653/v1/2023.trustnlp-1.28
[BibTeX] [Abstract] [Link]
Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes.
@inproceedings{portillo-wightman-etal-2023-strength, title = "Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement", author = "Portillo Wightman, Gwenyth and Delucia, Alexandra and Dredze, Mark", editor = "Ovalle, Anaelia and Chang, Kai-Wei and Mehrabi, Ninareh and Pruksachatkun, Yada and Galystan, Aram and Dhamala, Jwala and Verma, Apurv and Cao, Trista and Kumar, Anoop and Gupta, Rahul", booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.trustnlp-1.28", doi = "10.18653/v1/2023.trustnlp-1.28", pages = "326--362", abstract = "Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes.", } - J. Gwinnup, T. Anderson, B. Ore, E. Hansen, and K. Duh, “Enhancing Video Translation Context with Object Labels,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Toronto, Canada (in-person and online), 2023, p. 130–137. doi:10.18653/v1/2023.iwslt-1.8
[BibTeX] [Abstract] [Link]
We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset.
@inproceedings{gwinnup-etal-2023-enhancing, title = "Enhancing Video Translation Context with Object Labels", author = "Gwinnup, Jeremy and Anderson, Tim and Ore, Brian and Hansen, Eric and Duh, Kevin", editor = "Salesky, Elizabeth and Federico, Marcello and Carpuat, Marine", booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)", month = jul, year = "2023", address = "Toronto, Canada (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.iwslt-1.8", doi = "10.18653/v1/2023.iwslt-1.8", pages = "130--137", abstract = "We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset.", } - K. Harrigian, A. Zirikly, B. Chee, A. Ahmad, A. Links, S. Saha, M. C. Beach, and M. Dredze, “Characterization of Stigmatizing Language in Medical Records,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 312–329. doi:10.18653/v1/2023.acl-short.28
[BibTeX] [Abstract] [Link]
Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.
@inproceedings{harrigian-etal-2023-characterization, title = "Characterization of Stigmatizing Language in Medical Records", author = "Harrigian, Keith and Zirikly, Ayah and Chee, Brant and Ahmad, Alya and Links, Anne and Saha, Somnath and Beach, Mary Catherine and Dredze, Mark", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-short.28", doi = "10.18653/v1/2023.acl-short.28", pages = "312--329", abstract = "Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.", } - E. Stengel-Eskin, J. Guallar-Blasco, Y. Zhou, and B. Van Durme, “Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 10220–10237. doi:10.18653/v1/2023.acl-long.569
[BibTeX] [Abstract] [Link]
Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision.
@inproceedings{stengel-eskin-etal-2023-chicken, title = "Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in {VQA}", author = "Stengel-Eskin, Elias and Guallar-Blasco, Jimena and Zhou, Yi and Van Durme, Benjamin", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.569", doi = "10.18653/v1/2023.acl-long.569", pages = "10220--10237", abstract = "Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision.", } - A. Mallen, A. Asai, V. Zhong, R. Das, D. Khashabi, and H. Hajishirzi, “When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 9802–9822. doi:10.18653/v1/2023.acl-long.546
[BibTeX] [Abstract] [Link]
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs{‘} strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary.
@inproceedings{mallen-etal-2023-trust, title = "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories", author = "Mallen, Alex and Asai, Akari and Zhong, Victor and Das, Rajarshi and Khashabi, Daniel and Hajishirzi, Hannaneh", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.546", doi = "10.18653/v1/2023.acl-long.546", pages = "9802--9822", abstract = "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs{'} strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary.", } - N. Gandhi, A. Field, and E. Strubell, “Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 10543–10558. doi:10.18653/v1/2023.acl-long.588
[BibTeX] [Abstract] [Link]
Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14{\%} improvement in average F1 without increasing annotator time.
@inproceedings{gandhi-etal-2023-annotating, title = "Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution", author = "Gandhi, Nupoor and Field, Anjalie and Strubell, Emma", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.588", doi = "10.18653/v1/2023.acl-long.588", pages = "10543--10558", abstract = "Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14{\%} improvement in average F1 without increasing annotator time.", } - Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-Instruct: Aligning Language Models with Self-Generated Instructions,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 13484–13508. doi:10.18653/v1/2023.acl-long.754
[BibTeX] [Abstract] [Link]
Large {“}instruction-tuned{”} language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33{\%} absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5{\%} absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.
@inproceedings{wang-etal-2023-self-instruct, title = "Self-Instruct: Aligning Language Models with Self-Generated Instructions", author = "Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.754", doi = "10.18653/v1/2023.acl-long.754", pages = "13484--13508", abstract = "Large {``}instruction-tuned{''} language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33{\%} absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5{\%} absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.", } - N. Selvam, S. Dev, D. Khashabi, T. Khot, and K. Chang, “The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, 2023, p. 1373–1386. doi:10.18653/v1/2023.acl-short.118
[BibTeX] [Abstract] [Link]
How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases.
@inproceedings{selvam-etal-2023-tail, title = "The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks", author = "Selvam, Nikil and Dev, Sunipa and Khashabi, Daniel and Khot, Tushar and Chang, Kai-Wei", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-short.118", doi = "10.18653/v1/2023.acl-short.118", pages = "1373--1386", abstract = "How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases.", } - 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", } - 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", } - 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", } - 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", } - 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", } - 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", } - K. Duh and X. Zhang, “AutoML for NLP,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Dubrovnik, Croatia, 2023, p. 25–26. doi:10.18653/v1/2023.eacl-tutorials.5
[BibTeX] [Abstract] [Link]
Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.
@inproceedings{duh-zhang-2023-automl, title = "{A}uto{ML} for {NLP}", author = "Duh, Kevin and Zhang, Xuan", editor = "Zanzotto, Fabio Massimo and Pradhan, Sameer", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-tutorials.5", doi = "10.18653/v1/2023.eacl-tutorials.5", pages = "25--26", abstract = "Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.", } - W. Tan, K. Heffernan, H. Schwenk, and P. Koehn, “Multilingual Representation Distillation with Contrastive Learning,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 1477–1490. doi:10.18653/v1/2023.eacl-main.108
[BibTeX] [Abstract] [Link]
Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.
@inproceedings{tan-etal-2023-multilingual, title = "Multilingual Representation Distillation with Contrastive Learning", author = "Tan, Weiting and Heffernan, Kevin and Schwenk, Holger and Koehn, Philipp", editor = "Vlachos, Andreas and Augenstein, Isabelle", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.108", doi = "10.18653/v1/2023.eacl-main.108", pages = "1477--1490", abstract = "Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.", } - G. Qin, Y. Feng, and B. Van Durme, “The NLP Task Effectiveness of Long-Range Transformers,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 3774–3790. doi:10.18653/v1/2023.eacl-main.273
[BibTeX] [Abstract] [Link]
Transformer models cannot easily scale to long sequences due to their O(N{\^{}}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.
@inproceedings{qin-etal-2023-nlp, title = "The {NLP} Task Effectiveness of Long-Range Transformers", author = "Qin, Guanghui and Feng, Yukun and Van Durme, Benjamin", editor = "Vlachos, Andreas and Augenstein, Isabelle", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.273", doi = "10.18653/v1/2023.eacl-main.273", pages = "3774--3790", abstract = "Transformer models cannot easily scale to long sequences due to their O(N{\^{}}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.", } - Y. Chen, W. Gantt, W. Gu, T. Chen, A. White, and B. Van Durme, “Iterative Document-level Information Extraction via Imitation Learning,” in Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2023, p. 1858–1874. doi:10.18653/v1/2023.eacl-main.136
[BibTeX] [Abstract] [Link]
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{‘}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {–} 4-ary relation extraction on SciREX and template extraction on MUC-4 {–} as well as a strong baseline on the new BETTER Granular task.
@inproceedings{chen-etal-2023-iterative, title = "Iterative Document-level Information Extraction via Imitation Learning", author = "Chen, Yunmo and Gantt, William and Gu, Weiwei and Chen, Tongfei and White, Aaron and Van Durme, Benjamin", editor = "Vlachos, Andreas and Augenstein, Isabelle", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.136", doi = "10.18653/v1/2023.eacl-main.136", pages = "1858--1874", abstract = "We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{'}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {--} 4-ary relation extraction on SciREX and template extraction on MUC-4 {--} as well as a strong baseline on the new BETTER Granular task.", } - Liangyu Chen, Yutong Bai, A. Yuille, and Zongwei Zhou, “Making Your First Choice: To Address Cold Start Problem in Medical Active Learning,” in International Conference on Medical Imaging with Deep Learning, 2023.
[BibTeX] [Link]@inproceedings{259373518, title = {Making Your First Choice: To Address Cold Start Problem in Medical Active Learning}, author = {{Liangyu Chen} and {Yutong Bai} and {A. Yuille} and {Zongwei Zhou}}, year = 2023, booktitle = {International Conference on Medical Imaging with Deep Learning}, url = {https://www.semanticscholar.org/paper/251516c1549fc4566b801788c932ef1f18f343b3}, } - 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.
[BibTeX] [Link]@inproceedings{260909100, title = {Segmental SpeechCLIP: Utilizing Pretrained Image-text Models for Audio-Visual Learning}, author = {{Saurabhchand Bhati} and {J. Villalba} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak}}, year = 2023, month = {8}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/1617d389b7947161f2943e2d30afeb1856052b14}, } - S. Styles, Victoria Y. H. Chua, Fei Ting Woon, Hexin Liu, Leibny Paola García Perera, S. Khudanpur, Andy W. H. Khong, and J. Dauwels, “Investigating model performance in language identification: beyond simple error statistics,” in Interspeech, 2023.
[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 García Perera} and {S. Khudanpur} and {Andy W. H. Khong} and {J. Dauwels}}, year = 2023, month = {5}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/5550d0db7030aa77480bfb10c4ec00862bb233eb}, } - Chen Wang, Angtian Wang, Junbo Li, A. Yuille, and Cihang Xie, “Benchmarking Robustness in Neural Radiance Fields,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 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 = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, url = {https://www.semanticscholar.org/paper/eaf0c04e9784d6efc8f9ce16d1d9c3ae43506ad9}, } - J. Maillard, C. Gao, E. Kalbassi, K. R. Sadagopan, V. Goswami, P. Koehn, A. Fan, and F. Guzman, “Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, 2023, p. 2740–2756. doi:10.18653/v1/2023.acl-long.154
[BibTeX] [Abstract] [Link]
For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.
@inproceedings{maillard-etal-2023-small, title = "Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation", author = "Maillard, Jean and Gao, Cynthia and Kalbassi, Elahe and Sadagopan, Kaushik Ram and Goswami, Vedanuj and Koehn, Philipp and Fan, Angela and Guzman, Francisco", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.154", doi = "10.18653/v1/2023.acl-long.154", pages = "2740--2756", abstract = "For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.", } - Haoran Xu, Maha Elbayad, Kenton Murray, Jean Maillard, and Vedanuj Goswami, “Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity,” in Conference on Empirical Methods in Natural Language Processing, 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 = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/09d3a354d22d75deddb0dcc99870f307aae2fd3c}, } - 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}, } - 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}, } - M. Post, T. Gowda, R. Grundkiewicz, H. Khayrallah, R. Jain, and M. Junczys-Dowmunt, “SOTASTREAM: A Streaming Approach to Machine Translation Training,” in Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), Singapore, 2023, p. 110–119. doi:10.18653/v1/2023.nlposs-1.13
[BibTeX] [Abstract] [Link]
Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.
@inproceedings{post-etal-2023-sotastream, title = "{SOTASTREAM}: A Streaming Approach to Machine Translation Training", author = "Post, Matt and Gowda, Thamme and Grundkiewicz, Roman and Khayrallah, Huda and Jain, Rohit and Junczys-Dowmunt, Marcin", editor = "Tan, Liling and Milajevs, Dmitrijs and Chauhan, Geeticka and Gwinnup, Jeremy and Rippeth, Elijah", booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.nlposs-1.13", doi = "10.18653/v1/2023.nlposs-1.13", pages = "110--119", abstract = "Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.", } - 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.
[BibTeX] [Link]@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}, url = {https://www.semanticscholar.org/paper/c8e2b9f68bd585759d31741193516f58b5619584}, } - J. Villalba, Jonas Borgstrom, Maliha Jahan, Saurabh Kataria, Leibny Paola García, P. Torres-Carrasquillo, and N. Dehak, “Advances in Language Recognition in Low Resource African Languages: The JHU-MIT Submission for NIST LRE22,” in Interspeech, 2023.
[BibTeX] [Link]@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 García} and {P. Torres-Carrasquillo} and {N. Dehak}}, year = 2023, month = {8}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/51aca07d500c44ebde896b8df3b0388dd3ade489}, } - Zihao Xiao, A. Yuille, and Yi-Ting Chen, “Learning Road Scene-level Representations via Semantic Region Prediction,” in Conference on Robot Learning, 2023.
[BibTeX] [Link]@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}, } - Lingfeng Shen, Sihao Chen, Linfeng Song, Lifeng Jin, Baolin Peng, Haitao Mi, Daniel Khashabi, and Dong Yu, “The Trickle-down Impact of Reward (In-)consistency on RLHF,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{263134276, title = {The Trickle-down Impact of Reward (In-)consistency on RLHF}, author = {{Lingfeng Shen} and {Sihao Chen} and {Linfeng Song} and {Lifeng Jin} and {Baolin Peng} and {Haitao Mi} and {Daniel Khashabi} and {Dong Yu}}, year = 2023, month = {9}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/541b66bad4a9bf9b7fd97f13f94ab9061c7c0c47}, } - Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, A. Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, A. Tabassum, Arul Menezes, Arun Kirubarajan, A. Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. R. Roberts, B. S. Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, B. Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Christopher Callison-Burch, Christian Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Daniel H Garrette, Dan Hendrycks, D. Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, E. D. Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, E. Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, E. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, F. Siar, Fernando Martínez-Plumed, Francesca Happé, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hanna Hajishirzi, Harsh Mehta, H. Bogar, Henry Shevlin, Hinrich Schütze, H. Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jaehoon Lee, J. Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Narain Sohl-Dickstein, Jason Phang, Jason Wei, J. Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Oluwadara Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Jane W Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jorg Frohberg, Jos Rozen, J. Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Josh Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, K. Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Luca Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, M. J. Ramírez-Quintana, M. Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, M. Schubert, Medina Baitemirova, Melody Arnaud, M. McElrath, Michael A. 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Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphael Milliere, Rhythm Garg, Richard Barnes, R. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, R. L. Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Samuel Wiseman, Samuel Gruetter, Samuel R. Bowman, S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi S. Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, S. Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Debnath, Siamak Shakeri, Simon Thormeyer, S. Melzi, Siva Reddy, S. 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Wang, Zirui Wang, and Ziyi Wu, “Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,” in Trans. Mach. Learn. Res., 2023.
[BibTeX] [Link]@inproceedings{271601672, title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models}, author = {{Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Abu Awal Md Shoeb} and {Abubakar Abid} and {Adam Fisch} and {Adam R. Brown} and {Adam Santoro} and {Aditya Gupta} and {Adrià Garriga-Alonso} and {Agnieszka Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Parrish} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Amanda Dsouza} and {Ambrose Slone} and {Ameet Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmüller} and {Andrew M. Dai} and {Andrew La} and {Andrew K. Lampinen} and {Andy Zou} and {Angela Jiang} and {Angelica Chen} and {Anh Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {A. Tabassum} and {Arul Menezes} and {Arun Kirubarajan} and {A. Mullokandov} and {Ashish Sabharwal} and {Austin Herrick} and {Avia Efrat} and {Aykut Erdem} and {Ayla Karakas} and {B. R. Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Özyurt} and {Behnam Hedayatnia} and {Behnam Neyshabur} and {Benjamin Inden} and {Benno Stein} and {Berk Ekmekci} and {Bill Yuchen Lin} and {B. Howald} and {Bryan Orinion} and {Cameron Diao} and {Cameron Dour} and {Catherine Stinson} and {Cedrick Argueta} and {Cèsar Ferri Ramírez} and {Chandan Singh} and {Charles Rathkopf} and {Chenlin Meng} and {Chitta Baral} and {Chiyu Wu} and {Christopher Callison-Burch} and {Christian Waites} and {Christian Voigt} and {Christopher D. Manning} and {Christopher Potts} and {Cindy Ramirez} and {Clara E. Rivera} and {Clemencia Siro} and {Colin Raffel} and {Courtney Ashcraft} and {Cristina Garbacea} and {Damien Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {D. Kilman} and {Dan Roth} and {Daniel Freeman} and {Daniel Khashabi} and {Daniel Levy} and {Daniel Moseguí González} and {Danielle Perszyk} and {Danny Hernandez} and {Danqi Chen} and {Daphne Ippolito} and {D. Gilboa} and {David Dohan} and {D. Drakard} and {David Jurgens} and {Debajyoti Datta} and {Deep Ganguli} and {Denis Emelin} and {Denis Kleyko} and {Deniz Yuret} and {Derek Chen} and {Derek Tam} and {Dieuwke Hupkes} and {Diganta Misra} and {Dilyar Buzan} and {Dimitri Coelho Mollo} and {Diyi Yang} and {Dong-Ho Lee} and {Dylan Schrader} and {Ekaterina Shutova} and {E. D. Cubuk} and {Elad Segal} and {Eleanor Hagerman} and {Elizabeth Barnes} and {E. Donoway} and {Ellie Pavlick} and {Emanuele Rodolà} and {Emma Lam} and {Eric Chu} and {Eric Tang} and {Erkut Erdem} and {Ernie Chang} and {Ethan A. Chi} and {Ethan Dyer} and {E. Jerzak} and {Ethan Kim} and {Eunice Engefu Manyasi} and {Evgenii Zheltonozhskii} and {Fanyue Xia} and {F. Siar} and {Fernando Martínez-Plumed} and {Francesca Happé} and {François Chollet} and {Frieda Rong} and {Gaurav Mishra} and {Genta Indra Winata} and {Gerard de Melo} and {Germán Kruszewski} and {Giambattista Parascandolo} and {Giorgio Mariani} and {Gloria Xinyue Wang} and {Gonzalo Jaimovitch-López} and {Gregor Betz} and {Guy Gur-Ari} and {Hana Galijasevic} and {Hannah Kim} and {Hannah Rashkin} and {Hanna Hajishirzi} and {Harsh Mehta} and {H. Bogar} and {Henry Shevlin} and {Hinrich Schütze} and {H. Yakura} and {Hongming Zhang} and {Hugh Mee Wong} and {Ian Ng} and {Isaac Noble} and {Jaap Jumelet} and {Jack Geissinger} and {John Kernion} and {Jacob Hilton} and {Jaehoon Lee} and {J. Fisac} and {James B. Simon} and {James Koppel} and {James Zheng} and {James Zou} and {Jan Kocoń} and {Jana Thompson} and {Janelle Wingfield} and {Jared Kaplan} and {Jarema Radom} and {Jascha Narain Sohl-Dickstein} and {Jason Phang} and {Jason Wei} and {J. Yosinski} and {Jekaterina Novikova} and {Jelle Bosscher} and {Jennifer Marsh} and {Jeremy Kim} and {Jeroen Taal} and {Jesse Engel} and {Jesujoba Oluwadara Alabi} and {Jiacheng Xu} and {Jiaming Song} and {Jillian Tang} and {Jane W Waweru} and {John Burden} and {John Miller} and {John U. Balis} and {Jonathan Batchelder} and {Jonathan Berant} and {Jorg Frohberg} and {Jos Rozen} and {J. Hernández-Orallo} and {Joseph Boudeman} and {Joseph Guerr} and {Joseph Jones} and {Joshua B. Tenenbaum} and {Josh Rule} and {Joyce Chua} and {Kamil Kanclerz} and {Karen Livescu} and {Karl Krauth} and {Karthik Gopalakrishnan} and {Katerina Ignatyeva} and {K. Markert} and {Kaustubh D. Dhole} and {Kevin Gimpel} and {Kevin Omondi} and {K. Mathewson} and {Kristen Chiafullo} and {Ksenia Shkaruta} and {Kumar Shridhar} and {Kyle McDonell} and {Kyle Richardson} and {Laria Reynolds} and {Leo Gao} and {Li Zhang} and {Liam Dugan} and {Lianhui Qin} and {Lidia Contreras Ochando} and {Louis-Philippe Morency} and {Luca Moschella} and {Luca Lam} and {Lucy Noble} and {Ludwig Schmidt} and {Luheng He} and {Luis Oliveros Colón} and {Luke Metz} and {Lütfi Kerem Senel} and {Maarten Bosma} and {Maarten Sap} and {Maartje ter Hoeve} and {Maheen Farooqi} and {Manaal Faruqui} and {Mantas Mazeika} and {Marco Baturan} and {Marco Marelli} and {Marco Maru} and {M. J. Ramírez-Quintana} and {M. Tolkiehn} and {Mario Giulianelli} and {Martha Lewis} and {Martin Potthast} and {Matthew L. Leavitt} and {Matthias Hagen} and {M. Schubert} and {Medina Baitemirova} and {Melody Arnaud} and {M. McElrath} and {Michael A. Yee} and {Michael Cohen} and {Michael Gu} and {Michael Ivanitskiy} and {Michael Starritt} and {M. Strube} and {Michal Swedrowski} and {Michele Bevilacqua} and {Michihiro Yasunaga} and {Mihir Kale} and {Mike Cain} and {Mimee Xu} and {Mirac Suzgun} and {Mitch Walker} and {Mohit Tiwari} and {Mohit Bansal} and {Moin Aminnaseri} and {Mor Geva} and {Mozhdeh Gheini} and {T. MukundVarma} and {Nanyun Peng} and {Nathan A. Chi} and {Nayeon Lee} and {Neta Gur-Ari Krakover} and {Nicholas Cameron} and {Nicholas Roberts} and {Nick Doiron} and {Nicole Martinez} and {Nikita Nangia} and {Niklas Deckers} and {Niklas Muennighoff} and {N. Keskar} and {Niveditha Iyer} and {Noah Constant} and {Noah Fiedel} and {Nuan Wen} and {Oliver Zhang} and {Omar Agha} and {Omar Elbaghdadi} and {Omer Levy} and {Owain Evans} and {Pablo Antonio Moreno Casares} and {Parth Doshi} and {Pascale Fung} and {P. Liang} and {Paul Vicol} and {Pegah Alipoormolabashi} and {Peiyuan Liao} and {Percy Liang} and {Peter Chang} and {Peter Eckersley} and {Phu Mon Htut} and {P. Hwang} and {P. Milkowski} and {Piyush Patil} and {Pouya Pezeshkpour} and {Priti Oli} and {Qiaozhu Mei} and {Qing Lyu} and {Qinlang Chen} and {Rabin Banjade} and {Rachel Etta Rudolph} and {Raefer Gabriel} and {Rahel Habacker} and {Ramon Risco} and {Raphael Milliere} and {Rhythm Garg} and {Richard Barnes} and {R. Saurous} and {Riku Arakawa} and {Robbe Raymaekers} and {Robert Frank} and {Rohan Sikand} and {Roman Novak} and {Roman Sitelew} and {R. L. Bras} and {Rosanne Liu} and {Rowan Jacobs} and {Rui Zhang} and {Ruslan Salakhutdinov} and {Ryan Chi} and {Ryan Lee} and {Ryan Stovall} and {Ryan Teehan} and {Rylan Yang} and {Sahib Singh} and {Saif Mohammad} and {Sajant Anand} and {Sam Dillavou} and {Sam Shleifer} and {Samuel Wiseman} and {Samuel Gruetter} and {Samuel R. Bowman} and {S. Schoenholz} and {Sanghyun Han} and {Sanjeev Kwatra} and {Sarah A. Rous} and {Sarik Ghazarian} and {Sayan Ghosh} and {Sean Casey} and {Sebastian Bischoff} and {Sebastian Gehrmann} and {Sebastian Schuster} and {Sepideh Sadeghi} and {Shadi S. Hamdan} and {Sharon Zhou} and {Shashank Srivastava} and {Sherry Shi} and {Shikhar Singh} and {Shima Asaadi} and {S. Gu} and {Shubh Pachchigar} and {Shubham Toshniwal} and {Shyam Upadhyay} and {Shyamolima Debnath} and {Siamak Shakeri} and {Simon Thormeyer} and {S. Melzi} and {Siva Reddy} and {S. Makini} and {Soo-Hwan Lee} and {Spencer Bradley Torene} and {Sriharsha Hatwar} and {S. Dehaene} and {Stefan Divic} and {Stefano Ermon} and {Stella Biderman} and {Stephanie Lin} and {Stephen Prasad} and {Steven T Piantadosi} and {Stuart M. Shieber} and {Summer Misherghi} and {S. Kiritchenko} and {Swaroop Mishra} and {Tal Linzen} and {Tal Schuster} and {Tao Li} and {Tao Yu} and {Tariq Ali} and {Tatsunori Hashimoto} and {Te-Lin Wu} and {T. 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[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. Clephas} and {Dilan Aydin} and {M. Gent} and {M. Szymanski} and {M. Rienstra} and {M. Heuvel} and {C. 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}, } - Qihao Liu, Adam Kortylewski, Yutong Bai, Song Bai, and A. Yuille, “Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search,” in International Conference on Learning Representations, 2023.
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Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raf-786, Courtney Ashcraft, Cristina Garbacea, Dan Sileo, Daniel H Garrette, Dan Hendrycks, Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. Drakard, David Ju-792, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Tam, mán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-807 López, Gregor Betz, Guy Gur-Ari, Hana Galijase-808 vic, Hannah Kim, Harsh Mehta, H. Bogar, Henry Shevlin, Hinrich Schütze, H. Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jae-813 hoon Lee, J. Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Ko-815 co´n, Jana Thompson, Jared Kaplan, Jarema Radom, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gim-827 pel, Kevin Omondi, K. Mathewson, Kristen Chi-828 afullo, Ksenia Shkaruta, Kumar Shridhar, Kyle Mc-829 Donell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Maarten ¸Senel, Maarten Bosma, Manaal Farooqi, Mantas Faruqui, Marco Mazeika, Marco Baturan, Marco Marelli, Maria Jose Maru, Marie Ramírez Quintana, Tolkiehn Mario, Martha Giulianelli, Martin Lewis, L. PotthastMatthew, Matthew L. Leavitt, Mátyás Schu-840 bert Hagen, Medina Orduna, Melody Baitemirova, Arnaud Melvin, Michael A McElrath, Michael A. Yee, Michael Co-842 hen, Michael Gu, Michael Ivanitskiy, Michael Star-843 ritt, M. Strube, Michele Sw˛edrowski, Michihiro Bevilacqua, Mihir Yasunaga, Mike Kale, Mimee Cain, Mirac Xu, Mo Suzgun, Monica Tiwari, Moin Bansal, Mor Aminnaseri, Mozhdeh Geva, Mukund Gheini, T. Varma, Nanyun Peng, tish Shirish Keskar, Niveditha Iyer, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio, Moreno Casares, Parth Doshi, Jason Wei, Maarten Bosma, Vincent Y. Zhao, Adams Wei Guu, Brian Yu, Nan Lester, An-921 Du, M. Dai, Quoc V. Le, Finetuned, Adina Williams, Nikita Nangia, Samuel R. Bowman, Thomas Wolf, Lysandre Debut, Clement Chaumond, Anthony Delangue, Pier-339 Moi, Tim ric Cistac, Rémi Rault, Morgan Louf, Funtow-900 Joe, Sam Davison, Patrick Shleifer, V. Platen, Clara Ma, Yacine Jernite, J. Plu, Canwen Xu, Sylvain Gugger, Mariama Drame, Yinfei Yang, Yuan Zhang, Chris Tar, Hailey Schoelkopf, Niklas Muen-954, Alham Fikri, D. Adelani, M. Saiful Bari, Lintang Sutawika, Zhihan Zhang, Wenhao Yu, Mengxia Yu, Zhichun Guo, and Jonathan May, “What are Adapters Really Efficient At?.” 2023.
[BibTeX] [Link]@inproceedings{259267429, title = {What are Adapters Really Efficient At?}, author = {{Alexis Conneau} and {Kartikay Khandelwal} and {Naman Goyal} and {Mitesh M. Khapra} and {Pratyush Kumar} and {V. Rudra} and {Murthy Anoop} and {Kunchukuttan. 2022} and {Naama-677} and {Adam Roberts} and {Stella Biderman} and {Teven Le Scao} and {Saiful Bari} and {Sheng Shen} and {Zheng-Xin Yong} and {Hai-682 ley Schoelkopf} and {Xiangru Tang} and {Dragomir R. Radev} and {Al-683 ham} and {Fikri Aji} and {Khalid Almubarak} and {Samuel Albanie} and {Zaid Alyafeai} and {Albert Webson} and {Edward Raff} and {Jonas Pfeiffer} and {Aishwarya Kamath} and {Andreas Rücklé} and {Kyunghyun Cho} and {Iryna Gurevych} and {Clifton Poth} and {Aishwarya} and {Ivan Kamath} and {Sebastian Vuli´c} and {Kyunghyun Ruder} and {Gregor Geigle} and {Max Glockner} and {Jonas Beck} and {Nils Pfeiffer} and {Reimers Iryna} and {Victor Sanh} and {Colin Raffel} and {Lintang Bach} and {Zaid Sutawika} and {Antoine Alyafeai} and {Arnaud Chaffin} and {Arun Stiegler} and {Manan Raja} and {Dey Saiful} and {Canwen Bari} and {Urmish Xu} and {Thakker} and {Shanya Sharma} and {Eliza Szczechla} and {Taewoon} and {Gunjan Kim} and {Nihal Chhablani} and {Nayak} and {Debajyoti} and {Jonathan Datta} and {Mike Tian-Jian Chang} and {Han Jiang} and {Matteo Wang} and {S. Mânica} and {Zheng Xin Shen} and {Yong} and {Harshit Pandey} and {Rachel Bawden} and {Thomas Wang} and {Tripathi Neeraj} and {Jos Rozen} and {Abheesht Sharma} and {A. Santilli} and {Thibault Févry} and {Jason Alan Fries} and {Maarten Sap} and {Hannah Rashkin} and {Derek Chen} and {Ronan} and {Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Adam R. Brown} and {Adam Santoro} and {Adrià Gupta} and {Agnieszka Garriga-Alonso} and {Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Par-765} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmüller} and {Andrew M. Dai} and {Andrew La} and {Andrew K. Lampinen} and {Angela Zou} and {Angela Jiang} and {Anh Chen} and {Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {Arfa Tabas-773} and {Arul Menezes} and {Arun Kirubarajan} and {Asher Mul-774} and {Ashish lokandov} and {Austin Sabharwal} and {Herrick} and {Avia} and {A. Efrat} and {Ayla Erdem} and {B. Karaka¸s} and {Ryan Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Özyurt} and {Behnam Hedayatnia} and {Behnam} and {Benjamin Neyshabur} and {Benno Inden} and {Berk Stein} and {Ek-779 mekci} and {Bill Yuchen} and {B. Lin} and {Cameron Howald} and {Cameron Diao} and {Catherine Dour} and {Cedrick Stinson} and {Ar-781 César} and {Chandan Ferri Ramírez} and {Charles Singh} and {Christopher D. Manning} and {Christopher Potts} and {Cindy Ramirez} and {Clara E. Rivera} and {Clemencia Siro} and {Colin Raf-786} and {Courtney Ashcraft} and {Cristina Garbacea} and {Dan Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {Kilman} and {Dan Roth} and {Daniel Freeman} and {Daniel Khashabi} and {Daniel Levy} and {Daniel Moseguí González} and {Perszyk} and {Danny Hernandez} and {Danqi Chen} and {Daphne Ippolito} and {D. Gilboa} and {David Dohan} and {D. Drakard} and {David Ju-792} and {Debajyoti Datta} and {Deep Ganguli} and {Denis Emelin} and {Denis Kleyko} and {Deniz Yuret} and {Derek Tam} and {mán Kruszewski} and {Giambattista Parascandolo} and {Giorgio Mariani} and {Gloria Wang} and {Gonzalo Jaimovitch-807 López} and {Gregor Betz} and {Guy Gur-Ari} and {Hana Galijase-808 vic} and {Hannah Kim} and {Harsh Mehta} and {H. Bogar} and {Henry Shevlin} and {Hinrich Schütze} and {H. Yakura} and {Hongming Zhang} and {Hugh Mee Wong} and {Ian Ng} and {Isaac Noble} and {Jaap Jumelet} and {Jack Geissinger} and {John Kernion} and {Jacob Hilton} and {Jae-813 hoon Lee} and {J. Fisac} and {James B. Simon} and {James Koppel} and {James Zheng} and {James Zou} and {Jan Ko-815 co´n} and {Jana Thompson} and {Jared Kaplan} and {Jarema Radom} and {Joyce Chua} and {Kamil Kanclerz} and {Karen Livescu} and {Karl Krauth} and {Karthik Gopalakrishnan} and {Katerina Ignatyeva} and {K. Markert} and {Kaustubh D. Dhole} and {Kevin Gim-827 pel} and {Kevin Omondi} and {K. Mathewson} and {Kristen Chi-828 afullo} and {Ksenia Shkaruta} and {Kumar Shridhar} and {Kyle Mc-829 Donell} and {Kyle Richardson} and {Laria Reynolds} and {Leo Gao} and {Li Zhang} and {Liam Dugan} and {Lianhui Qin} and {Lidia Contreras Ochando} and {Louis-Philippe Morency} and {Luca Moschella} and {Maarten ¸Senel} and {Maarten Bosma} and {Manaal Farooqi} and {Mantas Faruqui} and {Marco Mazeika} and {Marco Baturan} and {Marco Marelli} and {Maria Jose Maru} and {Marie Ramírez Quintana} and {Tolkiehn Mario} and {Martha Giulianelli} and {Martin Lewis} and {L. PotthastMatthew} and {Matthew L. Leavitt} and {Mátyás Schu-840 bert Hagen} and {Medina Orduna} and {Melody Baitemirova} and {Arnaud Melvin} and {Michael A McElrath} and {Michael A. Yee} and {Michael Co-842 hen} and {Michael Gu} and {Michael Ivanitskiy} and {Michael Star-843 ritt} and {M. Strube} and {Michele Sw˛edrowski} and {Michihiro Bevilacqua} and {Mihir Yasunaga} and {Mike Kale} and {Mimee Cain} and {Mirac Xu} and {Mo Suzgun} and {Monica Tiwari} and {Moin Bansal} and {Mor Aminnaseri} and {Mozhdeh Geva} and {Mukund Gheini} and {T. Varma} and {Nanyun Peng} and {tish Shirish Keskar} and {Niveditha Iyer} and {Noah Fiedel} and {Nuan Wen} and {Oliver Zhang} and {Omar Agha} and {Omar Elbaghdadi} and {Omer Levy} and {Owain Evans} and {Pablo Antonio} and {Moreno Casares} and {Parth Doshi} and {Jason Wei} and {Maarten Bosma} and {Vincent Y. Zhao} and {Adams Wei Guu} and {Brian Yu} and {Nan Lester} and {An-921 Du} and {M. Dai} and {Quoc V. Le} and {Finetuned} and {Adina Williams} and {Nikita Nangia} and {Samuel R. Bowman} and {Thomas Wolf} and {Lysandre Debut} and {Clement Chaumond} and {Anthony Delangue} and {Pier-339 Moi} and {Tim ric Cistac} and {Rémi Rault} and {Morgan Louf} and {Funtow-900 Joe} and {Sam Davison} and {Patrick Shleifer} and {V. Platen} and {Clara Ma} and {Yacine Jernite} and {J. Plu} and {Canwen Xu} and {Sylvain Gugger} and {Mariama Drame} and {Yinfei Yang} and {Yuan Zhang} and {Chris Tar} and {Hailey Schoelkopf} and {Niklas Muen-954} and {Alham Fikri} and {D. Adelani} and {M. Saiful Bari} and {Lintang Sutawika} and {Zhihan Zhang} and {Wenhao Yu} and {Mengxia Yu} and {Zhichun Guo} and {Jonathan May}}, year = 2023, booktitle = {}, url = {https://www.semanticscholar.org/paper/6ce31ab55c3cad599b91e1a36c5e2928d31e3986}, } - 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}, } - Sai Saketh Rambhatla, Ishan Misra, R. Chellappa, and Abhinav Shrivastava, “MOST: Multiple Object localization with Self-supervised Transformers for object discovery,” in IEEE International Conference on Computer Vision, 2023.
[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 = {IEEE International Conference on Computer Vision}, url = {https://www.semanticscholar.org/paper/3be837073f08eecc01e1bc742c541c5f0e644946}, } - Malsha V. Perera and Vishal M. Patel, “Analyzing Bias in Diffusion-based Face Generation Models,” in 2023 IEEE International Joint Conference on Biometrics (IJCB), 2023.
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[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 = {IEEE International Conference on Computer Vision}, url = {https://www.semanticscholar.org/paper/cf6f0b77e006083e74d5f08bae59bd207d0e4ac6}, } - Anjalie Field, Prateek Verma, Nay San, J. Eberhardt, and Dan Jurafsky, “Developing Speech Processing Pipelines for Police Accountability,” in Interspeech, 2023.
[BibTeX] [Link]@inproceedings{259129355, title = {Developing Speech Processing Pipelines for Police Accountability}, author = {{Anjalie Field} and {Prateek Verma} and {Nay San} and {J. Eberhardt} and {Dan Jurafsky}}, year = 2023, month = {6}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/38c2e4e54a50ea5027c7a06ab325c22ece7b6c40}, } - Yuhui Xu, Lingxi Xie, Cihang Xie, Wenrui Dai, Jieru Mei, Siyuan Qiao, Wei Shen, H. Xiong, and A. Yuille, “BNET: Batch Normalization With Enhanced Linear Transformation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[BibTeX] [Link]@inproceedings{255669634, title = {BNET: Batch Normalization With Enhanced Linear Transformation}, author = {{Yuhui Xu} and {Lingxi Xie} and {Cihang Xie} and {Wenrui Dai} and {Jieru Mei} and {Siyuan Qiao} and {Wei Shen} and {H. Xiong} and {A. Yuille}}, year = 2023, month = {1}, booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, url = {https://www.semanticscholar.org/paper/edcf374466f791118acf3bbd8430d4fd73e4ea79}, } - 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}, } - Anjalie Field, Amanda Coston, Nupoor Gandhi, A. Chouldechova, Emily Putnam-Hornstein, David Steier, and Yulia Tsvetkov, “Examining risks of racial biases in NLP tools for child protective services,” in Conference on Fairness, Accountability and Transparency, 2023.
[BibTeX] [Link]@inproceedings{258987867, title = {Examining risks of racial biases in NLP tools for child protective services}, author = {{Anjalie Field} and {Amanda Coston} and {Nupoor Gandhi} and {A. Chouldechova} and {Emily Putnam-Hornstein} and {David Steier} and {Yulia Tsvetkov}}, year = 2023, month = {5}, booktitle = {Conference on Fairness, Accountability and Transparency}, url = {https://www.semanticscholar.org/paper/346e4f35a5a81ef893792133ec1fec18f23c1768}, } - 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}, } - Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati, Alejandro Galindo, and Vishal M. Patel, “Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation,” in Computer Vision and Pattern Recognition, 2023.
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[BibTeX] [Link]@inproceedings{257632366, title = {CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition}, author = {{Deepti Hegde} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}}, year = 2023, month = {3}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, url = {https://www.semanticscholar.org/paper/b460a263abec8b1aaa039963be9b371a581e7b21}, } - Haoran Xu, Weiting Tan, Shuyue Stella Li, Yunmo Chen, Benjamin Van Durme, Philipp Koehn, and Kenton Murray, “Condensing Multilingual Knowledge with Lightweight Language-Specific Modules,” in Conference on Empirical Methods in Natural Language Processing, 2023.
[BibTeX] [Link]@inproceedings{258841086, title = {Condensing Multilingual Knowledge with Lightweight Language-Specific Modules}, author = {{Haoran Xu} and {Weiting Tan} and {Shuyue Stella Li} and {Yunmo Chen} and {Benjamin Van Durme} and {Philipp Koehn} and {Kenton Murray}}, year = 2023, month = {5}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/cf99558d35891d35ba5429cfce5ca2e2ae383b71}, } - Kate Sanders, David Etter, Reno Kriz, and Benjamin Van Durme, “MultiVENT: Multilingual Videos of Events with Aligned Natural Text,” in Neural Information Processing Systems, 2023.
[BibTeX] [Link]@inproceedings{259361037, title = {MultiVENT: Multilingual Videos of Events with Aligned Natural Text}, author = {{Kate Sanders} and {David Etter} and {Reno Kriz} and {Benjamin Van Durme}}, year = 2023, month = {7}, booktitle = {Neural Information Processing Systems}, url = {https://www.semanticscholar.org/paper/d6592cd383da0ebae9f4ff78a4df96ab635acfa2}, } - 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}, } - Tianjian Li and Kenton Murray, “Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution,” in Annual Meeting of the Association for Computational Linguistics, 2023.
[BibTeX] [Link]@inproceedings{258959429, title = {Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution}, author = {{Tianjian Li} and {Kenton Murray}}, year = 2023, month = {5}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/aa933e27c470eeecbe7bbec5debdd8c5d2faa4be}, } - Hexin Liu, Leibny Paola García, Xiangyu Zhang, Andy W. H. Khong, and S. Khudanpur, “Enhancing Code-Switching Speech Recognition With Interactive Language Biases,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023.
[BibTeX] [Link]@inproceedings{263310495, title = {Enhancing Code-Switching Speech Recognition With Interactive Language Biases}, author = {{Hexin Liu} and {Leibny Paola García} and {Xiangyu Zhang} and {Andy W. H. Khong} and {S. Khudanpur}}, year = 2023, month = {9}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/47c14df80f649488c64f5659fa49ad356ff59470}, } - Lingfeng Shen, Weiting Tan, Boyuan Zheng, and Daniel Khashabi, “Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,” in Conference on Empirical Methods in Natural Language Processing, 2023.
[BibTeX] [Link]@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 = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/b8ba16a107621f760e7830ddaab8c3d5c5ff06b0}, } - 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}, } - 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.
[BibTeX] [Link]@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}, } - Elias Stengel-Eskin, Kyle Rawlins, and Benjamin Van Durme, “Zero and Few-shot Semantic Parsing with Ambiguous Inputs,” in International Conference on Learning Representations, 2023.
[BibTeX] [Link]@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 = {International Conference on Learning Representations}, url = {https://www.semanticscholar.org/paper/b8e49068441a43aaf039527c6063a033368dd357}, } - 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, Afshin Beheshti, Stuart Chalk, Guillermo M. Delgado-Aparicio, Melissa Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, John Kalantari, Kia Khezeli, Svetlana V. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Héctor García Martín, Christopher E. Mason, Mona Matar, G. Mias, J. Myers, Charlotte A. Nelson, Jonathan Oribello, P. Parsons-Wingerter, R. K. Prabhu, A. Qutub, J. Rask, Amanda M. Saravia-Butler, S. Saria, N. Singh, M. Snyder, Frank Soboczenski, Karthik Soman, David Van Valen, K. Venkateswaran, L. Warren, Liz Worthey, Jason H. Yang, M. Zitnik, and S. Costes, “Biomonitoring and precision health in deep space supported by artificial intelligence,” in Nature Machine Intelligence, 2023.
[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 {Afshin 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 {Kia 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}, } - Angtian Wang, Wufei Ma, A. Yuille, and Adam Kortylewski, “Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023.
[BibTeX] [Link]@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 = {IEEE Workshop/Winter Conference on Applications of Computer Vision}, url = {https://www.semanticscholar.org/paper/7752104e311751cc786a677f050e2815281039f8}, } - Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, A. Yuille, and Zongwei Zhou, “AbdomenAtlas-8K: Annotating 8, 000 CT Volumes for Multi-Organ Segmentation in Three Weeks,” in Neural Information Processing Systems, 2023.
[BibTeX] [Link]@inproceedings{258714648, title = {AbdomenAtlas-8K: Annotating 8, 000 CT Volumes for Multi-Organ Segmentation in Three Weeks}, author = {{Chongyu Qu} and {Tiezheng Zhang} and {Hualin Qiao} and {Jie Liu} and {Yucheng Tang} and {A. Yuille} and {Zongwei Zhou}}, year = 2023, month = {5}, booktitle = {Neural Information Processing Systems}, url = {https://www.semanticscholar.org/paper/40927a6916f00f8e7ad43ad7009decb25a861767}, } - 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}, } - Lingfeng Shen, Aayush Mishra, and Daniel Khashabi, “Do pretrained Transformers Really Learn In-context by Gradient Descent?,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{268499126, title = {Do pretrained Transformers Really Learn In-context by Gradient Descent?}, author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}}, year = 2023, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/a9d460f8eb9001b1bed11b7fb2af555185c70fcf}, } - Jieneng Chen, Yingda Xia, Jiawen Yao, K. Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Ming Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, X. Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, A. Yuille, Zai-De Liu, and Ling Zhang, “CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans,” in IEEE International Conference on Computer Vision, 2023.
[BibTeX] [Link]@inproceedings{256389683, title = {CancerUniT: 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 = {IEEE International Conference on Computer Vision}, url = {https://www.semanticscholar.org/paper/30be551bfce603f76ba526f047ec4e610b0c2376}, } - A. DeLucia, M. Dredze, and A. L. Buczak, “A Multi-instance Learning Approach to Civil Unrest Event Detection on Twitter,” in Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text, Varna, Bulgaria, 2023, p. 18–33.
[BibTeX] [Abstract] [Link]
Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.
@inproceedings{delucia-etal-2023-multi, title = "A Multi-instance Learning Approach to Civil Unrest Event Detection on {T}witter", author = "DeLucia, Alexandra and Dredze, Mark and Buczak, Anna L.", editor = {H{\"u}rriyeto{\u{g}}lu, Ali and Tanev, Hristo and Zavarella, Vanni and Yeniterzi, Reyyan and Y{\"o}r{\"u}k, Erdem and Slavcheva, Milena}, booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text", month = sep, year = "2023", address = "Varna, Bulgaria", publisher = "INCOMA Ltd., Shoumen, Bulgaria", url = "https://aclanthology.org/2023.case-1.3", pages = "18--33", abstract = "Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.", } - Daniel R. Mendat, Jonah P. Sengupta, Gaspar Tognetti, M. Villemur, P. Pouliquen, Sergio Montano, Kayode A. Sanni, J. Molin, Nishant Zachariah, I. Doxas, and A. Andreou, “A RISC-V Neuromorphic Micro-Controller Unit (vMCU) with Event-Based Physical Interface and Computational Memory for Low-Latency Machine Perception and Intelligence at the Edge,” in International Symposium on Circuits and Systems, 2023.
[BibTeX] [Link]@inproceedings{260003158, title = {A RISC-V Neuromorphic Micro-Controller Unit (vMCU) with Event-Based Physical Interface and Computational Memory for Low-Latency Machine Perception and Intelligence at the Edge}, author = {{Daniel R. Mendat} and {Jonah P. Sengupta} and {Gaspar Tognetti} and {M. Villemur} and {P. Pouliquen} and {Sergio Montano} and {Kayode A. Sanni} and {J. Molin} and {Nishant Zachariah} and {I. Doxas} and {A. Andreou}}, year = 2023, month = {5}, booktitle = {International Symposium on Circuits and Systems}, url = {https://www.semanticscholar.org/paper/0d2f0f6eb40d3be7b97a19315439721cf7ae8469}, } - 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}, } - 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}, } - Ju He, Jieneng Chen, Ming-Xian Lin, Qihang Yu, and A. Yuille, “Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation,” in Computer Vision and Pattern Recognition, 2023.
[BibTeX] [Link]@inproceedings{259145290, title = {Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation}, author = {{Ju He} and {Jieneng Chen} and {Ming-Xian Lin} and {Qihang Yu} and {A. Yuille}}, year = 2023, month = {6}, booktitle = {Computer Vision and Pattern Recognition}, url = {https://www.semanticscholar.org/paper/c49666de550031cd63514dacc74b5c4a632da6a6}, } - 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}, } - 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.
[BibTeX] [Link]@inproceedings{257766789, title = {Label-Free Liver Tumor Segmentation}, author = {{Qixing Hu} and {Yixiong Chen} and {Junfei Xiao} and {Shuwen Sun} and {Jieneng Chen} and {A. Yuille} and {Zongwei Zhou}}, year = 2023, month = {3}, booktitle = {Computer Vision and Pattern Recognition}, url = {https://www.semanticscholar.org/paper/74fc777becc43b9e94c2fb59ed3ee78d212ca01e}, } - 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.
[BibTeX] [Link]@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}, } - 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, Thomas K. M. Cudjoe, S. Saria, Esther Oh, N. Lundebjerg, C. Chute, Phillip Phan, Quincy M. Samus, and Nancy L. Schoenborn, “The promise of AI and technology to improve quality of life and care for older adults,” in Nature Aging, 2023.
[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 {Thomas K. M. Cudjoe} and {S. Saria} and {Esther Oh} and {N. Lundebjerg} and {C. Chute} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn}}, year = 2023, month = {5}, booktitle = {Nature Aging}, url = {https://www.semanticscholar.org/paper/24eafaf005bd6d73870b66525e8978b760e7b3ad}, } - Lauren M. Sanders, Ryan T. Scott, Jason H. Yang, Amina Ann Qutub, Héctor García Martín, D. Berrios, Jaden J. A. Hastings, J. Rask, Graham Mackintosh, A. Hoarfrost, Stuart Chalk, John Kalantari, Kia Khezeli, E. Antonsen, Joel Babdor, Richard Barker, S. Baranzini, Afshin Beheshti, Guillermo M. Delgado-Aparicio, B. Glicksberg, Casey S. Greene, Melissa Haendel, Arif A. Hamid, P. Heller, Daniel Jamieson, K. Jarvis, Svetlana V. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Christopher E. Mason, Mona Matar, G. Mias, Jack M. Miller, J. Myers, Charlotte A. Nelson, Jonathan Oribello, Seung-min Park, P. Parsons-Wingerter, R. K. Prabhu, R. Reynolds, Amanda M. Saravia-Butler, S. Saria, A. Sawyer, N. Singh, M. Snyder, Frank Soboczenski, Karthik Soman, C. Theriot, David Van Valen, K. Venkateswaran, L. Warren, Liz Worthey, M. Zitnik, and S. Costes, “Biological research and self-driving labs in deep space supported by artificial intelligence,” in Nature Machine Intelligence, 2023.
[BibTeX] [Link]@inproceedings{257697867, title = {Biological research and self-driving labs in deep space supported by artificial intelligence}, author = {{Lauren M. Sanders} and {Ryan T. Scott} and {Jason H. Yang} and {Amina Ann Qutub} and {Héctor García Martín} and {D. Berrios} and {Jaden J. A. Hastings} and {J. Rask} and {Graham Mackintosh} and {A. Hoarfrost} and {Stuart Chalk} and {John Kalantari} and {Kia Khezeli} and {E. Antonsen} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {Afshin Beheshti} and {Guillermo M. Delgado-Aparicio} and {B. Glicksberg} and {Casey S. Greene} and {Melissa Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {Svetlana V. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {Jack M. Miller} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {Seung-min Park} and {P. Parsons-Wingerter} and {R. K. Prabhu} and {R. Reynolds} and {Amanda M. Saravia-Butler} and {S. Saria} and {A. Sawyer} and {N. Singh} and {M. Snyder} and {Frank Soboczenski} and {Karthik Soman} and {C. Theriot} and {David Van Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {S. Costes}}, year = 2023, month = {3}, booktitle = {Nature Machine Intelligence}, url = {https://www.semanticscholar.org/paper/880e7f45c1952189e350545dd98a73ef47465cba}, } - Leibny Paola García Perera, Y. H. V. Chua, Hexin Liu, Fei Ting Woon, Andy W. H. Khong, J. Dauwels, S. Khudanpur, and S. Styles, “MERLIon CCS Challenge Evaluation Plan.” 2023.
[BibTeX] [Link]@inproceedings{258987816, title = {MERLIon CCS Challenge Evaluation Plan}, author = {{Leibny Paola García Perera} and {Y. H. V. Chua} and {Hexin Liu} and {Fei Ting Woon} and {Andy W. H. Khong} and {J. Dauwels} and {S. Khudanpur} and {S. Styles}}, year = 2023, month = {5}, booktitle = {}, url = {https://www.semanticscholar.org/paper/6616c330539e1f38b8d80d5aec6eaf0be98f9314}, } - Samuele Cornell, Matthew Wiesner, Shinji Watanabe, Desh Raj, Xuankai Chang, Paola García, Yoshiki Masuyama, Zhong-Qiu Wang, S. Squartini, and S. Khudanpur, “The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios,” in 7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023), 2023.
[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 {Zhong-Qiu Wang} and {S. Squartini} and {S. Khudanpur}}, year = 2023, month = {6}, booktitle = {7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023)}, url = {https://www.semanticscholar.org/paper/d4d5fe4a35e9de845877015075f727415e83d18f}, } - H. E. Wang, Jonathan P. Weiner, S. Saria, and Hadi Kharrazi, “Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis,” in Journal of Medical Internet Research, 2023.
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[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}, } - 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.
[BibTeX] [Link]@inproceedings{259991385, title = {GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos}, author = {{Nisarg A. Shah} and {S. Sikder} and {S. Vedula} and {Vishal M. Patel}}, year = 2023, month = {7}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/330bf5c3162606581ebfba1f744e1f7da90c5de4}, } - 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}, } - Qihao Liu, Junfeng Wu, Yi Jiang, Xiang Bai, A. Yuille, and S. Bai, “InstMove: Instance Motion for Object-centric Video Segmentation,” in Computer Vision and Pattern Recognition, 2023.
[BibTeX] [Link]@inproceedings{257505032, title = {InstMove: Instance Motion for Object-centric Video Segmentation}, author = {{Qihao Liu} and {Junfeng Wu} and {Yi Jiang} and {Xiang Bai} and {A. Yuille} and {S. Bai}}, year = 2023, month = {3}, booktitle = {Computer Vision and Pattern Recognition}, url = {https://www.semanticscholar.org/paper/0e60c1229d7963b605b83cb10a90ed6a8cf79149}, } - Huali Xu, Shuaifeng Zhi, Shuzhou Sun, Vishal M. Patel, and Li Liu, “Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey,” in arXiv.org, 2023.
[BibTeX] [Link]@inproceedings{257532548, title = {Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey}, author = {{Huali Xu} and {Shuaifeng Zhi} and {Shuzhou Sun} and {Vishal M. Patel} and {Li Liu}}, year = 2023, month = {3}, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/095138d9207da38bce4914c569e2f312927213b5}, } - Jay N. Paranjape, S. Sikder, Vishal M. Patel, and S. Vedula, “Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
[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 = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, url = {https://www.semanticscholar.org/paper/648a8bc5f5e6354aa56a899c327715bfaa80944d}, } - 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}, } - Chen Wei, K. Mangalam, Po-Yao (Bernie) Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, A. Yuille, and Christoph Feichtenhofer, “Diffusion Models as Masked Autoencoders,” in IEEE International Conference on Computer Vision, 2023.
[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 = {IEEE International Conference on Computer Vision}, url = {https://www.semanticscholar.org/paper/b032f324a0d4a24fd917551345bd100dc368e41a}, } - Denis Newman-Griffis, Bart Desmet, Ayah Zirikly, Suzanne Tamang, and Chih-Hung Chang, “Editorial: Artificial intelligence for human function and disability,” in Frontiers Digit. Health, 2023.
[BibTeX] [Link]@inproceedings{261637808, title = {Editorial: Artificial intelligence for human function and disability}, author = {{Denis Newman-Griffis} and {Bart Desmet} and {Ayah Zirikly} and {Suzanne Tamang} and {Chih-Hung Chang}}, year = 2023, month = {9}, booktitle = {Frontiers Digit. Health}, url = {https://www.semanticscholar.org/paper/251b6ab5f8aa64447bcab84b2078a7198afc4ac3}, } - 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}, } - 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}, } - E. Schumacher, J. Mayfield, and M. Dredze, “Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models,” in Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 38–51. doi:10.18653/v1/2022.mrl-1.4
[BibTeX] [Abstract] [Link]
Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.
@inproceedings{schumacher-etal-2022-zero, title = "Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models", author = "Schumacher, Elliot and Mayfield, James and Dredze, Mark", editor = {Ataman, Duygu and Gonen, Hila and Ruder, Sebastian and Firat, Orhan and G{\"u}l Sahin, G{\"o}zde and Mirzakhalov, Jamshidbek}, booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.mrl-1.4", doi = "10.18653/v1/2022.mrl-1.4", pages = "38--51", abstract = "Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.", } - Kangda Wei, Dawn J Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller, “When Do Decompositions Help for Machine Reading?,” in Conference on Empirical Methods in Natural Language Processing, 2022.
[BibTeX] [Link]@inproceedings{254877586, title = {When Do Decompositions Help for Machine Reading?}, author = {{Kangda Wei} and {Dawn J Lawrie} and {Benjamin Van Durme} and {Yunmo Chen} and {Orion Weller}}, year = 2022, month = {12}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/624ea7bdaf7e8e3f7bd76f72aa665b562f0dd70a}, } - 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}, } - 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}, } - W. Gu, B. Zheng, Y. Chen, T. Chen, and B. Van Durme, “An Empirical Study on Finding Spans,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 3976–3983. doi:10.18653/v1/2022.emnlp-main.264
[BibTeX] [Abstract] [Link]
We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.
@inproceedings{gu-etal-2022-empirical, title = "An Empirical Study on Finding Spans", author = "Gu, Weiwei and Zheng, Boyuan and Chen, Yunmo and Chen, Tongfei and Van Durme, Benjamin", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.264", doi = "10.18653/v1/2022.emnlp-main.264", pages = "3976--3983", abstract = "We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.", } - 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}, } - E. Stengel-Eskin, E. A. Platanios, A. Pauls, S. Thomson, H. Fang, B. Van Durme, J. Eisner, and Y. Su, “When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11473–11487. doi:10.18653/v1/2022.emnlp-main.789
[BibTeX] [Abstract] [Link]
In natural language understanding (NLU) production systems, users{‘} evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.
@inproceedings{stengel-eskin-etal-2022-data, title = "When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems", author = "Stengel-Eskin, Elias and Platanios, Emmanouil Antonios and Pauls, Adam and Thomson, Sam and Fang, Hao and Van Durme, Benjamin and Eisner, Jason and Su, Yu", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.789", doi = "10.18653/v1/2022.emnlp-main.789", pages = "11473--11487", abstract = "In natural language understanding (NLU) production systems, users{'} evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.", } - F. Casacuberta, G. Foster, G. Huang, P. Koehn, G. Kovacs, L. Liu, S. Shi, T. Watanabe, and C. Zong, “Findings of the Word-Level AutoCompletion Shared Task in WMT 2022,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 812–820.
[BibTeX] [Abstract] [Link]
Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.
@inproceedings{casacuberta-etal-2022-findings, title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2022", author = "Casacuberta, Francisco and Foster, George and Huang, Guoping and Koehn, Philipp and Kovacs, Geza and Liu, Lemao and Shi, Shuming and Watanabe, Taro and Zong, Chengqing", editor = {Koehn, Philipp and Barrault, Lo{\"\i}c and Bojar, Ond{\v{r}}ej and Bougares, Fethi and Chatterjee, Rajen and Costa-juss{\`a}, Marta R. and Federmann, Christian and Fishel, Mark and Fraser, Alexander and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Guzman, Paco and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Kocmi, Tom and Martins, Andr{\'e} and Morishita, Makoto and Monz, Christof and Nagata, Masaaki and Nakazawa, Toshiaki and Negri, Matteo and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Popel, Martin and Turchi, Marco and Zampieri, Marcos}, booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wmt-1.75", pages = "812--820", abstract = "Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.", } - I. Lin, L. Njoo, A. Field, A. Sharma, K. Reinecke, T. Althoff, and Y. Tsvetkov, “Gendered Mental Health Stigma in Masked Language Models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2152–2170. doi:10.18653/v1/2022.emnlp-main.139
[BibTeX] [Abstract] [Link]
Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models{‘} propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32{\%} vs. 19{\%}), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models{‘} gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models{‘} social biases.
@inproceedings{lin-etal-2022-gendered, title = "Gendered Mental Health Stigma in Masked Language Models", author = "Lin, Inna and Njoo, Lucille and Field, Anjalie and Sharma, Ashish and Reinecke, Katharina and Althoff, Tim and Tsvetkov, Yulia", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.139", doi = "10.18653/v1/2022.emnlp-main.139", pages = "2152--2170", abstract = "Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models{'} propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32{\%} vs. 19{\%}), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models{'} gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models{'} social biases.", } - R. Wicks and M. Post, “Does Sentence Segmentation Matter for Machine Translation?,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 843–854.
[BibTeX] [Abstract] [Link]
For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, {“}does segmentation matter?{”} and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.
@inproceedings{wicks-post-2022-sentence, title = "Does Sentence Segmentation Matter for Machine Translation?", author = "Wicks, Rachel and Post, Matt", editor = {Koehn, Philipp and Barrault, Lo{\"\i}c and Bojar, Ond{\v{r}}ej and Bougares, Fethi and Chatterjee, Rajen and Costa-juss{\`a}, Marta R. and Federmann, Christian and Fishel, Mark and Fraser, Alexander and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Guzman, Paco and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Kocmi, Tom and Martins, Andr{\'e} and Morishita, Makoto and Monz, Christof and Nagata, Masaaki and Nakazawa, Toshiaki and Negri, Matteo and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Popel, Martin and Turchi, Marco and Zampieri, Marcos}, booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wmt-1.78", pages = "843--854", abstract = "For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, {``}does segmentation matter?{''} and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.", } - Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, A. Yuille, Yuyin Zhou, and Cihang Xie, “Unleashing the Power of Visual Prompting At the Pixel Level,” in Trans. Mach. Learn. Res., 2022.
[BibTeX] [Link]@inproceedings{254877370, title = {Unleashing the Power of Visual Prompting At the Pixel Level}, author = {{Junyang Wu} and {Xianhang Li} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}}, year = 2022, month = {12}, booktitle = {Trans. Mach. Learn. Res.}, url = {https://www.semanticscholar.org/paper/7786825fd653b398c3975c3ff876459307d871f4}, } - H. Kim, Y. Yu, L. Jiang, X. Lu, D. Khashabi, G. Kim, Y. Choi, and M. Sap, “ProsocialDialog: A Prosocial Backbone for Conversational Agents,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 4005–4029. doi:10.18653/v1/2022.emnlp-main.267
[BibTeX] [Abstract] [Link]
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.
@inproceedings{kim-etal-2022-prosocialdialog, title = "{P}rosocial{D}ialog: A Prosocial Backbone for Conversational Agents", author = "Kim, Hyunwoo and Yu, Youngjae and Jiang, Liwei and Lu, Ximing and Khashabi, Daniel and Kim, Gunhee and Choi, Yejin and Sap, Maarten", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.267", doi = "10.18653/v1/2022.emnlp-main.267", pages = "4005--4029", abstract = "Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.", } - Y. Feng, P. Xia, B. Van Durme, and J. Sedoc, “Automatic Document Selection for Efficient Encoder Pretraining,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 9522–9530. doi:10.18653/v1/2022.emnlp-main.647
[BibTeX] [Abstract] [Link]
Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.
@inproceedings{feng-etal-2022-automatic, title = "Automatic Document Selection for Efficient Encoder Pretraining", author = "Feng, Yukun and Xia, Patrick and Van Durme, Benjamin and Sedoc, Jo{\~a}o", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.647", doi = "10.18653/v1/2022.emnlp-main.647", pages = "9522--9530", abstract = "Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.", } - Y. Feng, F. Li, and P. Koehn, “Toward the Limitation of Code-Switching in Cross-Lingual Transfer,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 5966–5971. doi:10.18653/v1/2022.emnlp-main.400
[BibTeX] [Abstract] [Link]
Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores.
@inproceedings{feng-etal-2022-toward, title = "Toward the Limitation of Code-Switching in Cross-Lingual Transfer", author = "Feng, Yukun and Li, Feng and Koehn, Philipp", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.400", doi = "10.18653/v1/2022.emnlp-main.400", pages = "5966--5971", abstract = "Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores.", } - 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}, } - 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}, } - K. Marchisio, A. Saad-Eldin, K. Duh, C. Priebe, and P. Koehn, “Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2545–2561. doi:10.18653/v1/2022.emnlp-main.164
[BibTeX] [Abstract] [Link]
Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.
@inproceedings{marchisio-etal-2022-bilingual, title = "Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport", author = "Marchisio, Kelly and Saad-Eldin, Ali and Duh, Kevin and Priebe, Carey and Koehn, Philipp", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.164", doi = "10.18653/v1/2022.emnlp-main.164", pages = "2545--2561", abstract = "Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.", } - Trevor Meyer, L. Moro-Velázquez, Seneca Motley, A. Butala, Ashley M Paul, Quincy M. Samus, Pedro P. Irazoqui, N. Dehak, and Esther S. Oh, “Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2022.
[BibTeX] [Link]@inproceedings{254879636, title = {Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer's Disease}, author = {{Trevor Meyer} and {L. Moro-Velázquez} and {Seneca Motley} and {A. Butala} and {Ashley M Paul} and {Quincy M. Samus} and {Pedro P. Irazoqui} and {N. Dehak} and {Esther S. Oh}}, year = 2022, month = {12}, booktitle = {Alzheimer's & Dementia}, url = {https://www.semanticscholar.org/paper/e5a0988cdd73b981611be9fe06e0b7328ff1c0d0}, } - E. Rippeth and M. Post, “Additive Interventions Yield Robust Multi-Domain Machine Translation Models,” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 220–232.
[BibTeX] [Abstract] [Link]
Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder{‘}s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.
@inproceedings{rippeth-post-2022-additive, title = "Additive Interventions Yield Robust Multi-Domain Machine Translation Models", author = "Rippeth, Elijah and Post, Matt", editor = {Koehn, Philipp and Barrault, Lo{\"\i}c and Bojar, Ond{\v{r}}ej and Bougares, Fethi and Chatterjee, Rajen and Costa-juss{\`a}, Marta R. and Federmann, Christian and Fishel, Mark and Fraser, Alexander and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Guzman, Paco and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Kocmi, Tom and Martins, Andr{\'e} and Morishita, Makoto and Monz, Christof and Nagata, Masaaki and Nakazawa, Toshiaki and Negri, Matteo and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Popel, Martin and Turchi, Marco and Zampieri, Marcos}, booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wmt-1.14", pages = "220--232", abstract = "Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder{'}s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.", } - E. Stengel-Eskin and B. Van Durme, “The Curious Case of Control,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11065–11076. doi:10.18653/v1/2022.emnlp-main.760
[BibTeX] [Abstract] [Link]
Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.
@inproceedings{stengel-eskin-van-durme-2022-curious, title = "The Curious Case of Control", author = "Stengel-Eskin, Elias and Van Durme, Benjamin", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.760", doi = "10.18653/v1/2022.emnlp-main.760", pages = "11065--11076", abstract = "Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.", } - H. Xu, P. Koehn, and K. Murray, “The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 170–183. doi:10.18653/v1/2022.emnlp-main.13
[BibTeX] [Abstract] [Link]
Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT{‘}14 dataset.
@inproceedings{xu-etal-2022-importance, title = "The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains", author = "Xu, Haoran and Koehn, Philipp and Murray, Kenton", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.13", doi = "10.18653/v1/2022.emnlp-main.13", pages = "170--183", abstract = "Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT{'}14 dataset.", } - O. Ogundepo, X. Zhang, S. Sun, K. Duh, and J. Lin, “AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 8721–8728. doi:10.18653/v1/2022.emnlp-main.597
[BibTeX] [Abstract] [Link]
Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers{‘} ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse{–}dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.
@inproceedings{ogundepo-etal-2022-africlirmatrix, title = "{A}fri{CLIRM}atrix: Enabling Cross-Lingual Information Retrieval for {A}frican Languages", author = "Ogundepo, Odunayo and Zhang, Xinyu and Sun, Shuo and Duh, Kevin and Lin, Jimmy", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.597", doi = "10.18653/v1/2022.emnlp-main.597", pages = "8721--8728", abstract = "Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers{'} ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse{--}dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.", } - Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Naik, A. Ashok, A. S. Dhanasekaran, A. Arunkumar, D. Stap, E. Pathak, G. Karamanolakis, H. Lai, I. Purohit, I. Mondal, J. Anderson, K. Kuznia, K. Doshi, K. K. Pal, M. Patel, M. Moradshahi, M. Parmar, M. Purohit, N. Varshney, P. R. Kaza, P. Verma, R. S. Puri, R. Karia, S. Doshi, S. K. Sampat, S. Mishra, S. Reddy A, S. Patro, T. Dixit, and X. Shen, “Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 5085–5109. doi:10.18653/v1/2022.emnlp-main.340
[BibTeX] [Abstract] [Link]
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{–-}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.
@inproceedings{wang-etal-2022-super, title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks", author = "Wang, Yizhong and Mishra, Swaroop and Alipoormolabashi, Pegah and Kordi, Yeganeh and Mirzaei, Amirreza and Naik, Atharva and Ashok, Arjun and Dhanasekaran, Arut Selvan and Arunkumar, Anjana and Stap, David and Pathak, Eshaan and Karamanolakis, Giannis and Lai, Haizhi and Purohit, Ishan and Mondal, Ishani and Anderson, Jacob and Kuznia, Kirby and Doshi, Krima and Pal, Kuntal Kumar and Patel, Maitreya and Moradshahi, Mehrad and Parmar, Mihir and Purohit, Mirali and Varshney, Neeraj and Kaza, Phani Rohitha and Verma, Pulkit and Puri, Ravsehaj Singh and Karia, Rushang and Doshi, Savan and Sampat, Shailaja Keyur and Mishra, Siddhartha and Reddy A, Sujan and Patro, Sumanta and Dixit, Tanay and Shen, Xudong", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.340", doi = "10.18653/v1/2022.emnlp-main.340", pages = "5085--5109", abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.", } - 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}, } - 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}, } - 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}, } - 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}, } - K. Deb, X. Zhang, and K. Duh, “Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines,” in Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 51–61. doi:10.18653/v1/2022.blackboxnlp-1.5
[BibTeX] [Abstract] [Link]
Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions.
@inproceedings{deb-etal-2022-post, title = "Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines", author = "Deb, Kiron and Zhang, Xuan and Duh, Kevin", editor = "Bastings, Jasmijn and Belinkov, Yonatan and Elazar, Yanai and Hupkes, Dieuwke and Saphra, Naomi and Wiegreffe, Sarah", booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.blackboxnlp-1.5", doi = "10.18653/v1/2022.blackboxnlp-1.5", pages = "51--61", abstract = "Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions.", } - 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}, } - K. Marchisio, N. Verma, K. Duh, and P. Koehn, “IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 6019–6033. doi:10.18653/v1/2022.emnlp-main.404
[BibTeX] [Abstract] [Link]
The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces{–-}their degree of {“}isomorphism.{”} We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec.
@inproceedings{marchisio-etal-2022-isovec, title = "{I}so{V}ec: Controlling the Relative Isomorphism of Word Embedding Spaces", author = "Marchisio, Kelly and Verma, Neha and Duh, Kevin and Koehn, Philipp", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.404", doi = "10.18653/v1/2022.emnlp-main.404", pages = "6019--6033", abstract = "The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces{---}their degree of {``}isomorphism.{''} We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec.", } - T. Kocmi, R. Bawden, O. Bojar, A. Dvorkovich, C. Federmann, M. Fishel, T. Gowda, Y. Graham, R. Grundkiewicz, B. Haddow, R. Knowles, P. Koehn, C. Monz, M. Morishita, M. Nagata, T. Nakazawa, M. Novák, M. Popel, and M. Popović, “Findings of the 2022 Conference on Machine Translation (WMT22),” in Proceedings of the Seventh Conference on Machine Translation (WMT), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 1–45.
[BibTeX] [Abstract] [Link]
This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).
@inproceedings{kocmi-etal-2022-findings, title = "Findings of the 2022 Conference on Machine Translation ({WMT}22)", author = "Kocmi, Tom and Bawden, Rachel and Bojar, Ond{\v{r}}ej and Dvorkovich, Anton and Federmann, Christian and Fishel, Mark and Gowda, Thamme and Graham, Yvette and Grundkiewicz, Roman and Haddow, Barry and Knowles, Rebecca and Koehn, Philipp and Monz, Christof and Morishita, Makoto and Nagata, Masaaki and Nakazawa, Toshiaki and Nov{\'a}k, Michal and Popel, Martin and Popovi{\'c}, Maja", editor = {Koehn, Philipp and Barrault, Lo{\"\i}c and Bojar, Ond{\v{r}}ej and Bougares, Fethi and Chatterjee, Rajen and Costa-juss{\`a}, Marta R. and Federmann, Christian and Fishel, Mark and Fraser, Alexander and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Guzman, Paco and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Kocmi, Tom and Martins, Andr{\'e} and Morishita, Makoto and Monz, Christof and Nagata, Masaaki and Nakazawa, Toshiaki and Negri, Matteo and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Popel, Martin and Turchi, Marco and Zampieri, Marcos}, booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wmt-1.1", pages = "1--45", abstract = "This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).", } - 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}, } - S. Sia, K. Jaidka, H. Ahuja, N. Chhaya, and K. Duh, “Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 11939–11950. doi:10.18653/v1/2022.emnlp-main.818
[BibTeX] [Abstract] [Link]
In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.
@inproceedings{sia-etal-2022-offer, title = "Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates", author = "Sia, Suzanna and Jaidka, Kokil and Ahuja, Hansin and Chhaya, Niyati and Duh, Kevin", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.818", doi = "10.18653/v1/2022.emnlp-main.818", pages = "11939--11950", abstract = "In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.", } - Z. Jiang, A. Liu, and B. Van Durme, “Calibrating Zero-shot Cross-lingual (Un-)structured Predictions,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, p. 2648–2674. doi:10.18653/v1/2022.emnlp-main.170
[BibTeX] [Abstract] [Link]
We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration.
@inproceedings{jiang-etal-2022-calibrating, title = "Calibrating Zero-shot Cross-lingual (Un-)structured Predictions", author = "Jiang, Zhengping and Liu, Anqi and Van Durme, Benjamin", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.170", doi = "10.18653/v1/2022.emnlp-main.170", pages = "2648--2674", abstract = "We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration.", } - 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}, } - M. Keymanesh, A. Benton, and M. Dredze, “What Makes Data-to-Text Generation Hard for Pretrained Language Models?,” in Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), Abu Dhabi, United Arab Emirates (Hybrid), 2022, p. 539–554. doi:10.18653/v1/2022.gem-1.50
[BibTeX] [Abstract] [Link]
Expressing natural language descriptions of structured facts or relations {–} data-to-text generation (D2T) {–} increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.
@inproceedings{keymanesh-etal-2022-makes, title = "What Makes Data-to-Text Generation Hard for Pretrained Language Models?", author = "Keymanesh, Moniba and Benton, Adrian and Dredze, Mark", editor = "Bosselut, Antoine and Chandu, Khyathi and Dhole, Kaustubh and Gangal, Varun and Gehrmann, Sebastian and Jernite, Yacine and Novikova, Jekaterina and Perez-Beltrachini, Laura", booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.gem-1.50", doi = "10.18653/v1/2022.gem-1.50", pages = "539--554", abstract = "Expressing natural language descriptions of structured facts or relations {--} data-to-text generation (D2T) {--} increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.", } - 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}, } - 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", } - 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", } - V. Rennoll, Ian McLane, Mounya Elhilali, and James E. West, “Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors,” in Italian National Conference on Sensors, 2022.
[BibTeX] [Link]@inproceedings{253903852, title = {Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors}, author = {{V. Rennoll} and {Ian McLane} and {Mounya Elhilali} and {James E. West}}, year = 2022, month = {11}, booktitle = {Italian National Conference on Sensors}, url = {https://www.semanticscholar.org/paper/0d7b6b5a15b47c1cd1d688f043fd06ff6822d5a1}, } - 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}, } - 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}, } - 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}, } - Yuanze Lin, Chen Wei, Huiyu Wang, A. Yuille, and Cihang Xie, “SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training,” in IEEE International Conference on Computer Vision, 2022.
[BibTeX] [Link]@inproceedings{253735003, title = {SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training}, author = {{Yuanze Lin} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Cihang Xie}}, year = 2022, month = {11}, booktitle = {IEEE International Conference on Computer Vision}, url = {https://www.semanticscholar.org/paper/210f6ffbed4bf3a0f020cfcb48dab9d6a9939cdb}, } - 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. doi:10.18653/v1/2022.aacl-short.19
[BibTeX] [Abstract] [Link]
Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models either translating from or into many languages. The model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. The location and content of the prefix can vary and many use different approaches without much justification towards one approach or another. As a guidance to future researchers and directions for future work, we present a series of experiments that show how the positioning and type of a target language prefix token effects translation performance. We show that source side prefixes improve performance. Further, we find that the best language information to denote via tokens depends on the supported language set.
@inproceedings{wicks-duh-2022-effects, title = "The Effects of Language Token Prefixing for Multilingual Machine Translation", author = "Wicks, Rachel and Duh, Kevin", editor = "He, Yulan and Ji, Heng and Li, Sujian and Liu, Yang and Chang, Chua-Hui", booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", month = nov, year = "2022", address = "Online only", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.aacl-short.19", doi = "10.18653/v1/2022.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.", } - Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, S. Venkatesh, and R. Arora, “On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation,” in AAAI Conference on Artificial Intelligence, 2022.
[BibTeX] [Link]@inproceedings{253801674, title = {On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation}, author = {{Thanh Nguyen-Tang} and {Ming Yin} and {Sunil Gupta} and {S. Venkatesh} and {R. Arora}}, year = 2022, month = {11}, booktitle = {AAAI Conference on Artificial Intelligence}, url = {https://www.semanticscholar.org/paper/b61a3d718a192e39a437d32a6ed4037b8c29cc41}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - Hexin Liu, Haihua Xu, Leibny Paola García, Andy W. H. Khong, Yi He, and S. Khudanpur, “Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
[BibTeX] [Link]@inproceedings{253116576, title = {Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization}, author = {{Hexin Liu} and {Haihua Xu} and {Leibny Paola García} and {Andy W. H. Khong} and {Yi He} and {S. Khudanpur}}, year = 2022, month = {10}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/1fab5a425ad712bb8245741c5abec00aa80fc123}, } - G. Botev, A. D. McCarthy, W. Wu, and D. Yarowsky, “Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages,” in Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, 2022, p. 5309–5326.
[BibTeX] [Abstract] [Link]
This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.
@inproceedings{botev-etal-2022-deciphering, title = "Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages", author = "Botev, Georgie and McCarthy, Arya D. and Wu, Winston and Yarowsky, David", editor = "Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.472", pages = "5309--5326", abstract = "This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.", } - W. Wu and D. Yarowsky, “Known Words Will Do: Unknown Concept Translation via Lexical Relations,” in Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022), Gyeongju, Republic of Korea, 2022, p. 15–22.
[BibTeX] [Abstract] [Link]
Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.
@inproceedings{wu-yarowsky-2022-known, title = "Known Words Will Do: Unknown Concept Translation via Lexical Relations", author = "Wu, Winston and Yarowsky, David", editor = "Ojha, Atul Kr. and Liu, Chao-Hong and Vylomova, Ekaterina and Abbott, Jade and Washington, Jonathan and Oco, Nathaniel and Pirinen, Tommi A and Malykh, Valentin and Logacheva, Varvara and Zhao, Xiaobing", booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.loresmt-1.3", pages = "15--22", abstract = "Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.", } - 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.", } - 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}, } - 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}, } - 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}, } - 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}, } - 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}, } - S. Sia and K. Duh, “Prefix Embeddings for In-context Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 45–57.
[BibTeX] [Abstract] [Link]
Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001{\%} of the model parameters, the simple method improves {\textasciitilde}0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings{‘} training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.
@inproceedings{sia-duh-2022-prefix, title = "Prefix Embeddings for In-context Machine Translation", author = "Sia, Suzanna and Duh, Kevin", editor = "Duh, Kevin and Guzm{\'a}n, Francisco", booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)", month = sep, year = "2022", address = "Orlando, USA", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2022.amta-research.4", pages = "45--57", abstract = "Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001{\%} of the model parameters, the simple method improves {\textasciitilde}0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings{'} training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.", } - D. Licht, C. Gao, J. Lam, F. Guzman, M. Diab, and P. Koehn, “Consistent Human Evaluation of Machine Translation across Language Pairs,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 309–321.
[BibTeX] [Abstract] [Link]
Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.
@inproceedings{licht-etal-2022-consistent, title = "Consistent Human Evaluation of Machine Translation across Language Pairs", author = "Licht, Daniel and Gao, Cynthia and Lam, Janice and Guzman, Francisco and Diab, Mona and Koehn, Philipp", editor = "Duh, Kevin and Guzm{\'a}n, Francisco", booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)", month = sep, year = "2022", address = "Orlando, USA", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2022.amta-research.24", pages = "309--321", abstract = "Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.", } - N. Verma, K. Murray, and K. Duh, “Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 31–44.
[BibTeX] [Abstract] [Link]
Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART{‘}s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.
@inproceedings{verma-etal-2022-strategies, title = "Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation", author = "Verma, Neha and Murray, Kenton and Duh, Kevin", editor = "Duh, Kevin and Guzm{\'a}n, Francisco", booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)", month = sep, year = "2022", address = "Orlando, USA", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2022.amta-research.3", pages = "31--44", abstract = "Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART{'}s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.", } - W. Tan, S. Ding, H. Khayrallah, and P. Koehn, “Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 157–174.
[BibTeX] [Abstract] [Link]
Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.
@inproceedings{tan-etal-2022-doubly, title = "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation", author = "Tan, Weiting and Ding, Shuoyang and Khayrallah, Huda and Koehn, Philipp", editor = "Duh, Kevin and Guzm{\'a}n, Francisco", booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)", month = sep, year = "2022", address = "Orlando, USA", publisher = "Association for Machine Translation in the Americas", url = "https://aclanthology.org/2022.amta-research.12", pages = "157--174", abstract = "Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.", } - A. Blair-stanek and B. Van Durme, “Improved Induction of Narrative Chains via Cross-Document Relations,” in Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, Seattle, Washington, 2022, p. 208–212. doi:10.18653/v1/2022.starsem-1.18
[BibTeX] [Abstract] [Link]
The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.
@inproceedings{blair-stanek-van-durme-2022-improved, title = "Improved Induction of Narrative Chains via Cross-Document Relations", author = "Blair-stanek, Andrew and Van Durme, Benjamin", editor = "Nastase, Vivi and Pavlick, Ellie and Pilehvar, Mohammad Taher and Camacho-Collados, Jose and Raganato, Alessandro", booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics", month = jul, year = "2022", address = "Seattle, Washington", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.starsem-1.18", doi = "10.18653/v1/2022.starsem-1.18", pages = "208--212", abstract = "The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.", } - O. Weller, M. Marone, V. Braverman, D. Lawrie, and B. Van Durme, “Pretrained Models for Multilingual Federated Learning,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 1413–1421. doi:10.18653/v1/2022.naacl-main.101
[BibTeX] [Abstract] [Link]
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.
@inproceedings{weller-etal-2022-pretrained, title = "Pretrained Models for Multilingual Federated Learning", author = "Weller, Orion and Marone, Marc and Braverman, Vladimir and Lawrie, Dawn and Van Durme, Benjamin", editor = "Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.101", doi = "10.18653/v1/2022.naacl-main.101", pages = "1413--1421", abstract = "Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.", } - A. Zirikly and M. Dredze, “Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks,” in Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, Seattle, USA, 2022, p. 30–39. doi:10.18653/v1/2022.clpsych-1.3
[BibTeX] [Abstract] [Link]
Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.
@inproceedings{zirikly-dredze-2022-explaining, title = "Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks", author = "Zirikly, Ayah and Dredze, Mark", editor = "Zirikly, Ayah and Atzil-Slonim, Dana and Liakata, Maria and Bedrick, Steven and Desmet, Bart and Ireland, Molly and Lee, Andrew and MacAvaney, Sean and Purver, Matthew and Resnik, Rebecca and Yates, Andrew", booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology", month = jul, year = "2022", address = "Seattle, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.clpsych-1.3", doi = "10.18653/v1/2022.clpsych-1.3", pages = "30--39", abstract = "Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.", } - A. Tsakalidis, J. Chim, I. M. Bilal, A. Zirikly, D. Atzil-Slonim, F. Nanni, P. Resnik, M. Gaur, K. Roy, B. Inkster, J. Leintz, and M. Liakata, “Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts,” in Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, Seattle, USA, 2022, p. 184–198. doi:10.18653/v1/2022.clpsych-1.16
[BibTeX] [Abstract] [Link]
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of {`}Moments of Change{‘} in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year{‘}s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual{‘}s mood (dras- tic changes-{`}Switches{‘}- and gradual changes -{`}Escalations{‘}- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual {–} a continuation of the CLPsych 2019 Shared Task{–} where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).
@inproceedings{tsakalidis-etal-2022-overview, title = "Overview of the {CLP}sych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts", author = "Tsakalidis, Adam and Chim, Jenny and Bilal, Iman Munire and Zirikly, Ayah and Atzil-Slonim, Dana and Nanni, Federico and Resnik, Philip and Gaur, Manas and Roy, Kaushik and Inkster, Becky and Leintz, Jeff and Liakata, Maria", editor = "Zirikly, Ayah and Atzil-Slonim, Dana and Liakata, Maria and Bedrick, Steven and Desmet, Bart and Ireland, Molly and Lee, Andrew and MacAvaney, Sean and Purver, Matthew and Resnik, Rebecca and Yates, Andrew", booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology", month = jul, year = "2022", address = "Seattle, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.clpsych-1.16", doi = "10.18653/v1/2022.clpsych-1.16", pages = "184--198", abstract = "We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of {`}Moments of Change{'} in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year{'}s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual{'}s mood (dras- tic changes-{`}Switches{'}- and gradual changes -{`}Escalations{'}- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual {--} a continuation of the CLPsych 2019 Shared Task{--} where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).", } - R. Volum, S. Rao, M. Xu, G. DesGarennes, C. Brockett, B. Van Durme, O. Deng, A. Malhotra, and B. Dolan, “Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code,” in Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022), Seattle, United States, 2022, p. 25–43. doi:10.18653/v1/2022.wordplay-1.3
[BibTeX] [Abstract] [Link]
Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players{‘} interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.
@inproceedings{volum-etal-2022-craft, title = "Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code", author = "Volum, Ryan and Rao, Sudha and Xu, Michael and DesGarennes, Gabriel and Brockett, Chris and Van Durme, Benjamin and Deng, Olivia and Malhotra, Akanksha and Dolan, Bill", editor = "C{\^o}t{\'e}, Marc-Alexandre and Yuan, Xingdi and Ammanabrolu, Prithviraj", booktitle = "Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022)", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.wordplay-1.3", doi = "10.18653/v1/2022.wordplay-1.3", pages = "25--43", abstract = "Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players{'} interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.", } - C. Zhang, B. Van Durme, Z. Li, and E. Stengel-Eskin, “Visual Commonsense in Pretrained Unimodal and Multimodal Models,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 5321–5335. doi:10.18653/v1/2022.naacl-main.390
[BibTeX] [Abstract] [Link]
Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.
@inproceedings{zhang-etal-2022-visual, title = "Visual Commonsense in Pretrained Unimodal and Multimodal Models", author = "Zhang, Chenyu and Van Durme, Benjamin and Li, Zhuowan and Stengel-Eskin, Elias", editor = "Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.390", doi = "10.18653/v1/2022.naacl-main.390", pages = "5321--5335", abstract = "Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.", } - R. Shin and B. Van Durme, “Few-Shot Semantic Parsing with Language Models Trained on Code,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022, p. 5417–5425. doi:10.18653/v1/2022.naacl-main.396
[BibTeX] [Abstract] [Link]
Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.
@inproceedings{shin-van-durme-2022-shot, title = "Few-Shot Semantic Parsing with Language Models Trained on Code", author = "Shin, Richard and Van Durme, Benjamin", editor = "Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.396", doi = "10.18653/v1/2022.naacl-main.396", pages = "5417--5425", abstract = "Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.", } - P. McNamee and K. Duh, “The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 910–918.
[BibTeX] [Abstract] [Link]
Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21{\%} was observed, demonstrating the utility of the corpus.
@inproceedings{mcnamee-duh-2022-multilingual, title = "The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text", author = "McNamee, Paul and Duh, Kevin", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.96", pages = "910--918", abstract = "Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21{\%} was observed, demonstrating the utility of the corpus.", } - W. Wu and D. Yarowsky, “On the Robustness of Cognate Generation Models,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 4299–4305.
[BibTeX] [Abstract] [Link]
We evaluate two popular neural cognate generation models{‘} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{‘} results with respect to each error type to explain how and why these models perform as they do.
@inproceedings{wu-yarowsky-2022-robustness, title = "On the Robustness of Cognate Generation Models", author = "Wu, Winston and Yarowsky, David", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.458", pages = "4299--4305", abstract = "We evaluate two popular neural cognate generation models{'} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{'} results with respect to each error type to explain how and why these models perform as they do.", } - K. Batsuren, O. Goldman, S. Khalifa, N. Habash, W. Kiera{‘s}, G. Bella, B. Leonard, G. Nicolai, K. Gorman, Y. G. Ate, M. Ryskina, S. Mielke, E. Budianskaya, C. El-Khaissi, T. Pimentel, M. Gasser, W. A. Lane, M. Raj, M. Coler, J. R. M. Samame, D. S. Camaiteri, E. Z. Rojas, D. López Francis, A. Oncevay, J. López Bautista, G. C. S. Villegas, L. T. Hennigen, A. Ek, D. Guriel, P. Dirix, J. Bernardy, A. Scherbakov, A. Bayyr-ool, A. Anastasopoulos, R. Zariquiey, K. Sheifer, S. Ganieva, H. Cruz, R. Karahó{v{g}}a, S. Markantonatou, G. Pavlidis, M. Plugaryov, E. Klyachko, A. Salehi, C. Angulo, J. Baxi, A. Krizhanovsky, N. Krizhanovskaya, E. Salesky, C. Vania, S. Ivanova, J. White, R. H. Maudslay, J. Valvoda, R. Zmigrod, P. Czarnowska, I. Nikkarinen, A. Salchak, B. Bhatt, C. Straughn, Z. Liu, J. N. Washington, Y. Pinter, D. Ataman, M. Wolinski, T. Suhardijanto, A. Yablonskaya, N. Stoehr, H. Dolatian, Z. Nuriah, S. Ratan, F. M. Tyers, E. M. Ponti, G. Aiton, A. Arora, R. J. Hatcher, R. Kumar, J. Young, D. Rodionova, A. Yemelina, T. Andrushko, I. Marchenko, P. Mashkovtseva, A. Serova, E. Prud{‘}hommeaux, M. Nepomniashchaya, F. Giunchiglia, E. Chodroff, M. Hulden, M. Silfverberg, A. D. McCarthy, D. Yarowsky, R. Cotterell, R. Tsarfaty, and E. Vylomova, “UniMorph 4.0: Universal Morphology,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 840–855.
[BibTeX] [Abstract] [Link]
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.
@inproceedings{batsuren-etal-2022-unimorph, title = "{U}ni{M}orph 4.0: {U}niversal {M}orphology", author = "Batsuren, Khuyagbaatar and Goldman, Omer and Khalifa, Salam and Habash, Nizar and Kiera{\'s}, Witold and Bella, G{\'a}bor and Leonard, Brian and Nicolai, Garrett and Gorman, Kyle and Ate, Yustinus Ghanggo and Ryskina, Maria and Mielke, Sabrina and Budianskaya, Elena and El-Khaissi, Charbel and Pimentel, Tiago and Gasser, Michael and Lane, William Abbott and Raj, Mohit and Coler, Matt and Samame, Jaime Rafael Montoya and Camaiteri, Delio Siticonatzi and Rojas, Esa{\'u} Zumaeta and L{\'o}pez Francis, Didier and Oncevay, Arturo and L{\'o}pez Bautista, Juan and Villegas, Gema Celeste Silva and Hennigen, Lucas Torroba and Ek, Adam and Guriel, David and Dirix, Peter and Bernardy, Jean-Philippe and Scherbakov, Andrey and Bayyr-ool, Aziyana and Anastasopoulos, Antonios and Zariquiey, Roberto and Sheifer, Karina and Ganieva, Sofya and Cruz, Hilaria and Karah{\'o}{\v{g}}a, Ritv{\'a}n and Markantonatou, Stella and Pavlidis, George and Plugaryov, Matvey and Klyachko, Elena and Salehi, Ali and Angulo, Candy and Baxi, Jatayu and Krizhanovsky, Andrew and Krizhanovskaya, Natalia and Salesky, Elizabeth and Vania, Clara and Ivanova, Sardana and White, Jennifer and Maudslay, Rowan Hall and Valvoda, Josef and Zmigrod, Ran and Czarnowska, Paula and Nikkarinen, Irene and Salchak, Aelita and Bhatt, Brijesh and Straughn, Christopher and Liu, Zoey and Washington, Jonathan North and Pinter, Yuval and Ataman, Duygu and Wolinski, Marcin and Suhardijanto, Totok and Yablonskaya, Anna and Stoehr, Niklas and Dolatian, Hossep and Nuriah, Zahroh and Ratan, Shyam and Tyers, Francis M. and Ponti, Edoardo M. and Aiton, Grant and Arora, Aryaman and Hatcher, Richard J. and Kumar, Ritesh and Young, Jeremiah and Rodionova, Daria and Yemelina, Anastasia and Andrushko, Taras and Marchenko, Igor and Mashkovtseva, Polina and Serova, Alexandra and Prud{'}hommeaux, Emily and Nepomniashchaya, Maria and Giunchiglia, Fausto and Chodroff, Eleanor and Hulden, Mans and Silfverberg, Miikka and McCarthy, Arya D. and Yarowsky, David and Cotterell, Ryan and Tsarfaty, Reut and Vylomova, Ekaterina", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.89", pages = "840--855", abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.", } - A. Zirikly, B. Desmet, J. Porcino, J. Camacho Maldonado, P. Ho, R. Jimenez Silva, and M. Sacco, “A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 2850–2855.
[BibTeX] [Abstract] [Link]
Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.
@inproceedings{zirikly-etal-2022-whole, title = "A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach", author = "Zirikly, Ayah and Desmet, Bart and Porcino, Julia and Camacho Maldonado, Jonathan and Ho, Pei-Shu and Jimenez Silva, Rafael and Sacco, Maryanne", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.305", pages = "2850--2855", abstract = "Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.", } - L. Kanashiro Pereira, “Attention-Focused Adversarial Training for Robust Temporal Reasoning,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France, 2022, p. 7352–7359.
[BibTeX] [Abstract] [Link]
We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.
@inproceedings{kanashiro-pereira-2022-attention, title = "Attention-Focused Adversarial Training for Robust Temporal Reasoning", author = "Kanashiro Pereira, Lis", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.800", pages = "7352--7359", abstract = "We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.", } - N. Weber, A. Belyy, N. Holzenberger, R. Rudinger, and B. Van Durme, “Human Schema Curation via Causal Association Rule Mining,” in Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, Marseille, France, 2022, p. 139–150.
[BibTeX] [Abstract] [Link]
Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to {“}program{”} complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.
@inproceedings{weber-etal-2022-human, title = "Human Schema Curation via Causal Association Rule Mining", author = "Weber, Noah and Belyy, Anton and Holzenberger, Nils and Rudinger, Rachel and Van Durme, Benjamin", editor = "Pradhan, Sameer and Kuebler, Sandra", booktitle = "Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.law-1.17", pages = "139--150", abstract = "Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to {``}program{''} complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.", } - S. Wu, B. Van Durme, and M. Dredze, “Zero-shot Cross-lingual Transfer is Under-specified Optimization,” in Proceedings of the 7th Workshop on Representation Learning for NLP, Dublin, Ireland, 2022, p. 236–248. doi:10.18653/v1/2022.repl4nlp-1.25
[BibTeX] [Abstract] [Link]
Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.
@inproceedings{wu-etal-2022-zero, title = "Zero-shot Cross-lingual Transfer is Under-specified Optimization", author = "Wu, Shijie and Van Durme, Benjamin and Dredze, Mark", editor = "Gella, Spandana and He, He and Majumder, Bodhisattwa Prasad and Can, Burcu and Giunchiglia, Eleonora and Cahyawijaya, Samuel and Min, Sewon and Mozes, Maximilian and Li, Xiang Lorraine and Augenstein, Isabelle and Rogers, Anna and Cho, Kyunghyun and Grefenstette, Edward and Rimell, Laura and Dyer, Chris", booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.repl4nlp-1.25", doi = "10.18653/v1/2022.repl4nlp-1.25", pages = "236--248", abstract = "Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.", } - S. Panthaplackel, A. Benton, and M. Dredze, “Updated Headline Generation: Creating Updated Summaries for Evolving News Stories,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 6438–6461. doi:10.18653/v1/2022.acl-long.446
[BibTeX] [Abstract] [Link]
We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.
@inproceedings{panthaplackel-etal-2022-updated, title = "Updated Headline Generation: Creating Updated Summaries for Evolving News Stories", author = "Panthaplackel, Sheena and Benton, Adrian and Dredze, Mark", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.446", doi = "10.18653/v1/2022.acl-long.446", pages = "6438--6461", abstract = "We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.", } - A. Belyy, C. Huang, J. Andreas, E. A. Platanios, S. Thomson, R. Shin, S. Roy, A. Nisnevich, C. Chen, and B. Van Durme, “Guided K-best Selection for Semantic Parsing Annotation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Dublin, Ireland, 2022, p. 114–126. doi:10.18653/v1/2022.acl-demo.11
[BibTeX] [Abstract] [Link]
Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.
@inproceedings{belyy-etal-2022-guided, title = "Guided K-best Selection for Semantic Parsing Annotation", author = "Belyy, Anton and Huang, Chieh-yang and Andreas, Jacob and Platanios, Emmanouil Antonios and Thomson, Sam and Shin, Richard and Roy, Subhro and Nisnevich, Aleksandr and Chen, Charles and Van Durme, Benjamin", editor = "Basile, Valerio and Kozareva, Zornitsa and Stajner, Sanja", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-demo.11", doi = "10.18653/v1/2022.acl-demo.11", pages = "114--126", abstract = "Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.", } - K. Yang, O. Deng, C. Chen, R. Shin, S. Roy, and B. Van Durme, “Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation,” in Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, 2022, p. 3685–3695. doi:10.18653/v1/2022.findings-acl.291
[BibTeX] [Abstract] [Link]
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33{\%} relative improvement over a non-data-augmented baseline in top-1 match.
@inproceedings{yang-etal-2022-addressing, title = "Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation", author = "Yang, Kevin and Deng, Olivia and Chen, Charles and Shin, Richard and Roy, Subhro and Van Durme, Benjamin", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-acl.291", doi = "10.18653/v1/2022.findings-acl.291", pages = "3685--3695", abstract = "We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33{\%} relative improvement over a non-data-augmented baseline in top-1 match.", } - A. Anastasopoulos, L. Barrault, L. Bentivogli, M. Zanon Boito, O. Bojar, R. Cattoni, A. Currey, G. Dinu, K. Duh, M. Elbayad, C. Emmanuel, Y. Estève, M. Federico, C. Federmann, S. Gahbiche, H. Gong, R. Grundkiewicz, B. Haddow, B. Hsu, D. Javorský, V. Kloudová, S. Lakew, X. Ma, P. Mathur, P. McNamee, K. Murray, M. N{v{a}}dejde, S. Nakamura, M. Negri, J. Niehues, X. Niu, J. Ortega, J. Pino, E. Salesky, J. Shi, M. Sperber, S. Stüker, K. Sudoh, M. Turchi, Y. Virkar, A. Waibel, C. Wang, and S. Watanabe, “Findings of the IWSLT 2022 Evaluation Campaign,” in Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), Dublin, Ireland (in-person and online), 2022, p. 98–157. doi:10.18653/v1/2022.iwslt-1.10
[BibTeX] [Abstract] [Link]
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.
@inproceedings{anastasopoulos-etal-2022-findings, title = "Findings of the {IWSLT} 2022 Evaluation Campaign", author = {Anastasopoulos, Antonios and Barrault, Lo{\"\i}c and Bentivogli, Luisa and Zanon Boito, Marcely and Bojar, Ond{\v{r}}ej and Cattoni, Roldano and Currey, Anna and Dinu, Georgiana and Duh, Kevin and Elbayad, Maha and Emmanuel, Clara and Est{\`e}ve, Yannick and Federico, Marcello and Federmann, Christian and Gahbiche, Souhir and Gong, Hongyu and Grundkiewicz, Roman and Haddow, Barry and Hsu, Benjamin and Javorsk{\'y}, D{\'a}vid and Kloudov{\'a}, V{\u{e}}ra and Lakew, Surafel and Ma, Xutai and Mathur, Prashant and McNamee, Paul and Murray, Kenton and N{\v{a}}dejde, Maria and Nakamura, Satoshi and Negri, Matteo and Niehues, Jan and Niu, Xing and Ortega, John and Pino, Juan and Salesky, Elizabeth and Shi, Jiatong and Sperber, Matthias and St{\"u}ker, Sebastian and Sudoh, Katsuhito and Turchi, Marco and Virkar, Yogesh and Waibel, Alexander and Wang, Changhan and Watanabe, Shinji}, editor = "Salesky, Elizabeth and Federico, Marcello and Costa-juss{\`a}, Marta", booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)", month = may, year = "2022", address = "Dublin, Ireland (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.iwslt-1.10", doi = "10.18653/v1/2022.iwslt-1.10", pages = "98--157", abstract = "The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.", } - T. Nguyen, A. Yates, A. Zirikly, B. Desmet, and A. Cohan, “Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 8446–8459. doi:10.18653/v1/2022.acl-long.578
[BibTeX] [Abstract] [Link]
Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9{‘}s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.
@inproceedings{nguyen-etal-2022-improving, title = "Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires", author = "Nguyen, Thong and Yates, Andrew and Zirikly, Ayah and Desmet, Bart and Cohan, Arman", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.578", doi = "10.18653/v1/2022.acl-long.578", pages = "8446--8459", abstract = "Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9{'}s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.", } - J. Yang, A. Hussein, M. Wiesner, and S. Khudanpur, “JHU IWSLT 2022 Dialect Speech Translation System Description,” in Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), Dublin, Ireland (in-person and online), 2022, p. 319–326. doi:10.18653/v1/2022.iwslt-1.29
[BibTeX] [Abstract] [Link]
This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.
@inproceedings{yang-etal-2022-jhu, title = "{JHU} {IWSLT} 2022 Dialect Speech Translation System Description", author = "Yang, Jinyi and Hussein, Amir and Wiesner, Matthew and Khudanpur, Sanjeev", editor = "Salesky, Elizabeth and Federico, Marcello and Costa-juss{\`a}, Marta", booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)", month = may, year = "2022", address = "Dublin, Ireland (in-person and online)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.iwslt-1.29", doi = "10.18653/v1/2022.iwslt-1.29", pages = "319--326", abstract = "This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.", } - S. Sun, A. Fan, J. Cross, V. Chaudhary, C. Tran, P. Koehn, and F. Guzmán, “Alternative Input Signals Ease Transfer in Multilingual Machine Translation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 5291–5305. doi:10.18653/v1/2022.acl-long.363
[BibTeX] [Abstract] [Link]
Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble {–} training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference, outperforms strong ensemble baselines by 1.3 BLEU points on both language families. Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5{\%} of the total training data is accessible. Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems.
@inproceedings{sun-etal-2022-alternative, title = "Alternative Input Signals Ease Transfer in Multilingual Machine Translation", author = "Sun, Simeng and Fan, Angela and Cross, James and Chaudhary, Vishrav and Tran, Chau and Koehn, Philipp and Guzm{\'a}n, Francisco", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.363", doi = "10.18653/v1/2022.acl-long.363", pages = "5291--5305", abstract = "Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble {--} training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference, outperforms strong ensemble baselines by 1.3 BLEU points on both language families. Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5{\%} of the total training data is accessible. Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems.", } - M. Yuan, P. Xia, C. May, B. Van Durme, and J. Boyd-Graber, “Adapting Coreference Resolution Models through Active Learning,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022, p. 7533–7549. doi:10.18653/v1/2022.acl-long.519
[BibTeX] [Abstract] [Link]
Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.
@inproceedings{yuan-etal-2022-adapting, title = "Adapting Coreference Resolution Models through Active Learning", author = "Yuan, Michelle and Xia, Patrick and May, Chandler and Van Durme, Benjamin and Boyd-Graber, Jordan", editor = "Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.519", doi = "10.18653/v1/2022.acl-long.519", pages = "7533--7549", abstract = "Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.", } - 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", } - R. Cotterell and J. Eisner, “A Functionalist Account of Vowel System Typology,” in Proceedings of the Association for Computational Linguistics (ACL), Dublin, 2022.
[BibTeX] [Link]@InProceedings{cotterell-eisner-2022, author = "Ryan Cotterell and Jason Eisner", title = "A Functionalist Account of Vowel System Typology", booktitle = "Proceedings of the Association for Computational Linguistics (ACL)", year = "2022", month = may, address = "Dublin", note = "Paper was accepted, but we withdrew it in order to add more experiments and analysis before publication.", URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2022", } - 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", } - Vikas Raunak, Matt Post, and Arul Menezes, “SALTED: A Framework for SAlient Long-Tail Translation Error Detection,” in Conference on Empirical Methods in Natural Language Processing, 2022.
[BibTeX] [Link]@inproceedings{248965209, title = {SALTED: A Framework for SAlient Long-Tail Translation Error Detection}, author = {{Vikas Raunak} and {Matt Post} and {Arul Menezes}}, year = 2022, month = {5}, booktitle = {Conference on Empirical Methods in Natural Language Processing}, url = {https://www.semanticscholar.org/paper/a349bcb86ba80ef543e5deaadbb7e0ff5daef5e7}, } - Sandeep Reddy Kothinti and Mounya Elhilali, “Temporal Contrastive-Loss for Audio Event Detection,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
[BibTeX] [Link]@inproceedings{249437588, title = {Temporal Contrastive-Loss for Audio Event Detection}, author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}}, year = 2022, month = {5}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, url = {https://www.semanticscholar.org/paper/34fe9aa0f5e26768d196087ed146e2b3a576d73e}, } - Boyuan Zheng, Patrick Xia, M. Yarmohammadi, and Benjamin Van Durme, “Multilingual Coreference Resolution in Multiparty Dialogue,” in Transactions of the Association for Computational Linguistics, 2022.
[BibTeX] [Link]@inproceedings{251253396, title = {Multilingual Coreference Resolution in Multiparty Dialogue}, author = {{Boyuan Zheng} and {Patrick Xia} and {M. Yarmohammadi} and {Benjamin Van Durme}}, year = 2022, month = {8}, booktitle = {Transactions of the Association for Computational Linguistics}, url = {https://www.semanticscholar.org/paper/ced26dee12fe5fa2f666bcc2ba5b0a1969240887}, } - 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}, } - 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, booktitle = {arXiv.org}, url = {https://www.semanticscholar.org/paper/132751a80632e80a90d7c3d3cd8a361f48fdb9b4}, } - Christos Sapsanis, M. Sophocleous, A. Andreou, and J. Georgiou, “Trade-Offs in Sensor Systems Design: A Tutorial,” in IEEE Sensors Journal, 2022.
[BibTeX] [Link]@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}, } - P. Xia and B. Van Durme, “Online Neural Coreference Resolution with Rollback,” in Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference, Gyeongju, Republic of Korea, 2022, p. 13–21.
[BibTeX] [Abstract] [Link]
Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.
@inproceedings{xia-van-durme-2022-online, title = "Online Neural Coreference Resolution with Rollback", author = "Xia, Patrick and Van Durme, Benjamin", editor = "Ogrodniczuk, Maciej and Pradhan, Sameer and Nedoluzhko, Anna and Ng, Vincent and Poesio, Massimo", booktitle = "Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.crac-1.2", pages = "13--21", abstract = "Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.", } - H. Kayser, H. Hermansky, and B. Meyer, “Spatial speech detection for binaural hearing aids using deep phoneme classifiers,” in Acta acustica. European Acoustics Association, 2022.
[BibTeX] [Link]@inproceedings{250105551, title = {Spatial speech detection for binaural hearing aids using deep phoneme classifiers}, author = {{H. Kayser} and {H. Hermansky} and {B. Meyer}}, year = 2022, month = {6}, booktitle = {Acta acustica. European Acoustics Association}, url = {https://www.semanticscholar.org/paper/5bf8888705bfa1cdbf08784606d5ebf6e6a0e2f8}, } - W. G. C. Bandara and Vishal M. Patel, “A Transformer-Based Siamese Network for Change Detection,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
[BibTeX] [Link]@inproceedings{245668909, title = {A Transformer-Based Siamese Network for Change Detection}, author = {{W. G. C. Bandara} and {Vishal M. Patel}}, year = 2022, month = {1}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, url = {https://www.semanticscholar.org/paper/ef3b15260a610473c95662f5df2c195ac19f64d6}, } - 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}, } - Bruce Y Lee, J. Ordovás, E. J. Parks, Cheryl AM Anderson, A. Barabási, S. Clinton, K. Haye, V. Duffy, P. Franks, Elizabeth M Ginexi, K. Hammond, Erin C. Hanlon, Michael Hittle, Emily Ho, A. Horn, R. Isaacson, P. Mabry, Susan E. Malone, Corby K. Martin, J. Mattei, S. Meydani, Lorene M. Nelson, M. Neuhouser, N. Pronk, S. Saria, Frank Ajl Scheer, E. Segal, M. Sevick, T. Spector, Linda B Van Horn, K. Varady, V. S. Voruganti, and Marie F Martinez, “Microsoft Word-nqac237.docx.” 2022.
[BibTeX] [Link]@inproceedings{253253453, title = {Microsoft Word-nqac237.docx}, author = {{Bruce Y Lee} and {J. Ordovás} and {E. J. Parks} and {Cheryl AM Anderson} and {A. Barabási} and {S. Clinton} and {K. Haye} and {V. Duffy} and {P. Franks} and {Elizabeth M Ginexi} and {K. Hammond} and {Erin C. Hanlon} and {Michael Hittle} and {Emily Ho} and {A. Horn} and {R. Isaacson} and {P. Mabry} and {Susan E. Malone} and {Corby K. Martin} and {J. Mattei} and {S. Meydani} and {Lorene M. Nelson} and {M. Neuhouser} and {N. Pronk} and {S. Saria} and {Frank Ajl Scheer} and {E. Segal} and {M. Sevick} and {T. Spector} and {Linda B Van Horn} and {K. Varady} and {V. S. Voruganti} and {Marie F Martinez}}, year = 2022, booktitle = {}, url = {https://www.semanticscholar.org/paper/ae27ca3ffeb8273f258fb6a41a1cc4803adb716b}, } - 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.
[BibTeX] [Link]@inproceedings{252383306, title = {NBD-GAP: Non-Blind Image Deblurring without Clean Target Images}, author = {{Nithin Gopalakrishnan Nair} and {R. Yasarla} and {Vishal M. Patel}}, year = 2022, month = {9}, booktitle = {International Conference on Information Photonics}, url = {https://www.semanticscholar.org/paper/28a43c5d52c421b1ccc24d15f39b2cdb82ed84de}, } - 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}, } - 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.
[BibTeX] [Link]@inproceedings{251647228, title = {Image BERT Pre-training with Online Tokenizer}, author = {{Jinghao Zhou} and {Chen Wei} and {Huiyu Wang} and {Wei Shen} and {Cihang Xie} and {A. Yuille} and {Tao Kong}}, year = 2022, booktitle = {International Conference on Learning Representations}, url = {https://www.semanticscholar.org/paper/ff169d09a933756e8798021dbf9e24a0bbfd9b38}, } - J. Villalba, B. J. Borgstrom, Saurabh Kataria, Jaejin Cho, P. Torres-Carrasquillo, and N. Dehak, “Advances in Speaker Recognition for Multilingual Conversational Telephone Speech: The JHU-MIT System for NIST SRE20 CTS Challenge,” in The Speaker and Language Recognition Workshop, 2022.
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Gonz’alez, Danielle R. Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, E. D. Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, E. Donoway, Ellie Pavlick, E. Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, E. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, F. Siar, Fernando Mart’inez-Plumed, Francesca Happ’e, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-L’opez, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, H. Bogar, Henry Shevlin, Hinrich Schutze, H. Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jaehoon Lee, J. Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Koco’n, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Narain Sohl-Dickstein, Jason Phang, Jason Wei, J. Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Oluwadara Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Jane W Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jorg Frohberg, Jos Rozen, J. Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, K. Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, K. Mathewson, Kristen Chiafullo, Ksenia Shkaruta, K. Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Luca Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Col’on, Luke Metz, Lutfi Kerem cSenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ram’irez Quintana, M. Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, M. Schubert, Medina Baitemirova, Melody Arnaud, M. McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, M. Strube, Michal Swkedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Monica Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, T. MukundVarma, Nanyun Peng, Nathan A. 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[BibTeX] [Link]@inproceedings{263625818, title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models}, author = {{Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Abu Awal Md Shoeb} and {Abubakar Abid} and {Adam Fisch} and {Adam R. Brown} and {Adam Santoro} and {Aditya Gupta} and {Adrià Garriga-Alonso} and {Agnieszka Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Parrish} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Amanda Dsouza} and {Ambrose Slone} and {Ameet Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {Andrea Santilli} and {Andreas Stuhlmuller} and {Andrew M. Dai} and {Andrew 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. Mullokandov} and {Ashish Sabharwal} and {Austin Herrick} and {Avia Efrat} and {Aykut Erdem} and {Ayla Karakacs} and {B. R. Roberts} and {B. S. Loe} and {Barret Zoph} and {Bartlomiej Bojanowski} and {Batuhan Ozyurt} and {Behnam Hedayatnia} and {Behnam Neyshabur} and {Benjamin Inden} and {Benno Stein} and {Berk Ekmekci} and {Bill Yuchen Lin} and {B. Howald} and {Bryan Orinion} and {Cameron Diao} and {Cameron Dour} and {Catherine Stinson} and {Cedrick Argueta} and {C'esar Ferri Ram'irez} and {Chandan Singh} and {Charles Rathkopf} and {Chenlin Meng} and {Chitta Baral} and {Chiyu Wu} and {Chris Callison-Burch} and {Chris Waites} and {Christian Voigt} and {Christopher D. Manning} and {Christopher Potts} and {Cindy Ramirez} and {Clara E. Rivera} and {Clemencia Siro} and {Colin Raffel} and {Courtney Ashcraft} and {Cristina Garbacea} and {Damien Sileo} and {Daniel H Garrette} and {Dan Hendrycks} and {D. Kilman} and {Dan Roth} and {Daniel Freeman} and {Daniel Khashabi} and {Daniel Levy} and {D. Gonz'alez} and {Danielle R. Perszyk} and {Danny Hernandez} and {Danqi Chen} and {Daphne Ippolito} and {D. Gilboa} and {David Dohan} and {D. Drakard} and {David Jurgens} and {Debajyoti Datta} and {Deep Ganguli} and {Denis Emelin} and {Denis Kleyko} and {Deniz Yuret} and {Derek Chen} and {Derek Tam} and {Dieuwke Hupkes} and {Diganta Misra} and {Dilyar Buzan} and {Dimitri Coelho Mollo} and {Diyi Yang} and {Dong-Ho Lee} and {Dylan Schrader} and {Ekaterina Shutova} and {E. D. Cubuk} and {Elad Segal} and {Eleanor Hagerman} and {Elizabeth Barnes} and {E. Donoway} and {Ellie Pavlick} and {E. Rodolà} and {Emma Lam} and {Eric Chu} and {Eric Tang} and {Erkut Erdem} and {Ernie Chang} and {Ethan A. Chi} and {Ethan Dyer} and {E. Jerzak} and {Ethan Kim} and {Eunice Engefu Manyasi} and {Evgenii Zheltonozhskii} and {Fanyue Xia} and {F. Siar} and {Fernando Mart'inez-Plumed} and {Francesca Happ'e} and {François Chollet} and {Frieda Rong} and {Gaurav Mishra} and {Genta Indra Winata} and {Gerard de Melo} and {Germán Kruszewski} and {Giambattista Parascandolo} and {Giorgio Mariani} and {Gloria Xinyue Wang} and {Gonzalo Jaimovitch-L'opez} and {Gregor Betz} and {Guy Gur-Ari} and {Hana Galijasevic} and {Hannah Kim} and {Hannah Rashkin} and {Hannaneh Hajishirzi} and {Harsh Mehta} and {H. Bogar} and {Henry Shevlin} and {Hinrich Schutze} and {H. Yakura} and {Hongming Zhang} and {Hugh Mee Wong} and {Ian Ng} and {Isaac Noble} and {Jaap Jumelet} and {Jack Geissinger} and {John Kernion} and {Jacob Hilton} and {Jaehoon Lee} and {J. Fisac} and {James B. Simon} and {James Koppel} and {James Zheng} and {James Zou} and {Jan Koco'n} and {Jana Thompson} and {Janelle Wingfield} and {Jared Kaplan} and {Jarema Radom} and {Jascha Narain Sohl-Dickstein} and {Jason Phang} and {Jason Wei} and {J. Yosinski} and {Jekaterina Novikova} and {Jelle Bosscher} and {Jennifer Marsh} and {Jeremy Kim} and {Jeroen Taal} and {Jesse Engel} and {Jesujoba Oluwadara Alabi} and {Jiacheng Xu} and {Jiaming Song} and {Jillian Tang} and {Jane W Waweru} and {John Burden} and {John Miller} and {John U. Balis} and {Jonathan Batchelder} and {Jonathan Berant} and {Jorg Frohberg} and {Jos Rozen} and {J. Hernández-Orallo} and {Joseph Boudeman} and {Joseph Guerr} and {Joseph Jones} and {Joshua B. Tenenbaum} and {Joshua S. Rule} and {Joyce Chua} and {Kamil Kanclerz} and {Karen Livescu} and {K. Krauth} and {Karthik Gopalakrishnan} and {Katerina Ignatyeva} and {K. Markert} and {Kaustubh D. Dhole} and {Kevin Gimpel} and {Kevin Omondi} and {K. Mathewson} and {Kristen Chiafullo} and {Ksenia Shkaruta} and {K. Shridhar} and {Kyle McDonell} and {Kyle Richardson} and {Laria Reynolds} and {Leo Gao} and {Li Zhang} and {Liam Dugan} and {Lianhui Qin} and {Lidia Contreras-Ochando} and {Louis-Philippe Morency} and {Luca Moschella} and {Luca Lam} and {Lucy Noble} and {Ludwig Schmidt} and {Luheng He} and {Luis Oliveros Col'on} and {Luke Metz} and {Lutfi Kerem cSenel} and {Maarten Bosma} and {Maarten Sap} and {Maartje ter Hoeve} and {Maheen Farooqi} and {Manaal Faruqui} and {Mantas Mazeika} and {Marco Baturan} and {Marco Marelli} and {Marco Maru} and {Maria Jose Ram’irez Quintana} and {M. Tolkiehn} and {Mario Giulianelli} and {Martha Lewis} and {Martin Potthast} and {Matthew L. Leavitt} and {Matthias Hagen} and {M. Schubert} and {Medina Baitemirova} and {Melody Arnaud} and {M. McElrath} and {Michael A. Yee} and {Michael Cohen} and {Michael Gu} and {Michael Ivanitskiy} and {Michael Starritt} and {M. Strube} and {Michal Swkedrowski} and {Michele Bevilacqua} and {Michihiro Yasunaga} and {Mihir Kale} and {Mike Cain} and {Mimee Xu} and {Mirac Suzgun} and {Mitch Walker} and {Monica Tiwari} and {Mohit Bansal} and {Moin Aminnaseri} and {Mor Geva} and {Mozhdeh Gheini} and {T. MukundVarma} and {Nanyun Peng} and {Nathan A. Chi} and {Nayeon Lee} and {Neta Gur-Ari Krakover} and {Nicholas Cameron} and {Nicholas Roberts} and {Nick Doiron} and {Nicole Martinez} and {Nikita Nangia} and {Niklas Deckers} and {Niklas Muennighoff} and {N. Keskar} and {Niveditha Iyer} and {Noah Constant} and {Noah Fiedel} and {Nuan Wen} and {Oliver Zhang} and {Omar Agha} and {Omar Elbaghdadi} and {Omer Levy} and {Owain Evans} and {Pablo Antonio Moreno Casares} and {P. Doshi} and {Pascale Fung} and {P. Liang} and {Paul Vicol} and {Pegah Alipoormolabashi} and {Peiyuan Liao} and {Percy Liang} and {Peter Chang} and {P. Eckersley} and {Phu Mon Htut} and {P. Hwang} and {P. Milkowski} and {P. 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Hamdan} and {Sharon Zhou} and {Shashank Srivastava} and {Sherry Shi} and {Shikhar Singh} and {Shima Asaadi} and {S. Gu} and {Shubh Pachchigar} and {Shubham Toshniwal} and {Shyam Upadhyay} and {Shyamolima Debnath} and {Siamak Shakeri} and {Simon Thormeyer} and {S. Melzi} and {Siva Reddy} and {S. Makini} and {Soo-Hwan Lee} and {Spencer Bradley Torene} and {Sriharsha Hatwar} and {S. Dehaene} and {Stefan Divic} and {Stefano Ermon} and {Stella Biderman} and {Stephanie Lin} and {Stephen Prasad} and {Steven T Piantadosi} and {Stuart M. Shieber} and {Summer Misherghi} and {S. Kiritchenko} and {Swaroop Mishra} and {Tal Linzen} and {Tal Schuster} and {Tao Li} and {Tao Yu} and {Tariq Ali} and {Tatsunori Hashimoto} and {Te-Lin Wu} and {T. Desbordes} and {Theodore Rothschild} and {Thomas Phan} and {Tianle Wang} and {Tiberius Nkinyili} and {Timo Schick} and {T. Kornev} and {T. Tunduny} and {Tobias Gerstenberg} and {T. 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[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}, } - 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/2a1b41221def527e17eb1ca04f4f32442fa09ba7}, } - 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}, } - 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.
[BibTeX] [Link]@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}, } - Samik Sadhu and H. Hermansky, “Blind Signal Dereverberation for Machine Speech Recognition,” in arXiv.org, 2022.
[BibTeX] [Link]@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}, } - A. Javaid, Fawzi Zghyer, Chang H Kim, Erin M. Spaulding, Nino Isakadze, Jie Ding, Daniel Kargillis, Yumin Gao, Faisal Rahman, Donald E. Brown, S. Saria, Seth S. Martin, C. Kramer, R. Blumenthal, and F. Marvel, “Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology,” in American Journal of Preventive Cardiology, 2022.
[BibTeX] [Link]@inproceedings{251936798, title = {Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology}, author = {{A. Javaid} and {Fawzi Zghyer} and {Chang H Kim} and {Erin M. Spaulding} and {Nino Isakadze} and {Jie Ding} and {Daniel Kargillis} and {Yumin Gao} and {Faisal Rahman} and {Donald E. Brown} and {S. Saria} and {Seth S. Martin} and {C. Kramer} and {R. Blumenthal} and {F. Marvel}}, year = 2022, month = {8}, booktitle = {American Journal of Preventive Cardiology}, url = {https://www.semanticscholar.org/paper/fe2f3307cb21f446a2e1272a008b2938cfd3d402}, } - B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, Gary S. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, B. Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. Mccradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Author Correction: Regenerative and restorative medicine for eye disease,” in Nature Network Boston, 2022.
[BibTeX] [Link]@inproceedings{251539930, title = {Author Correction: Regenerative and restorative medicine for eye disease}, author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {Gary S. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {B. Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. Mccradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}}, year = 2022, month = {8}, booktitle = {Nature Network Boston}, url = {https://www.semanticscholar.org/paper/a22215acadb4ad4ec04624025021023acf7261d6}, } - B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, Gary S. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, B. Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. Mccradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in Nature Network Boston, 2022.
[BibTeX] [Link]@inproceedings{248890002, title = {Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI}, author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {Gary S. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {B. Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. Mccradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}}, year = 2022, month = {5}, booktitle = {Nature Network Boston}, url = {https://www.semanticscholar.org/paper/83b6a76ba5112d27bdbfca3efd2ed918d8e73db5}, } - Jeya Maria Jose Valanarasu, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, J. Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, and Vishal M. Patel, “Interactive Portrait Harmonization,” in International Conference on Learning Representations, 2022.
[BibTeX] [Link]@inproceedings{247476364, title = {Interactive Portrait Harmonization}, author = {{Jeya Maria Jose Valanarasu} and {He Zhang} and {Jianming Zhang} and {Yilin Wang} and {Zhe Lin} and {J. Echevarria} and {Yinglan Ma} and {Zijun Wei} and {Kalyan Sunkavalli} and {Vishal M. Patel}}, year = 2022, month = {3}, booktitle = {International Conference on Learning Representations}, url = {https://www.semanticscholar.org/paper/c423c8ef2d8101676a4c2ba403ad5970c0364f09}, } - Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser,” in Interspeech, 2022.
[BibTeX] [Link]@inproceedings{252346818, title = {Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser}, author = {{Sonal Joshi} and {Saurabh Kataria} and {Yiwen Shao} and {Piotr Żelasko} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}}, year = 2022, month = {9}, booktitle = {Interspeech}, url = {https://www.semanticscholar.org/paper/b8c3c97f239a1048b460d659a14110cc7f7a499e}, } - Kangfu Mei, Yiqun Mei, and Vishal M. Patel, “Thermal to Visible Image Synthesis Under Atmospheric Turbulence,” in International Conference on Information Photonics, 2022.
[BibTeX] [Link]@inproceedings{248006370, title = {Thermal to Visible Image Synthesis Under Atmospheric Turbulence}, author = {{Kangfu Mei} and {Yiqun Mei} and {Vishal M. Patel}}, year = 2022, month = {4}, booktitle = {International Conference on Information Photonics}, url = {https://www.semanticscholar.org/paper/0a123eb1a768cc151ff9ebb004cc2461414a53a3}, } - 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}, } - 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.
[BibTeX] [Link]@inproceedings{252333098, title = {Addressing the 'coin flip model' and the role of 'process of care' variables in the analysis of TREWS}, author = {{R. Adams} and {K. Henry} and {S. Saria}}, year = 2022, month = {9}, booktitle = {medRxiv}, url = {https://www.semanticscholar.org/paper/39383cc7a62fdd63e05873096d7283d5f1b90d59}, } - 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.
[BibTeX]