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].

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.
[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)",
year = "2023",
month = jul,
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.
[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,
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.
[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,
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.
[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,
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.
[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,
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.
[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,
URL = "http://cs.jhu.edu/~jason/papers/#opedal-et-al-2023",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{tan-etal-2023-multilingual,
title = "Multilingual Representation Distillation with Contrastive Learning",
author = "Tan, Weiting and
Heffernan, Kevin and
Schwenk, Holger and
Koehn, Philipp",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.108",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{chen-etal-2023-iterative,
title = "Iterative Document-level Information Extraction via Imitation Learning",
author = "Chen, Yunmo and
Gantt, William and
Gu, Weiwei and
Chen, Tongfei and
White, Aaron and
Van Durme, Benjamin",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.136",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{qin-etal-2023-nlp,
title = "The {NLP} Task Effectiveness of Long-Range Transformers",
author = "Qin, Guanghui and
Feng, Yukun and
Van Durme, Benjamin",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.273",
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.",
}

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

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

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},
}

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

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

Alicia M. Braxton, A. Kiemen, Mia P. Grahn, André Forjaz, Jaanvi Mahesh Babu, Lily Zheng, Li-yu Jiang, H. Cheng, Q. Song, Rebecca Reichel, Sarah Graham, A. Damanakis, Catherine G. Fischer, Stephanie Mou, Cameron Metz, Julie Granger, Xiao-ding Liu, N. Bachmann, Cristina Almagro-Pérez, A. C. Jiang, Jeonghyun Yoo, Bridgette Kim, Scott Du, Eli Foster, Jocelyn Y Hsu, P.A. Rivera, L. Chu, Fengze Liu, N. Niknafs, E. Fishman, A. Yuille, Nicholas J. Roberts, E. Thompson, R. Scharpf, T. Cornish, Y. Jiao, R. Karchin, R. Hruban, Pei-Hsun Wu, D. Wirtz, and L. Wood, “Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions,” in bioRxiv, 2023.
[BibTeX] [Link]

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

Samik Sadhu and H. Hermansky, “Self-supervised Learning with Speech Modulation Dropout,” in arXiv.org, 2023.
[BibTeX] [Link]

@inproceedings{257687723,
title = {Self-supervised Learning with Speech Modulation Dropout},
author = {{Samik Sadhu} and {H. Hermansky}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/eb6358eca5f4ee632f929cb384d07b6a5f04e0ef},
}

Qixing Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, A. Yuille, and Zongwei Zhou, “Label-Free Liver Tumor Segmentation,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/74fc777becc43b9e94c2fb59ed3ee78d212ca01e},
}

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

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

Qihao Liu, Junfeng Wu, Yi Jiang, Xiang Bai, A. Yuille, and S. Bai, “InstMove: Instance Motion for Object-centric Video Segmentation,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0e60c1229d7963b605b83cb10a90ed6a8cf79149},
}

Jonah P. Sengupta and A. Andreou, “Retinomorphic Channel Design and Considerations,” in Annual Conference on Information Sciences and Systems, 2023.
[BibTeX] [Link]

@inproceedings{258074434,
title = {Retinomorphic Channel Design and Considerations},
author = {{Jonah P. Sengupta} and {A. Andreou}},
year = 2023,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/7f97effeed913a6089ca98d576d585401e251f9b},
}

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

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

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

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

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

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

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

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

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

@inproceedings{257532548,
title = {Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey},
author = {{Huali Xu} and {S. 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},
}

Qihao Liu, Adam Kortylewski, and A. Yuille, “PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation,” in arXiv.org, 2023.
[BibTeX] [Link]

@inproceedings{257495961,
title = {PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation},
author = {{Qihao Liu} and {Adam Kortylewski} and {A. Yuille}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/85fcce7ef6f5eec2d5e5bce82fc7246e8a90696c},
}

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

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

Jieneng Chen, Yingda Xia, Jiawen Yao, K. Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Ming Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, X. Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, A. Yuille, Zai-De Liu, and Ling Zhang, “Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans,” in arXiv.org, 2023.
[BibTeX] [Link]

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

V. Vibashan, Ning Yu, Chen Xing, Can Qin, M. Gao, Juan Carlos Niebles, Vishal M. Patel, and Ran Xu, “Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations,” in arXiv.org, 2023.
[BibTeX] [Link]

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

L. Eudy and Matt Post, “AC Transit Fuel Cell Electric Bus Progress Report (Data Period Focus: Jan. 2020 through Dec. 2020).” 2023.
[BibTeX] [Link]

@inproceedings{257848515,
title = {AC Transit Fuel Cell Electric Bus Progress Report (Data Period Focus: Jan. 2020 through Dec. 2020)},
author = {{L. Eudy} and {Matt Post}},
year = 2023,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/69cf8aae9aa20f261b91ad67636fc064a2376e7a},
}

Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, P. Kambadur, D. Rosenberg, and Gideon Mann, “BloombergGPT: A Large Language Model for Finance,” in arXiv.org, 2023.
[BibTeX] [Link]

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

Marc Marone and Benjamin Van Durme, “Data Portraits: Recording Foundation Model Training Data,” in arXiv.org, 2023.
[BibTeX] [Link]

@inproceedings{257378087,
title = {Data Portraits: Recording Foundation Model Training Data},
author = {{Marc Marone} and {Benjamin Van Durme}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/572b92972eff7501ca2b109b8998cdcb69aa1958},
}

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

@inproceedings{258236142,
title = {OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images},
author = {{Bingchen Zhao} and {Jiahao Wang} and {Wufei Ma} and {Artur Jesslen} and {Si-Jia Yang} and {Shaozuo Yu} and {O. Zendel} and {C. Theobalt} and {A. Yuille} and {Adam Kortylewski}},
year = 2023,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7c8fdef38766748ffb11559f6a45949dd0afe5b8},
}

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

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

Malsha V. Perera and Vishal M. Patel, “Analyzing Bias in Diffusion-based Face Generation Models,” in arXiv.org, 2023.
[BibTeX] [Link]

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

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},
}

Jie Liu, Yixiao Zhang, Jieneng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, A. Yuille, Yucheng Tang, and Zongwei Zhou, “CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection,” in arXiv.org, 2023.
[BibTeX] [Link]

@inproceedings{255372928,
title = {CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection},
author = {{Jie Liu} and {Yixiao Zhang} and {Jieneng Chen} and {Junfei Xiao} and {Yongyi Lu} and {Bennett A. Landman} and {Yixuan Yuan} and {A. Yuille} and {Yucheng Tang} and {Zongwei Zhou}},
year = 2023,
month = {1},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/cd7d70a3fc45608c2c17b784539a2add8ccffcf9},
}

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

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

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

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

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

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

A. Favaro, Chelsie Motley, Tianyu Cao, Miguel Iglesias, A. Butala, E. Oh, R. Stevens, J. Villalba, N. Dehak, and L. Moro-Velázquez, “A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders,” in Spoken Language Technology Workshop, 2023.
[BibTeX] [Link]

@inproceedings{256353599,
title = {A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders},
author = {{A. Favaro} and {Chelsie Motley} and {Tianyu Cao} and {Miguel Iglesias} and {A. Butala} and {E. Oh} and {R. Stevens} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2023,
month = {1},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/40eb935374d67b7b9979e0c9333c291d188c472b},
}

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

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

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

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

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

Andrew Blair-Stanek, Nils Holzenberger, and Benjamin Van Durme, “Can GPT-3 Perform Statutory Reasoning?,” in arXiv.org, 2023.
[BibTeX] [Link]

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

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

@inproceedings{258714648,
title = {Annotating 8, 000 Abdominal CT Volumes for Multi-Organ Segmentation in Three Weeks},
author = {{Chongyu Qu} and {Tiezheng Zhang} and {Hualin Qiao} and {Jie Liu} and {Yucheng Tang} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/39a915bd2a67b0a81996e65a74a2896c757fe10b},
}

Orion Weller, Dawn J Lawrie, and Benjamin Van Durme, “NevIR: Negation in Neural Information Retrieval,” in arXiv.org, 2023.
[BibTeX] [Link]

@inproceedings{258676146,
title = {NevIR: Negation in Neural Information Retrieval},
author = {{Orion Weller} and {Dawn J Lawrie} and {Benjamin Van Durme}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/402cbd67c202b0b2dc184861472bd99ebc0e69a4},
}

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},
}

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

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

Yutong Bai, Angtian Wang, Adam Kortylewski, and A. Yuille, “CoKe: Contrastive Learning for Robust Keypoint Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023.
[BibTeX] [Link]

@inproceedings{256649514,
title = {CoKe: Contrastive Learning for Robust Keypoint Detection},
author = {{Yutong Bai} and {Angtian Wang} and {Adam Kortylewski} and {A. Yuille}},
year = 2023,
month = {1},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/e7464dec34a34f257efd5ab3b195e4c98b222e50},
}

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

@inproceedings{258079344,
title = {Regularizing Contrastive Predictive Coding for Speech Applications},
author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2023,
month = {4},
booktitle = {},
url = {https://www.semanticscholar.org/paper/47ac48e7ee37e7cf4d3bb183477e42d6c5632b64},
}

A. Favaro, L. Moro-Velázquez, A. Butala, Chelsie Motley, Tianyu Cao, R. Stevens, J. Villalba, and N. Dehak, “Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson’s disease,” in Frontiers in Neurology, 2023.
[BibTeX] [Link]

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

Kangfu Mei, Mo Zhou, and Vishal M. Patel, “T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities.” 2023.
[BibTeX] [Link]

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

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

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

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},
}

Lingfeng Shen, Weiting Tan, Boyuan Zheng, and Daniel Khashabi, “Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f92dd36f1e08b9a8fc68896a3d72166d602280e2},
}

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

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

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},
}

Yu Zeng, Mo Zhou, Yuan Xue, and Vishal M. Patel, “Securing Deep Generative Models with Universal Adversarial Signature.” 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 = {},
url = {https://www.semanticscholar.org/paper/98e87cd4c19dad7018270b4561dc64b0109ee360},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{duh-zhang-2023-automl,
title = "{A}uto{ML} for {NLP}",
author = "Duh, Kevin and
Zhang, Xuan",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-tutorials.5",
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.",
}

Shiyue Zhang, Shijie Wu, Ozan Irsoy, Steven Lu, Mohit Bansal, Mark Dredze, and D. Rosenberg, “MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies.” 2023.
[BibTeX] [Link]

@inproceedings{258947786,
title = {MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies},
author = {{Shiyue Zhang} and {Shijie Wu} and {Ozan Irsoy} and {Steven Lu} and {Mohit Bansal} and {Mark Dredze} and {D. Rosenberg}},
year = 2023,
month = {5},
booktitle = {},
url = {https://www.semanticscholar.org/paper/d8d578d4ece329f17b025946587b1751721b9144},
}

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

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

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

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

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

@inproceedings{258060050,
title = {MOST: Multiple Object localization with Self-supervised Transformers for object discovery},
author = {{Sai Saketh Rambhatla} and {Ishan Misra} and {R. Chellappa} and {Abhinav Shrivastava}},
year = 2023,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a6200befc66a3efb1fdf5629db180e5b433b905e},
}

K. 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 Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 2023.
[BibTeX] [Link]

@inproceedings{258383693,
title = {Leveraging synthetic data for robust gesture recognition},
author = {{K. 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 = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications},
url = {https://www.semanticscholar.org/paper/922198774621861436721bd923dc0f0028872a84},
}

Enayat Ullah, Harry Lang, R. Arora, and V. Braverman, “Clustering using Approximate Nearest Neighbour Oracles,” in Trans. Mach. Learn. Res., 2023.
[BibTeX] [Link]

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

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

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

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},
}

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

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

Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, and Benjamin Van Durme, “Natural Language Decomposition and Interpretation of Complex Utterances,” in arXiv.org, 2023.
[BibTeX] [Link]

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

Ashwin Bellur, Karan Thakkar, and Mounya Elhilali, “Explicit-memory multiresolution adaptive framework for speech and music separation,” in EURASIP Journal on Audio, Speech, and Music Processing, 2023.
[BibTeX] [Link]

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

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

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

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

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

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

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

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.” 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 = {},
url = {https://www.semanticscholar.org/paper/c4e399ceeef9f99c8b633657f4c13de8378cde41},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{kim-etal-2022-prosocialdialog,
title = "{P}rosocial{D}ialog: A Prosocial Backbone for Conversational Agents",
author = "Kim, Hyunwoo and
Yu, Youngjae and
Jiang, Liwei and
Lu, Ximing and
Khashabi, Daniel and
Kim, Gunhee and
Choi, Yejin and
Sap, Maarten",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.267",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{stengel-eskin-van-durme-2022-curious,
title = "The Curious Case of Control",
author = "Stengel-Eskin, Elias and
Van Durme, Benjamin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.760",
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.",
}

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.
[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",
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",
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.",
}

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 arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/5f696247d384af650d07d3de30bd023a6128f048},
}

Desh Raj, Daniel Povey, and S. Khudanpur, “GPU-accelerated Guided Source Separation for Meeting Transcription,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7a8cb19ddec6b697111b220746def89570956ddf},
}

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},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{sia-etal-2022-offer,
title = "Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates",
author = "Sia, Suzanna and
Jaidka, Kokil and
Ahuja, Hansin and
Chhaya, Niyati and
Duh, Kevin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.818",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{keymanesh-etal-2022-makes,
title = "What Makes Data-to-Text Generation Hard for Pretrained Language Models?",
author = "Keymanesh, Moniba and
Benton, Adrian and
Dredze, Mark",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.50",
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.",
}

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

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

@inproceedings{schumacher-etal-2022-zero,
title = "Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models",
author = "Schumacher, Elliot and
Mayfield, James and
Dredze, Mark",
booktitle = "Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mrl-1.4",
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.",
}

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},
}

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},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{wang-etal-2022-super,
title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks",
author = "Wang, Yizhong and
Mishra, Swaroop and
Alipoormolabashi, Pegah and
Kordi, Yeganeh and
Mirzaei, Amirreza and
Naik, Atharva and
Ashok, Arjun and
Dhanasekaran, Arut Selvan and
Arunkumar, Anjana and
Stap, David and
Pathak, Eshaan and
Karamanolakis, Giannis and
Lai, Haizhi and
Purohit, Ishan and
Mondal, Ishani and
Anderson, Jacob and
Kuznia, Kirby and
Doshi, Krima and
Pal, Kuntal Kumar and
Patel, Maitreya and
Moradshahi, Mehrad and
Parmar, Mihir and
Purohit, Mirali and
Varshney, Neeraj and
Kaza, Phani Rohitha and
Verma, Pulkit and
Puri, Ravsehaj Singh and
Karia, Rushang and
Doshi, Savan and
Sampat, Shailaja Keyur and
Mishra, Siddhartha and
Reddy A, Sujan and
Patro, Sumanta and
Dixit, Tanay and
Shen, Xudong",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.340",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{deb-etal-2022-post,
title = "Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines",
author = "Deb, Kiron and
Zhang, Xuan and
Duh, Kevin",
booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.blackboxnlp-1.5",
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.",
}

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi, “Self-Instruct: Aligning Language Model with Self Generated Instructions,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{254877310,
title = {Self-Instruct: Aligning Language Model with Self Generated Instructions},
author = {{Yizhong Wang} and {Yeganeh Kordi} and {Swaroop Mishra} and {Alisa Liu} and {Noah A. Smith} and {Daniel Khashabi} and {Hannaneh Hajishirzi}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e65b346d442e9962a4276dc1c1af2956d9d5f1eb},
}

Kangda Wei, Dawn J Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller, “When Do Decompositions Help for Machine Reading?,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{254877586,
title = {When Do Decompositions Help for Machine Reading?},
author = {{Kangda Wei} and {Dawn J Lawrie} and {Benjamin Van Durme} and {Yunmo Chen} and {Orion Weller}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/624ea7bdaf7e8e3f7bd76f72aa665b562f0dd70a},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{xu-etal-2022-importance,
title = "The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains",
author = "Xu, Haoran and
Koehn, Philipp and
Murray, Kenton",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.13",
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.",
}

Zhuowan Li, Cihang Xie, Benjamin Van Durme, Alan Yuille Johns Hopkins University, U. California, and Santa Cruz, “Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?,” in arXiv.org, 2022.
[BibTeX] [Link]

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

Kangfu Mei and Vishal M. Patel, “VIDM: Video Implicit Diffusion Models,” in arXiv.org, 2022.
[BibTeX] [Link]

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

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},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{lin-etal-2022-gendered,
title = "Gendered Mental Health Stigma in Masked Language Models",
author = "Lin, Inna and
Njoo, Lucille and
Field, Anjalie and
Sharma, Ashish and
Reinecke, Katharina and
Althoff, Tim and
Tsvetkov, Yulia",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.139",
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.",
}

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

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

@inproceedings{rippeth-post-2022-additive,
title = "Additive Interventions Yield Robust Multi-Domain Machine Translation Models",
author = "Rippeth, Elijah and
Post, Matt",
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.14",
pages = "220--232",
abstract = "Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder{'}s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.",
}

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.
[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",
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",
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.",
}

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

@inproceedings{258685264,
title = {Defending Against Misinformation Attacks in Open-Domain Question Answering},
author = {{Orion Weller} and {Aleem Khan} and {Nathaniel Weir} and {Dawn J Lawrie} and {Benjamin Van Durme}},
year = 2022,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/55dca1a431f3de1fc3abceb6d5ff1d424936dd6c},
}

Nathaniel Weir, Ryan Thomas, Randolph D’Amore, Kellie Hill, Benjamin Van Durme, and Harsh Jhamtani, “Ontologically Faithful Generation of Non-Player Character Dialogues,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{254926993,
title = {Ontologically Faithful Generation of Non-Player Character Dialogues},
author = {{Nathaniel Weir} and {Ryan Thomas} and {Randolph D'Amore} and {Kellie Hill} and {Benjamin Van Durme} and {Harsh Jhamtani}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/68f1b94bbc900d2b5c60192a7e9eea4b046dd18a},
}

A. Favaro, Seneca Motley, Q. 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 {Q. 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},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{jiang-etal-2022-calibrating,
title = "Calibrating Zero-shot Cross-lingual (Un-)structured Predictions",
author = "Jiang, Zhengping and
Liu, Anqi and
Van Durme, Benjamin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.170",
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.",
}

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},
}

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

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

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

Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Unite and Conquer: Plug&Play Multi-Modal Synthesis using Diffusion Models.” 2022.
[BibTeX] [Link]

@inproceedings{258236725,
title = {Unite and Conquer: Plug&Play Multi-Modal Synthesis using Diffusion Models},
author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
year = 2022,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/24e6c62fd28da4ecf748620e1f25eae7337bad40},
}

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},
}

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},
}

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

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

@inproceedings{casacuberta-etal-2022-findings,
title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2022",
author = "Casacuberta, Francisco and
Foster, George and
Huang, Guoping and
Koehn, Philipp and
Kovacs, Geza and
Liu, Lemao and
Shi, Shuming and
Watanabe, Taro and
Zong, Chengqing",
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.75",
pages = "812--820",
abstract = "Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.",
}

Alex Mallen, Akari Asai, Victor Zhong, R. Das, Hannaneh Hajishirzi, and Daniel Khashabi, “When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{254877603,
title = {When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories},
author = {{Alex Mallen} and {Akari Asai} and {Victor Zhong} and {R. Das} and {Hannaneh Hajishirzi} and {Daniel Khashabi}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/6870b2deac76db453dffc9c0084d3a5e8146ebc3},
}

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

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

@inproceedings{wicks-post-2022-sentence,
title = "Does Sentence Segmentation Matter for Machine Translation?",
author = "Wicks, Rachel and
Post, Matt",
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.78",
pages = "843--854",
abstract = "For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, {``}does segmentation matter?{''} and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.",
}

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

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

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},
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{feng-etal-2022-toward,
title = "Toward the Limitation of Code-Switching in Cross-Lingual Transfer",
author = "Feng, Yukun and
Li, Feng and
Koehn, Philipp",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.400",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{feng-etal-2022-automatic,
title = "Automatic Document Selection for Efficient Encoder Pretraining",
author = "Feng, Yukun and
Xia, Patrick and
Van Durme, Benjamin and
Sedoc, Jo{\~a}o",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.647",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{gu-etal-2022-empirical,
title = "An Empirical Study on Finding Spans",
author = "Gu, Weiwei and
Zheng, Boyuan and
Chen, Yunmo and
Chen, Tongfei and
Van Durme, Benjamin",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.264",
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.",
}

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{marchisio-etal-2022-bilingual,
title = "Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport",
author = "Marchisio, Kelly and
Saad-Eldin, Ali and
Duh, Kevin and
Priebe, Carey and
Koehn, Philipp",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.164",
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, Q. Samus, Pedro Irazoqui, N. Dehak, and Esther S. Oh, “Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer’s Disease,” in Alzheimer’s & Dementia, 2022.
[BibTeX] [Link]

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

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",
}

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

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

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},
}

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},
}

Elias Stengel-Eskin, Jimena Guallar-Blasco, Yi Zhou, and Benjamin Van Durme, “Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{253510682,
title = {Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA},
author = {{Elias Stengel-Eskin} and {Jimena Guallar-Blasco} and {Yi Zhou} and {Benjamin Van Durme}},
year = 2022,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8536182e4379687e10517fd8ab587679641f983b},
}

Bardia Safaei, V. 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 {V. 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},
}

Elias Stengel-Eskin and Benjamin Van Durme, “Calibrated Interpretation: Confidence Estimation in Semantic Parsing,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b15cddd33b36d1f38a8e59412026f6dfde0ca38d},
}

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},
}

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

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

Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, J. Collomosse, Jason Kuen, and Vishal M. Patel, “SceneComposer: Any-Level Semantic Image Synthesis,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/4cc5266166478592ec8539a2b940740b8d380cdd},
}

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},
}

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},
}

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},
}

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 arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a135632a05cc1f3311859fdebcd1350b4e9e1ee7},
}

Yuanze Lin, Chen Wei, Huiyu Wang, A. Yuille, and Cihang Xie, “SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training,” in arXiv.org, 2022.
[BibTeX] [Link]

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

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},
}

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},
}

S. Welleck, Ximing Lu, Peter West, Faeze Brahman, T. Shen, Daniel Khashabi, and Yejin Choi, “Generating Sequences by Learning to Self-Correct,” in arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/538288d24bdad73d831dfed44b706958287ed318},
}

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},
}

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

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

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},
}

V. 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 = {{V. 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},
}

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},
}

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

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

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},
}

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},
}

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

@inproceedings{252907521,
title = {Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution},
author = {{Nupoor Gandhi} and {Anjalie Field} and {Emma Strubell}},
year = 2022,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/43f09be116b87046334d395a71919ab423b204a1},
}

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},
}

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

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

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

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},
}

G. Botev, A. D. McCarthy, W. Wu, and D. Yarowsky, “Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages,” in Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, 2022, p. 5309–5326.
[BibTeX] [Abstract] [Link]

This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.

@inproceedings{botev-etal-2022-deciphering,
title = "Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages",
author = "Botev, Georgie and
McCarthy, Arya D. and
Wu, Winston and
Yarowsky, David",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.472",
pages = "5309--5326",
abstract = "This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.",
}

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},
}

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 arXiv.org, 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 = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a8a2a8229f99c291bf71ec92b801a073854c52e2},
}

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},
}

Nikil Selvam, Sunipa Dev, Daniel Khashabi, Tushar Khot, and Kai-Wei Chang, “The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{252968208,
title = {The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks},
author = {{Nikil Selvam} and {Sunipa Dev} and {Daniel Khashabi} and {Tushar Khot} and {Kai-Wei Chang}},
year = 2022,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/74dd68b4ca6444f56bad9079289c99878e051a0f},
}

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

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

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

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

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

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

K. Marchisio, C. Xiong, and P. Koehn, “Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings,” in Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), Orlando, USA, 2022, p. 264–273.
[BibTeX] [Abstract] [Link]

A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New methods that outperform GIZA++ primarily rely on large machine translation models, massively multilingual language models, or supervision from GIZA++ alignments itself. We introduce Embedding-Enhanced GIZA++, and outperform GIZA++ without any of the aforementioned factors. Taking advantage of monolingual embedding spaces of source and target language only, we exceed GIZA++{‘}s performance in every tested scenario for three languages pairs. In the lowest-resource setting, we outperform GIZA++ by 8.5, 10.9, and 12 AER for RoEn, De-En, and En-Fr, respectively. We release our code at www.blind-review.code.

@inproceedings{marchisio-etal-2022-embedding,
title = "Embedding-Enhanced {GIZA}++: Improving Low-Resource Word Alignment Using Embeddings",
author = "Marchisio, Kelly and
Xiong, Conghao and
Koehn, Philipp",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.20",
pages = "264--273",
abstract = "A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New methods that outperform GIZA++ primarily rely on large machine translation models, massively multilingual language models, or supervision from GIZA++ alignments itself. We introduce Embedding-Enhanced GIZA++, and outperform GIZA++ without any of the aforementioned factors. Taking advantage of monolingual embedding spaces of source and target language only, we exceed GIZA++{'}s performance in every tested scenario for three languages pairs. In the lowest-resource setting, we outperform GIZA++ by 8.5, 10.9, and 12 AER for RoEn, De-En, and En-Fr, respectively. We release our code at www.blind-review.code.",
}

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

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

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

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

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

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

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

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

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

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 [email protected] and induces qualitatively better narrative chains.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@inproceedings{wu-yarowsky-2022-robustness,
title = "On the Robustness of Cognate Generation Models",
author = "Wu, Winston and
Yarowsky, David",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.458",
pages = "4299--4305",
abstract = "We evaluate two popular neural cognate generation models{'} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{'} results with respect to each error type to explain how and why these models perform as they do.",
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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",
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",
}

Junfeng Wu, Qihao Liu, Yi Jiang, S. Bai, A. Yuille, and Xiang Bai, “In Defense of Online Models for Video Instance Segmentation,” in European Conference on Computer Vision, 2022.
[BibTeX] [Link]

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

Suzanna Sia, Kokil Jaidka, Niyati Chayya, and Kevin Duh, “Modeling Constraints Can Identify Winning Arguments in Multi-Party Interactions (Student Abstract),” in AAAI Conference on Artificial Intelligence, 2022.
[BibTeX] [Link]

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

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,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/89d794843eadb7eca6889e24f9fb374334fd85f7},
}

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

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

@inproceedings{kanashiro-pereira-2022-attention,
title = "Attention-Focused Adversarial Training for Robust Temporal Reasoning",
author = "Kanashiro Pereira, Lis",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.800",
pages = "7352--7359",
abstract = "We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.",
}

Nathaniel Weir and Benjamin Van Durme, “Dynamic Generation of Interpretable Inference Rules in a Neuro-Symbolic Expert System,” in arXiv.org, 2022.
[BibTeX] [Link]

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

Jared Markowitz, Ryan W. Gardner, A. Llorens, R. Arora, and I.-J. Wang, “A Risk-Sensitive Approach to Policy Optimization,” in arXiv.org, 2022.
[BibTeX] [Link]

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

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

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

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

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.
[BibTeX] [Abstract] [Link]

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

@inproceedings{stengel-eskin-etal-2022-data,
title = "When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems",
author = "Stengel-Eskin, Elias and
Platanios, Emmanouil Antonios and
Pauls, Adam and
Thomson, Sam and
Fang, Hao and
Van Durme, Benjamin and
Eisner, Jason and
Su, Yu",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.789",
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.",
}

K. Henry, R. Kornfield, A. Sridharan, Robert C. Linton, Catherine Groh, Tony Wang, Albert W Wu, Bilge Mutlu, and S. Saria, “Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system,” in npj Digital Medicine, 2022.
[BibTeX] [Link]

@inproceedings{250703036,
title = {Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system},
author = {{K. Henry} and {R. Kornfield} and {A. Sridharan} and {Robert C. Linton} and {Catherine Groh} and {Tony Wang} and {Albert W Wu} and {Bilge Mutlu} and {S. Saria}},
year = 2022,
month = {7},
booktitle = {npj Digital Medicine},
url = {https://www.semanticscholar.org/paper/22ea0aa6c750d327529053d66e4f0a9457485402},
}

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

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

Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning,” in International Journal of Computer Vision, 2022.
[BibTeX] [Link]

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

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},
}

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

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

Pirazh Khorramshahi, V. Shenoy, M. Pack, and R. Chellappa, “Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking,” in arXiv.org, 2022.
[BibTeX] [Link]

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

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

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

Martin Sustek, Samik Sadhu, and H. Hermansky, “Dealing with Unknowns in Continual Learning for End-to-end Automatic Speech Recognition,” in Interspeech, 2022.
[BibTeX] [Link]

@inproceedings{252337895,
title = {Dealing with Unknowns in Continual Learning for End-to-end Automatic Speech Recognition},
author = {{Martin Sustek} and {Samik Sadhu} and {H. Hermansky}},
year = 2022,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/dea2103e2b666413670b3f5c81a2e3ca318ea2d4},
}

Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, B. Green, E. Engle, Guillermo Almodovar, Ryan Walk, S. Soto-Diaz, J. Taube, A. Szalay, and A. Yuille, “A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation,” in [email protected], 2022.
[BibTeX] [Link]

@inproceedings{252440016,
title = {A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation},
author = {{Yixiao Zhang} and {Adam Kortylewski} and {Qing Liu} and {Seyoun Park} and {B. Green} and {E. Engle} and {Guillermo Almodovar} and {Ryan Walk} and {S. Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
year = 2022,
booktitle = {[email protected]},
url = {https://www.semanticscholar.org/paper/0a60e89b5bdbac414be744f8d7fb2347c709df64},
}

Shraman Pramanick, E. Nowara, Joshua Gleason, C. Castillo, and R. Chellappa, “Where in the World is this Image? Transformer-based Geo-localization in the Wild,” in European Conference on Computer Vision, 2022.
[BibTeX] [Link]

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

Y. Xia, Q. Yu, L. Chu, S. Kawamoto, S. Park, F. Liu, J. Chen, Z. Zhu, B. Li, Z. Zhou, Y. Lu, Y. Wang, W. Shen, L. Xie, Y. Zhou, elliot k fishman, A. Javed, D. Fouladi, S. Shayesteh, J. Graves, A. Blanco, E. Zinreich, B. Kinny-Koster, K. Kinzler, R. Hruban, B. Vogelstein, A. Yuille, and E. Fishman, “The FELIX Project: Deep Networks To Detect Pancreatic Neoplasms,” in medRxiv, 2022.
[BibTeX] [Link]

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

W. G. C. Bandara and Vishal M. Patel, “HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening,” in Computer Vision and Pattern Recognition, 2022.
[BibTeX] [Link]

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

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},
}

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},
}

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

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

Geoffrey M. Gray, L. Ahumada, Ayah Zirikly, Masoud Rouhizadeh, Thomas Richards, and E. Hatef, “Application of Natural Language Processing to Identify Social Needs from The Electronic Health Record’s Free-Text Notes,” in American Medical Informatics Association Annual Symposium, 2022.
[BibTeX] [Link]

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

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

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

Malsha V. Perera, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “SAR Despeckling Using a Denoising Diffusion Probabilistic Model,” in IEEE Geoscience and Remote Sensing Letters, 2022.
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title = {SAR Despeckling Using a Denoising Diffusion Probabilistic Model},
author = {{Malsha V. Perera} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
year = 2022,
month = {6},
booktitle = {IEEE Geoscience and Remote Sensing Letters},
url = {https://www.semanticscholar.org/paper/d49713b2126f4b224a75b3bfea3e00c63c7e51e3},
}

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

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

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

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

R. Arora, Raef Bassily, Crist’obal Guzm’an, Michael Menart, and Enayat Ullah, “Differentially Private Generalized Linear Models Revisited,” in Neural Information Processing Systems, 2022.
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url = {https://www.semanticscholar.org/paper/6c1ae5bd9aa32a08141bf630c90b00523ef4ffb1},
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Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, A. Yuille, Yuyin Zhou, and Cihang Xie, “Masked Autoencoders Enable Efficient Knowledge Distillers,” in arXiv.org, 2022.
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B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, G. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, Bart F Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. McCradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in British medical journal, 2022.
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Saksham Suri, Saketh Rambhatla, R. Chellappa, and Abhinav Shrivastava, “R-SSL: Region based Semi-Supervised Learning for Sparsely Annotated Object Detection.” 2022.
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url = {https://www.semanticscholar.org/paper/e2e159205030b9d3e3d742b4bdbebd7e94201d3f},
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Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, and N. Dehak, “Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification,” in Interspeech, 2022.
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Jaejin Cho, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Non-Contrastive Self-Supervised Learning for Utterance-Level Information Extraction From Speech,” in IEEE Journal on Selected Topics in Signal Processing, 2022.
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Zili Huang, Shinji Watanabe, Shu-wen Yang, Leibny Paola García-Perera, and S. Khudanpur, “Investigating Self-Supervised Learning for Speech Enhancement and Separation,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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Harminder Singh, R. Sharma, and R. Arora, “A Novel Dual-band filtenna for 2.4 and 5.8 GHz Wireless Local Area for Network Applications,” in 2022 Interdisciplinary Research in Technology and Management (IRTM), 2022.
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Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in arXiv.org, 2022.
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Jaejin Cho, R. Pappagari, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations,” in Interspeech, 2022.
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Samik Sadhu and H. Hermansky, “Complex Frequency Domain Linear Prediction: A Tool to Compute Modulation Spectrum of Speech,” in Interspeech, 2022.
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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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Shota Horiguchi, Shinji Watanabe, Leibny Paola García-Perera, Yuki Takashima, and Y. Kawaguchi, “Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2022.
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Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation,” in Computer Vision and Pattern Recognition, 2022.
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Cheng Peng and R. Chellappa, “PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images,” in arXiv.org, 2022.
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Rajeev Yasarla, C. Priebe, and Vishal M. Patel, “ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images,” in European Conference on Computer Vision, 2022.
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V. Vibashan, Poojan Oza, and Vishal M. Patel, “Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection,” in arXiv.org, 2022.
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Magdalena Rybicka, J. Villalba, N. Dehak, and K. Kowalczyk, “End-to-End Neural Speaker Diarization with an Iterative Refinement of Non-Autoregressive Attention-based Attractors,” in Interspeech, 2022.
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A. Hussein, S. A. Chowdhury, Ahmed Abdelali, N. Dehak, Ahmed M. Ali, and S. Khudanpur, “Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition,” in Spoken Language Technology Workshop, 2022.
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Amir Alipour-Fanid, Monireh Dabaghchian, R. Arora, and K. Zeng, “Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach,” in IEEE Transactions on Cognitive Communications and Networking, 2022.
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Nithin Gopalakrishnan Nair, Rajeev Yasarla, and Vishal M. Patel, “NBD-GAP: Non-Blind Image Deblurring without Clean Target Images,” in International Conference on Information Photonics, 2022.
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Vipul Gupta, Zhuowan Li, Adam Kortylewski, Chenyu Zhang, Yingwei Li, and A. Yuille, “SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering,” in Computer Vision and Pattern Recognition, 2022.
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Rajeev Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel, “Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN,” in International Conference on Pattern Recognition, 2022.
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Nithin Gopalakrishnan Nair, Kangfu Mei, and Vishal M. Patel, “A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration,” in International Conference on Information Photonics, 2022.
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Sai Saketh Rambhatla, Saksham Suri, R. Chellappa, and Abhinav Shrivastava, “Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach,” in arXiv.org, 2022.
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Nils Holzenberger, Yunmo Chen, and Benjamin Van Durme, “Asking the Right Questions in Low Resource Template Extraction,” in arXiv.org, 2022.
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url = {https://www.semanticscholar.org/paper/196b71b4e8465dd632954cf499f0467754cbd9d4},
}

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

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

Rajeev Yasarla and Vishal M. Patel, “CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2022.
[BibTeX] [Link]

@inproceedings{249929578,
title = {CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence},
author = {{Rajeev Yasarla} and {Vishal M. Patel}},
year = 2022,
month = {4},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/59f5937d4d7a81185a7a0501059c42cee271432f},
}

Bruce Y Lee, J. Ordovás, E. J. Parks, Cheryl AM Anderson, A. Barabasi, S. Clinton, K. Haye, V. Duffy, P. Franks, E. Ginexi, K. Hammond, E. Hanlon, Michael Hittle, Emily Ho, A. Horn, R. Isaacson, P. Mabry, Susan E. Malone, Corby K. Martin, J. Mattei, S. Meydani, L. 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. Barabasi} and {S. Clinton} and {K. Haye} and {V. Duffy} and {P. Franks} and {E. Ginexi} and {K. Hammond} and {E. 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 {L. 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},
}

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.",
}

Xianhang Li, Huiyu Wang, Chen Wei, Jieru Mei, A. Yuille, Yuyin Zhou, and Cihang Xie, “In Defense of Image Pre-Training for Spatiotemporal Recognition,” in European Conference on Computer Vision, 2022.
[BibTeX] [Link]

@inproceedings{248512944,
title = {In Defense of Image Pre-Training for Spatiotemporal Recognition},
author = {{Xianhang Li} and {Huiyu Wang} and {Chen Wei} and {Jieru Mei} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
year = 2022,
month = {5},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/c0c0139333b9c642fe7789f4fe8f27bc647c280d},
}

W. G. C. Bandara, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{249953553,
title = {DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models},
author = {{W. G. C. Bandara} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
year = 2022,
month = {6},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/69a483159b03543e0a750776057218674287953b},
}

V. Vibashan, Poojan Oza, and Vishal M. Patel, “Towards Online Domain Adaptive Object Detection,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
[BibTeX] [Link]

@inproceedings{248085083,
title = {Towards Online Domain Adaptive Object Detection},
author = {{V. Vibashan} and {Poojan Oza} and {Vishal M. Patel}},
year = 2022,
month = {4},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/ae1a767e40ce43b3cdcc2440a91dfe4a77cad901},
}

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},
}

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},
}

Suraj Nair, Eugene Yang, Dawn J Lawrie, Kevin Duh, Paul McNamee, Kenton Murray, J. Mayfield, and D. Oard, “Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models,” in European Conference on Information Retrieval, 2022.
[BibTeX] [Link]

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

Sangwook Park, Sandeep Reddy Kothinti, and Mounya Elhilali, “Temporal coding with magnitude-phase regularization for sound event detection,” in Interspeech, 2022.
[BibTeX] [Link]

@inproceedings{252337295,
title = {Temporal coding with magnitude-phase regularization for sound event detection},
author = {{Sangwook Park} and {Sandeep Reddy Kothinti} and {Mounya Elhilali}},
year = 2022,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/2c502d4f5eeb29bbf282d78d111cab0ed5d4cc00},
}

J. Villalba, B. J. Borgstrom, Saurabh Kataria, Magdalena Rybicka, C. Castillo, Jaejin Cho, Leibny Paola García-Perera, P. Torres-Carrasquillo, and N. Dehak, “Advances in Cross-Lingual and Cross-Source Audio-Visual Speaker Recognition: The JHU-MIT System for NIST SRE21,” in The Speaker and Language Recognition Workshop, 2022.
[BibTeX] [Link]

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

Ramraj Chandradevan, Eugene Yang, M. Yarmohammadi, and Eugene Agichtein, “Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval,” in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022.
[BibTeX] [Link]

@inproceedings{250340438,
title = {Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval},
author = {{Ramraj Chandradevan} and {Eugene Yang} and {M. Yarmohammadi} and {Eugene Agichtein}},
year = 2022,
month = {7},
booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
url = {https://www.semanticscholar.org/paper/f0c4f3cb741548c70a4db105fee227fc4f59dfd2},
}

A. Javaid, Fawzi Zghyer, Chang H Kim, Erin M. Spaulding, N. 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 {N. 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},
}

H. Goldman, Julia Porcino, G. Divita, Ayah Zirikly, Bart Desmet, Maryanne Sacco, E. Marfeo, Christine McDonough, E. Rasch, and L. Chan, “Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.,” in Psychiatric Services, 2022.
[BibTeX] [Link]

@inproceedings{249276757,
title = {Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.},
author = {{H. Goldman} and {Julia Porcino} and {G. Divita} and {Ayah Zirikly} and {Bart Desmet} and {Maryanne Sacco} and {E. Marfeo} and {Christine McDonough} and {E. Rasch} and {L. Chan}},
year = 2022,
month = {6},
booktitle = {Psychiatric Services},
url = {https://www.semanticscholar.org/paper/99410edf5a03b98ff66fa16e86bc39412fefa2e6},
}

Jeya Maria Jose Valanarasu, Pengfei Guo, V. Vibashan, and Vishal M. Patel, “On-the-Fly Test-time Adaptation for Medical Image Segmentation,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{247411201,
title = {On-the-Fly Test-time Adaptation for Medical Image Segmentation},
author = {{Jeya Maria Jose Valanarasu} and {Pengfei Guo} and {V. Vibashan} and {Vishal M. Patel}},
year = 2022,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3b8c4a2a005df6dc7e9fb0b9e2e81a887ace5a6c},
}

Aimon Rahman, Jeya Maria Jose Valanarasu, I. Hacihaliloglu, and Vishal M. Patel, “Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
[BibTeX] [Link]

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

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

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

Gerardo Flores, George H. Chen, T. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce Ho, and Tristan Naumann, “Conference on Health, Inference, and Learning (CHIL) 2022,” in ACM Conference on Health, Inference, and Learning, 2022.
[BibTeX] [Link]

@inproceedings{248397783,
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,
month = {3},
booktitle = {ACM Conference on Health, Inference, and Learning},
url = {https://www.semanticscholar.org/paper/20d7a0ea43dfc3c086fd41ca90f8885ea892f965},
}

Jeya Maria Jose Valanarasu, He Zhang, Jianming Zhang, Yilin Wang, Zhe L. Lin, J. Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, and Vishal M. Patel, “Interactive Portrait Harmonization,” in arXiv.org, 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 L. Lin} and {J. Echevarria} and {Yinglan Ma} and {Zijun Wei} and {Kalyan Sunkavalli} and {Vishal M. Patel}},
year = 2022,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/432a1bedd67619e66580fed6de48d8df852c36bf},
}

A. Kala, E. McCollum, and Mounya Elhilali, “Implications of clinical variability on computer-aided lung auscultation classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022.
[BibTeX] [Link]

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

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},
}

K. Henry, R. Adams, Cassandra Parent, Hossein Soleimani, A. Sridharan, Lauren Johnson, D. Hager, S. Cosgrove, Andrew Markowski, E. Klein, E. Chen, M. Saheed, Maureen Henley, S. Miranda, Katrina Houston, Robert C. Linton, Anushree R Ahluwalia, Albert W Wu, and S. Saria, “Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing,” in Nature Network Boston, 2022.
[BibTeX] [Link]

@inproceedings{250953863,
title = {Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing},
author = {{K. Henry} and {R. Adams} and {Cassandra Parent} and {Hossein Soleimani} and {A. Sridharan} and {Lauren Johnson} and {D. Hager} and {S. Cosgrove} and {Andrew Markowski} and {E. Klein} and {E. Chen} and {M. Saheed} and {Maureen Henley} and {S. Miranda} and {Katrina Houston} and {Robert C. Linton} and {Anushree R Ahluwalia} and {Albert W Wu} and {S. Saria}},
year = 2022,
month = {7},
booktitle = {Nature Network Boston},
url = {https://www.semanticscholar.org/paper/4cf1afc1e27d26a77aca58d7a5ec7fe3d6b7ffad},
}

Nithin Gopalakrishnan Nair, Kangfu Mei, and Vishal M. Patel, “AT-DDPM: Restoring Faces Degraded by Atmospheric Turbulence Using Denoising Diffusion Probabilistic Models,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
[BibTeX] [Link]

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

Feng Wang, Huiyu Wang, Chen Wei, A. Yuille, and Wei Shen, “CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation,” in European Conference on Computer Vision, 2022.
[BibTeX] [Link]

@inproceedings{247596632,
title = {CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation},
author = {{Feng Wang} and {Huiyu Wang} and {Chen Wei} and {A. Yuille} and {Wei Shen}},
year = 2022,
month = {3},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/3eb748f6279de5cfc582b3179bd1012bbd95614e},
}

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

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

A. Hussein, S. A. Chowdhury, Ahmed Abdelali, N. Dehak, and Ahmed M. Ali, “Code-Switching Text Augmentation for Multilingual Speech Processing,” in arXiv.org, 2022.
[BibTeX] [Link]

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

Lianhui Qin, S. Welleck, Daniel Khashabi, and Yejin Choi, “COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics,” in Neural Information Processing Systems, 2022.
[BibTeX] [Link]

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

Emma Bigelow, S. Saria, B. Piening, B. Curti, A. Dowdell, R. Weerasinghe, C. Bifulco, W. Urba, N. Finkelstein, E. Fertig, A. Baras, N. Zaidi, E. Jaffee, and M. Yarchoan, “A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy,” in Cancer Informatics, 2022.
[BibTeX] [Link]

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

Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{248069341,
title = {Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser},
author = {{Sonal Joshi} and {Saurabh Kataria} and {Yiwen Shao} and {Piotr Żelasko} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}},
year = 2022,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/49011d1b139bbb65fe273fd9e4b2197cee237385},
}

W. G. C. Bandara and Vishal M. Patel, “Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{248227361,
title = {Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images},
author = {{W. G. C. Bandara} and {Vishal M. Patel}},
year = 2022,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3e0fda3d87152ddafdc8357765036c52a8f477d7},
}

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

@inproceedings{258588228,
title = {Representation Projection Invariance Mitigates Representation Collapse},
author = {{Anastasia Razdaibiedina} and {A. Khetan} and {Zohar S. Karnin} and {Daniel Khashabi} and {Vishaal Kapoor} and {V. Madan}},
year = 2022,
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Chan Young Park, Julia Mendelsohn, Anjalie Field, and Yulia Tsvetkov, “Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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author = {{Chan Young Park} and {Julia Mendelsohn} and {Anjalie Field} and {Yulia Tsvetkov}},
year = 2022,
month = {5},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/b616154578751e156b21561e1a5d5ed833a3506f},
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Kelly Marchisio, Conghao Xiong, and Philipp Koehn, “Embedding-Enhanced GIZA++: Improving Word Alignment Using Embeddings.” 2022.
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title = {Embedding-Enhanced GIZA++: Improving Word Alignment Using Embeddings},
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booktitle = {},
url = {https://www.semanticscholar.org/paper/f40adef732f55b1fe4206339d3b51f18da65d4d4},
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Tae Soo Kim, Bohoon Shim, Michael Peven, Weichao Qiu, A. Yuille, and Gregory Hager, “Learning from Synthetic Vehicles,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2022.
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year = 2022,
month = {1},
booktitle = {2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)},
url = {https://www.semanticscholar.org/paper/b5ac4931672397df6d9135e0d5b615351f490a44},
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Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, A. Yuille, Yuyin Zhou, and Cihang Xie, “A Simple Data Mixing Prior for Improving Self-Supervised Learning,” in Computer Vision and Pattern Recognition, 2022.
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month = {6},
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url = {https://www.semanticscholar.org/paper/5ed5dcb0763af9e6283dcdcf4af75248d9d19c95},
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B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, G. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, Bart F Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. McCradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in Nature Network Boston, 2022.
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url = {https://www.semanticscholar.org/paper/83b6a76ba5112d27bdbfca3efd2ed918d8e73db5},
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, I. Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, M. Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, S. Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi, Noah A. Smith, and Daniel Khashabi, “Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks,” in arXiv.org, 2022.
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url = {https://www.semanticscholar.org/paper/ec64e324ce1210fe5245dfd0fb5a92058732e5b9},
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Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, A. Yuille, and Cihang Xie, “F AST A DV P ROP.” 2022.
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url = {https://www.semanticscholar.org/paper/f2eaaf8afc89a86035fd7127305f2bb9d2169495},
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Nithin Gopalakrishnan Nair and Vishal M. Patel, “T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2022.
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url = {https://www.semanticscholar.org/paper/fc49634e80ab31929799786a97b7ea63834bbdb1},
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Jieneng Chen, Shuyang Sun, Ju He, Philip H. S. Torr, A. Yuille, and Song Bai, “Supplementary Materials: ”TransMix: Attend to Mix for Vision Transformers”.” 2022.
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url = {https://www.semanticscholar.org/paper/d378dc21ab5cfbde24b295ab759c9947f820bc94},
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Haoran Xu and Kenton Murray, “Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer,” in NAACL-HLT, 2022.
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month = {4},
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url = {https://www.semanticscholar.org/paper/811a5c79d8c0f6f5b57697e7be0e84b5f9a94ce8},
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Malsha V. Perera, W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SAR Despeckling Using Overcomplete Convolutional Networks,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
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month = {5},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
url = {https://www.semanticscholar.org/paper/c4911e20fb50f6da552c812bda9ef4fdb525b939},
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Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “Appendix for k -means Mask Transformer.” 2022.
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B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, G. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, Bart F Geerts, Mudathir Ibrahim, Xiaoxuan Liu, B. Mateen, P. Mathur, M. McCradden, L. Morgan, Johan Ordish, Campbell Rogers, S. Saria, D. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in Nature Network Boston, 2022.
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Boyuan Zheng, Patrick Xia, M. Yarmohammadi, and Benjamin Van Durme, “Multilingual Coreference Resolution in Multiparty Dialogue,” in arXiv.org, 2022.
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Nllb team, M. Costa-jussà, James Cross, Onur cCelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Alison Youngblood, Bapi Akula, Loïc Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, S. Spruit, C. Tran, Pierre Yves Andrews, N. F. Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzm’an, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang, “No Language Left Behind: Scaling Human-Centered Machine Translation,” in arXiv.org, 2022.
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year = 2022,
month = {7},
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url = {https://www.semanticscholar.org/paper/0e638ce20f3e9b4dd1c10c32a29495c798425e63},
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Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, and A. Yuille, “VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis,” in arXiv.org, 2022.
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year = 2022,
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url = {https://www.semanticscholar.org/paper/31e79b62a9483dcdf2575603469e6ff888e7f234},
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Matthew Maciejewski, Jing Shi, Shinji Watanabe, and S. Khudanpur, “A dilemma of ground truth in noisy speech separation and an approach to lessen the impact of imperfect training data,” in Computer Speech and Language, 2022.
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year = 2022,
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url = {https://www.semanticscholar.org/paper/e3b6ab2d2e1a0e734bf505fbb34dc6fe723ab37e},
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Hexin Liu, Leibny Paola García Perera, Andy W. H. Khong, S. Styles, and S. Khudanpur, “PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification,” in Interspeech, 2022.
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url = {https://www.semanticscholar.org/paper/3f7542a6f77db123632ac723ab49f5a62f6184e3},
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Yiwen Shao, J. Villalba, Sonal Joshi, Saurabh Kataria, S. Khudanpur, and N. Dehak, “Chunking Defense for Adversarial Attacks on ASR,” in Interspeech, 2022.
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year = 2022,
month = {9},
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url = {https://www.semanticscholar.org/paper/ace27d0f6e93765439e19203e69570cf00f09e63},
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Nathaniel Weir and Benjamin Van Durme, “Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System.” 2022.
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Kangfu Mei, Yiqun Mei, and Vishal M. Patel, “Thermal to Visible Image Synthesis Under Atmospheric Turbulence,” in International Conference on Information Photonics, 2022.
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Yukun Feng, Feng Li, Ziang Song, Boyuan Zheng, and Philipp Koehn, “Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation,” in NAACL-HLT, 2022.
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Xiangyu Zhang, Zhanhong He, Shuyu Li, R. Togneri, and Leibny Paola García-Perera, “Investigating self-supervised learning for lyrics recognition,” in arXiv.org, 2022.
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Pengfei Guo, Yiqun Mei, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer,” in arXiv.org, 2022.
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Xiaolei Huang, Franck Dernoncourt, and Mark Dredze, “Enriching Unsupervised User Embedding via Medical Concepts,” in ACM Conference on Health, Inference, and Learning, 2022.
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Tasnim Mohiuddin, Philipp Koehn, Vishrav Chaudhary, James Cross, Shruti Bhosale, and Shafiq R. Joty, “Data Selection Curriculum for Neural Machine Translation,” in Conference on Empirical Methods in Natural Language Processing, 2022.
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R. Adams, K. Henry, A. Sridharan, Hossein Soleimani, A. Zhan, Nishi Rawat, Lauren Johnson, D. Hager, S. Cosgrove, Andrew Markowski, E. Klein, E. Chen, M. Saheed, Maureen Henley, S. Miranda, Katrina Houston, Robert C. Linton, Anushree R Ahluwalia, Albert W Wu, and S. Saria, “Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis,” in Nature Network Boston, 2022.
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R. Arora, Raef Bassily, Tom’as Gonz’alez, Crist’obal Guzm’an, Michael Menart, and Enayat Ullah, “Faster Rates of Convergence to Stationary Points in Differentially Private Optimization,” in arXiv.org, 2022.
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@inproceedings{249282662,
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Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hatwig Adam, A. Yuille, and Liang-Chieh Chen, “k-means Mask Transformer,” in European Conference on Computer Vision, 2022.
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Rajeev Yasarla, Renliang Weng, Wongun Choi, Vishal M. Patel, and Amir Sadeghian, “3SD: Self-Supervised Saliency Detection With No Labels,” in arXiv.org, 2022.
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url = {https://www.semanticscholar.org/paper/2a78e1c0412cbcc851ba60224c15c501debe2049},
}

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,
month = {6},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/132751a80632e80a90d7c3d3cd8a361f48fdb9b4},
}

Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, A. Yuille, and Tao Kong, “I BOT : I MAGE BERT P RE -T RAINING WITH O NLINE T OKENIZER.” 2022.
[BibTeX] [Link]

@inproceedings{248067560,
title = {I BOT : I MAGE BERT P RE -T RAINING WITH O NLINE T OKENIZER},
author = {{Jinghao Zhou} and {Chen Wei} and {Huiyu Wang} and {Wei Shen} and {Cihang Xie} and {A. Yuille} and {Tao Kong}},
year = 2022,
booktitle = {},
url = {https://www.semanticscholar.org/paper/251c6206338bc92e07a77f9e7043d49399217679},
}

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},
}

Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, A. Yuille, and Cihang Xie, “Fast AdvProp,” in International Conference on Learning Representations, 2022.
[BibTeX] [Link]

@inproceedings{248300043,
title = {Fast AdvProp},
author = {{Jieru Mei} and {Yucheng Han} and {Yutong Bai} and {Yixiao Zhang} and {Yingwei Li} and {Xianhang Li} and {A. Yuille} and {Cihang Xie}},
year = 2022,
month = {4},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/bbc8df27808cf1831087a080be5c76e29657c0b2},
}

Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté, Matthew J. Hausknecht, R. Laroche, I. Momennejad, H. V. Seijen, and Benjamin Van Durme, “One-Shot Learning from a Demonstration with Hierarchical Latent Language,” in Adaptive Agents and Multi-Agent Systems, 2022.
[BibTeX] [Link]

@inproceedings{247318673,
title = {One-Shot Learning from a Demonstration with Hierarchical Latent Language},
author = {{Nathaniel Weir} and {Xingdi Yuan} and {Marc-Alexandre Côté} and {Matthew J. Hausknecht} and {R. Laroche} and {I. Momennejad} and {H. V. Seijen} and {Benjamin Van Durme}},
year = 2022,
month = {3},
booktitle = {Adaptive Agents and Multi-Agent Systems},
url = {https://www.semanticscholar.org/paper/1d41a0ddda57caa6c8d268dd1703e4c9b35db18b},
}

Anjalie Field, Chan Young Park, Antônio Theóphilo, J. Watson-Daniels, and Yulia Tsvetkov, “An analysis of emotions and the prominence of positivity in #BlackLivesMatter tweets,” in Proceedings of the National Academy of Sciences of the United States of America, 2022.
[BibTeX] [Link]

@inproceedings{251766132,
title = {An analysis of emotions and the prominence of positivity in #BlackLivesMatter tweets},
author = {{Anjalie Field} and {Chan Young Park} and {Antônio Theóphilo} and {J. Watson-Daniels} and {Yulia Tsvetkov}},
year = 2022,
month = {8},
booktitle = {Proceedings of the National Academy of Sciences of the United States of America},
url = {https://www.semanticscholar.org/paper/6dadf66d41b5c5bf4ce8f49fce38bc4f44889246},
}

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

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

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

Yingwei Li, A. Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo-Xun Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, A. Yuille, and Mingxing Tan, “DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection,” in Computer Vision and Pattern Recognition, 2022.
[BibTeX] [Link]

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

A. Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, A. Hussain, Amanda Askell, Amanda Dsouza, A. Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmuller, Andrew M. Dai, Andrew D. La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, A. Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakacs, B. R. Roberts, B. S. Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan Ozyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, B. Howald, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, C’esar Ferri Ram’irez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, C. Ramirez, Clara Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Daniel H Garrette, Dan Hendrycks, D. Kilman, D. Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Gonz’alez, Danny Hernandez, Danqi Chen, Daphne Ippolito, D. Gilboa, David Dohan, D. Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, D. Kleyko, Deniz Yuret, Derek Chen, Derek Tam, D. Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Ekaterina Shutova, E. D. Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, E. Donoway, Ellie Pavlick, E. Rodolà, E. Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, E. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fan 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, G. Mariani, Gloria Wang, Gonzalo Jaimovitch-L’opez, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, H. Kim, Hannah Rashkin, Hanna Hajishirzi, Harsh Mehta, H. Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hubert Wong, I. Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, John Kernion, Jacob Hilton, Jaehoon Lee, J. Fisac, J. B. Simon, James Koppel, James Zheng, James Zou, Jan Koco’n, Jana Thompson, Jared Kaplan, Jarema Radom, Jascha Narain Sohl-Dickstein, Jason Phang, Jason Wei, J. Yosinski, Jekaterina Novikova, Jelle Bosscher, Jenni 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 Berant, Jorg Frohberg, Jos Rozen, J. Hernández-Orallo, Joseph Boudeman, Joseph Jones, J. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, K. Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, K. Markert, Kaustubh D. Dhole, Kevin Gimpel, K. 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, Madotto Andrea, M. Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, M. Marelli, Marco Maru, M. Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew Leavitt, Matthias Hagen, M. Schubert, Medina Baitemirova, M. Arnaud, M. McElrath, Michael A. Yee, Michael Cohen, Mi Gu, Michael I. Ivanitskiy, Michael Starritt, M. Strube, Michal Swkedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Monica Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, T. MukundVarma, Nanyun Peng, Nathan Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas S. Roberts, Nicholas Doiron, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, N. Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, P. Doshi, Pascale Fung, P. Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter W. Chang, P. Eckersley, Phu Mon Htut, Pi-Bei Hwang, P. Milkowski, P. Patil, Pouya Pezeshkpour, P. Oli, Q. Mei, QING LYU, Qinlang Chen, Rabin Banjade, R. Rudolph, Raefer Gabriel, Rahel Habacker, R. Delgado, Raphaël Millière, Rhythm Garg, Richard Barnes, R. Saurous, Riku Arakawa, Robbe Raymaekers, R. Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, R. Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib J. Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Sam Bowman, S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, S. Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi S. Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, S. Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Debnath, Siamak Shakeri, Simon Thormeyer, S. Melzi, Siva Reddy, S. Makini, Soo-hwan Lee, Spencer Bradley Torene, Sriharsha Hatwar, S. Dehaene, Stefan Divic, S. Ermon, Stella Rose Biderman, Stephanie C. Lin, S. Prasad, S. Piantadosi, S. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq A. Ali, Tatsuo Hashimoto, Te-Lin Wu, T. Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, T. Kornev, Timothy Telleen-Lawton, T. Tunduny, Tobias Gerstenberg, T. Chang, Trishala Neeraj, Tushar Khot, T. Shultz, Uri Shaham, Vedant Misra, V. Demberg, Victoria Nyamai, Vikas Raunak, V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, W. Fedus, W. Saunders, William Zhang, W. Vossen, Xiang Ren, Xiaoyu Tong, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yang Song, Yasaman Bahri, Y. Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yu Hou, Yuntao Bai, Zachary Seid, Zhao Xinran, Zhuoye Zhao, Z. Wang, Zijie J. Wang, Zirui Wang, Ziyi Wu, Sahib Singh, and Uri Shaham, “Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models,” in arXiv.org, 2022.
[BibTeX] [Link]

@inproceedings{249538544,
title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
author = {{A. Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Abu Awal Md Shoeb} and {Abubakar Abid} and {Adam Fisch} and {Adam R. Brown} and {Adam Santoro} and {Aditya Gupta} and {Adrià Garriga-Alonso} and {Agnieszka Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Parrish} and {Allen Nie} and {A. Hussain} and {Amanda Askell} and {Amanda Dsouza} and {A. Rahane} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Santilli} and {Andreas Stuhlmuller} and {Andrew M. Dai} and {Andrew D. La} and {Andrew Kyle Lampinen} and {Andy Zou} and {Angela Jiang} and {Angelica Chen} and {Anh Vuong} and {Animesh Gupta} and {Anna Gottardi} and {Antonio Norelli} and {Anu Venkatesh} and {Arash Gholamidavoodi} and {Arfa Tabassum} and {Arul Menezes} and {Arun Kirubarajan} and {A. 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 {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 {C. Ramirez} and {Clara 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 {D. Roth} and {Daniel Freeman} and {Daniel Khashabi} and {Daniel Levy} and {Daniel Gonz'alez} 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 {D. Kleyko} and {Deniz Yuret} and {Derek Chen} and {Derek Tam} and {D. Hupkes} and {Diganta Misra} and {Dilyar Buzan} and {Dimitri Coelho Mollo} and {Diyi Yang} and {Dong-Ho Lee} 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 {E. 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 {Fan 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 {G. Mariani} and {Gloria Wang} and {Gonzalo Jaimovitch-L'opez} and {Gregor Betz} and {Guy Gur-Ari} and {Hana Galijasevic} and {H. Kim} and {Hannah Rashkin} and {Hanna Hajishirzi} and {Harsh Mehta} and {H. Bogar} and {Henry Shevlin} and {Hinrich Schütze} and {Hiromu Yakura} and {Hongming Zhang} and {Hubert Wong} and {I. Ng} and {Isaac Noble} and {Jaap Jumelet} and {Jack Geissinger} and {John Kernion} and {Jacob Hilton} and {Jaehoon Lee} and {J. Fisac} and {J. B. Simon} and {James Koppel} and {James Zheng} and {James Zou} and {Jan Koco'n} and {Jana Thompson} 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 {Jenni 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 Berant} and {Jorg Frohberg} and {Jos Rozen} and {J. Hernández-Orallo} and {Joseph Boudeman} and {Joseph Jones} and {J. 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 {K. 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 {Madotto Andrea} and {M. Farooqi} and {Manaal Faruqui} and {Mantas Mazeika} and {Marco Baturan} and {M. Marelli} and {Marco Maru} and {M. Quintana} and {Marie Tolkiehn} and {Mario Giulianelli} and {Martha Lewis} and {Martin Potthast} and {Matthew Leavitt} and {Matthias Hagen} and {M. Schubert} and {Medina Baitemirova} and {M. Arnaud} and {M. McElrath} and {Michael A. Yee} and {Michael Cohen} and {Mi Gu} and {Michael I. 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 {Monica Tiwari} and {Mohit Bansal} and {Moin Aminnaseri} and {Mor Geva} and {Mozhdeh Gheini} and {T. MukundVarma} and {Nanyun Peng} and {Nathan Chi} and {Nayeon Lee} and {Neta Gur-Ari Krakover} and {Nicholas Cameron} and {Nicholas S. Roberts} and {Nicholas Doiron} 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 W. Chang} and {P. Eckersley} and {Phu Mon Htut} and {Pi-Bei Hwang} and {P. Milkowski} and {P. Patil} and {Pouya Pezeshkpour} and {P. Oli} and {Q. Mei} and {QING LYU} and {Qinlang Chen} and {Rabin Banjade} and {R. Rudolph} and {Raefer Gabriel} and {Rahel Habacker} and {R. Delgado} and {Raphaël Millière} and {Rhythm Garg} and {Richard Barnes} and {R. Saurous} and {Riku Arakawa} and {Robbe Raymaekers} and {R. Frank} and {Rohan Sikand} and {Roman Novak} and {Roman Sitelew} and {Ronan Le Bras} and {Rosanne Liu} and {Rowan Jacobs} and {Rui Zhang} and {R. Salakhutdinov} and {Ryan Chi} and {Ryan Lee} and {Ryan Stovall} and {Ryan Teehan} and {Rylan Yang} and {Sahib J. Singh} and {Saif M. Mohammad} and {Sajant Anand} and {Sam Dillavou} and {Sam Shleifer} and {Sam Wiseman} and {Samuel Gruetter} and {Sam Bowman} and {S. Schoenholz} and {Sanghyun Han} and {Sanjeev Kwatra} and {Sarah A. Rous} and {Sarik Ghazarian} and {Sayan Ghosh} and {S. Casey} and {Sebastian Bischoff} and {Sebastian Gehrmann} and {Sebastian Schuster} and {Sepideh Sadeghi} and {Shadi S. 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Sonal Joshi, Saurabh Kataria, Yiwen Shao, Piotr Żelasko, J. Villalba, S. Khudanpur, and N. Dehak, “Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser,” in Interspeech, 2022.
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V. Vibashan, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Target and Task specific Source-Free Domain Adaptive Image Segmentation,” in arXiv.org, 2022.
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Sonal Joshi, Saurabh Kataria, J. Villalba, and N. Dehak, “AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification,” in Interspeech, 2022.
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Jian Xue, Peidong Wang, Jinyu Li, Matt Post, and Yashesh Gaur, “Large-Scale Streaming End-to-End Speech Translation with Neural Transducers,” in Interspeech, 2022.
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booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/5a5704382fd8c980937e10618713d641c846b313},
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Aimon Rahman, W. G. C. Bandara, Jeya Maria Jose Valanarasu, I. Hacihaliloglu, and Vishal M. Patel, “Orientation-guided Graph Convolutional Network for Bone Surface Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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Shao-Yuan Lo, Wei Wang, Jim Thomas, Jingjing Zheng, Vishal M. Patel, and Cheng-Hao Kuo, “Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation,” in IEEE/RJS International Conference on Intelligent RObots and Systems, 2022.
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Pirazh Khorramshahi, V. Shenoy, and R. Chellappa, “Scalable Vehicle Re-Identification via Self-Supervision,” in arXiv.org, 2022.
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Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, Elvira Stellato, G. Carrafiello, Vishal M. Patel, and H. Roth, “Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation,” in European Conference on Computer Vision, 2022.
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Mo Zhou and Vishal M. Patel, “Enhancing Adversarial Robustness for Deep Metric Learning,” in Computer Vision and Pattern Recognition, 2022.
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url = {https://www.semanticscholar.org/paper/5bcdc704df91b425b76fc6b64f1582667505cfae},
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Cheng Peng, Pengfei Guo, S. K. Zhou, Vishal M. Patel, and Ramalingam Chellappa, “Towards performant and reliable undersampled MR reconstruction via diffusion model sampling,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022.
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Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Auditing Predictive Uncertainty Under Covariate Shift,” in Neural Information Processing Systems, 2022.
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url = {https://www.semanticscholar.org/paper/4fb13897dad166844ca020e3cef1563b8dc81775},
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Jonah P. Sengupta, M. Villemur, P. Pouliquen, P. Julián, and A. Andreou, “Embedded Processing Pipeline Exploration For Neuromorphic Event Based Perceptual Systems,” in International Symposium on Circuits and Systems, 2022.
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Samik Sadhu and H. Hermansky, “Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022.
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url = {https://www.semanticscholar.org/paper/3ce501d4d81d9a78c2e506df7f6de0d79ca91a5b},
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Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe L. Lin, and A. Yuille, “Lite Vision Transformer with Enhanced Self-Attention,” in Computer Vision and Pattern Recognition, 2021.
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@inproceedings{245353696,
title = {Lite Vision Transformer with Enhanced Self-Attention},
author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zijun Wei} and {Zhe L. Lin} and {A. Yuille}},
year = 2021,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/72e81bc41ffae1d414836169107910025aaacb75},
}

S. Saria, K. Henry, Hossein Soleimani, R. Adams, A. Zhan, Nishi Rawat, E. Chen, and Albert W Wu, “1429: LEAD TIME AND ACCURACY OF TREWS, A MACHINE LEARNING-BASED SEPSIS ALERT,” in Critical Care Medicine, 2021.
[BibTeX] [Link]

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

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

@inproceedings{245425181,
title = {Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health},
author = {{Ryan T. Scott} and {E. Antonsen} and {L. Sanders} and {Jaden J. A. Hastings} and {Seung-min Park} and {Graham Mackintosh} and {R. Reynolds} and {A. Hoarfrost} and {A. Sawyer} and {C. Greene} and {Benjamin S. Glicksberg} and {C. Theriot} and {D. Berrios} and {Jack M. Miller} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {S. Chalk} and {Guillermo M. Delgado-Aparicio} and {M. Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {John Kalantari} and {K. Khezeli} and {S. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {H. Martín} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {J. Myers} and {Jr.} and {Charlotte A. Nelson} and {Jonathan Oribello} and {P. Parsons-Wingerter} and {R. Prabhu} and {A. Qutub} and {J. Rask} and {Amanda M. Saravia-Butler} and {S. Saria} and {N. Singh} and {Frank Soboczenski} and {M. Snyder} and {Karthik Soman} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {Jason H. Yang} and {M. Zitnik} and {S. V. C. Kbr} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Tx} and {Blue Marble Space Institute of Science} and {Departmentof Physiology} and {Biophysics} and {Weill Cornell Medicine} and {N. York} and {Ny} and {Departments of Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Bay Area Environmental Research Institute} and {Mortality ResearchConsulting} and {Inc.} and {Universities Space Research Association} and {UC Space Health} and {D. Surgery} and {U. California} and {San Francisco} and {AI CenterforHealth} and {D. Biochemistry} and {Molecular Genetics} and {University of Maryland School of Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Human Health} and {Performance Directorate} and {NASAMarshall Space Flight Center} and {D. Microbiology} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of San Francisco} and {The Gilroy AstroBiology Research Group} and {The University of Wisconsin} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Peru} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Mn} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {Center for Individualized Medicine} and {D. Surgery} and {Department of Astrophysical Sciences} and {M. Clinic} and {Rochester} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {M. University} and {Montreal.} and {Quebec.} and {Canada.} and {Faculty of Veterinary Medicine} and {Cancer} and {I. -. London} and {London} and {SymbioSeq Llc} and {Ashburn} and {Va} and {Center for Data Driven Discovery} and {California Institute of Technology.} and {Pasadena} and {Waitt Advanced Biophotonics Center} and {Chan-Zuckerberg Imaging Scientist Fellow} and {Salk Institute for Biological Studies} and {La Jolla} and {Biological Systems} and {Engineering Division} and {Lawrence Berkeley National Lab.} and {Berkeley} and {Doe Agile BioFoundry} and {Emeryville} and {Joint BioEnergy Institute} and {Human Research Program Cross-cutting Computational Model Project} and {N. R. Center} and {Cleveland} and {Oh} and {Institute for Computational Science} and {Engineering} and {M. Biology} and {M. University} and {E. Lansing.} and {Mi} and {Low Exploration Gravity Technology} and {AI Matrix Consortium} and {Department of Electrical Engineering} and {U. Texas} and {S. Antonio} and {UT Health Sciences} and {Office of the Director} and {Logyx} and {Computer Science} and {Statistics} and {H. Policy} and {J. University} and {Baltimore.} and {Md.} and {Ml} and {Ai} and {Healthcare Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {J. P. Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {Department of Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {D. Science} and {U. Alabama} and {Birmingham} and {Al} and {Center for Emerging} and {Re-Emerging Pathogens} and {Biochemistry} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Department of Biomedical Informatics} and {Harvard Medical School} and {Harvard Data Science} and {Broad Institute of Mit} and {Harvard} and {Harvard University} and {Boston} and {Ma.}},
year = 2021,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0d6d142dc49cf7537ece045d8d469fd014a5d3b6},
}

Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jieneng Chen, Shuai Liu, Cheng Yang, and A. Yuille, “PartImageNet: A Large, High-Quality Dataset of Parts,” in European Conference on Computer Vision, 2021.
[BibTeX] [Link]

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

Yuyin Zhou, D. Dreizin, Yan Wang, Fengze Liu, Wei-lei Shen, and A. Yuille, “External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data,” in IEEE Transactions on Medical Imaging, 2021.
[BibTeX] [Link]

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

Chen Wei, Haoqi Fan, Saining Xie, Chaoxia Wu, A. Yuille, and Christoph Feichtenhofer, “Masked Feature Prediction for Self-Supervised Visual Pre-Training,” in Computer Vision and Pattern Recognition, 2021.
[BibTeX] [Link]

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

R. Adams, K. Henry, Hossein Soleimani, Nishi Rawat, M. Saheed, E. Chen, Albert W Wu, and S. Saria, “1405: ASSESSING CLINICAL USE AND PERFORMANCE OF A MACHINE LEARNING SEPSIS ALERT FOR SEX AND RACIAL BIAS,” in Critical Care Medicine, 2021.
[BibTeX] [Link]

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

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

@inproceedings{244909001,
title = {LTT-GAN: Looking Through Turbulence by Inverting GANs},
author = {{Kangfu Mei} and {Vishal M. Patel}},
year = 2021,
month = {12},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/2b969be9a39ea220fb09f8888c6cca460c3da189},
}

Jingye Chen, Jieneng Chen, Zongwei Zhou, Bin Li, A. Yuille, and Yongyi Lu, “MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification,” in arXiv.org, 2021.
[BibTeX] [Link]

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

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

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

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

@inproceedings{245424654,
title = {Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs},
author = {{Lauren M. Sanders} and {Jason H. Yang} and {Ryan T. Scott} and {A. Qutub} and {H. Martín} and {D. Berrios} and {Jaden J. A. Hastings} and {J. Rask} and {Graham Mackintosh} and {A. Hoarfrost} and {Stuart Chalk} and {John Kalantari} and {K. Khezeli} and {E. Antonsen} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {A. Beheshti} and {Guillermo M. Delgado-Aparicio} and {B. Glicksberg} and {C. Greene} and {M. Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {S. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {Jack M. Miller} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {Seung-min Park} and {P. Parsons-Wingerter} and {R. Prabhu} and {R. Reynolds} and {Amanda M. Saravia-Butler} and {S. Saria} and {A. Sawyer} and {N. Singh} and {Frank Soboczenski} and {Michael Snyder} and {Karthik Soman} and {C. Theriot} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {Sylvain V. Costes Blue Marble Space Institute of Science} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Center for Emerging} and {Re-Emerging Pathogens} and {D. Microbiology} and {Biochemistry} and {Molecular Genetics} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Kbr} and {AI Matrix Consortium} and {Department of Electrical Engineering} and {U. Texas} and {San Antonio} and {UT Health Sciences} and {Tx} and {Biological Systems} and {Engineering Division} and {Lawrence Berkeley National Lab.} and {Berkeley} and {Doe Agile BioFoundry} and {Emeryville} and {Joint BioEnergy Institute} and {Departmentof Physiology} and {Biophysics} and {Weill Cornell Medicine} and {N. York} and {Ny} and {Office of the Director} and {Bay Area Environmental Research Institute} and {Universities Space Research Association} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {Center for Individualized Medicine} and {D. Surgery} and {Department of Astrophysical Sciences} and {M. Clinic} and {Rochester} and {Mn} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of San Francisco} and {San Francisco} and {The Gilroy AstroBiology Research Group} and {Theodore Madison} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Peru} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {AI CenterforHealth} and {D. Biochemistry} and {University of Maryland School of Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {UC Space Health} and {D. Surgery} and {U. California} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {M. University} and {Montreal.} and {Quebec.} and {Canada.} and {Faculty of Veterinary Medicine} and {Dept of Surgery} and {Cancer} and {I. -. London} and {London} and {SymbioSeq Llc} and {NASAMarshall Space Flight Center} and {Ashburn} and {Va} and {Center for Data Driven Discovery} and {California Institute of Technology.} and {Pasadena} and {Waitt Advanced Biophotonics Center} and {Chan-Zuckerberg Imaging Scientist Fellow} and {Salk Institute for Biological Studies} and {La Jolla} and {Human Research Program Cross-cutting Computational Model Project} and {N. R. Center} and {Cleveland} and {Oh} and {Institute for Computational Science} and {Engineering} and {M. Biology} and {M. University} and {E. Lansing.} and {Mi} and {Departments of Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Low Exploration Gravity Technology} and {Human Research Program Cross-cutting Computational Model Project} and {Mortality ResearchConsulting} and {Inc.} and {Logyx} and {Computer Science} and {Statistics} and {H. Policy} and {J. University} and {Baltimore.} and {Md.} and {Ml} and {Ai} and {Healthcare Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {J. P. Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {C. Usa} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Tx Usa} and {Human Health} and {Performance Directorate} and {Department of Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {D. Science} and {U. Alabama} and {Birmingham} and {Al} and {Department of Biomedical Informatics} and {Harvard Medical School} and {Harvard Data Science} and {Broad Institute of Mit} and {Harvard} and {Harvard University} and {Boston} and {Ma.}},
year = 2021,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0c7240e91af2778ab1e65cc79c20704aa61e106b},
}

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

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

@inproceedings{tran-etal-2021-facebook,
title = "{F}acebook {AI}{'}s {WMT}21 News Translation Task Submission",
author = "Tran, Chau and
Bhosale, Shruti and
Cross, James and
Koehn, Philipp and
Edunov, Sergey and
Fan, Angela",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.19",
pages = "205--215",
abstract = "We describe Facebook{'}s multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models. We utilize data from all available sources {---} WMT, large-scale data mining, and in-domain backtranslation {---} to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all eight languages. Our final submission is an ensemble of dense and sparse Mixture-of-Expert multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year{'}s winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.",
}

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

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

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

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

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

Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M. Patel, “TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions,” in Computer Vision and Pattern Recognition, 2021.
[BibTeX] [Link]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@inproceedings{alam-etal-2021-findings,
title = "Findings of the {WMT} Shared Task on Machine Translation Using Terminologies",
author = "Alam, Md Mahfuz Ibn and
Kvapil{\'\i}kov{\'a}, Ivana and
Anastasopoulos, Antonios and
Besacier, Laurent and
Dinu, Georgiana and
Federico, Marcello and
Gall{\'e}, Matthias and
Jung, Kweonwoo and
Koehn, Philipp and
Nikoulina, Vassilina",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.69",
pages = "652--663",
abstract = "Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.",
}

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

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

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

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

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

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

Zili Huang, Marc Delcroix, Leibny Paola García-Perera, Shinji Watanabe, Desh Raj, and S. Khudanpur, “Joint speaker diarization and speech recognition based on region proposal networks,” in Computer Speech and Language, 2021.
[BibTeX] [Link]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, S. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge,” in NeurIPS Datasets and Benchmarks, 2021.
[BibTeX] [Link]

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

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

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

Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, A. Yuille, and Tao Kong, “iBOT: Image BERT Pre-Training with Online Tokenizer,” in arXiv.org, 2021.
[BibTeX] [Link]

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

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

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

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

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

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

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

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

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

Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, B. Green, Elizabeth L. Engle, Guillermo Almodovar, Ryan Walk, S. Soto-Diaz, J. Taube, A. Szalay, and A. Yuille, “A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation.” 2021.
[BibTeX] [Link]

@inproceedings{251467805,
title = {A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation},
author = {{Yixiao Zhang} and {Adam Kortylewski} and {Qing Liu} and {Seyoun Park} and {B. Green} and {Elizabeth L. Engle} and {Guillermo Almodovar} and {Ryan Walk} and {S. Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
year = 2021,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/36b9a20c24bb33ac66feccd9dd8e1dc472f791b6},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A. Hamad, Alan Finn, A. Fahmy, Atsushi Irie, Baihua Xiao, Changping Liu, Cheng-Lin Liu, Christian Nitschke, C. Chrysouli, Chunrong Dong, Chung-Ling Huang, Cui wei Liu, D. Dai, Daxue Liu, F. Lauze, Genquan Duan, H. Wang, H. Sahbi, Hiroki Unten, A. Hidaka, A. Yuille, A. Nakazawa, C. Sun, C. Igel, Christian Peñaloza, Chuan-Xian Ren, and Chun Zhou, “Author list,” in The First Asian Conference on Pattern Recognition, 2021.
[BibTeX] [Link]

@inproceedings{33161741,
title = {Author list},
author = {{A. Hamad} and {Alan Finn} and {A. Fahmy} and {Atsushi Irie} and {Baihua Xiao} and {Changping Liu} and {Cheng-Lin Liu} and {Christian Nitschke} and {C. Chrysouli} and {Chunrong Dong} and {Chung-Ling Huang} and {Cui wei Liu} and {D. Dai} and {Daxue Liu} and {F. Lauze} and {Genquan Duan} and {H. Wang} and {H. Sahbi} and {Hiroki Unten} and {A. Hidaka} and {A. Yuille} and {A. Nakazawa} and {C. Sun} and {C. Igel} and {Christian Peñaloza} and {Chuan-Xian Ren} and {Chun Zhou}},
year = 2021,
month = {10},
booktitle = {The First Asian Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/4181a7995477907641905f93411e51fe311c025f},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Patricia M. Johnson, Geunu Jeong, K. Hammernik, Jo Schlemper, C. Qin, J. Duan, D. Rueckert, Jingu Lee, Nicola Pezzotti, E. Weerdt, Sahar Yousefi, M. Elmahdy, Jeroen Van Gemert, C. Schülke, M. Doneva, T. Nielsen, Sergey Kastryulin, B. Lelieveldt, M. Osch, M. Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, Nikita Miriakov, B. Bakker, M. Caan, Max Welling, Matthew Muckley, and F. Knoll, “Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge,” in [email protected], 2021.
[BibTeX] [Link]

@inproceedings{238862886,
title = {Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge},
author = {{Patricia M. Johnson} and {Geunu Jeong} and {K. Hammernik} and {Jo Schlemper} and {C. Qin} and {J. Duan} and {D. Rueckert} and {Jingu Lee} and {Nicola Pezzotti} and {E. Weerdt} and {Sahar Yousefi} and {M. Elmahdy} and {Jeroen Van Gemert} and {C. Schülke} and {M. Doneva} and {T. Nielsen} and {Sergey Kastryulin} and {B. Lelieveldt} and {M. Osch} and {M. Staring} and {Eric Z. Chen} and {Puyang Wang} and {Xiao Chen} and {Terrence Chen} and {Vishal M. Patel} and {Shanhui Sun} and {Hyungseob Shin} and {Yohan Jun} and {Taejoon Eo} and {Sewon Kim} and {Taeseong Kim} and {D. Hwang} and {P. Putzky} and {D. Karkalousos} and {J. Teuwen} and {Nikita Miriakov} and {B. Bakker} and {M. Caan} and {Max Welling} and {Matthew Muckley} and {F. Knoll}},
year = 2021,
booktitle = {[email protected]},
url = {https://www.semanticscholar.org/paper/744702eddf5a0851e1f74af5525666109e730d53},
}

M. Illa, B. Halpern, R. V. Son, L. Moro-Velázquez, and O. Scharenborg, “Pathological voice adaptation with autoencoder-based voice conversion,” in 11th ISCA Speech Synthesis Workshop (SSW 11), 2021.
[BibTeX] [Link]

@inproceedings{235446502,
title = {Pathological voice adaptation with autoencoder-based voice conversion},
author = {{M. Illa} and {B. Halpern} and {R. V. Son} and {L. Moro-Velázquez} and {O. Scharenborg}},
year = 2021,
month = {6},
booktitle = {11th ISCA Speech Synthesis Workshop (SSW 11)},
url = {https://www.semanticscholar.org/paper/28519a0c7bf3de835b184f3c85f01f4d1d8746d8},
}

Noah Weber, Anton Belyy, Nils Holzenberger, Rachel Rudinger, and Benjamin Van Durme, “Schema Curation via Causal Association Rule Mining,” in arXiv.org, 2021.
[BibTeX] [Link]

@inproceedings{233296523,
title = {Schema Curation via Causal Association Rule Mining},
author = {{Noah Weber} and {Anton Belyy} and {Nils Holzenberger} and {Rachel Rudinger} and {Benjamin Van Durme}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1da3dc6f997599a197b1510f2c2bdd86e2d86e49},
}

W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction,” in IEEE Transactions on Geoscience and Remote Sensing, 2021.
[BibTeX] [Link]

@inproceedings{235743099,
title = {Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction},
author = {{W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
year = 2021,
month = {7},
booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
url = {https://www.semanticscholar.org/paper/6dffdd9ad229900de79646f53cc73715ad261508},
}

Tiange Xiang, Yongyi Lu, A. Yuille, Chaoyi Zhang, Weidong (Tom) Cai, and Zongwei Zhou, “In-painting Radiography Images for Unsupervised Anomaly Detection,” in arXiv.org, 2021.
[BibTeX] [Link]

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

Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractors for End-to-End Neural Diarization,” in IEEE/ACM Transactions on Audio Speech and Language Processing, 2021.
[BibTeX] [Link]

@inproceedings{247685739,
title = {Encoder-Decoder Based Attractors for End-to-End Neural Diarization},
author = {{Shota Horiguchi} and {Yusuke Fujita} and {Shinji Watanabe} and {Yawen Xue} and {Leibny Paola García-Perera}},
year = 2021,
month = {6},
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/8c7628641450203b0aa959b5a69729ff906760ff},
}

Chenxu Luo, Xiaodong Yang, and A. Yuille, “Exploring Simple 3D Multi-Object Tracking for Autonomous Driving,” in IEEE International Conference on Computer Vision, 2021.
[BibTeX] [Link]

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

Kelly Marchisio, Youngser Park, Ali Saad-Eldin, A. Alyakin, Kevin Duh, C. Priebe, and Philipp Koehn, “An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces,” in Conference on Empirical Methods in Natural Language Processing, 2021.
[BibTeX] [Link]

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

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

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

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

Anshul B. Shah, Shlok Kumar Mishra, Ankan Bansal, Jun-Cheng Chen, R. Chellappa, and Abhinav Shrivastava, “Pose and Joint-Aware Action Recognition-Supplementary Material.” 2021.
[BibTeX] [Link]

@inproceedings{247112044,
title = {Pose and Joint-Aware Action Recognition-Supplementary Material},
author = {{Anshul B. Shah} and {Shlok Kumar Mishra} and {Ankan Bansal} and {Jun-Cheng Chen} and {R. Chellappa} and {Abhinav Shrivastava}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7400177a4165c13d22da45a242ab8180e32a3d38},
}

Nicholas Ichien, Qing Liu, Shuhao Fu, K. Holyoak, A. Yuille, and Hongjing Lu, “Visual analogy: Deep learning versus compositional models,” in arXiv.org, 2021.
[BibTeX] [Link]

@inproceedings{234742304,
title = {Visual analogy: Deep learning versus compositional models},
author = {{Nicholas Ichien} and {Qing Liu} and {Shuhao Fu} and {K. Holyoak} and {A. Yuille} and {Hongjing Lu}},
year = 2021,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1013e151d0aeeddac9e3c59db226c2ecf7d28c3f},
}

L. Moro-Velázquez, J. Gómez-García, N. Dehak, and J. I. Godino-Llorente, “New tools for the differential evaluation of Parkinson’s disease using voice and speech processing,” in IberSPEECH Conference, 2021.
[BibTeX] [Link]

@inproceedings{232285765,
title = {New tools for the differential evaluation of Parkinson's disease using voice and speech processing},
author = {{L. Moro-Velázquez} and {J. Gómez-García} and {N. Dehak} and {J. I. Godino-Llorente}},
year = 2021,
month = {3},
booktitle = {IberSPEECH Conference},
url = {https://www.semanticscholar.org/paper/c76e00b4e7c3fa5774cb61a194535086f53b7802},
}

Ehsan, Adeli, Yan Wang, Le Lu, A. Yuille, and Yuyin Zhou, “Download A Visual Segmentation Method For Temporal Smart Card Data.” 2021.
[BibTeX] [Link]

@inproceedings{245536726,
title = {Download A Visual Segmentation Method For Temporal Smart Card Data},
author = {{Ehsan} and {Adeli} and {Yan Wang} and {Le Lu} and {A. Yuille} and {Yuyin Zhou}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a557000dd26cdc5ef1e8c5da76a092aa362b6a81},
}

Guoguo Chen, Shuzhou Chai, Guan-Bo Wang, Jiayu Du, Weiqiang Zhang, Chao Weng, Dan Su, Daniel Povey, J. Trmal, Junbo Zhang, Mingjie Jin, S. Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, and Zhiyong Yan, “GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio,” in Interspeech, 2021.
[BibTeX] [Link]

@inproceedings{235422086,
title = {GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio},
author = {{Guoguo Chen} and {Shuzhou Chai} and {Guan-Bo Wang} and {Jiayu Du} and {Weiqiang Zhang} and {Chao Weng} and {Dan Su} and {Daniel Povey} and {J. Trmal} and {Junbo Zhang} and {Mingjie Jin} and {S. Khudanpur} and {Shinji Watanabe} and {Shuaijiang Zhao} and {Wei Zou} and {Xiangang Li} and {Xuchen Yao} and {Yongqing Wang} and {Yujun Wang} and {Zhao You} and {Zhiyong Yan}},
year = 2021,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/6f1ca0249eafa36a5762ac53f6ba2a4ee2133456},
}

N. Dehak, Réda Dehak, P. Kenny, N. Brummer, P. Ouellet, and P. Dumouchel, “Support Vector Machines versus Fast Scoring in the Low-Dimensional Total Variability Space for Speaker Veriﬁcation.” 2021.
[BibTeX] [Link]

@inproceedings{253571800,
title = {Support Vector Machines versus Fast Scoring in the Low-Dimensional Total Variability Space for Speaker Veriﬁcation},
author = {{N. Dehak} and {Réda Dehak} and {P. Kenny} and {N. Brummer} and {P. Ouellet} and {P. Dumouchel}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/f5424b452b7dd8e4a90b2344a95daa776129f947},
}

Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, and A. Yuille, “CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models,” in arXiv.org, 2021.
[BibTeX] [Link]

@inproceedings{232379984,
title = {CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models},
author = {{Qing Liu} and {Adam Kortylewski} and {Zhishuai Zhang} and {Zizhang Li} and {Mengqi Guo} and {Qihao Liu} and {Xiaoding Yuan} and {Jiteng Mu} and {Weichao Qiu} and {A. Yuille}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/88923b7b455e3ebe63810ebf8dbd1c0c47e79a3c},
}

Ju He, Adam Kortylewski, and A. Yuille, “CORL: Compositional Representation Learning for Few-Shot Classification,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
[BibTeX] [Link]

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

Benjamin Skerritt-Davis and Mounya Elhilali, “Neural Encoding of Auditory Statistics,” in Journal of Neuroscience, 2021.
[BibTeX] [Link]

@inproceedings{235696523,
title = {Neural Encoding of Auditory Statistics},
author = {{Benjamin Skerritt-Davis} and {Mounya Elhilali}},
year = 2021,
month = {6},
booktitle = {Journal of Neuroscience},
url = {https://www.semanticscholar.org/paper/77543e1c8dc684e5a5343ee8001e5cc41d72ddd6},
}

Piotr Żelasko, R. Pappagari, and N. Dehak, “What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition,” in Transactions of the Association for Computational Linguistics, 2021.
[BibTeX] [Link]

@inproceedings{235742745,
title = {What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition},
author = {{Piotr Żelasko} and {R. Pappagari} and {N. Dehak}},
year = 2021,
month = {7},
booktitle = {Transactions of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/f3173cd86ae95a53f44f0d1093e85df4988a459a},
}

Nanxin Chen, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition,” in Interspeech, 2021.
[BibTeX] [Link]

@inproceedings{237634474,
title = {Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition},
author = {{Nanxin Chen} and {Piotr Żelasko} and {L. Moro-Velázquez} and {J. Villalba} and {N. Dehak}},
year = 2021,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/2161383af6d420450f69ada26f2e310e554750f8},
}

Domenick Poster, Matthew Thielke, R. Nguyen, Srinivasan Rajaraman, Xing Di, Cedric Nimpa Fondje, Vishal M. Patel, Nathan J. Short, B. Riggan, N. Nasrabadi, and Shuowen Hu, “A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
[BibTeX] [Link]

@inproceedings{230125224,
title = {A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset},
author = {{Domenick Poster} and {Matthew Thielke} and {R. Nguyen} and {Srinivasan Rajaraman} and {Xing Di} and {Cedric Nimpa Fondje} and {Vishal M. Patel} and {Nathan J. Short} and {B. Riggan} and {N. Nasrabadi} and {Shuowen Hu}},
year = 2021,
month = {1},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/d88d4a05e076a070e1209245a40d57a0e9c211a2},
}

Mounya Elhilali, “Adaptive Listening to Everyday Soundscapes,” in Interspeech, 2021.
[BibTeX] [Link]

@inproceedings{247439732,
title = {Adaptive Listening to Everyday Soundscapes},
author = {{Mounya Elhilali}},
year = 2021,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/fe2ecaf07328112ffbfd40c932b6356c41262198},
}

Ruizhi Li, Gregory Sell, and H. Hermansky, “Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream end-to-end ASR,” in Spoken Language Technology Workshop, 2021.
[BibTeX] [Link]

@inproceedings{231839599,
title = {Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream end-to-end ASR},
author = {{Ruizhi Li} and {Gregory Sell} and {H. Hermansky}},
year = 2021,
month = {1},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/0052e22c1f07dfd3cc2c79d88e2c78fc89a11ff3},
}

Xing Di, He Zhang, and Vishal M. Patel, “Thermal-to-Visible Face Synthesis and Recognition,” in Advances in Computer Vision and Pattern Recognition, 2021.
[BibTeX] [Link]

@inproceedings{238912924,
title = {Thermal-to-Visible Face Synthesis and Recognition},
author = {{Xing Di} and {He Zhang} and {Vishal M. Patel}},
year = 2021,
booktitle = {Advances in Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/c02d3e2e51d8fcea46e36822b110b26c140d5d24},
}

Pirazh Khorramshahi, Sai Saketh Rambhatla, and R. Chellappa, “Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
[BibTeX] [Link]

@inproceedings{235657291,
title = {Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems},
author = {{Pirazh Khorramshahi} and {Sai Saketh Rambhatla} and {R. Chellappa}},
year = 2021,
month = {6},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/8be99c2d0802d6222e233dd67d2927c75a0bed24},
}

S. Saria, Peter F. Schulam, Brian J. Yeh, Daniel Burke, S. Mooney, C. Fong, Jacob E. Sunshine, Dustin R. Long, and V. O’Reilly-Shah, “Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients,” in Critical Care Explorations, 2021.
[BibTeX] [Link]

@inproceedings{235371744,
title = {Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients},
author = {{S. Saria} and {Peter F. Schulam} and {Brian J. Yeh} and {Daniel Burke} and {S. Mooney} and {C. Fong} and {Jacob E. Sunshine} and {Dustin R. Long} and {V. O’Reilly-Shah}},
year = 2021,
month = {6},
booktitle = {Critical Care Explorations},
url = {https://www.semanticscholar.org/paper/947fad4b54ebf666dc5c73837beb9bf3e018e7c6},
}

Chun Pong Lau, C. Castillo, and R. Chellappa, “ATFaceGAN: Single Face Semantic Aware Image Restoration and Recognition From Atmospheric Turbulence,” in IEEE Transactions on Biometrics Behavior and Identity Science, 2021.
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@inproceedings{232373611,
title = {ATFaceGAN: Single Face Semantic Aware Image Restoration and Recognition From Atmospheric Turbulence},
author = {{Chun Pong Lau} and {C. Castillo} and {R. Chellappa}},
year = 2021,
month = {4},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/d5ef84d04a6f527158d22304ff0bf73990d6563d},
}

Sai Saketh Rambhatla, Michael Jones, and R. Chellappa, “To Boost or not to Boost: On the Limits of Boosted Neural Networks,” in arXiv.org, 2021.
[BibTeX] [Link]

@inproceedings{236493542,
title = {To Boost or not to Boost: On the Limits of Boosted Neural Networks},
author = {{Sai Saketh Rambhatla} and {Michael Jones} and {R. Chellappa}},
year = 2021,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/cf94610981c556cc8e8930c6f71f88f2186d446f},
}

Nanxin Chen, Shinji Watanabe, J. Villalba, Piotr Żelasko, and N. Dehak, “Non-Autoregressive Transformer for Speech Recognition,” in IEEE Signal Processing Letters, 2021.
[BibTeX] [Link]

@inproceedings{231715684,
title = {Non-Autoregressive Transformer for Speech Recognition},
author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {Piotr Żelasko} and {N. Dehak}},
year = 2021,
booktitle = {IEEE Signal Processing Letters},
url = {https://www.semanticscholar.org/paper/737aff546a9112127d7a13a5b835e27a6e1e935e},
}

Xinyue Wei, Weichao Qiu, Yi Zhang, Zihao Xiao, and A. Yuille, “Supplementary Materials of NLS.” 2021.
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@inproceedings{248067452,
title = {Supplementary Materials of NLS},
author = {{Xinyue Wei} and {Weichao Qiu} and {Yi Zhang} and {Zihao Xiao} and {A. Yuille}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/d31880f8e3aa03e9366ff5f582cbc70427a10783},
}

Desh Raj and S. Khudanpur, “Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem,” in Interspeech, 2021.
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@inproceedings{233025093,
title = {Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem},
author = {{Desh Raj} and {S. Khudanpur}},
year = 2021,
month = {4},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/88dfc766aeff22a4e5fbdb81ce6161994c745039},
}

R. Pappagari, Piotr Żelasko, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Beyond Isolated Utterances: Conversational Emotion Recognition,” in Automatic Speech Recognition & Understanding, 2021.
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@inproceedings{237492280,
title = {Beyond Isolated Utterances: Conversational Emotion Recognition},
author = {{R. Pappagari} and {Piotr Żelasko} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2021,
month = {9},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/6b39bd717627d97c7e69e46801fdbb38ef4eb946},
}

Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, X. Bai, Serge J. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation,” in arXiv.org, 2021.
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@inproceedings{231749862,
title = {Occluded Video Instance Segmentation},
author = {{Jiyang Qi} and {Yan Gao} and {Yao Hu} and {Xinggang Wang} and {Xiaoyu Liu} and {X. Bai} and {Serge J. Belongie} and {A. Yuille} and {Philip H. S. Torr} and {S. Bai}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1c6121acbe33ab5327509629425605982b5a0ec5},
}

Aviad Shtrosberg, J. Villalba, N. Dehak, Azaria Cohen, and Bar Ben-Yair, “Invariant Representation Learning for Robust Far-Field Speaker Recognition,” in International Conference on Statistical Language and Speech Processing, 2021.
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@inproceedings{239039731,
title = {Invariant Representation Learning for Robust Far-Field Speaker Recognition},
author = {{Aviad Shtrosberg} and {J. Villalba} and {N. Dehak} and {Azaria Cohen} and {Bar Ben-Yair}},
year = 2021,
booktitle = {International Conference on Statistical Language and Speech Processing},
url = {https://www.semanticscholar.org/paper/f157b429553c4a6165856783ec879cd8d0f6a4cd},
}

Zach Wood-Doughty, I. Shpitser, and Mark Dredze, “Generating Synthetic Text Data to Evaluate Causal Inference Methods,” in arXiv.org, 2021.
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@inproceedings{231861828,
title = {Generating Synthetic Text Data to Evaluate Causal Inference Methods},
author = {{Zach Wood-Doughty} and {I. Shpitser} and {Mark Dredze}},
year = 2021,
month = {2},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/9adc1a3307c05ff3c9b0ae595cb57b1de041713f},
}

Liming Wang, Xinsheng Wang, M. Hasegawa-Johnson, O. Scharenborg, and N. Dehak, “Align or attend? Toward More Efficient and Accurate Spoken Word Discovery Using Speech-to-Image Retrieval,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
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@inproceedings{235223299,
title = {Align or attend? Toward More Efficient and Accurate Spoken Word Discovery Using Speech-to-Image Retrieval},
author = {{Liming Wang} and {Xinsheng Wang} and {M. Hasegawa-Johnson} and {O. Scharenborg} and {N. Dehak}},
year = 2021,
month = {6},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/83677a13503f5413f28026290d95c615de58f49d},
}

Yingda Xia, Dong Yang, Wenqi Li, A. Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, P. An, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, Elvira Stellato, G. Carrafiello, A. Ierardi, A. Yuille, and H. Roth, “Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation,” in arXiv.org, 2021.
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@inproceedings{233324290,
title = {Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation},
author = {{Yingda Xia} and {Dong Yang} and {Wenqi Li} and {A. Myronenko} and {Daguang Xu} and {Hirofumi Obinata} and {Hitoshi Mori} and {P. An} and {S. Harmon} and {E. Turkbey} and {B. Turkbey} and {B. Wood} and {F. Patella} and {Elvira Stellato} and {G. Carrafiello} and {A. Ierardi} and {A. Yuille} and {H. Roth}},
year = 2021,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7ba0e1864f094da06410af52166dc6c4e7a74adf},
}

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title = {One-Class Classification: A Survey},
author = {{Pramuditha Perera} and {Poojan Oza} and {Vishal M. Patel}},
year = 2021,
month = {1},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/bd6262ebdd1a865e8e6859ab7dd8dc576d2a90e6},
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Haoran Xu and Philipp Koehn, “Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions,” in arXiv.org, 2021.
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title = {Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions},
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month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f38b33d863f9554a00cd9798484e0cc8b0236579},
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Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman, R. Cattoni, Matteo Negri, M. Turchi, D. Oard, and Matt Post, “The Multilingual TEDx Corpus for Speech Recognition and Translation,” in Interspeech, 2021.
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Pramuditha Perera and Vishal M. Patel, “Geometric Transformation-Based Network Ensemble for Open-Set Recognition,” in IEEE International Conference on Multimedia and Expo, 2021.
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booktitle = {IEEE International Conference on Multimedia and Expo},
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year = 2021,
month = {7},
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S. Saria, M. Ghassemi, Z. Obermeyer, Karandeep Singh, P. Hsueh, and E. Topol, “Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery,” in American Medical Informatics Association Annual Symposium, 2021.
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booktitle = {American Medical Informatics Association Annual Symposium},
url = {https://www.semanticscholar.org/paper/d41dfad8ea7ae4ead9e38fcc425c02749a1a8d64},
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L. Chu, Seyoun Park, S. Kawamoto, A. Yuille, R. Hruban, and E. Fishman, “Current Status of Radiomics and Deep Learning in Liver Imaging,” in Journal of computer assisted tomography, 2021.
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C. Castillo-Sanchez and Leibny Paola García-Perera, “The CLIR-CLSP System for the IberSPEECH-RTVE 2020 Speaker Diarization and Identity Assignment Challenge,” in IberSPEECH Conference, 2021.
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year = 2021,
month = {3},
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Jonah P. Sengupta, M. Villemur, Daniel R. Mendat, Gaspar Tognetti, and A. Andreou, “Architecture and Algorithm Co-Design Framework for Embedded Processors in Event-Based Cameras,” in International Symposium on Circuits and Systems, 2021.
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N. Higgins, Ambar Monjaras, Breanne D Yerkes, David F. Little, J. Nave-Blodgett, Mounya Elhilali, and J. Snyder, “Resetting of Auditory and Visual Segregation Occurs After Transient Stimuli of the Same Modality,” in Frontiers in Psychology, 2021.
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Matt Landers, S. Saria, and A. Espay, “Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?,” in Journal of Parkinson’s Disease, 2021.
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Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, and A. Yuille, “Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning,” in arXiv.org, 2021.
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Matthew Maciejewski, Shinji Watanabe, and S. Khudanpur, “Speaker Verification-Based Evaluation of Single-Channel Speech Separation,” in Interspeech, 2021.
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Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, D. Cremers, L. Leal-Taixé, A. Yuille, Florian Schroff, Hartwig Adam, and Liang-Chieh Chen, “DeepLab2: A TensorFlow Library for Deep Labeling,” in arXiv.org, 2021.
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Hao Ding, Siyuan Qiao, A. Yuille, and Wei Shen, “Deeply Shape-guided Cascade for Instance Segmentation,” in Computer Vision and Pattern Recognition, 2021.
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Ayah Zirikly, Bart Desmet, D. Newman-Griffis, Beth Marfeo, C. McDonough, H. Goldman, and L. Chan, “An Information Extraction Framework for Disability Determination: A mental Functioning Use-Case (Preprint).” 2021.
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Chenxu Luo, Xiaodong Yang, and A. Yuille, “Self-Supervised Pillar Motion Learning for Autonomous Driving,” in Computer Vision and Pattern Recognition, 2021.
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Yuval Pinter, A. Stent, Mark Dredze, and Jacob Eisenstein, “Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information,” in arXiv.org, 2021.
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P. Resnik, M. de Choudhury, Katherine Musacchio Schafer, and Glen A. Coppersmith, “Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”,” in Journal of Medical Internet Research, 2021.
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Prakhar Kaushik, Alex Gain, Adam Kortylewski, and A. Yuille, “Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping,” in arXiv.org, 2021.
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year = 2021,
month = {2},
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url = {https://www.semanticscholar.org/paper/be864a16ec597c76d1ab36453d01471723a37bac},
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Tiago Pimentel, Maria Ryskina, Sabrina J. Mielke, Shijie Wu, Eleanor Chodroff, Brian Leonard, Garrett Nicolai, Yustinus Ghanggo Ate, Salam Khalifa, Charbel El-Khaissi, Omer Goldman, M. Gasser, William Lane, M. Coler, Arturo Oncevay, Jaime Rafael Montoya Samame, Gema Celeste Silva Villegas, Adam Ek, Jean-Philippe Bernardy, A. Shcherbakov, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, E. Klyachko, A. Salehi, A. A. Krizhanovsky, Natalia Krizhanovsky, Clara Vania, Sardana Ivanova, A. Salchak, Christopher A. Straughn, Zoey Liu, J. North, Duygu Ataman, Witold Kieraś, Marcin Woliński, T. Suhardijanto, Niklas Stoehr, Z. Nuriah, S. Ratan, Francis M. Tyers, E. M. Ponti, Grant Aiton, R. Hatcher, Ritesh Kumar, Mans Hulden, B. Barta, Dorina Lakatos, Gábor Szolnok, Judit Ács, Mohith S Raj, David Yarowsky, Ryan Cotterell, Ben Ambridge, and Ekaterina Vylomova, “SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages,” in Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, 2021.
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Jiteng Mu, Weichao Qiu, Adam Kortylewski, A. Yuille, N. Vasconcelos, and Xiaolong Wang, “A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation,” in IEEE International Conference on Computer Vision, 2021.
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url = {https://www.semanticscholar.org/paper/289f55883db1b91ff1c8d9e4a36dbdd6c3e2782e},
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Sangwook Park, Angeles Salles, K. Allen, C. Moss, and Mounya Elhilali, “Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks,” in eNeuro, 2021.
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Hexin Liu, Leibny Paola García Perera, Xinyi Zhang, J. Dauwels, Andy W. H. Khong, S. Khudanpur, and S. Styles, “End-to-End Language Diarization for Bilingual Code-Switching Speech,” in Interspeech, 2021.
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Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, and A. Yuille, “Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles,” in Computer Vision and Pattern Recognition, 2021.
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Subhrajit Roy, S. Pfohl, G. Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, and Emily Alsentzer, “Machine Learning for Health (ML4H) 2021,” in [email protected], 2021.
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Neehar Peri, Joshua Gleason, C. Castillo, T. Bourlai, Vishal M. Patel, and R. Chellappa, “A Synthesis-Based Approach for Thermal-to-Visible Face Verification,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
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Jeya Maria Jose Valanarasu, Christina Chen, and Vishal M. Patel, “R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection,” in arXiv.org, 2021.
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Poojan Oza and Vishal M. Patel, “Federated Learning-based Active Authentication on Mobile Devices,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
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K. Henry, R. Adams, C. Parent, A. Sridharan, L. Johnson, D. Hager, S. Cosgrove, A. Markowski, E. Klein, E. Chen, M. Henley, S. Miranda, K. Houston, R. C. Linton, A. Ahluwalia, A. Wu, and S. Saria, “Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System,” in medRxiv, 2021.
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S. G. Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, J. Zittrain, I. Kohane, and S. Saria, “The Clinician and Dataset Shift in Artificial Intelligence.,” in New England Journal of Medicine, 2021.
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Matthew Wiesner, Mousmita Sarma, Ashish Arora, Desh Raj, Dongji Gao, Ruizhe Huang, Supreet Preet, Moris Johnson, Zikra Iqbal, N. Goel, J. Trmal, Leibny Paola García Perera, and S. Khudanpur, “Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition,” in Interspeech, 2021.
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Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, E. Fishman, and A. Yuille, “Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction,” in IEEE Transactions on Medical Imaging, 2021.
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booktitle = "Proceedings of the Society for Computation in Linguistics 2021",
month = feb,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
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Chun Pong Lau, Amit Kumar, and R. Chellappa, “Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images,” in IEEE Journal on Selected Topics in Signal Processing, 2021.
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R. Pappagari, Jaejin Cho, Sonal Joshi, L. Moro-Velázquez, Piotr Żelasko, J. Villalba, and N. Dehak, “Automatic Detection and Assessment of Alzheimer Disease Using Speech and Language Technologies in Low-Resource Scenarios,” in Interspeech, 2021.
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D. Dreizin, Tina Chen, Yuanyuan Liang, Yuyin Zhou, Fabio M. Paes, Yan Wang, A. Yuille, Patrick Roth, Kathryn Champ, Guang Li, Ashley McLenithan, and J. Morrison, “Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis,” in Abdominal Radiology, 2021.
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R. Pappagari, Piotr Żelasko, Agnieszka Mikołajczyk, Piotr Pęzik, and N. Dehak, “Joint Prediction of Truecasing and Punctuation for Conversational Speech in Low-Resource Scenarios,” in Automatic Speech Recognition & Understanding, 2021.
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Sonal Joshi, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems,” in IEEE Transactions on Information Forensics and Security, 2021.
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Nithin Gopalakrishnan Nair and Vishal M. Patel, “Confidence Guided Network For Atmospheric Turbulence Mitigation,” in International Conference on Information Photonics, 2021.
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Md Mahfuz Ibn Alam, Antonios Anastasopoulos, L. Besacier, James Cross, Matthias Gallé, Philipp Koehn, and Vassilina Nikoulina, “On the Evaluation of Machine Translation for Terminology Consistency,” in arXiv.org, 2021.
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Prithviraj Dhar, Joshua Gleason, A. Roy, C. Castillo, and R. Chellappa, “PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition,” in IEEE International Conference on Computer Vision, 2021.
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W. G. C. Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving,” in IEEE International Conference on Robotics and Automation, 2021.
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Ju He, Adam Kortylewski, and A. Yuille, “COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification,” in arXiv.org, 2021.
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Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, S. Belongie, A. Yuille, Philip H. S. Torr, and S. Bai, “Occluded Video Instance Segmentation: A Benchmark,” in International Journal of Computer Vision, 2021.
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Shota Horiguchi, Shinji Watanabe, Leibny Paola García-Perera, Yawen Xue, Yuki Takashima, and Y. Kawaguchi, “Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors,” in Automatic Speech Recognition & Understanding, 2021.
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Jejin Cho, J. Villalba, and N. Dehak, “The JHU submission to VoxSRC-21: Track 3,” in arXiv.org, 2021.
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N. Balachandran, Jun-Cheng Chen, and R. Chellappa, “LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network,” in arXiv.org, 2021.
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url = {https://www.semanticscholar.org/paper/a8d2b6e0d180f0b5f3d444c6ac7302f531dc90c2},
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author = {{Enayat Ullah} and {Tung Mai} and {Anup B. Rao} and {Ryan A. Rossi} and {R. Arora}},
year = 2021,
month = {2},
booktitle = {Annual Conference Computational Learning Theory},
url = {https://www.semanticscholar.org/paper/0fa360d5bb8ce649155c6816fd19e5bffac4e07c},
}

Jonah P. Sengupta, M. Villemur, and A. Andreou, “A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering,” in Annual Conference on Information Sciences and Systems, 2021.
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title = {A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering},
author = {{Jonah P. Sengupta} and {M. Villemur} and {A. Andreou}},
year = 2021,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/76791fe786d8fd412ee15ca19b65c8e5b3103bc1},
}

Michelle Yuan, Patrick Xia, Benjamin Van Durme, and Jordan L. Boyd-Graber, “Adaptive Active Learning for Coreference Resolution,” in arXiv.org, 2021.
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title = {Adaptive Active Learning for Coreference Resolution},
author = {{Michelle Yuan} and {Patrick Xia} and {Benjamin Van Durme} and {Jordan L. Boyd-Graber}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c3e2fab0a498e1c18997f0a293b2e0ed624d9939},
}

Jiefu Ou, Nathaniel Weir, Anton Belyy, Felix Yu, and Benjamin Van Durme, “InFillmore: Neural Frame Lexicalization for Narrative Text Infilling,” in arXiv.org, 2021.
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@inproceedings{232147511,
title = {InFillmore: Neural Frame Lexicalization for Narrative Text Infilling},
author = {{Jiefu Ou} and {Nathaniel Weir} and {Anton Belyy} and {Felix Yu} and {Benjamin Van Durme}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f21e78a3f3e55fc081358176fe908910ef4571ea},
}

Seyoun Park, E. Fishman, and A. Yuille, “Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations,” in arXiv.org, 2021.
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@inproceedings{232168564,
title = {Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations},
author = {{Seyoun Park} and {E. Fishman} and {A. Yuille}},
year = 2021,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/20ac864f361512c85577eab83115a7cfa48dc0d7},
}

Ju He, Jieneng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, and A. Yuille, “TransFG: A Transformer Architecture for Fine-grained Recognition,” in AAAI Conference on Artificial Intelligence, 2021.
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@inproceedings{232233178,
title = {TransFG: A Transformer Architecture for Fine-grained Recognition},
author = {{Ju He} and {Jieneng Chen} and {Shuai Liu} and {Adam Kortylewski} and {Cheng Yang} and {Yutong Bai} and {Changhu Wang} and {A. Yuille}},
year = 2021,
month = {3},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/860e24025c67487b9dd87b442c7b44e5bbf5a054},
}

Sonal Joshi, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Adversarial Attacks and Defenses for Speaker Identification Systems,” in arXiv.org, 2021.
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@inproceedings{231693283,
title = {Adversarial Attacks and Defenses for Speaker Identification Systems},
author = {{Sonal Joshi} and {J. Villalba} and {Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {N. Dehak}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b595a080a4376bab6edd2e8b8c4bfa3cede54f3b},
}

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

Magdalena Rybicka, J. Villalba, Piotr Żelasko, N. Dehak, and K. Kowalczyk, “Spine2Net: SpineNet with Res2Net and Time-Squeeze-and-Excitation Blocks for Speaker Recognition,” in Interspeech, 2021.
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@inproceedings{239671591,
title = {Spine2Net: SpineNet with Res2Net and Time-Squeeze-and-Excitation Blocks for Speaker Recognition},
author = {{Magdalena Rybicka} and {J. Villalba} and {Piotr Żelasko} and {N. Dehak} and {K. Kowalczyk}},
year = 2021,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/62a007787bdf51bb58668d2a88df18850c4e9e28},
}

Daniel R. Mendat, Jonah P. Sengupta, Drake K. Foreman, and A. Andreou, “Parallel Computation of Event-Based Visual Features Using Relational Graphs,” in Annual Conference on Information Sciences and Systems, 2021.
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@inproceedings{233332283,
title = {Parallel Computation of Event-Based Visual Features Using Relational Graphs},
author = {{Daniel R. Mendat} and {Jonah P. Sengupta} and {Drake K. Foreman} and {A. Andreou}},
year = 2021,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/e00046bd84c1efded8589ee44d907f385d4b7e99},
}

Shao-Yuan Lo, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Overcomplete Representations Against Adversarial Videos,” in International Conference on Information Photonics, 2020.
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@inproceedings{227739091,
title = {Overcomplete Representations Against Adversarial Videos},
author = {{Shao-Yuan Lo} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
year = 2020,
month = {12},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/f045e09aa1a97cbb96560aa1c6a7647ceb2ab0e5},
}

Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zhe L. Lin, and A. Yuille, “Meticulous Object Segmentation,” in arXiv.org, 2020.
[BibTeX] [Link]

@inproceedings{229153995,
title = {Meticulous Object Segmentation},
author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zhe L. Lin} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/67b4298db52b5082e851ff6bbd7fbcebeb1c33fc},
}

D. Newman-Griffis, G. Divita, Bart Desmet, Ayah Zirikly, C. Rosé, and E. Fosler-Lussier, “Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets,” in J. Am. Medical Informatics Assoc., 2020.
[BibTeX] [Link]

@inproceedings{229173551,
title = {Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets},
author = {{D. Newman-Griffis} and {G. Divita} and {Bart Desmet} and {Ayah Zirikly} and {C. Rosé} and {E. Fosler-Lussier}},
year = 2020,
month = {12},
booktitle = {J. Am. Medical Informatics Assoc.},
url = {https://www.semanticscholar.org/paper/e38e5957a05b5bd21f7d18a41a56d15e6549d3c7},
}

X. Ma, J. Pino, and P. Koehn, “SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation,” in Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, Suzhou, China, 2020, p. 582–587.
[BibTeX] [Abstract] [Link]

We investigate how to adapt simultaneous text translation methods such as wait-$k$ and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.

@inproceedings{ma-etal-2020-simulmt,
title = "{S}imul{MT} to {S}imul{ST}: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation",
author = "Ma, Xutai and
Pino, Juan and
Koehn, Philipp",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.58",
pages = "582--587",
abstract = "We investigate how to adapt simultaneous text translation methods such as wait-$k$ and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.",
}

Shota Horiguchi, Leibny Paola García-Perera, Yusuke Fujita, Shinji Watanabe, and Kenji Nagamatsu, “End-To-End Speaker Diarization as Post-Processing,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
[BibTeX] [Link]

@inproceedings{229331970,
title = {End-To-End Speaker Diarization as Post-Processing},
author = {{Shota Horiguchi} and {Leibny Paola García-Perera} and {Yusuke Fujita} and {Shinji Watanabe} and {Kenji Nagamatsu}},
year = 2020,
month = {12},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/7374494ee88608ef76f74b58a8f8c26ab06adfb9},
}

D. Lewis, W. Wu, A. D. McCarthy, and D. Yarowsky, “Neural Transduction for Multilingual Lexical Translation,” in Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online), 2020, p. 4373–4384. doi:10.18653/v1/2020.coling-main.387
[BibTeX] [Abstract] [Link]

We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.

@inproceedings{lewis-etal-2020-neural,
title = "Neural Transduction for Multilingual Lexical Translation",
author = "Lewis, Dylan and
Wu, Winston and
McCarthy, Arya D. and
Yarowsky, David",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.387",
doi = "10.18653/v1/2020.coling-main.387",
pages = "4373--4384",
abstract = "We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.",
}

Harpreet Singh, S. Kusuda, R. McAdams, Shubham Gupta, Jayant Kalra, R. Kaur, Ritu Das, Saket Anand, Ashish Kumar Pandey, S. Cho, S. Saluja, J. Boutilier, S. Saria, J. Palma, A. Kaur, Gautam Yadav, and Yao Sun, “Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study,” in Children, 2020.
[BibTeX] [Link]

@inproceedings{229720553,
title = {Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study},
author = {{Harpreet Singh} and {S. Kusuda} and {R. McAdams} and {Shubham Gupta} and {Jayant Kalra} and {R. Kaur} and {Ritu Das} and {Saket Anand} and {Ashish Kumar Pandey} and {S. Cho} and {S. Saluja} and {J. Boutilier} and {S. Saria} and {J. Palma} and {A. Kaur} and {Gautam Yadav} and {Yao Sun}},
year = 2020,
month = {12},
booktitle = {Children},
url = {https://www.semanticscholar.org/paper/9279ffd9cc9c0753d2f737b204fff479e24bad42},
}

Huiyu Wang, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers,” in Computer Vision and Pattern Recognition, 2020.
[BibTeX] [Link]

@inproceedings{227248077,
title = {MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers},
author = {{Huiyu Wang} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
year = 2020,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/787119e3c3f819244c82b7d97779473773e60696},
}

Thanh Thieu, Jonathan Camacho Maldonado, Pei-Shu Ho, Min Ding, Alex R Marr, D. Brandt, D. Newman-Griffis, Ayah Zirikly, L. Chan, and E. Rasch, “A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling,” in Int. J. Medical Informatics, 2020.
[BibTeX] [Link]

@inproceedings{230784020,
title = {A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling},
author = {{Thanh Thieu} and {Jonathan Camacho Maldonado} and {Pei-Shu Ho} and {Min Ding} and {Alex R Marr} and {D. Brandt} and {D. Newman-Griffis} and {Ayah Zirikly} and {L. Chan} and {E. Rasch}},
year = 2020,
month = {12},
booktitle = {Int. J. Medical Informatics},
url = {https://www.semanticscholar.org/paper/0380a40df2833b48c509af21ada2e755300d8389},
}

Ilya Kavalerov, Weilin Li, W. Czaja, and R. Chellappa, “3-D Fourier Scattering Transform and Classification of Hyperspectral Images,” in IEEE Transactions on Geoscience and Remote Sensing, 2020.
[BibTeX] [Link]

@inproceedings{234523589,
title = {3-D Fourier Scattering Transform and Classification of Hyperspectral Images},
author = {{Ilya Kavalerov} and {Weilin Li} and {W. Czaja} and {R. Chellappa}},
year = 2020,
month = {12},
booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
url = {https://www.semanticscholar.org/paper/74b6910c70e9990b06b6ec9a55b976765b238a16},
}

N. Finkelstein, R. Adams, S. Saria, and I. Shpitser, “Partial Identifiability in Discrete Data With Measurement Error,” in Conference on Uncertainty in Artificial Intelligence, 2020.
[BibTeX] [Link]

@inproceedings{229363765,
title = {Partial Identifiability in Discrete Data With Measurement Error},
author = {{N. Finkelstein} and {R. Adams} and {S. Saria} and {I. Shpitser}},
year = 2020,
month = {12},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/6c0484885b9de17e2da47e1e73c5fa7416f08383},
}

Cheng Peng, Haofu Liao, G. Wong, Jiebo Luo, S. Zhou, and R. Chellappa, “XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors,” in AAAI Conference on Artificial Intelligence, 2020.
[BibTeX] [Link]

@inproceedings{227305790,
title = {XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors},
author = {{Cheng Peng} and {Haofu Liao} and {G. Wong} and {Jiebo Luo} and {S. Zhou} and {R. Chellappa}},
year = 2020,
month = {12},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/dbe6bff16563ba3b821f8fd5a93d298d0fd9517a},
}

Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe L. Lin, N. Xu, Yutong Bai, and A. Yuille, “Mask Guided Matting via Progressive Refinement Network,” in Computer Vision and Pattern Recognition, 2020.
[BibTeX] [Link]

@inproceedings{229156417,
title = {Mask Guided Matting via Progressive Refinement Network},
author = {{Qihang Yu} and {Jianming Zhang} and {He Zhang} and {Yilin Wang} and {Zhe L. Lin} and {N. Xu} and {Yutong Bai} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/2dd4b5e8633a5587ce2ebf73284134f21d1bc6a9},
}

Pengfei Guo, Puyang Wang, Rajeev Yasarla, Jinyuan Zhou, Vishal M. Patel, and Shanshan Jiang, “Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs,” in IEEE Transactions on Medical Imaging, 2020.
[BibTeX] [Link]

@inproceedings{229687263,
title = {Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs},
author = {{Pengfei Guo} and {Puyang Wang} and {Rajeev Yasarla} and {Jinyuan Zhou} and {Vishal M. Patel} and {Shanshan Jiang}},
year = 2020,
month = {12},
booktitle = {IEEE Transactions on Medical Imaging},
url = {https://www.semanticscholar.org/paper/cfc1473fa1ee01d64a15cb12713b06797fd7d739},
}

Christian Cosgrove, Adam Kortylewski, Chenglin Yang, and A. Yuille, “Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks,” in arXiv.org, 2020.
[BibTeX] [Link]

@inproceedings{227239366,
title = {Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks},
author = {{Christian Cosgrove} and {Adam Kortylewski} and {Chenglin Yang} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/53ae52ef49a8c2ffa4d893332fa0ea9ca7b20805},
}

Xiaoding Yuan, Adam Kortylewski, Yihong Sun, and A. Yuille, “Robust Instance Segmentation through Reasoning about Multi-Object Occlusion,” in Computer Vision and Pattern Recognition, 2020.
[BibTeX] [Link]

@inproceedings{227254685,
title = {Robust Instance Segmentation through Reasoning about Multi-Object Occlusion},
author = {{Xiaoding Yuan} and {Adam Kortylewski} and {Yihong Sun} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/016596ac909d0230f78e9173d43ccc9937246b30},
}

Mengqi Guo, Yutong Bai, Zhishuai Zhang, Adam Kortylewski, and A. Yuille, “Unsupervised Part Discovery via Feature Alignment,” in arXiv.org, 2020.
[BibTeX] [Link]

@inproceedings{227239052,
title = {Unsupervised Part Discovery via Feature Alignment},
author = {{Mengqi Guo} and {Yutong Bai} and {Zhishuai Zhang} and {Adam Kortylewski} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/2f2879a07875a94e0e04bc59068807924ea17f97},
}

Jonathan D. Jones, Cathryn S. Cortesa, A. Shelton, B. Landau, S. Khudanpur, and Gregory Hager, “Fine-Grained Activity Recognition for Assembly Videos,” in IEEE Robotics and Automation Letters, 2020.
[BibTeX] [Link]

@inproceedings{227247996,
title = {Fine-Grained Activity Recognition for Assembly Videos},
author = {{Jonathan D. Jones} and {Cathryn S. Cortesa} and {A. Shelton} and {B. Landau} and {S. Khudanpur} and {Gregory Hager}},
year = 2020,
month = {12},
booktitle = {IEEE Robotics and Automation Letters},
url = {https://www.semanticscholar.org/paper/b48e6990bda8f29bde11f0f3f6b7c1a9e0785312},
}

Siyuan Qiao, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation,” in Computer Vision and Pattern Recognition, 2020.
[BibTeX] [Link]

@inproceedings{228083552,
title = {ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation},
author = {{Siyuan Qiao} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
year = 2020,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/86f88bc71034122eb9d4f8ea16371ebd3efd42cc},
}

W. Wu and D. Yarowsky, “Wiktionary Normalization of Translations and Morphological Information,” in Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online), 2020, p. 4683–4692. doi:10.18653/v1/2020.coling-main.413
[BibTeX] [Abstract] [Link]

We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation{–-}clipping, contraction, and eye dialect{–-}and improve upon a standard attention baseline by using copy attention.

@inproceedings{wu-yarowsky-2020-wiktionary,
title = "{W}iktionary Normalization of Translations and Morphological Information",
author = "Wu, Winston and
Yarowsky, David",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.413",
doi = "10.18653/v1/2020.coling-main.413",
pages = "4683--4692",
abstract = "We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation{---}clipping, contraction, and eye dialect{---}and improve upon a standard attention baseline by using copy attention.",
}

Joshua C. Chang, Patrick Fletcher, Ju Han, Ted L.Chang, S. Vattikuti, Bart Desmet, Ayah Zirikly, and C. Chow, “Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization,” in International Conference on Learning Representations, 2020.
[BibTeX] [Link]

@inproceedings{227745021,
title = {Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization},
author = {{Joshua C. Chang} and {Patrick Fletcher} and {Ju Han} and {Ted L.Chang} and {S. Vattikuti} and {Bart Desmet} and {Ayah Zirikly} and {C. Chow}},
year = 2020,
month = {12},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/302c5388dfc37671ce109d65349a3c8cf0746788},
}

H. Mei, T. Wan, and J. Eisner, “Noise-Contrastive Estimation for Multivariate Point Processes,” in Advances in Neural Information Processing Systems (NeurIPS), 2020, p. 5204–5214.
[BibTeX] [Link]

@InProceedings{mei-wan-eisner-2020,
author = "Hongyuan Mei and Tom Wan and Jason Eisner",
title = "Noise-Contrastive Estimation for Multivariate Point
Processes",
booktitle = "Advances in Neural Information Processing Systems
(NeurIPS)",
pages = "5204--5214",
year = "2020",
month = dec,
URL = "http://cs.jhu.edu/~jason/papers/#mei-wan-eisner-2020",
}

S. Wu and M. Dredze, “Do Explicit Alignments Robustly Improve Multilingual Encoders?,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 4471–4482. doi:10.18653/v1/2020.emnlp-main.362
[BibTeX] [Abstract] [Link]

Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.

@inproceedings{wu-dredze-2020-explicit,
title = "Do Explicit Alignments Robustly Improve Multilingual Encoders?",
author = "Wu, Shijie and
Dredze, Mark",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.362",
doi = "10.18653/v1/2020.emnlp-main.362",
pages = "4471--4482",
abstract = "Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.",
}

S. Sun and K. Duh, “CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 4160–4170. doi:10.18653/v1/2020.emnlp-main.340
[BibTeX] [Abstract] [Link]

We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139×138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.

@inproceedings{sun-duh-2020-clirmatrix,
title = "{CLIRM}atrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval",
author = "Sun, Shuo and
Duh, Kevin",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.340",
doi = "10.18653/v1/2020.emnlp-main.340",
pages = "4160--4170",
abstract = "We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.",
}

Desh Raj, J. Villalba, Daniel Povey, and S. Khudanpur, “Frustratingly Easy Noise-aware Training of Acoustic Models,” in arXiv.org, 2020.
[BibTeX] [Link]

@inproceedings{226246188,
title = {Frustratingly Easy Noise-aware Training of Acoustic Models},
author = {{Desh Raj} and {J. Villalba} and {Daniel Povey} and {S. Khudanpur}},
year = 2020,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3b2eb1a573dcdb5a27103b857d32bd0c4d5ef60a},
}

A. Fine, P. Crutchley, J. Blase, J. Carroll, and G. Coppersmith, “Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data,” in Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Online, 2020, p. 50–54. doi:10.18653/v1/2020.nlpcss-1.6
[BibTeX] [Abstract] [Link]

Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.

@inproceedings{fine-etal-2020-assessing,
title = "Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using {NLP} applied to social media data",
author = "Fine, Alex and
Crutchley, Patrick and
Blase, Jenny and
Carroll, Joshua and
Coppersmith, Glen",
booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcss-1.6",
doi = "10.18653/v1/2020.nlpcss-1.6",
pages = "50--54",
abstract = "Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.",
}

R. Adams, S. Saria, and M. Rosenblum, “The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.,” in arXiv: Methodology, 2020.
[BibTeX] [Link]

@inproceedings{227238661,
title = {The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.},
author = {{R. Adams} and {S. Saria} and {M. Rosenblum}},
year = 2020,
month = {11},
booktitle = {arXiv: Methodology},
url = {https://www.semanticscholar.org/paper/4fbea743d7e81b8a1cd48376a264ea30df9ea6f2},
}

Desh Raj, Leibny Paola García-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, A. Stolcke, and S. Khudanpur, “DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs,” in Spoken Language Technology Workshop, 2020.
[BibTeX] [Link]

@inproceedings{226246280,
title = {DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs},
author = {{Desh Raj} and {Leibny Paola García-Perera} and {Zili Huang} and {Shinji Watanabe} and {Daniel Povey} and {A. Stolcke} and {S. Khudanpur}},
year = 2020,
month = {11},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/6c59a6ad00d82ca9f76fef92232ff3e2f3c1acc8},
}

S. Vashishtha, A. Poliak, Y. K. Lal, B. Van Durme, and A. S. White, “Temporal Reasoning in Natural Language Inference,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 4070–4078. doi:10.18653/v1/2020.findings-emnlp.363
[BibTeX] [Abstract] [Link]

We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration{–-}how long an event lasts{–-}and event ordering{–-}how events are temporally arranged{–-}into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.

@inproceedings{vashishtha-etal-2020-temporal,
title = "Temporal Reasoning in Natural Language Inference",
author = "Vashishtha, Siddharth and
Poliak, Adam and
Lal, Yash Kumar and
Van Durme, Benjamin and
White, Aaron Steven",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.363",
doi = "10.18653/v1/2020.findings-emnlp.363",
pages = "4070--4078",
abstract = "We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration{---}how long an event lasts{---}and event ordering{---}how events are temporally arranged{---}into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.",
}

L. Specia, Z. Li, J. Pino, V. Chaudhary, F. Guzmán, G. Neubig, N. Durrani, Y. Belinkov, P. Koehn, H. Sajjad, P. Michel, and X. Li, “Findings of the WMT 2020 Shared Task on Machine Translation Robustness,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 76–91.
[BibTeX] [Abstract] [Link]

We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs {–} English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for {”}catastrophic errors{”}. We received 59 submissions by 11 participating teams from a variety of types of institutions.

@inproceedings{specia-etal-2020-findings,
title = "Findings of the {WMT} 2020 Shared Task on Machine Translation Robustness",
author = "Specia, Lucia and
Li, Zhenhao and
Pino, Juan and
Chaudhary, Vishrav and
Guzm{\'a}n, Francisco and
Neubig, Graham and
Durrani, Nadir and
Belinkov, Yonatan and
Koehn, Philipp and
Sajjad, Hassan and
Michel, Paul and
Li, Xian",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.4",
pages = "76--91",
abstract = "We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs {--} English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for {''}catastrophic errors{''}. We received 59 submissions by 11 participating teams from a variety of types of institutions.",
}

A. Yuille and Chenxi Liu, “Deep Nets: What have They Ever Done for Vision?,” in International Journal of Computer Vision, 2020.
[BibTeX] [Link]

@inproceedings{255098046,
title = {Deep Nets: What have They Ever Done for Vision?},
author = {{A. Yuille} and {Chenxi Liu}},
year = 2020,
month = {11},
booktitle = {International Journal of Computer Vision},
url = {https://www.semanticscholar.org/paper/cf2707c43feebe4e999bc3f5f514335339962db9},
}

Jesús Antonio Villalba López, D. Garcia-Romero, Nanxin Chen, Gregory Sell, Jonas Borgstrom, A. McCree, Leibny Paola García-Perera, Saurabh Kataria, P. S. Nidadavolu, Pedro Torres-Carrasquiilo, and N. Dehak, “Advances in Speaker Recognition for Telephone and Audio-Visual Data: the JHU-MIT Submission for NIST SRE19,” in The Speaker and Language Recognition Workshop, 2020.
[BibTeX] [Link]

@inproceedings{219505334,
title = {Advances in Speaker Recognition for Telephone and Audio-Visual Data: the JHU-MIT Submission for NIST SRE19},
author = {{Jesús Antonio Villalba López} and {D. Garcia-Romero} and {Nanxin Chen} and {Gregory Sell} and {Jonas Borgstrom} and {A. McCree} and {Leibny Paola García-Perera} and {Saurabh Kataria} and {P. S. Nidadavolu} and {Pedro Torres-Carrasquiilo} and {N. Dehak}},
year = 2020,
month = {11},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/de00fffe4b64aef3797e05e74b5d3d07065b20ee},
}

A. D. McCarthy, A. Williams, S. Liu, D. Yarowsky, and R. Cotterell, “Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5664–5675. doi:10.18653/v1/2020.emnlp-main.456
[BibTeX] [Abstract] [Link]

A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages{‘} gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.

@inproceedings{mccarthy-etal-2020-measuring,
title = "Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions",
author = "McCarthy, Arya D. and
Williams, Adina and
Liu, Shijia and
Yarowsky, David and
Cotterell, Ryan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.456",
doi = "10.18653/v1/2020.emnlp-main.456",
pages = "5664--5675",
abstract = "A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages{'} gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.",
}

J. Bremerman, H. Khayrallah, D. Oard, and M. Post, “On the Evaluation of Machine Translation n-best Lists,” in Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, Online, 2020, p. 60–68. doi:10.18653/v1/2020.eval4nlp-1.7
[BibTeX] [Abstract] [Link]

The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a {`}good{‘} n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with pref