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].

  1. 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",
    }

  2. 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",
    }

  3. 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",
    }

  4. 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",
    }

  5. 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",
    }

  6. 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",
    }

  7. 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.",
    }

  8. 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.",
    }

  9. 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.",
    }

  10. 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},
    }

  11. 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},
    }

  12. 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},
    }

  13. 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},
    }

  14. 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},
    }

  15. 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},
    }

  16. 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},
    }

  17. 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},
    }

  18. 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},
    }

  19. 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},
    }

  20. 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},
    }

  21. 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},
    }

  22. 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},
    }

  23. 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},
    }

  24. 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},
    }

  25. 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},
    }

  26. 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},
    }

  27. 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},
    }

  28. 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},
    }

  29. 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},
    }

  30. 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},
    }

  31. 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},
    }

  32. 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},
    }

  33. 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},
    }

  34. 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},
    }

  35. 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},
    }

  36. 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},
    }

  37. 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},
    }

  38. 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},
    }

  39. 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},
    }

  40. 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},
    }

  41. 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},
    }

  42. 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},
    }

  43. 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},
    }

  44. 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},
    }

  45. 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},
    }

  46. 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},
    }

  47. 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},
    }

  48. 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},
    }

  49. 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},
    }

  50. 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},
    }

  51. 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},
    }

  52. 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},
    }

  53. 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},
    }

  54. 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},
    }

  55. 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},
    }

  56. 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},
    }

  57. 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},
    }

  58. 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.",
    }

  59. 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},
    }

  60. 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},
    }

  61. 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},
    }

  62. 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},
    }

  63. 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},
    }

  64. 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},
    }

  65. 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},
    }

  66. 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},
    }

  67. 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},
    }

  68. 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},
    }

  69. 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},
    }

  70. 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},
    }

  71. 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},
    }

  72. 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},
    }

  73. 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},
    }

  74. 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.",
    }

  75. 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.",
    }

  76. 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.",
    }

  77. 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},
    }

  78. 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},
    }

  79. 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},
    }

  80. 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.",
    }

  81. 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.",
    }

  82. 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.",
    }

  83. 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},
    }

  84. 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},
    }

  85. 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.",
    }

  86. 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.",
    }

  87. 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},
    }

  88. 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},
    }

  89. 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.",
    }

  90. 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},
    }

  91. 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},
    }

  92. 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},
    }

  93. 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.",
    }

  94. 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.",
    }

  95. 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.",
    }

  96. 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},
    }

  97. 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},
    }

  98. 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},
    }

  99. 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.",
    }

  100. 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},
    }

  101. 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).",
    }

  102. 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},
    }

  103. 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},
    }

  104. 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},
    }

  105. 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.",
    }

  106. 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},
    }

  107. 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.",
    }

  108. 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},
    }

  109. 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},
    }

  110. 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.",
    }

  111. 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.",
    }

  112. 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.",
    }

  113. 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.",
    }

  114. 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},
    }

  115. 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",
    }

  116. 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",
    }

  117. 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},
    }

  118. 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},
    }

  119. 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},
    }

  120. 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},
    }

  121. 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},
    }

  122. 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},
    }

  123. 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},
    }

  124. 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},
    }

  125. 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},
    }

  126. 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},
    }

  127. 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},
    }

  128. 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},
    }

  129. 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},
    }

  130. 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},
    }

  131. 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},
    }

  132. 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},
    }

  133. 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},
    }

  134. 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},
    }

  135. 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},
    }

  136. 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},
    }

  137. 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},
    }

  138. 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},
    }

  139. 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},
    }

  140. 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},
    }

  141. 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},
    }

  142. 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},
    }

  143. 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},
    }

  144. 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.",
    }

  145. 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},
    }

  146. 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.",
    }

  147. 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},
    }

  148. 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},
    }

  149. 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},
    }

  150. 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},
    }

  151. 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.",
    }

  152. 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.",
    }

  153. 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.",
    }

  154. 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.",
    }

  155. 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.",
    }

  156. 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.",
    }

  157. 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.",
    }

  158. 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.",
    }

  159. 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.",
    }

  160. 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.",
    }

  161. 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.",
    }

  162. 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.",
    }

  163. 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.",
    }

  164. 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.",
    }

  165. 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.",
    }

  166. 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.",
    }

  167. 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.",
    }

  168. 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.",
    }

  169. 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.",
    }

  170. 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.",
    }

  171. 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.",
    }

  172. 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.",
    }

  173. 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.",
    }

  174. 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.",
    }

  175. 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.",
    }

  176. 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",
    }

  177. 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",
    }

  178. 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",
    }

  179. 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},
    }

  180. 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},
    }

  181. 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},
    }

  182. 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.",
    }

  183. 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},
    }

  184. 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},
    }

  185. 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.",
    }

  186. 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.",
    }

  187. 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},
    }

  188. 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},
    }

  189. 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},
    }

  190. 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},
    }

  191. 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},
    }

  192. 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},
    }

  193. 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},
    }

  194. 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},
    }

  195. 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},
    }

  196. 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},
    }

  197. 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},
    }

  198. 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},
    }

  199. 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},
    }

  200. 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},
    }

  201. 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},
    }

  202. 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},
    }

  203. 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},
    }

  204. Malsha V. Perera, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “SAR Despeckling Using a Denoising Diffusion Probabilistic Model,” in IEEE Geoscience and Remote Sensing Letters, 2022.
    [BibTeX] [Link]
    @inproceedings{249538421,
    title = {SAR Despeckling Using a Denoising Diffusion Probabilistic Model},
    author = {{Malsha V. Perera} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
    year = 2022,
    month = {6},
    booktitle = {IEEE Geoscience and Remote Sensing Letters},
    url = {https://www.semanticscholar.org/paper/d49713b2126f4b224a75b3bfea3e00c63c7e51e3},
    }

  205. 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).",
    }

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

  207. 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.
    [BibTeX] [Link]
    @inproceedings{248562546,
    title = {Differentially Private Generalized Linear Models Revisited},
    author = {{R. Arora} and {Raef Bassily} and {Crist'obal Guzm'an} and {Michael Menart} and {Enayat Ullah}},
    year = 2022,
    month = {5},
    booktitle = {Neural Information Processing Systems},
    url = {https://www.semanticscholar.org/paper/6c1ae5bd9aa32a08141bf630c90b00523ef4ffb1},
    }

  208. 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.
    [BibTeX] [Link]
    @inproceedings{251800257,
    title = {Masked Autoencoders Enable Efficient Knowledge Distillers},
    author = {{Yutong Bai} and {Zeyu Wang} and {Junfei Xiao} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
    year = 2022,
    month = {8},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/a7cd547c539d69f99f17855242cb07bd80047f9a},
    }

  209. 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.
    [BibTeX] [Link]
    @inproceedings{248864294,
    title = {Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI},
    author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {G. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {Bart F Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. McCradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
    year = 2022,
    month = {5},
    booktitle = {British medical journal},
    url = {https://www.semanticscholar.org/paper/3a8c344f67d5081ead5f7dd5ebf0f760d69fc01d},
    }

  210. Saksham Suri, Saketh Rambhatla, R. Chellappa, and Abhinav Shrivastava, “R-SSL: Region based Semi-Supervised Learning for Sparsely Annotated Object Detection.” 2022.
    [BibTeX] [Link]
    @inproceedings{255750913,
    title = {R-SSL: Region based Semi-Supervised Learning for Sparsely Annotated Object Detection},
    author = {{Saksham Suri} and {Saketh Rambhatla} and {R. Chellappa} and {Abhinav Shrivastava}},
    year = 2022,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/e2e159205030b9d3e3d742b4bdbebd7e94201d3f},
    }

  211. 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.
    [BibTeX] [Link]
    @inproceedings{247839251,
    title = {Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification},
    author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {N. Dehak}},
    year = 2022,
    month = {3},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/d58ebbc34e8ea987da5dda1bb132823b3e9105d3},
    }

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

  213. 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.
    [BibTeX] [Link]
    @inproceedings{247450789,
    title = {Investigating Self-Supervised Learning for Speech Enhancement and Separation},
    author = {{Zili Huang} and {Shinji Watanabe} and {Shu-wen Yang} and {Leibny Paola García-Perera} and {S. Khudanpur}},
    year = 2022,
    month = {3},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/d5634a21b3727258822b78f5c5ababf7261a5c79},
    }

  214. 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.
    [BibTeX] [Link]
    @inproceedings{249795778,
    title = {A Novel Dual-band filtenna for 2.4 and 5.8 GHz Wireless Local Area for Network Applications},
    author = {{Harminder Singh} and {R. Sharma} and {R. Arora}},
    year = 2022,
    month = {2},
    booktitle = {2022 Interdisciplinary Research in Technology and Management (IRTM)},
    url = {https://www.semanticscholar.org/paper/a773c6edcc796c34a4cd477d6a39043cab45d037},
    }

  215. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{251018520,
    title = {JAWS: Predictive Inference Under Covariate Shift},
    author = {{Drew Prinster} and {Anqi Liu} and {S. Saria}},
    year = 2022,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/e9b0db3dae9050413e3eda2861acf82bff41624b},
    }

  216. 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.
    [BibTeX] [Link]
    @inproceedings{251468156,
    title = {Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations},
    author = {{Jaejin Cho} and {R. Pappagari} and {Piotr Żelasko} and {L. Moro-Velázquez} and {J. Villalba} and {N. Dehak}},
    year = 2022,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/f3d7789c627d3e62d92c225a272e408f287c6317},
    }

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

  218. 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.
    [BibTeX] [Link]
    @inproceedings{249830266,
    title = {Advances in Speaker Recognition for Multilingual Conversational Telephone Speech: The JHU-MIT System for NIST SRE20 CTS Challenge},
    author = {{J. Villalba} and {B. J. Borgstrom} and {Saurabh Kataria} and {Jaejin Cho} and {P. Torres-Carrasquillo} and {N. Dehak}},
    year = 2022,
    month = {6},
    booktitle = {The Speaker and Language Recognition Workshop},
    url = {https://www.semanticscholar.org/paper/042e35459f6dfd8ad8be0dad72ae27f8e73cd4a8},
    }

  219. 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.
    [BibTeX] [Link]
    @inproceedings{249394670,
    title = {Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors},
    author = {{Shota Horiguchi} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Yuki Takashima} and {Y. Kawaguchi}},
    year = 2022,
    month = {6},
    booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
    url = {https://www.semanticscholar.org/paper/872c99ead3cc2644fbabd7dab37b82d233cc81cb},
    }

  220. 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.
    [BibTeX] [Link]
    @inproceedings{249890221,
    title = {CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation},
    author = {{Qihang Yu} and {Huiyu Wang} and {Dahun Kim} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
    year = 2022,
    month = {6},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/31a9744bd5421b3fbbad2ab38ce33bb2f352c77a},
    }

  221. Cheng Peng and R. Chellappa, “PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{251622408,
    title = {PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images},
    author = {{Cheng Peng} and {R. Chellappa}},
    year = 2022,
    month = {8},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/c900f690fdab5d17b0253d4362e7f1a7d9d2d495},
    }

  222. 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.
    [BibTeX] [Link]
    @inproceedings{247518863,
    title = {ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images},
    author = {{Rajeev Yasarla} and {C. Priebe} and {Vishal M. Patel}},
    year = 2022,
    month = {3},
    booktitle = {European Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/0bf4fd83f0f17b0fa94c18631a28d52ce5ea6042},
    }

  223. V. Vibashan, Poojan Oza, and Vishal M. Patel, “Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{247778633,
    title = {Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection},
    author = {{V. Vibashan} and {Poojan Oza} and {Vishal M. Patel}},
    year = 2022,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/c850d77f3ce8e8fa989cc4f7b466b63b113fd6db},
    }

  224. 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.
    [BibTeX] [Link]
    @inproceedings{252346611,
    title = {End-to-End Neural Speaker Diarization with an Iterative Refinement of Non-Autoregressive Attention-based Attractors},
    author = {{Magdalena Rybicka} and {J. Villalba} and {N. Dehak} and {K. Kowalczyk}},
    year = 2022,
    month = {9},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/916cfa98c48af9931559fe0d8953bcaf7bdf7f2c},
    }

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

  226. 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.
    [BibTeX] [Link]
    @inproceedings{246595318,
    title = {Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach},
    author = {{Amir Alipour-Fanid} and {Monireh Dabaghchian} and {R. Arora} and {K. Zeng}},
    year = 2022,
    month = {6},
    booktitle = {IEEE Transactions on Cognitive Communications and Networking},
    url = {https://www.semanticscholar.org/paper/ad0c8cc0a80c5873591e62ca9f47fa21b631c35f},
    }

  227. M. Villemur, Jonah P. Sengupta, P. Julián, and A. Andreou, “Morphological, Object Detection Framework for Embedded, Event-based Sensing,” in International Conference on Event-Based Control, Communication, and Signal Processing, 2022.
    [BibTeX] [Link]
    @inproceedings{251762249,
    title = {Morphological, Object Detection Framework for Embedded, Event-based Sensing},
    author = {{M. Villemur} and {Jonah P. Sengupta} and {P. Julián} and {A. Andreou}},
    year = 2022,
    month = {6},
    booktitle = {International Conference on Event-Based Control, Communication, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/dc774c02c8260a15a0098b2a193b7b5db7e3fdb1},
    }

  228. A. Shelton, E. Davis, Cathryn S. Cortesa, Jonathan D. Jones, Gregory Hager, S. Khudanpur, and B. Landau, “Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability,” in Cognitive Sciences, 2022.
    [BibTeX] [Link]
    @inproceedings{246239741,
    title = {Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability},
    author = {{A. Shelton} and {E. Davis} and {Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {B. Landau}},
    year = 2022,
    month = {1},
    booktitle = {Cognitive Sciences},
    url = {https://www.semanticscholar.org/paper/6482f52977f167c6db734f766b0b59e8c92d7e52},
    }

  229. 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.
    [BibTeX] [Link]
    @inproceedings{252383306,
    title = {NBD-GAP: Non-Blind Image Deblurring without Clean Target Images},
    author = {{Nithin Gopalakrishnan Nair} and {Rajeev Yasarla} and {Vishal M. Patel}},
    year = 2022,
    month = {9},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/28a43c5d52c421b1ccc24d15f39b2cdb82ed84de},
    }

  230. Xiang Xiang, Yuwen Tan, Qian Wan, Jing Ma, A. Yuille, and Gregory Hager, “Coarse-To-Fine Incremental Few-Shot Learning – Appendix.” 2022.
    [BibTeX] [Link]
    @inproceedings{253540960,
    title = {Coarse-To-Fine Incremental Few-Shot Learning - Appendix},
    author = {{Xiang Xiang} and {Yuwen Tan} and {Qian Wan} and {Jing Ma} and {A. Yuille} and {Gregory Hager}},
    year = 2022,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/4656e23147a7bf6cce8ef8702324da910d004bb4},
    }

  231. 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.
    [BibTeX] [Link]
    @inproceedings{247958394,
    title = {SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering},
    author = {{Vipul Gupta} and {Zhuowan Li} and {Adam Kortylewski} and {Chenyu Zhang} and {Yingwei Li} and {A. Yuille}},
    year = 2022,
    month = {4},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/0d2f848fff121133b3b77c7e691c6a2ba502be47},
    }

  232. 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.
    [BibTeX] [Link]
    @inproceedings{248377080,
    title = {Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN},
    author = {{Rajeev Yasarla} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
    year = 2022,
    month = {4},
    booktitle = {International Conference on Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/ee48b57139e1d84c60926796195f5f77c2d8b1db},
    }

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

  234. 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.
    [BibTeX] [Link]
    @inproceedings{248239720,
    title = {A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration},
    author = {{Nithin Gopalakrishnan Nair} and {Kangfu Mei} and {Vishal M. Patel}},
    year = 2022,
    month = {4},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/be3eb6827c645f176e204dffb5d740e5281dd67c},
    }

  235. Sai Saketh Rambhatla, Saksham Suri, R. Chellappa, and Abhinav Shrivastava, “Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{245877805,
    title = {Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach},
    author = {{Sai Saketh Rambhatla} and {Saksham Suri} and {R. Chellappa} and {Abhinav Shrivastava}},
    year = 2022,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9de73cf88055249db41c8179d4eb3b39d1ca81c2},
    }

  236. Nils Holzenberger, Yunmo Chen, and Benjamin Van Durme, “Asking the Right Questions in Low Resource Template Extraction,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{249062873,
    title = {Asking the Right Questions in Low Resource Template Extraction},
    author = {{Nils Holzenberger} and {Yunmo Chen} and {Benjamin Van Durme}},
    year = 2022,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/196b71b4e8465dd632954cf499f0467754cbd9d4},
    }

  237. 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},
    }

  238. 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},
    }

  239. 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},
    }

  240. 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.",
    }

  241. 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},
    }

  242. 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},
    }

  243. 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},
    }

  244. 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},
    }

  245. 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},
    }

  246. 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},
    }

  247. 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},
    }

  248. 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},
    }

  249. 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},
    }

  250. 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},
    }

  251. 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},
    }

  252. 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},
    }

  253. 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},
    }

  254. 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},
    }

  255. 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},
    }

  256. 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},
    }

  257. 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},
    }

  258. 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},
    }

  259. 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},
    }

  260. 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},
    }

  261. 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},
    }

  262. 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},
    }

  263. 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},
    }

  264. 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},
    }

  265. 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},
    }

  266. 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},
    }

  267. 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},
    }

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

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

  270. Kelly Marchisio, Conghao Xiong, and Philipp Koehn, “Embedding-Enhanced GIZA++: Improving Word Alignment Using Embeddings.” 2022.
    [BibTeX] [Link]
    @inproceedings{252816176,
    title = {Embedding-Enhanced GIZA++: Improving Word Alignment Using Embeddings},
    author = {{Kelly Marchisio} and {Conghao Xiong} and {Philipp Koehn}},
    year = 2022,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/f40adef732f55b1fe4206339d3b51f18da65d4d4},
    }

  271. 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.
    [BibTeX] [Link]
    @inproceedings{246872267,
    title = {Learning from Synthetic Vehicles},
    author = {{Tae Soo Kim} and {Bohoon Shim} and {Michael Peven} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
    year = 2022,
    month = {1},
    booktitle = {2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)},
    url = {https://www.semanticscholar.org/paper/b5ac4931672397df6d9135e0d5b615351f490a44},
    }

  272. 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.
    [BibTeX] [Link]
    @inproceedings{249037240,
    title = {A Simple Data Mixing Prior for Improving Self-Supervised Learning},
    author = {{Sucheng Ren} and {Huiyu Wang} and {Zhengqi Gao} and {Shengfeng He} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
    year = 2022,
    month = {6},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/5ed5dcb0763af9e6283dcdcf4af75248d9d19c95},
    }

  273. 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.
    [BibTeX] [Link]
    @inproceedings{248890002,
    title = {Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI},
    author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {G. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {Bart F Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. McCradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
    year = 2022,
    month = {5},
    booktitle = {Nature Network Boston},
    url = {https://www.semanticscholar.org/paper/83b6a76ba5112d27bdbfca3efd2ed918d8e73db5},
    }

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

  275. Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, A. Yuille, and Cihang Xie, “F AST A DV P ROP.” 2022.
    [BibTeX] [Link]
    @inproceedings{247738628,
    title = {F AST A DV P ROP},
    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,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/f2eaaf8afc89a86035fd7127305f2bb9d2169495},
    }

  276. 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.
    [BibTeX] [Link]
    @inproceedings{252368348,
    title = {T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models},
    author = {{Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
    year = 2022,
    month = {9},
    booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
    url = {https://www.semanticscholar.org/paper/fc49634e80ab31929799786a97b7ea63834bbdb1},
    }

  277. 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.
    [BibTeX] [Link]
    @inproceedings{249877931,
    title = {Supplementary Materials: ”TransMix: Attend to Mix for Vision Transformers”},
    author = {{Jieneng Chen} and {Shuyang Sun} and {Ju He} and {Philip H. S. Torr} and {A. Yuille} and {Song Bai}},
    year = 2022,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/d378dc21ab5cfbde24b295ab759c9947f820bc94},
    }

  278. 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.
    [BibTeX] [Link]
    @inproceedings{248476149,
    title = {Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer},
    author = {{Haoran Xu} and {Kenton Murray}},
    year = 2022,
    month = {4},
    booktitle = {NAACL-HLT},
    url = {https://www.semanticscholar.org/paper/811a5c79d8c0f6f5b57697e7be0e84b5f9a94ce8},
    }

  279. 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.
    [BibTeX] [Link]
    @inproceedings{249209486,
    title = {SAR Despeckling Using Overcomplete Convolutional Networks},
    author = {{Malsha V. Perera} and {W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
    year = 2022,
    month = {5},
    booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
    url = {https://www.semanticscholar.org/paper/c4911e20fb50f6da552c812bda9ef4fdb525b939},
    }

  280. Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell D. Collins, Yukun Zhu, Hartwig Adam, A. Yuille, and Liang-Chieh Chen, “Appendix for k -means Mask Transformer.” 2022.
    [BibTeX] [Link]
    @inproceedings{253513495,
    title = {Appendix for k -means Mask Transformer},
    author = {{Qihang Yu} and {Huiyu Wang} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
    year = 2022,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/46bfaa37e8b95f6bff810e5563d67e3404e78225},
    }

  281. 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.
    [BibTeX] [Link]
    @inproceedings{251539930,
    title = {Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI},
    author = {{B. Vasey} and {M. Nagendran} and {Bruce Campbell} and {D. Clifton} and {G. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {Bart F Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. McCradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
    year = 2022,
    month = {8},
    booktitle = {Nature Network Boston},
    url = {https://www.semanticscholar.org/paper/a22215acadb4ad4ec04624025021023acf7261d6},
    }

  282. Boyuan Zheng, Patrick Xia, M. Yarmohammadi, and Benjamin Van Durme, “Multilingual Coreference Resolution in Multiparty Dialogue,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{251253396,
    title = {Multilingual Coreference Resolution in Multiparty Dialogue},
    author = {{Boyuan Zheng} and {Patrick Xia} and {M. Yarmohammadi} and {Benjamin Van Durme}},
    year = 2022,
    month = {8},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/840945dddcfaca56f8cfb42dc890a6185212eae2},
    }

  283. 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.
    [BibTeX] [Link]
    @inproceedings{250425961,
    title = {No Language Left Behind: Scaling Human-Centered Machine Translation},
    author = {{Nllb team} and {M. Costa-jussà} and {James Cross} and {Onur cCelebi} and {Maha Elbayad} and {Kenneth Heafield} and {Kevin Heffernan} and {Elahe Kalbassi} and {Janice Lam} and {Daniel Licht} and {Jean Maillard} and {Anna Sun} and {Skyler Wang} and {Guillaume Wenzek} and {Alison Youngblood} and {Bapi Akula} and {Loïc Barrault} and {Gabriel Mejia Gonzalez} and {Prangthip Hansanti} and {John Hoffman} and {Semarley Jarrett} and {Kaushik Ram Sadagopan} and {Dirk Rowe} and {S. Spruit} and {C. Tran} and {Pierre Yves Andrews} and {N. F. Ayan} and {Shruti Bhosale} and {Sergey Edunov} and {Angela Fan} and {Cynthia Gao} and {Vedanuj Goswami} and {Francisco Guzm'an} and {Philipp Koehn} and {Alexandre Mourachko} and {Christophe Ropers} and {Safiyyah Saleem} and {Holger Schwenk} and {Jeff Wang}},
    year = 2022,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/0e638ce20f3e9b4dd1c10c32a29495c798425e63},
    }

  284. 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.
    [BibTeX] [Link]
    @inproceedings{249209554,
    title = {VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis},
    author = {{Angtian Wang} and {Peng Wang} and {Jian Sun} and {Adam Kortylewski} and {A. Yuille}},
    year = 2022,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/31e79b62a9483dcdf2575603469e6ff888e7f234},
    }

  285. 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.
    [BibTeX] [Link]
    @inproceedings{249386629,
    title = {A dilemma of ground truth in noisy speech separation and an approach to lessen the impact of imperfect training data},
    author = {{Matthew Maciejewski} and {Jing Shi} and {Shinji Watanabe} and {S. Khudanpur}},
    year = 2022,
    month = {6},
    booktitle = {Computer Speech and Language},
    url = {https://www.semanticscholar.org/paper/e3b6ab2d2e1a0e734bf505fbb34dc6fe723ab37e},
    }

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

  287. Yiwen Shao, J. Villalba, Sonal Joshi, Saurabh Kataria, S. Khudanpur, and N. Dehak, “Chunking Defense for Adversarial Attacks on ASR,” in Interspeech, 2022.
    [BibTeX] [Link]
    @inproceedings{252341100,
    title = {Chunking Defense for Adversarial Attacks on ASR},
    author = {{Yiwen Shao} and {J. Villalba} and {Sonal Joshi} and {Saurabh Kataria} and {S. Khudanpur} and {N. Dehak}},
    year = 2022,
    month = {9},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/ace27d0f6e93765439e19203e69570cf00f09e63},
    }

  288. Nathaniel Weir and Benjamin Van Durme, “Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System.” 2022.
    [BibTeX] [Link]
    @inproceedings{258762900,
    title = {Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System},
    author = {{Nathaniel Weir} and {Benjamin Van Durme}},
    year = 2022,
    month = {9},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/767c1338c001fdadb5bf4eed530fc78ede49e9ae},
    }

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

  290. 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.
    [BibTeX] [Link]
    @inproceedings{248505905,
    title = {Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation},
    author = {{Yukun Feng} and {Feng Li} and {Ziang Song} and {Boyuan Zheng} and {Philipp Koehn}},
    year = 2022,
    month = {5},
    booktitle = {NAACL-HLT},
    url = {https://www.semanticscholar.org/paper/4293121e2bef84aa8db5aab6634cfcd2d06947d4},
    }

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

  292. Pengfei Guo, Yiqun Mei, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{246240294,
    title = {ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer},
    author = {{Pengfei Guo} and {Yiqun Mei} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
    year = 2022,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/92a574d34837b970e6c0610226362e801ca83442},
    }

  293. Xiaolei Huang, Franck Dernoncourt, and Mark Dredze, “Enriching Unsupervised User Embedding via Medical Concepts,” in ACM Conference on Health, Inference, and Learning, 2022.
    [BibTeX] [Link]
    @inproceedings{247594586,
    title = {Enriching Unsupervised User Embedding via Medical Concepts},
    author = {{Xiaolei Huang} and {Franck Dernoncourt} and {Mark Dredze}},
    year = 2022,
    month = {3},
    booktitle = {ACM Conference on Health, Inference, and Learning},
    url = {https://www.semanticscholar.org/paper/78a4f90b348f5401e8fb6b84bca0e539142b2530},
    }

  294. 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.
    [BibTeX] [Link]
    @inproceedings{247762191,
    title = {Data Selection Curriculum for Neural Machine Translation},
    author = {{Tasnim Mohiuddin} and {Philipp Koehn} and {Vishrav Chaudhary} and {James Cross} and {Shruti Bhosale} and {Shafiq R. Joty}},
    year = 2022,
    month = {3},
    booktitle = {Conference on Empirical Methods in Natural Language Processing},
    url = {https://www.semanticscholar.org/paper/d6c4b31958fe9e4ff4f83e049ed5c6881653eb03},
    }

  295. 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.
    [BibTeX] [Link]
    @inproceedings{250954558,
    title = {Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis},
    author = {{R. Adams} and {K. Henry} and {A. Sridharan} and {Hossein Soleimani} and {A. Zhan} and {Nishi Rawat} 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/9ad55e7b87e1557983bdef0e9fe7eb0f4254dd94},
    }

  296. 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.
    [BibTeX] [Link]
    @inproceedings{249282662,
    title = {Faster Rates of Convergence to Stationary Points in Differentially Private Optimization},
    author = {{R. Arora} and {Raef Bassily} and {Tom'as Gonz'alez} and {Crist'obal Guzm'an} and {Michael Menart} and {Enayat Ullah}},
    year = 2022,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/230242936c3f2ea0773946c7a8fba4f0d209aa25},
    }

  297. 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.
    [BibTeX] [Link]
    @inproceedings{250408015,
    title = {k-means Mask Transformer},
    author = {{Qihang Yu} and {Huiyu Wang} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hatwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
    year = 2022,
    month = {7},
    booktitle = {European Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/4540b16bc97e329bd9d987c96321ffb210844edd},
    }

  298. Rajeev Yasarla, Renliang Weng, Wongun Choi, Vishal M. Patel, and Amir Sadeghian, “3SD: Self-Supervised Saliency Detection With No Labels,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{247318765,
    title = {3SD: Self-Supervised Saliency Detection With No Labels},
    author = {{Rajeev Yasarla} and {Renliang Weng} and {Wongun Choi} and {Vishal M. Patel} and {Amir Sadeghian}},
    year = 2022,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/2a78e1c0412cbcc851ba60224c15c501debe2049},
    }

  299. 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},
    }

  300. 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},
    }

  301. 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},
    }

  302. 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},
    }

  303. 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},
    }

  304. 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},
    }

  305. 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},
    }

  306. 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.",
    }

  307. 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},
    }

  308. 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. Hamdan} and {Sharon Zhou} and {Shashank Srivastava} and {Sherry Shi} and {Shikhar Singh} and {Shima Asaadi} and {S. Gu} and {Shubh Pachchigar} and {Shubham Toshniwal} and {Shyam Upadhyay} and {Shyamolima Debnath} and {Siamak Shakeri} and {Simon Thormeyer} and {S. Melzi} and {Siva Reddy} and {S. Makini} and {Soo-hwan Lee} and {Spencer Bradley Torene} and {Sriharsha Hatwar} and {S. Dehaene} and {Stefan Divic} and {S. Ermon} and {Stella Rose Biderman} and {Stephanie C. Lin} and {S. Prasad} and {S. Piantadosi} and {S. Shieber} and {Summer Misherghi} and {Svetlana Kiritchenko} and {Swaroop Mishra} and {Tal Linzen} and {Tal Schuster} and {Tao Li} and {Tao Yu} and {Tariq A. Ali} and {Tatsuo Hashimoto} and {Te-Lin Wu} and {T. Desbordes} and {Theodore Rothschild} and {Thomas Phan} and {Tianle Wang} and {Tiberius Nkinyili} and {Timo Schick} and {T. Kornev} and {Timothy Telleen-Lawton} and {T. Tunduny} and {Tobias Gerstenberg} and {T. Chang} and {Trishala Neeraj} and {Tushar Khot} and {T. Shultz} and {Uri Shaham} and {Vedant Misra} and {V. Demberg} and {Victoria Nyamai} and {Vikas Raunak} and {V. Ramasesh} and {Vinay Uday Prabhu} and {Vishakh Padmakumar} and {Vivek Srikumar} and {W. Fedus} and {W. Saunders} and {William Zhang} and {W. Vossen} and {Xiang Ren} and {Xiaoyu Tong} and {Xinyi Wu} and {Xudong Shen} and {Yadollah Yaghoobzadeh} and {Yair Lakretz} and {Yang Song} and {Yasaman Bahri} and {Y. Choi} and {Yichi Yang} and {Yiding Hao} and {Yifu Chen} and {Yonatan Belinkov} and {Yu Hou} and {Yu Hou} and {Yuntao Bai} and {Zachary Seid} and {Zhao Xinran} and {Zhuoye Zhao} and {Z. Wang} and {Zijie J. Wang} and {Zirui Wang} and {Ziyi Wu} and {Sahib Singh} and {Uri Shaham}},
    year = 2022,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/34503c0b6a615124eaf82cb0e4a1dab2866e8980},
    }

  309. Christos Sapsanis, M. Villemur, and A. Andreou, “Real Number Modeling of a SAR ADC behavior using SystemVerilog,” in International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, 2022.
    [BibTeX] [Link]
    @inproceedings{250463643,
    title = {Real Number Modeling of a SAR ADC behavior using SystemVerilog},
    author = {{Christos Sapsanis} and {M. Villemur} and {A. Andreou}},
    year = 2022,
    month = {6},
    booktitle = {International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design},
    url = {https://www.semanticscholar.org/paper/528b50e00ed3efece80bbc4557ecf4f8df98094a},
    }

  310. Bruce Y Lee, J. Ordovás, E. Parks, Cheryl Anderson, A. Barabasi, S. Clinton, K. de la Haye, V. Duffy, P. Franks, E. Ginexi, K. Hammond, E. Hanlon, Michael Hittle, E. Ho, A. Horn, R. Isaacson, P. Mabry, S. Malone, Corby K. Martin, J. Mattei, S. Meydani, L. Nelson, M. Neuhouser, Brendan Parent, N. Pronk, H. Roche, S. Saria, F. Scheer, E. Segal, M. Sevick, T. Spector, Linda B Van Horn, K. Varady, V. S. Voruganti, and Marie F Martinez, “Research gaps and opportunities in precision nutrition: an NIH workshop report.,” in American Journal of Clinical Nutrition, 2022.
    [BibTeX] [Link]
    @inproceedings{252045737,
    title = {Research gaps and opportunities in precision nutrition: an NIH workshop report.},
    author = {{Bruce Y Lee} and {J. Ordovás} and {E. Parks} and {Cheryl Anderson} and {A. Barabasi} and {S. Clinton} and {K. de la Haye} and {V. Duffy} and {P. Franks} and {E. Ginexi} and {K. Hammond} and {E. Hanlon} and {Michael Hittle} and {E. Ho} and {A. Horn} and {R. Isaacson} and {P. Mabry} and {S. Malone} and {Corby K. Martin} and {J. Mattei} and {S. Meydani} and {L. Nelson} and {M. Neuhouser} and {Brendan Parent} and {N. Pronk} and {H. Roche} and {S. Saria} and {F. Scheer} and {E. Segal} and {M. Sevick} and {T. Spector} and {Linda B Van Horn} and {K. Varady} and {V. S. Voruganti} and {Marie F Martinez}},
    year = 2022,
    month = {9},
    booktitle = {American Journal of Clinical Nutrition},
    url = {https://www.semanticscholar.org/paper/31b65b7ccc0ed5ba975753c8b0ba8da8df28a09c},
    }

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

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

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

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

  315. Yiqun Mei, Pengfei Guo, and Vishal M. Patel, “Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination,” in Computer Vision and Pattern Recognition, 2022.
    [BibTeX] [Link]
    @inproceedings{247839270,
    title = {Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination},
    author = {{Yiqun Mei} and {Pengfei Guo} and {Vishal M. Patel}},
    year = 2022,
    month = {3},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/5f7510530bc9d9655968fac8b3430772bd554816},
    }

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

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

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

  319. Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, Piotr Żelasko, and N. Dehak, “Time-domain speech super-resolution with GAN based modeling for telephony speaker verification.” 2022.
    [BibTeX] [Link]
    @inproceedings{252090174,
    title = {Time-domain speech super-resolution with GAN based modeling for telephony speaker verification},
    author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {Piotr Żelasko} and {N. Dehak}},
    year = 2022,
    month = {9},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/312a44c9d2d2719ca8d3eb22539edd215415229e},
    }

  320. Daniel E Park, Nora L. Watson, Christopher Focht, D. Feikin, L. Hammit, W. A. Brooks, S. Howie, K. Kotloff, O. Levine, S. Madhi, D. Murdoch, K. O’Brien, J. Scott, D. Thea, Tussanee Amorninthapichet, Juliet O. Awori, C. Bunthi, B. Ebruke, Mounya Elhilali, M. Higdon, L. Hossain, Y. Jahan, D. Moore, J. Mulindwa, L. Mwananyanda, Sathapana Naorat, Christine Prosperi, S. Thamthitiwat, C. Verwey, K. Jablonski, M. Power, H. Young, Maria Deloria Knoll, and E. McCollum, “Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study,” in BMJ Open Respiratory Research, 2022.
    [BibTeX] [Link]
    @inproceedings{248832550,
    title = {Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study},
    author = {{Daniel E Park} and {Nora L. Watson} and {Christopher Focht} and {D. Feikin} and {L. Hammit} and {W. A. Brooks} and {S. Howie} and {K. Kotloff} and {O. Levine} and {S. Madhi} and {D. Murdoch} and {K. O'Brien} and {J. Scott} and {D. Thea} and {Tussanee Amorninthapichet} and {Juliet O. Awori} and {C. Bunthi} and {B. Ebruke} and {Mounya Elhilali} and {M. Higdon} and {L. Hossain} and {Y. Jahan} and {D. Moore} and {J. Mulindwa} and {L. Mwananyanda} and {Sathapana Naorat} and {Christine Prosperi} and {S. Thamthitiwat} and {C. Verwey} and {K. Jablonski} and {M. Power} and {H. Young} and {Maria Deloria Knoll} and {E. McCollum}},
    year = 2022,
    month = {5},
    booktitle = {BMJ Open Respiratory Research},
    url = {https://www.semanticscholar.org/paper/dfedb313d8718de8aa162813060af3e24e8cbe28},
    }

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

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

  323. Qihao Liu, Yi Zhang, S. Bai, and A. Yuille, “Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation,” in European Conference on Computer Vision, 2022.
    [BibTeX] [Link]
    @inproceedings{251223772,
    title = {Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation},
    author = {{Qihao Liu} and {Yi Zhang} and {S. Bai} and {A. Yuille}},
    year = 2022,
    month = {7},
    booktitle = {European Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/6daa8592e9c895acd78ce0d798f5327add2902a4},
    }

  324. 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.
    [BibTeX] [Link]
    @inproceedings{248069457,
    title = {AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification},
    author = {{Sonal Joshi} and {Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
    year = 2022,
    month = {4},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/a8144dbb8481cb78e08fc34e452603984bb5aa01},
    }

  325. 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.
    [BibTeX] [Link]
    @inproceedings{248118691,
    title = {Large-Scale Streaming End-to-End Speech Translation with Neural Transducers},
    author = {{Jian Xue} and {Peidong Wang} and {Jinyu Li} and {Matt Post} and {Yashesh Gaur}},
    year = 2022,
    month = {4},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/5a5704382fd8c980937e10618713d641c846b313},
    }

  326. 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.
    [BibTeX] [Link]
    @inproceedings{249848080,
    title = {Orientation-guided Graph Convolutional Network for Bone Surface Segmentation},
    author = {{Aimon Rahman} and {W. G. C. Bandara} 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/bdcd82545a729552d83ed920bd117718c9f6948f},
    }

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

  328. Pirazh Khorramshahi, V. Shenoy, and R. Chellappa, “Scalable Vehicle Re-Identification via Self-Supervision,” in arXiv.org, 2022.
    [BibTeX] [Link]
    @inproceedings{248811372,
    title = {Scalable Vehicle Re-Identification via Self-Supervision},
    author = {{Pirazh Khorramshahi} and {V. Shenoy} and {R. Chellappa}},
    year = 2022,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9d69f0b6c916ac36e2bf28491d27c653eae245cd},
    }

  329. 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.
    [BibTeX] [Link]
    @inproceedings{247447734,
    title = {Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation},
    author = {{Pengfei Guo} and {Dong Yang} and {Ali Hatamizadeh} and {An Xu} and {Ziyue Xu} and {Wenqi Li} and {Can Zhao} and {Daguang Xu} and {S. Harmon} and {E. Turkbey} and {B. Turkbey} and {B. Wood} and {F. Patella} and {Elvira Stellato} and {G. Carrafiello} and {Vishal M. Patel} and {H. Roth}},
    year = 2022,
    month = {3},
    booktitle = {European Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/ea8889c3bbca75fcdd71ba60068df014dfb7d861},
    }

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

  331. 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.
    [BibTeX] [Link]
    @inproceedings{247318941,
    title = {Towards performant and reliable undersampled MR reconstruction via diffusion model sampling},
    author = {{Cheng Peng} and {Pengfei Guo} and {S. K. Zhou} and {Vishal M. Patel} and {Ramalingam Chellappa}},
    year = 2022,
    month = {3},
    booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
    url = {https://www.semanticscholar.org/paper/6a0bc6e194608b9e41cbf69794343888edb54378},
    }

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

  333. 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.
    [BibTeX] [Link]
    @inproceedings{253461961,
    title = {Embedded Processing Pipeline Exploration For Neuromorphic Event Based Perceptual Systems},
    author = {{Jonah P. Sengupta} and {M. Villemur} and {P. Pouliquen} and {P. Julián} and {A. Andreou}},
    year = 2022,
    month = {5},
    booktitle = {International Symposium on Circuits and Systems},
    url = {https://www.semanticscholar.org/paper/42845a69a8efd8e8dc7b697c3ce0a4a8f6dfae86},
    }

  334. 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.
    [BibTeX] [Link]
    @inproceedings{247922750,
    title = {Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives},
    author = {{Samik Sadhu} and {H. Hermansky}},
    year = 2022,
    month = {3},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/3ce501d4d81d9a78c2e506df7f6de0d79ca91a5b},
    }

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

  336. 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},
    }

  337. 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},
    }

  338. 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},
    }

  339. 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},
    }

  340. 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},
    }

  341. 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},
    }

  342. 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},
    }

  343. 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},
    }

  344. 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},
    }

  345. 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},
    }

  346. 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.",
    }

  347. 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},
    }

  348. 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.",
    }

  349. 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},
    }

  350. 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},
    }

  351. 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.",
    }

  352. 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},
    }

  353. 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},
    }

  354. 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.",
    }

  355. 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},
    }

  356. 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},
    }

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

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

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

  358. 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.",
    }

  359. 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.",
    }

  360. 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.",
    }

  361. 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.",
    }

  362. 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},
    }

  363. 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.",
    }

  364. 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.",
    }

  365. 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.",
    }

  366. 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.",
    }

  367. 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},
    }

  368. 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},
    }

  369. 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},
    }

  370. 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},
    }

  371. 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},
    }

  372. 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",
    }

  373. 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",
    }

  374. 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},
    }

  375. 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},
    }

  376. 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},
    }

  377. 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},
    }

  378. 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},
    }

  379. 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},
    }

  380. 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},
    }

  381. 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},
    }

  382. 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},
    }

  383. 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},
    }

  384. 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},
    }

  385. 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},
    }

  386. 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},
    }

  387. 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},
    }

  388. 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},
    }

  389. 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},
    }

  390. 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},
    }

  391. 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},
    }

  392. 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},
    }

  393. 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},
    }

  394. 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.",
    }

  395. 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.",
    }

  396. 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.",
    }

  397. 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.",
    }

  398. 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.",
    }

  399. 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.",
    }

  400. 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.",
    }

  401. 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.",
    }

  402. 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.",
    }

  403. 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.",
    }

  404. 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",
    }

  405. 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",
    }

  406. 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.",
    }

  407. 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.",
    }

  408. 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.",
    }

  409. 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.",
    }

  410. 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.",
    }

  411. 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",
    }

  412. 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",
    }

  413. 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.",
    }

  414. 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.",
    }

  415. 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.",
    }

  416. 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.",
    }

  417. 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.",
    }

  418. 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.",
    }

  419. 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},
    }

  420. 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},
    }

  421. 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},
    }

  422. 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},
    }

  423. 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},
    }

  424. 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},
    }

  425. 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},
    }

  426. 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},
    }

  427. 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.",
    }

  428. 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},
    }

  429. 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},
    }

  430. 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},
    }

  431. 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},
    }

  432. 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},
    }

  433. 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},
    }

  434. 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},
    }

  435. 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},
    }

  436. 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},
    }

  437. 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},
    }

  438. 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},
    }

  439. 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},
    }

  440. 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},
    }

  441. 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},
    }

  442. 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},
    }

  443. 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},
    }

  444. 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},
    }

  445. 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.
    [BibTeX] [Link]
    @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},
    }

  446. 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},
    }

  447. 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},
    }

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

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

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

  451. 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.
    [BibTeX] [Link]
    @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},
    }

  452. 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.
    [BibTeX] [Link]
    @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},
    }

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

  454. 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.
    [BibTeX] [Link]
    @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},
    }

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

  456. Pramuditha Perera, Poojan Oza, and Vishal M. Patel, “One-Class Classification: A Survey,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{231418911,
    title = {One-Class Classification: A Survey},
    author = {{Pramuditha Perera} and {Poojan Oza} and {Vishal M. Patel}},
    year = 2021,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/bd6262ebdd1a865e8e6859ab7dd8dc576d2a90e6},
    }

  457. Haoran Xu and Philipp Koehn, “Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{236133964,
    title = {Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions},
    author = {{Haoran Xu} and {Philipp Koehn}},
    year = 2021,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/f38b33d863f9554a00cd9798484e0cc8b0236579},
    }

  458. 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.
    [BibTeX] [Link]
    @inproceedings{231786401,
    title = {The Multilingual TEDx Corpus for Speech Recognition and Translation},
    author = {{Elizabeth Salesky} and {Matthew Wiesner} and {Jacob Bremerman} and {R. Cattoni} and {Matteo Negri} and {M. Turchi} and {D. Oard} and {Matt Post}},
    year = 2021,
    month = {2},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/8e1e7741b56455056ff369fff9889b4c5f998b58},
    }

  459. Pramuditha Perera and Vishal M. Patel, “Geometric Transformation-Based Network Ensemble for Open-Set Recognition,” in IEEE International Conference on Multimedia and Expo, 2021.
    [BibTeX] [Link]
    @inproceedings{236264069,
    title = {Geometric Transformation-Based Network Ensemble for Open-Set Recognition},
    author = {{Pramuditha Perera} and {Vishal M. Patel}},
    year = 2021,
    month = {7},
    booktitle = {IEEE International Conference on Multimedia and Expo},
    url = {https://www.semanticscholar.org/paper/6ef59ba79e3c4d725bfdd22174f2adc72d801245},
    }

  460. V. Vibashan, Jeya Maria Jose Valanarasu, Poojan Oza, and Vishal M. Patel, “Image Fusion Transformer,” in International Conference on Information Photonics, 2021.
    [BibTeX] [Link]
    @inproceedings{236087620,
    title = {Image Fusion Transformer},
    author = {{V. Vibashan} and {Jeya Maria Jose Valanarasu} and {Poojan Oza} and {Vishal M. Patel}},
    year = 2021,
    month = {7},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/48ec7f1bcf8953ac472384bcea88bc38774508f0},
    }

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

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

  463. 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.
    [BibTeX] [Link]
    @inproceedings{232289573,
    title = {The CLIR-CLSP System for the IberSPEECH-RTVE 2020 Speaker Diarization and Identity Assignment Challenge},
    author = {{C. Castillo-Sanchez} and {Leibny Paola García-Perera}},
    year = 2021,
    month = {3},
    booktitle = {IberSPEECH Conference},
    url = {https://www.semanticscholar.org/paper/43c6c43ef8a3ce0c7a77eb83471afa6714ebd0ac},
    }

  464. 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.
    [BibTeX] [Link]
    @inproceedings{235520107,
    title = {Architecture and Algorithm Co-Design Framework for Embedded Processors in Event-Based Cameras},
    author = {{Jonah P. Sengupta} and {M. Villemur} and {Daniel R. Mendat} and {Gaspar Tognetti} and {A. Andreou}},
    year = 2021,
    month = {5},
    booktitle = {International Symposium on Circuits and Systems},
    url = {https://www.semanticscholar.org/paper/c7bc38e1a275d8e17aa779f0d66c567398c5d0cb},
    }

  465. 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.
    [BibTeX] [Link]
    @inproceedings{236230536,
    title = {Resetting of Auditory and Visual Segregation Occurs After Transient Stimuli of the Same Modality},
    author = {{N. Higgins} and {Ambar Monjaras} and {Breanne D Yerkes} and {David F. Little} and {J. Nave-Blodgett} and {Mounya Elhilali} and {J. Snyder}},
    year = 2021,
    month = {6},
    booktitle = {Frontiers in Psychology},
    url = {https://www.semanticscholar.org/paper/5720f41225f3203b71d2b0d40a0776d9e7d5db15},
    }

  466. 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.
    [BibTeX] [Link]
    @inproceedings{235733826,
    title = {Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?},
    author = {{Matt Landers} and {S. Saria} and {A. Espay}},
    year = 2021,
    month = {6},
    booktitle = {Journal of Parkinson's Disease},
    url = {https://www.semanticscholar.org/paper/18b5479599330a9f09c06680f679fd22d5197963},
    }

  467. Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, and A. Yuille, “Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{235266081,
    title = {Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning},
    author = {{Ju He} and {Adam Kortylewski} and {Shaokang Yang} and {Shuai Liu} and {Cheng Yang} and {Changhu Wang} and {A. Yuille}},
    year = 2021,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/1c9b186efe4529493bad1b89cac8d837c5f121ee},
    }

  468. Matthew Maciejewski, Shinji Watanabe, and S. Khudanpur, “Speaker Verification-Based Evaluation of Single-Channel Speech Separation,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{239704028,
    title = {Speaker Verification-Based Evaluation of Single-Channel Speech Separation},
    author = {{Matthew Maciejewski} and {Shinji Watanabe} and {S. Khudanpur}},
    year = 2021,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/39c5740304b5f4072f92e4e012a4b57e7bc2e817},
    }

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

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

  471. 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.
    [BibTeX] [Link]
    @inproceedings{242764358,
    title = {An Information Extraction Framework for Disability Determination: A mental Functioning Use-Case (Preprint)},
    author = {{Ayah Zirikly} and {Bart Desmet} and {D. Newman-Griffis} and {Beth Marfeo} and {C. McDonough} and {H. Goldman} and {L. Chan}},
    year = 2021,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/f068c7251b4a014fbe8f6e9cb722fd1c0f45da81},
    }

  472. Chenxu Luo, Xiaodong Yang, and A. Yuille, “Self-Supervised Pillar Motion Learning for Autonomous Driving,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{233296254,
    title = {Self-Supervised Pillar Motion Learning for Autonomous Driving},
    author = {{Chenxu Luo} and {Xiaodong Yang} and {A. Yuille}},
    year = 2021,
    month = {4},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/94c22c98d38d983fdbd41d75488e2de5176082aa},
    }

  473. 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.
    [BibTeX] [Link]
    @inproceedings{236771967,
    title = {Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information},
    author = {{Yuval Pinter} and {A. Stent} and {Mark Dredze} and {Jacob Eisenstein}},
    year = 2021,
    month = {8},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9c2e4e5ee224c20a45c37244924138b50f3fe603},
    }

  474. 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.
    [BibTeX] [Link]
    @inproceedings{235461366,
    title = {Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”},
    author = {{P. Resnik} and {M. de Choudhury} and {Katherine Musacchio Schafer} and {Glen A. Coppersmith}},
    year = 2021,
    month = {6},
    booktitle = {Journal of Medical Internet Research},
    url = {https://www.semanticscholar.org/paper/d3437dc88d0943c041983c5a85a0288f8baa3575},
    }

  475. 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.
    [BibTeX] [Link]
    @inproceedings{232013967,
    title = {Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping},
    author = {{Prakhar Kaushik} and {Alex Gain} and {Adam Kortylewski} and {A. Yuille}},
    year = 2021,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/be864a16ec597c76d1ab36453d01471723a37bac},
    }

  476. Tiago Pimentel, Maria Ryskina, Sabrina J. Mielke, Shijie Wu, Eleanor Chodroff, Brian Leonard, Garrett Nicolai, Yustinus Ghanggo Ate, Salam Khalifa, Charbel El-Khaissi, Omer Goldman, M. Gasser, William Lane, M. Coler, Arturo Oncevay, Jaime Rafael Montoya Samame, Gema Celeste Silva Villegas, Adam Ek, Jean-Philippe Bernardy, A. Shcherbakov, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, E. Klyachko, A. Salehi, A. A. Krizhanovsky, Natalia Krizhanovsky, Clara Vania, Sardana Ivanova, A. Salchak, Christopher A. Straughn, Zoey Liu, J. North, Duygu Ataman, Witold Kieraś, Marcin Woliński, T. Suhardijanto, Niklas Stoehr, Z. Nuriah, S. Ratan, Francis M. Tyers, E. M. Ponti, Grant Aiton, R. Hatcher, Ritesh Kumar, Mans Hulden, B. Barta, Dorina Lakatos, Gábor Szolnok, Judit Ács, Mohith S Raj, David Yarowsky, Ryan Cotterell, Ben Ambridge, and Ekaterina Vylomova, “SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages,” in Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, 2021.
    [BibTeX] [Link]
    @inproceedings{244460160,
    title = {SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages},
    author = {{Tiago Pimentel} and {Maria Ryskina} and {Sabrina J. Mielke} and {Shijie Wu} and {Eleanor Chodroff} and {Brian Leonard} and {Garrett Nicolai} and {Yustinus Ghanggo Ate} and {Salam Khalifa} and {Charbel El-Khaissi} and {Omer Goldman} and {M. Gasser} and {William Lane} and {M. Coler} and {Arturo Oncevay} and {Jaime Rafael Montoya Samame} and {Gema Celeste Silva Villegas} and {Adam Ek} and {Jean-Philippe Bernardy} and {A. Shcherbakov} and {Karina Sheifer} and {Sofya Ganieva} and {Matvey Plugaryov} and {E. Klyachko} and {A. Salehi} and {A. A. Krizhanovsky} and {Natalia Krizhanovsky} and {Clara Vania} and {Sardana Ivanova} and {A. Salchak} and {Christopher A. Straughn} and {Zoey Liu} and {J. North} and {Duygu Ataman} and {Witold Kieraś} and {Marcin Woliński} and {T. Suhardijanto} and {Niklas Stoehr} and {Z. Nuriah} and {S. Ratan} and {Francis M. Tyers} and {E. M. Ponti} and {Grant Aiton} and {R. Hatcher} and {Ritesh Kumar} and {Mans Hulden} and {B. Barta} and {Dorina Lakatos} and {Gábor Szolnok} and {Judit Ács} and {Mohith S Raj} and {David Yarowsky} and {Ryan Cotterell} and {Ben Ambridge} and {Ekaterina Vylomova}},
    year = 2021,
    booktitle = {Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology},
    url = {https://www.semanticscholar.org/paper/136235d2a3dc4f1c995eaf977aec9c42114da850},
    }

  477. 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.
    [BibTeX] [Link]
    @inproceedings{233240742,
    title = {A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation},
    author = {{Jiteng Mu} and {Weichao Qiu} and {Adam Kortylewski} and {A. Yuille} and {N. Vasconcelos} and {Xiaolong Wang}},
    year = 2021,
    month = {4},
    booktitle = {IEEE International Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/289f55883db1b91ff1c8d9e4a36dbdd6c3e2782e},
    }

  478. Sangwook Park, Angeles Salles, K. Allen, C. Moss, and Mounya Elhilali, “Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks,” in eNeuro, 2021.
    [BibTeX] [Link]
    @inproceedings{233744082,
    title = {Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks},
    author = {{Sangwook Park} and {Angeles Salles} and {K. Allen} and {C. Moss} and {Mounya Elhilali}},
    year = 2021,
    month = {5},
    booktitle = {eNeuro},
    url = {https://www.semanticscholar.org/paper/33056958f57d7a3bdf0c28bafb4932e6443579a8},
    }

  479. 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.
    [BibTeX] [Link]
    @inproceedings{239651118,
    title = {End-to-End Language Diarization for Bilingual Code-Switching Speech},
    author = {{Hexin Liu} and {Leibny Paola García Perera} and {Xinyi Zhang} and {J. Dauwels} and {Andy W. H. Khong} and {S. Khudanpur} and {S. Styles}},
    year = 2021,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/9f08e77a8c072dd3994879f450d9de730b6cfe43},
    }

  480. 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.
    [BibTeX] [Link]
    @inproceedings{247939729,
    title = {Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles},
    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,
    month = {3},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/f67fe2f05ccbfa7eb45fe0f8ed99e2be4279e3e7},
    }

  481. S. Vuuren and H. Hermansky, “SPEECH VARIABILITY IN THE MODULATION SPECTRAL DOMAIN.” 2021.
    [BibTeX] [Link]
    @inproceedings{251497493,
    title = {SPEECH VARIABILITY IN THE MODULATION SPECTRAL DOMAIN},
    author = {{S. Vuuren} and {H. Hermansky}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/12f39a1702c038b858c9aef1d4cdc42d0c8dc5ca},
    }

  482. 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.
    [BibTeX] [Link]
    @inproceedings{248396784,
    title = {Machine Learning for Health (ML4H) 2021},
    author = {{Subhrajit Roy} and {S. Pfohl} and {G. Tadesse} and {Luis Oala} and {Fabian Falck} and {Yuyin Zhou} and {Liyue Shen} and {Ghada Zamzmi} and {Purity Mugambi} and {Ayah Zirikly} and {Matthew B. A. McDermott} and {Emily Alsentzer}},
    year = 2021,
    booktitle = {[email protected]},
    url = {https://www.semanticscholar.org/paper/c59c0ea24987b63df440fe9a7c8838874d948a02},
    }

  483. Neehar Peri, Joshua Gleason, C. Castillo, T. Bourlai, Vishal M. Patel, and R. Chellappa, “A Synthesis-Based Approach for Thermal-to-Visible Face Verification,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{237266437,
    title = {A Synthesis-Based Approach for Thermal-to-Visible Face Verification},
    author = {{Neehar Peri} and {Joshua Gleason} and {C. Castillo} and {T. Bourlai} and {Vishal M. Patel} and {R. Chellappa}},
    year = 2021,
    month = {8},
    booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
    url = {https://www.semanticscholar.org/paper/edcfc2e222d08c51a9f1087fb29252b659d9b071},
    }

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

  485. Poojan Oza and Vishal M. Patel, “Federated Learning-based Active Authentication on Mobile Devices,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
    [BibTeX] [Link]
    @inproceedings{233241170,
    title = {Federated Learning-based Active Authentication on Mobile Devices},
    author = {{Poojan Oza} and {Vishal M. Patel}},
    year = 2021,
    month = {4},
    booktitle = {2021 IEEE International Joint Conference on Biometrics (IJCB)},
    url = {https://www.semanticscholar.org/paper/e14c3885adfdbe3d1543b2c73b215c7f4d29083b},
    }

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

  487. 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.
    [BibTeX] [Link]
    @inproceedings{235908140,
    title = {The Clinician and Dataset Shift in Artificial Intelligence.},
    author = {{S. G. Finlayson} and {Adarsh Subbaswamy} and {Karandeep Singh} and {John Bowers} and {Annabel Kupke} and {J. Zittrain} and {I. Kohane} and {S. Saria}},
    year = 2021,
    month = {7},
    booktitle = {New England Journal of Medicine},
    url = {https://www.semanticscholar.org/paper/129c66d240883c735dbb08c8f025a6573328827b},
    }

  488. 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.
    [BibTeX] [Link]
    @inproceedings{239711135,
    title = {Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition},
    author = {{Matthew Wiesner} and {Mousmita Sarma} and {Ashish Arora} and {Desh Raj} and {Dongji Gao} and {Ruizhe Huang} and {Supreet Preet} and {Moris Johnson} and {Zikra Iqbal} and {N. Goel} and {J. Trmal} and {Leibny Paola García Perera} and {S. Khudanpur}},
    year = 2021,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/dc6c49acca0d3d6f3fad0971d0962f0990c45a7d},
    }

  489. Junfei Xiao, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, A. Yuille, and Yuyin Zhou, “CateNorm: Categorical Normalization for Robust Medical Image Segmentation,” in [email protected], 2021.
    [BibTeX] [Link]
    @inproceedings{251371341,
    title = {CateNorm: Categorical Normalization for Robust Medical Image Segmentation},
    author = {{Junfei Xiao} and {Lequan Yu} and {Zongwei Zhou} and {Yutong Bai} and {Lei Xing} and {A. Yuille} and {Yuyin Zhou}},
    year = 2021,
    month = {3},
    booktitle = {[email protected]},
    url = {https://www.semanticscholar.org/paper/895f1bf600c8be5a0a9dd1f6ae714ea1ac56b525},
    }

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

  491. Chenglin Yang, Siyuan Qiao, Adam Kortylewski, and A. Yuille, “Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{235829300,
    title = {Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms},
    author = {{Chenglin Yang} and {Siyuan Qiao} and {Adam Kortylewski} and {A. Yuille}},
    year = 2021,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/cca5a070dac2f434a10bcc12bd1377b8c7356e21},
    }

  492. 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.
    [BibTeX] [Link]
    @inproceedings{231964915,
    title = {Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction},
    author = {{Yan Wang} and {Peng Tang} and {Yuyin Zhou} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
    year = 2021,
    month = {2},
    booktitle = {IEEE Transactions on Medical Imaging},
    url = {https://www.semanticscholar.org/paper/703dd183e9e814ece9c8d01ee2a3ec27e1513441},
    }

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

  494. Alycen Wiacek, N. Dehak, and M. L. Lediju Bell, “Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses,” in IUS, 2021.
    [BibTeX] [Link]
    @inproceedings{244043942,
    title = {Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses},
    author = {{Alycen Wiacek} and {N. Dehak} and {M. L. Lediju Bell}},
    year = 2021,
    month = {9},
    booktitle = {IUS},
    url = {https://www.semanticscholar.org/paper/36a66d1519a846b05d014858fa611f8e9d500747},
    }

  495. Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, and Benjamin Van Durme, “Joint Universal Syntactic and Semantic Parsing,” in Transactions of the Association for Computational Linguistics, 2021.
    [BibTeX] [Link]
    @inproceedings{233210159,
    title = {Joint Universal Syntactic and Semantic Parsing},
    author = {{Elias Stengel-Eskin} and {Kenton Murray} and {Sheng Zhang} and {Aaron Steven White} and {Benjamin Van Durme}},
    year = 2021,
    month = {4},
    booktitle = {Transactions of the Association for Computational Linguistics},
    url = {https://www.semanticscholar.org/paper/4a44567f0165936e190f112cce998fe0a9328974},
    }

  496. Saurabh Kataria, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{233024923,
    title = {Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification},
    author = {{Saurabh Kataria} and {J. Villalba} and {Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {N. Dehak}},
    year = 2021,
    month = {4},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/c3bb7ff3eba44535c9b704ee52041f91bde7bcd0},
    }

  497. E. Stengel-Eskin, J. Guallar-Blasco, and B. Van Durme, “Human-Model Divergence in the Handling of Vagueness,” in Proceedings of the Society for Computation in Linguistics 2021, Online, 2021, p. 390–393.
    [BibTeX] [Link]
    @inproceedings{stengel-eskin-etal-2021-human-model,
    title = "Human-Model Divergence in the Handling of Vagueness",
    author = "Stengel-Eskin, Elias and
    Guallar-Blasco, Jimena and
    Van Durme, Benjamin",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2021",
    month = feb,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.scil-1.42",
    pages = "390--393",
    }

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

  499. 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.
    [BibTeX] [Link]
    @inproceedings{232062162,
    title = {Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images},
    author = {{Chun Pong Lau} and {Amit Kumar} and {R. Chellappa}},
    year = 2021,
    month = {2},
    booktitle = {IEEE Journal on Selected Topics in Signal Processing},
    url = {https://www.semanticscholar.org/paper/bfe23f726af27f611a81ffe2faf436ea00acb860},
    }

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

  501. Seyoun Park, J. Sham, S. Kawamoto, A. Blair, N. Rozich, D. Fouladi, S. Shayesteh, R. Hruban, Jin He, elliot k fishman, A. Yuille, E. Fishman, and L. Chu, “CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.,” in AJR. American journal of roentgenology, 2021.
    [BibTeX] [Link]
    @inproceedings{237375333,
    title = {CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.},
    author = {{Seyoun Park} and {J. Sham} and {S. Kawamoto} and {A. Blair} and {N. Rozich} and {D. Fouladi} and {S. Shayesteh} and {R. Hruban} and {Jin He} and {elliot k fishman} and {A. Yuille} and {E. Fishman} and {L. Chu}},
    year = 2021,
    month = {9},
    booktitle = {AJR. American journal of roentgenology},
    url = {https://www.semanticscholar.org/paper/c68fbb8e0ba372d01ed9c4c797369668274dc89d},
    }

  502. Hang Lv, Zhehuai Chen, Hainan Xu, Daniel Povey, Lei Xie, and S. Khudanpur, “An Asynchronous WFST-Based Decoder for Automatic Speech Recognition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
    [BibTeX] [Link]
    @inproceedings{232237963,
    title = {An Asynchronous WFST-Based Decoder for Automatic Speech Recognition},
    author = {{Hang Lv} and {Zhehuai Chen} and {Hainan Xu} and {Daniel Povey} and {Lei Xie} and {S. Khudanpur}},
    year = 2021,
    month = {3},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/ea221ef54fd47b3d1487a6f686871b2bccdc94c6},
    }

  503. 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.
    [BibTeX] [Link]
    @inproceedings{239653935,
    title = {Automatic Detection and Assessment of Alzheimer Disease Using Speech and Language Technologies in Low-Resource Scenarios},
    author = {{R. Pappagari} and {Jaejin Cho} and {Sonal Joshi} and {L. Moro-Velázquez} and {Piotr Żelasko} and {J. Villalba} and {N. Dehak}},
    year = 2021,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/7e3deabd44eccb0fe2823d8cecf1e182efeeb0f6},
    }

  504. Sangwook Park, D. Han, and Mounya Elhilali, “Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures,” in IEEE transactions on multimedia, 2021.
    [BibTeX] [Link]
    @inproceedings{235248024,
    title = {Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures},
    author = {{Sangwook Park} and {D. Han} and {Mounya Elhilali}},
    year = 2021,
    month = {5},
    booktitle = {IEEE transactions on multimedia},
    url = {https://www.semanticscholar.org/paper/a11ae02ea8b207dea32c7856ba5d7496c3aa9cc4},
    }

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

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

  507. S. Schwarcz and R. Chellappa, “Finding Facial Forgery Artifacts with Parts-Based Detectors,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
    [BibTeX] [Link]
    @inproceedings{235703532,
    title = {Finding Facial Forgery Artifacts with Parts-Based Detectors},
    author = {{S. Schwarcz} and {R. Chellappa}},
    year = 2021,
    month = {6},
    booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
    url = {https://www.semanticscholar.org/paper/eb752fd572ca2c984b56a06c9974fdfdf951acb6},
    }

  508. D. Dreizin, Tina Chen, Yuanyuan Liang, Yuyin Zhou, Fabio M. Paes, Yan Wang, A. Yuille, Patrick Roth, Kathryn Champ, Guang Li, Ashley McLenithan, and J. Morrison, “Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis,” in Abdominal Radiology, 2021.
    [BibTeX] [Link]
    @inproceedings{231651448,
    title = {Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis},
    author = {{D. Dreizin} and {Tina Chen} and {Yuanyuan Liang} and {Yuyin Zhou} and {Fabio M. Paes} and {Yan Wang} and {A. Yuille} and {Patrick Roth} and {Kathryn Champ} and {Guang Li} and {Ashley McLenithan} and {J. Morrison}},
    year = 2021,
    month = {1},
    booktitle = {Abdominal Radiology},
    url = {https://www.semanticscholar.org/paper/7f5547253cf023c093b2cd3c9f9412e53c58578e},
    }

  509. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Data-Assemble: Leveraging Multiple Datasets with Partial Labels.” 2021.
    [BibTeX] [Link]
    @inproceedings{245007204,
    title = {Data-Assemble: Leveraging Multiple Datasets with Partial Labels},
    author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/eeacac7a5f84274de89e185a975ca1f7584b858b},
    }

  510. Navaneeth Bodla, G. Shrivastava, R. Chellappa, and Abhinav Shrivastava, “Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{235691818,
    title = {Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions},
    author = {{Navaneeth Bodla} and {G. Shrivastava} and {R. Chellappa} and {Abhinav Shrivastava}},
    year = 2021,
    month = {6},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/302e4537b277384542d7f0b5cdc4db33abbaa1db},
    }

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

  512. 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.
    [BibTeX] [Link]
    @inproceedings{237491841,
    title = {Joint Prediction of Truecasing and Punctuation for Conversational Speech in Low-Resource Scenarios},
    author = {{R. Pappagari} and {Piotr Żelasko} and {Agnieszka Mikołajczyk} and {Piotr Pęzik} and {N. Dehak}},
    year = 2021,
    month = {9},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/cad80d9a6ba7c943da74be90c7d3302a2f463099},
    }

  513. 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.
    [BibTeX] [Link]
    @inproceedings{235652468,
    title = {Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems},
    author = {{Sonal Joshi} and {J. Villalba} and {Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {N. Dehak}},
    year = 2021,
    month = {1},
    booktitle = {IEEE Transactions on Information Forensics and Security},
    url = {https://www.semanticscholar.org/paper/46a3c701f9e013b9aba1e6f6d5dc3ff0998573a2},
    }

  514. Nithin Gopalakrishnan Nair and Vishal M. Patel, “Confidence Guided Network For Atmospheric Turbulence Mitigation,” in International Conference on Information Photonics, 2021.
    [BibTeX] [Link]
    @inproceedings{238681785,
    title = {Confidence Guided Network For Atmospheric Turbulence Mitigation},
    author = {{Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
    year = 2021,
    month = {9},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/34ff864bcef1d3f8bbacc3c241ee65cc6b13b84e},
    }

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

  516. 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.
    [BibTeX] [Link]
    @inproceedings{235593078,
    title = {On the Evaluation of Machine Translation for Terminology Consistency},
    author = {{Md Mahfuz Ibn Alam} and {Antonios Anastasopoulos} and {L. Besacier} and {James Cross} and {Matthias Gallé} and {Philipp Koehn} and {Vassilina Nikoulina}},
    year = 2021,
    month = {6},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/365d30a104d03acee14530327eeaf7b66baa3421},
    }

  517. 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.
    [BibTeX] [Link]
    @inproceedings{236956411,
    title = {PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition},
    author = {{Prithviraj Dhar} and {Joshua Gleason} and {A. Roy} and {C. Castillo} and {R. Chellappa}},
    year = 2021,
    month = {8},
    booktitle = {IEEE International Conference on Computer Vision},
    url = {https://www.semanticscholar.org/paper/5451ff6ea2e7bb3d40bb61889bb3494cf0eebb3e},
    }

  518. Qing Liu, Vignesh Ramanathan, D. Mahajan, A. Yuille, and Zhenheng Yang, “Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{232335658,
    title = {Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency},
    author = {{Qing Liu} and {Vignesh Ramanathan} and {D. Mahajan} and {A. Yuille} and {Zhenheng Yang}},
    year = 2021,
    month = {3},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/5b62f55ee46245b0c4e710efac9c7578666c68a3},
    }

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

  520. Poojan Oza, Vishwanath A. Sindagi, V. Vibashan, and Vishal M. Patel, “Unsupervised Domain Adaption of Object Detectors: A Survey,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
    [BibTeX] [Link]
    @inproceedings{235247842,
    title = {Unsupervised Domain Adaption of Object Detectors: A Survey},
    author = {{Poojan Oza} and {Vishwanath A. Sindagi} and {V. Vibashan} and {Vishal M. Patel}},
    year = 2021,
    month = {5},
    booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    url = {https://www.semanticscholar.org/paper/96bb5e0ee886b499e5d6d64b4636bc7be343ccc0},
    }

  521. 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.
    [BibTeX] [Link]
    @inproceedings{237532129,
    title = {SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving},
    author = {{W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
    year = 2021,
    month = {9},
    booktitle = {IEEE International Conference on Robotics and Automation},
    url = {https://www.semanticscholar.org/paper/1c8fe5d3882d2a67f87d7899289b69d028271150},
    }

  522. Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, A. Yuille, and Wei Shen, “Glance-and-Gaze Vision Transformer,” in Neural Information Processing Systems, 2021.
    [BibTeX] [Link]
    @inproceedings{235352495,
    title = {Glance-and-Gaze Vision Transformer},
    author = {{Qihang Yu} and {Yingda Xia} and {Yutong Bai} and {Yongyi Lu} and {A. Yuille} and {Wei Shen}},
    year = 2021,
    month = {6},
    booktitle = {Neural Information Processing Systems},
    url = {https://www.semanticscholar.org/paper/ac74a160e0ca53d3ffb15f79f0b9d3911df2fc28},
    }

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

  524. Jieneng Chen, K. Yan, Yu-Dong Zhang, Youbao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, A. Yuille, Ya Zhang, and Le Lu, “Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
    [BibTeX] [Link]
    @inproceedings{232168577,
    title = {Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining},
    author = {{Jieneng Chen} and {K. Yan} and {Yu-Dong Zhang} and {Youbao Tang} and {Xun Xu} and {Shuwen Sun} and {Qiuping Liu} and {Lingyun Huang} and {Jing Xiao} and {A. Yuille} and {Ya Zhang} and {Le Lu}},
    year = 2021,
    month = {3},
    booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
    url = {https://www.semanticscholar.org/paper/f3bbab69d8da5835868497409c9129d111ccf919},
    }

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

  526. Ayah Zirikly, Bart Desmet, D. Newman-Griffis, E. Marfeo, C. McDonough, Howard Goldman, and L. Chan, “Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case,” in JMIR Medical Informatics, 2021.
    [BibTeX] [Link]
    @inproceedings{247520370,
    title = {Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case},
    author = {{Ayah Zirikly} and {Bart Desmet} and {D. Newman-Griffis} and {E. Marfeo} and {C. McDonough} and {Howard Goldman} and {L. Chan}},
    year = 2021,
    month = {7},
    booktitle = {JMIR Medical Informatics},
    url = {https://www.semanticscholar.org/paper/66ce3e5f86256fb9b54ab94457b3aa6a0080e6b2},
    }

  527. Angtian Wang, Adam Kortylewski, and A. Yuille, “N E M O : N EURAL M ESH M ODELS OF C ONTRASTIVE F EATURES FOR R OBUST 3D P OSE E STIMATION.” 2021.
    [BibTeX] [Link]
    @inproceedings{249046613,
    title = {N E M O : N EURAL M ESH M ODELS OF C ONTRASTIVE F EATURES FOR R OBUST 3D P OSE E STIMATION},
    author = {{Angtian Wang} and {Adam Kortylewski} and {A. Yuille}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/c30a93c6003552d447f32c68ec8051cae6cd0b5e},
    }

  528. 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 Individual Nuisances in Real-World Out-of-Distribution Shifts.” 2021.
    [BibTeX] [Link]
    @inproceedings{244714909,
    title = {OOD-CV: A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts},
    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,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/26ea70c56fdb5036b16750e29fd23894266464f8},
    }

  529. Sangwook Park, Woohyun Choi, and Mounya Elhilali, “Sound Event Detection with Cross-Referencing Self-Training.” 2021.
    [BibTeX] [Link]
    @inproceedings{245930590,
    title = {Sound Event Detection with Cross-Referencing Self-Training},
    author = {{Sangwook Park} and {Woohyun Choi} and {Mounya Elhilali}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/14d161c0665ce7508c64270707ef62216b4e19a9},
    }

  530. Adarsh Subbaswamy, R. Adams, and S. Saria, “Evaluating Model Robustness and Stability to Dataset Shift,” in International Conference on Artificial Intelligence and Statistics, 2021.
    [BibTeX] [Link]
    @inproceedings{232234128,
    title = {Evaluating Model Robustness and Stability to Dataset Shift},
    author = {{Adarsh Subbaswamy} and {R. Adams} and {S. Saria}},
    year = 2021,
    booktitle = {International Conference on Artificial Intelligence and Statistics},
    url = {https://www.semanticscholar.org/paper/52af46fc925989036d1e4f21d5e870ea6e23358c},
    }

  531. Zefan Li, Chenxin Liu, A. Yuille, Bingbing Ni, Wenjun Zhang, and W. Gao, “Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{235391026,
    title = {Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement},
    author = {{Zefan Li} and {Chenxin Liu} and {A. Yuille} and {Bingbing Ni} and {Wenjun Zhang} and {W. Gao}},
    year = 2021,
    month = {6},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/43faa30a076c723c566228b73a6ed81a8039b806},
    }

  532. Piotr Żelasko, Sonal Joshi, Yiwen Shao, J. Villalba, J. Trmal, N. Dehak, and S. Khudanpur, “Adversarial Attacks and Defenses for Speech Recognition Systems,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{232427815,
    title = {Adversarial Attacks and Defenses for Speech Recognition Systems},
    author = {{Piotr Żelasko} and {Sonal Joshi} and {Yiwen Shao} and {J. Villalba} and {J. Trmal} and {N. Dehak} and {S. Khudanpur}},
    year = 2021,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/9d15685433a067c5beca67e5f6cc612b3dc29f66},
    }

  533. Rakhil Immidisetti, Shuowen Hu, and Vishal M. Patel, “Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN,” in 2021 IEEE International Joint Conference on Biometrics (IJCB), 2021.
    [BibTeX] [Link]
    @inproceedings{233231566,
    title = {Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN},
    author = {{Rakhil Immidisetti} and {Shuowen Hu} and {Vishal M. Patel}},
    year = 2021,
    month = {4},
    booktitle = {2021 IEEE International Joint Conference on Biometrics (IJCB)},
    url = {https://www.semanticscholar.org/paper/d27eac86c86a953a5b1ad13f7c7bc9d5fb127837},
    }

  534. Jaejin Cho, Piotr Żelasko, J. Villalba, and N. Dehak, “Improving Reconstruction Loss Based Speaker Embedding in Unsupervised and Semi-Supervised Scenarios,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
    [BibTeX] [Link]
    @inproceedings{235780607,
    title = {Improving Reconstruction Loss Based Speaker Embedding in Unsupervised and Semi-Supervised Scenarios},
    author = {{Jaejin Cho} and {Piotr Żelasko} and {J. Villalba} and {N. Dehak}},
    year = 2021,
    month = {6},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/2695593c166924372283e2a5802f7bca4c17a356},
    }

  535. Hongru Zhu, A. Yuille, and D. Kersten, “Three-dimensional pose discrimination in natural images of humans.,” in CogSci … Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference, 2021.
    [BibTeX] [Link]
    @inproceedings{239122639,
    title = {Three-dimensional pose discrimination in natural images of humans.},
    author = {{Hongru Zhu} and {A. Yuille} and {D. Kersten}},
    year = 2021,
    month = {7},
    booktitle = {CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference},
    url = {https://www.semanticscholar.org/paper/b11a13a4118fc032cb995ca601b01fe481c75665},
    }

  536. Angtian Wang, Adam Kortylewski, and A. Yuille, “NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation,” in International Conference on Learning Representations, 2021.
    [BibTeX] [Link]
    @inproceedings{231728364,
    title = {NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation},
    author = {{Angtian Wang} and {Adam Kortylewski} and {A. Yuille}},
    year = 2021,
    month = {1},
    booktitle = {International Conference on Learning Representations},
    url = {https://www.semanticscholar.org/paper/02200d454717ffea6c1daf64d635ab945d4fa140},
    }

  537. Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, E. Adeli, Yan Wang, Le Lu, A. Yuille, and Yuyin Zhou, “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{231847326,
    title = {TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation},
    author = {{Jieneng Chen} and {Yongyi Lu} and {Qihang Yu} and {Xiangde Luo} and {E. Adeli} and {Yan Wang} and {Le Lu} and {A. Yuille} and {Yuyin Zhou}},
    year = 2021,
    month = {2},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/24b8a0b02bcb7934967757fc59d273a71ba67e30},
    }

  538. Adarsh Subbaswamy, Bryant Chen, and S. Saria, “THE STABILITY AND ACCURACY TRADEOFF UNDER DATASET SHIFT: A CAUSAL GRAPHICAL ANALYSIS.” 2021.
    [BibTeX] [Link]
    @inproceedings{244909446,
    title = {THE STABILITY AND ACCURACY TRADEOFF UNDER DATASET SHIFT: A CAUSAL GRAPHICAL ANALYSIS},
    author = {{Adarsh Subbaswamy} and {Bryant Chen} and {S. Saria}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/7353a679102d49f5a66265c56f74a338edbeed16},
    }

  539. Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, F. Yu, Xianglong Liu, D. Song, A. Yuille, Philip H. S. Torr, and D. Tao, “RobustART: Benchmarking Robustness on Architecture Design and Training Techniques,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{237491819,
    title = {RobustART: Benchmarking Robustness on Architecture Design and Training Techniques},
    author = {{Shiyu Tang} and {Ruihao Gong} and {Yan Wang} and {Aishan Liu} and {Jiakai Wang} and {Xinyun Chen} and {F. Yu} and {Xianglong Liu} and {D. Song} and {A. Yuille} and {Philip H. S. Torr} and {D. Tao}},
    year = 2021,
    month = {9},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/fd3bee898ae69bd956af9f4aabd3f7b478de2cbd},
    }

  540. Fengze Liu, K. Yan, Adam P. Harrison, Dazhou Guo, Le Lu, A. Yuille, Lingyun Huang, G. Xie, Jing Xiao, X. Ye, and D. Jin, “SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021.
    [BibTeX] [Link]
    @inproceedings{237621545,
    title = {SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings},
    author = {{Fengze Liu} and {K. Yan} and {Adam P. Harrison} and {Dazhou Guo} and {Le Lu} and {A. Yuille} and {Lingyun Huang} and {G. Xie} and {Jing Xiao} and {X. Ye} and {D. Jin}},
    year = 2021,
    month = {9},
    booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
    url = {https://www.semanticscholar.org/paper/2950acc069210c93d5d25f615b82bdc403241046},
    }

  541. Samik Sadhu and H. Hermansky, “Radically Old Way of Computing Spectra: Applications in End-to-End ASR,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{233025579,
    title = {Radically Old Way of Computing Spectra: Applications in End-to-End ASR},
    author = {{Samik Sadhu} and {H. Hermansky}},
    year = 2021,
    month = {3},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/22895f07cbcbde21d115ebb744edf230ee7a7e18},
    }

  542. Chen Wei, Huiyu Wang, Wei Shen, and A. Yuille, “CO 2 : C ONSISTENT C ONTRAST FOR U NSUPERVISED V ISUAL R EPRESENTATION L EARNING.” 2021.
    [BibTeX] [Link]
    @inproceedings{233404939,
    title = {CO 2 : C ONSISTENT C ONTRAST FOR U NSUPERVISED V ISUAL R EPRESENTATION L EARNING},
    author = {{Chen Wei} and {Huiyu Wang} and {Wei Shen} and {A. Yuille}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/f69b228e133afda499745c08edd767ee776c1cac},
    }

  543. Yunjuan Wang, Poorya Mianjy, and R. Arora, “Robust Learning for Data Poisoning Attacks,” in International Conference on Machine Learning, 2021.
    [BibTeX] [Link]
    @inproceedings{235826166,
    title = {Robust Learning for Data Poisoning Attacks},
    author = {{Yunjuan Wang} and {Poorya Mianjy} and {R. Arora}},
    year = 2021,
    booktitle = {International Conference on Machine Learning},
    url = {https://www.semanticscholar.org/paper/c541fa104bc5297f3ebf967855d582ab9a37291d},
    }

  544. Rajeev Yasarla, Hamid Reza Vaezi Joze, and Vishal M. Patel, “Network Architecture Search for Face Enhancement,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{234681236,
    title = {Network Architecture Search for Face Enhancement},
    author = {{Rajeev Yasarla} and {Hamid Reza Vaezi Joze} and {Vishal M. Patel}},
    year = 2021,
    month = {5},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/02c7dcedee24ae9ca55a96180fae7b7000009ad0},
    }

  545. Hang Lv, Daniel Povey, M. Yarmohammadi, Ke Li, Yiming Wang, Lei Xie, and S. Khudanpur, “LET-Decoder: A WFST-Based Lazy-Evaluation Token-Group Decoder With Exact Lattice Generation,” in IEEE Signal Processing Letters, 2021.
    [BibTeX] [Link]
    @inproceedings{232238258,
    title = {LET-Decoder: A WFST-Based Lazy-Evaluation Token-Group Decoder With Exact Lattice Generation},
    author = {{Hang Lv} and {Daniel Povey} and {M. Yarmohammadi} and {Ke Li} and {Yiming Wang} and {Lei Xie} and {S. Khudanpur}},
    year = 2021,
    booktitle = {IEEE Signal Processing Letters},
    url = {https://www.semanticscholar.org/paper/8fc15d95dfbe10856b289eed48716e0ab758d09b},
    }

  546. D. Kelly, Max Spaderna, Vedrana Hodzic, Glen A. Coppersmith, Shuo Chen, and P. Resnik, “Can language use in social media help in the treatment of severe mental illness?,” in Current research in psychiatry, 2021.
    [BibTeX] [Link]
    @inproceedings{237533078,
    title = {Can language use in social media help in the treatment of severe mental illness?},
    author = {{D. Kelly} and {Max Spaderna} and {Vedrana Hodzic} and {Glen A. Coppersmith} and {Shuo Chen} and {P. Resnik}},
    year = 2021,
    month = {8},
    booktitle = {Current research in psychiatry},
    url = {https://www.semanticscholar.org/paper/69ac548c8855aad23d897b52b0ba8d9bc4d8e107},
    }

  547. Ju He, Adam Kortylewski, and A. Yuille, “COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{231719786,
    title = {COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification},
    author = {{Ju He} and {Adam Kortylewski} and {A. Yuille}},
    year = 2021,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/3ad1caf62e0f4353ac3a7bb563392f7683999d23},
    }

  548. V. Vibashan, Vikram Gupta, Poojan Oza, Vishwanath A. Sindagi, and Vishal M. Patel, “MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{232147762,
    title = {MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection},
    author = {{V. Vibashan} and {Vikram Gupta} and {Poojan Oza} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
    year = 2021,
    month = {3},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/5937d7c2c10da2418f7979cd55ad12fa1b93a58e},
    }

  549. R. Pappagari, Tianzi Wang, J. Villalba, Nanxin Chen, and N. Dehak, “Fe b 20 20 X-VECTORS MEET EMOTIONS : A STUDY ON DEPENDENCIES BETWEEN EMOTION AND SPEAKER RECOGNITION.” 2021.
    [BibTeX] [Link]
    @inproceedings{235611330,
    title = {Fe b 20 20 X-VECTORS MEET EMOTIONS : A STUDY ON DEPENDENCIES BETWEEN EMOTION AND SPEAKER RECOGNITION},
    author = {{R. Pappagari} and {Tianzi Wang} and {J. Villalba} and {Nanxin Chen} and {N. Dehak}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/4ea99eae00271944740936a2053f41375863c21e},
    }

  550. 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.
    [BibTeX] [Link]
    @inproceedings{244117311,
    title = {Occluded Video Instance Segmentation: A Benchmark},
    author = {{Jiyang Qi} and {Yan Gao} and {Yao Hu} and {Xinggang Wang} and {Xiaoyu Liu} and {Xiang Bai} and {S. Belongie} and {A. Yuille} and {Philip H. S. Torr} and {S. Bai}},
    year = 2021,
    month = {2},
    booktitle = {International Journal of Computer Vision},
    url = {https://www.semanticscholar.org/paper/f0597d0543b0b315e9290ec49017424aeeb8d3e5},
    }

  551. Shota Horiguchi, Shinji Watanabe, Leibny Paola García-Perera, Yawen Xue, Yuki Takashima, and Y. Kawaguchi, “Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors,” in Automatic Speech Recognition & Understanding, 2021.
    [BibTeX] [Link]
    @inproceedings{235732166,
    title = {Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors},
    author = {{Shota Horiguchi} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Yawen Xue} and {Yuki Takashima} and {Y. Kawaguchi}},
    year = 2021,
    month = {7},
    booktitle = {Automatic Speech Recognition & Understanding},
    url = {https://www.semanticscholar.org/paper/6f173939f6defe3ebae8fb12f19349ba96b7b5c4},
    }

  552. Jejin Cho, J. Villalba, and N. Dehak, “The JHU submission to VoxSRC-21: Track 3,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{238198440,
    title = {The JHU submission to VoxSRC-21: Track 3},
    author = {{Jejin Cho} and {J. Villalba} and {N. Dehak}},
    year = 2021,
    month = {9},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/ed2065a9cb6f31806aba9a70a4148b99225782a3},
    }

  553. N. Balachandran, Jun-Cheng Chen, and R. Chellappa, “LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{238226971,
    title = {LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network},
    author = {{N. Balachandran} and {Jun-Cheng Chen} and {R. Chellappa}},
    year = 2021,
    month = {9},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/c741349663272c0d4a61e52d5650ba123bbbc81e},
    }

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

  555. Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Leibny Paola García-Perera, and Kenji Nagamatsu, “Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{239675607,
    title = {Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers},
    author = {{Yawen Xue} and {Shota Horiguchi} and {Yusuke Fujita} and {Yuki Takashima} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Kenji Nagamatsu}},
    year = 2021,
    month = {8},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/cee96ee69adacfdeb648c230d2c9b01011724724},
    }

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

  557. Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Leibny Paola García-Perera, “Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{235489753,
    title = {Encoder-Decoder Based Attractor Calculation 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,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/8abd724b770348bd21b16b9aaf2ba0a77596b2ed},
    }

  558. Velat Kilic, Deepti Hegde, Vishwanath A. Sindagi, A. Cooper, M. Foster, and Vishal M. Patel, “Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{235899299,
    title = {Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection},
    author = {{Velat Kilic} and {Deepti Hegde} and {Vishwanath A. Sindagi} and {A. Cooper} and {M. Foster} and {Vishal M. Patel}},
    year = 2021,
    month = {7},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/e9edc2d44af422cb6b8d8ce494161c7779ba0895},
    }

  559. Shao-Yuan Lo and Vishal M. Patel, “Error Diffusion Halftoning Against Adversarial Examples,” in International Conference on Information Photonics, 2021.
    [BibTeX] [Link]
    @inproceedings{231698927,
    title = {Error Diffusion Halftoning Against Adversarial Examples},
    author = {{Shao-Yuan Lo} and {Vishal M. Patel}},
    year = 2021,
    month = {1},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/3b1fa137197c334b8929a326b607359e432cb68f},
    }

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

  561. Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, and S. Khudanpur, “Wake Word Detection with Streaming Transformers,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021.
    [BibTeX] [Link]
    @inproceedings{231855282,
    title = {Wake Word Detection with Streaming Transformers},
    author = {{Yiming Wang} and {Hang Lv} and {Daniel Povey} and {Lei Xie} and {S. Khudanpur}},
    year = 2021,
    month = {2},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/ca4b945ad7d109c3cbc2170a942ca3b0ecf6fcf5},
    }

  562. Saurabhchand Bhati, J. Villalba, Piotr Żelasko, L. Moro-Velázquez, and N. Dehak, “Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{235352541,
    title = {Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation},
    author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
    year = 2021,
    month = {6},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/642dab29e680f516eb25949d616a24e0ad147a19},
    }

  563. Rui Shao, Pramuditha Perera, P. Yuen, and Vishal M. Patel, “Federated Generalized Face Presentation Attack Detection,” in IEEE Transactions on Neural Networks and Learning Systems, 2021.
    [BibTeX] [Link]
    @inproceedings{233231746,
    title = {Federated Generalized Face Presentation Attack Detection},
    author = {{Rui Shao} and {Pramuditha Perera} and {P. Yuen} and {Vishal M. Patel}},
    year = 2021,
    month = {4},
    booktitle = {IEEE Transactions on Neural Networks and Learning Systems},
    url = {https://www.semanticscholar.org/paper/916556aedad592417e07fe78d0a2ce336a6074e8},
    }

  564. Yao Sun, R. Kaur, Shubham Gupta, Rahul Paul, Ritu Das, S. Cho, Saket Anand, J. Boutilier, S. Saria, J. Palma, S. Saluja, R. McAdams, A. Kaur, Gautam Yadav, and Harpreet Singh, “Development and validation of high definition phenotype-based mortality prediction in critical care units,” in JAMIA Open, 2021.
    [BibTeX] [Link]
    @inproceedings{232479139,
    title = {Development and validation of high definition phenotype-based mortality prediction in critical care units},
    author = {{Yao Sun} and {R. Kaur} and {Shubham Gupta} and {Rahul Paul} and {Ritu Das} and {S. Cho} and {Saket Anand} and {J. Boutilier} and {S. Saria} and {J. Palma} and {S. Saluja} and {R. McAdams} and {A. Kaur} and {Gautam Yadav} and {Harpreet Singh}},
    year = 2021,
    month = {1},
    booktitle = {JAMIA Open},
    url = {https://www.semanticscholar.org/paper/6077d1afa94008aceb63e81b4bdd6ad08e98f3d8},
    }

  565. Jalaj Upadhyay, Sarvagya Upadhyay, and R. Arora, “Differentially Private Analysis on Graph Streams,” in International Conference on Artificial Intelligence and Statistics, 2021.
    [BibTeX] [Link]
    @inproceedings{233236129,
    title = {Differentially Private Analysis on Graph Streams},
    author = {{Jalaj Upadhyay} and {Sarvagya Upadhyay} and {R. Arora}},
    year = 2021,
    booktitle = {International Conference on Artificial Intelligence and Statistics},
    url = {https://www.semanticscholar.org/paper/1bd5e498e2213528a94de1410f430db697dc28d1},
    }

  566. I. McLane, Dimitra Emmanouilidou, James E. West, and Mounya Elhilali, “Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings,” in IEEE journal of biomedical and health informatics, 2021.
    [BibTeX] [Link]
    @inproceedings{231805407,
    title = {Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings},
    author = {{I. McLane} and {Dimitra Emmanouilidou} and {James E. West} and {Mounya Elhilali}},
    year = 2021,
    month = {2},
    booktitle = {IEEE journal of biomedical and health informatics},
    url = {https://www.semanticscholar.org/paper/84d283da84a56296c925a6c383bad6e4cb345376},
    }

  567. Hui Che, Sumana Ramanathan, D. Foran, J. Nosher, Vishal M. Patel, and I. Hacihaliloglu, “Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease,” in [email protected], 2021.
    [BibTeX] [Link]
    @inproceedings{236447807,
    title = {Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease},
    author = {{Hui Che} and {Sumana Ramanathan} and {D. Foran} and {J. Nosher} and {Vishal M. Patel} and {I. Hacihaliloglu}},
    year = 2021,
    month = {7},
    booktitle = {[email protected]},
    url = {https://www.semanticscholar.org/paper/764ad2c50a028fa7e9b60f0d45fd6d9037a21696},
    }

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

  569. Jorge Andrés Gómez García, L. Moro-Velázquez, J. D. Arias-Londoño, and J. I. Godino-Llorente, “On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies,” in Biomedical Signal Processing and Control, 2021.
    [BibTeX] [Link]
    @inproceedings{233260951,
    title = {On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies},
    author = {{Jorge Andrés Gómez García} and {L. Moro-Velázquez} and {J. D. Arias-Londoño} and {J. I. Godino-Llorente}},
    year = 2021,
    booktitle = {Biomedical Signal Processing and Control},
    url = {https://www.semanticscholar.org/paper/1b59ac31271268c5cb70f2ff8659f57da4d31acd},
    }

  570. Rajeev Yasarla, Federico Perazzi, and Vishal M. Patel, “Deblurring Face Images Using Deep Networks,” in Advances in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{238923351,
    title = {Deblurring Face Images Using Deep Networks},
    author = {{Rajeev Yasarla} and {Federico Perazzi} and {Vishal M. Patel}},
    year = 2021,
    booktitle = {Advances in Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/8f28feea48abab5a40cb0926073fbb0cec4a77c9},
    }

  571. H. Inaguma, Yosuke Higuchi, Kevin Duh, Tatsuya Kawahara, and Shinji Watanabe, “Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{237453587,
    title = {Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring},
    author = {{H. Inaguma} and {Yosuke Higuchi} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
    year = 2021,
    month = {9},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/d79b613a67cf79740e1c08037f7d054585a12284},
    }

  572. Andrew Blair-Stanek and Benjamin Van Durme, “AI for Tax Analogies and Code Renumbering,” in Tax Law: Practitioner Series eJournal, 2021.
    [BibTeX] [Link]
    @inproceedings{236512351,
    title = {AI for Tax Analogies and Code Renumbering},
    author = {{Andrew Blair-Stanek} and {Benjamin Van Durme}},
    year = 2021,
    month = {5},
    booktitle = {Tax Law: Practitioner Series eJournal},
    url = {https://www.semanticscholar.org/paper/a0c8218fcac4fe920b55bb930147b9aea58d3de2},
    }

  573. Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, and Jamie Callan, “Complement Lexical Retrieval Model with Semantic Residual Embeddings,” in European Conference on Information Retrieval, 2021.
    [BibTeX] [Link]
    @inproceedings{232423090,
    title = {Complement Lexical Retrieval Model with Semantic Residual Embeddings},
    author = {{Luyu Gao} and {Zhuyun Dai} and {Tongfei Chen} and {Zhen Fan} and {Benjamin Van Durme} and {Jamie Callan}},
    year = 2021,
    booktitle = {European Conference on Information Retrieval},
    url = {https://www.semanticscholar.org/paper/1e4b28465d3166dd4fedeb5f23d4c768c170e859},
    }

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

  575. Prithviraj Dhar, Joshua Gleason, A. Roy, C. Castillo, and R. Chellappa, “Supplementary Material-PASS.” 2021.
    [BibTeX] [Link]
    @inproceedings{244312236,
    title = {Supplementary Material-PASS},
    author = {{Prithviraj Dhar} and {Joshua Gleason} and {A. Roy} and {C. Castillo} and {R. Chellappa}},
    year = 2021,
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/ee50fa46cd195e4b59330297d4285877906583b5},
    }

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

  577. Benjamin Skerritt-Davis and Mounya Elhilali, “Computational framework for investigating predictive processing in auditory perception,” in Journal of Neuroscience Methods, 2021.
    [BibTeX] [Link]
    @inproceedings{233186455,
    title = {Computational framework for investigating predictive processing in auditory perception},
    author = {{Benjamin Skerritt-Davis} and {Mounya Elhilali}},
    year = 2021,
    month = {4},
    booktitle = {Journal of Neuroscience Methods},
    url = {https://www.semanticscholar.org/paper/8046a293f376cce9d17b77d26cd04742019c50a3},
    }

  578. Xing Di, Shuowen Hu, and Vishal M. Patel, “Heterogeneous Face Frontalization via Domain Agnostic Learning,” in IEEE International Conference on Automatic Face & Gesture Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{236087940,
    title = {Heterogeneous Face Frontalization via Domain Agnostic Learning},
    author = {{Xing Di} and {Shuowen Hu} and {Vishal M. Patel}},
    year = 2021,
    month = {7},
    booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
    url = {https://www.semanticscholar.org/paper/cb854b48d871265a457a2fbb97d01ca40bb9f4c3},
    }

  579. Ankan Bansal, Jingxiao Zheng, and R. Chellappa, “Face Recognition from Still Images and Video,” in Encyclopedia of Cryptography, Security and Privacy, 2021.
    [BibTeX] [Link]
    @inproceedings{243104926,
    title = {Face Recognition from Still Images and Video},
    author = {{Ankan Bansal} and {Jingxiao Zheng} and {R. Chellappa}},
    year = 2021,
    booktitle = {Encyclopedia of Cryptography, Security and Privacy},
    url = {https://www.semanticscholar.org/paper/eddee7bdc03d5973cd98303c0d5850bc433069c1},
    }

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

  581. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{237940275,
    title = {Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels},
    author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
    year = 2021,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/b269e5ae28f360b7ea159135a63ae1f82d9effbf},
    }

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

  583. Mintong Kang, Yongyi Lu, A. Yuille, and Zongwei Zhou, “Label-Assemble: Leveraging Multiple Datasets with Partial Labels.” 2021.
    [BibTeX] [Link]
    @inproceedings{247245043,
    title = {Label-Assemble: Leveraging Multiple Datasets with Partial Labels},
    author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
    year = 2021,
    month = {9},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/ace00da928797186bf3c6e48e79149f4b8886418},
    }

  584. Yuki Takashima, Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Leibny Paola García-Perera, and Kenji Nagamatsu, “End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection,” in Spoken Language Technology Workshop, 2021.
    [BibTeX] [Link]
    @inproceedings{232413801,
    title = {End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection},
    author = {{Yuki Takashima} and {Yusuke Fujita} and {Shinji Watanabe} and {Shota Horiguchi} and {Leibny Paola García-Perera} and {Kenji Nagamatsu}},
    year = 2021,
    month = {1},
    booktitle = {Spoken Language Technology Workshop},
    url = {https://www.semanticscholar.org/paper/cbf9a2560eac548e7b3d5eb7074c40b7bb861909},
    }

  585. G. Kumar, Philipp Koehn, and S. Khudanpur, “Learning Policies for Multilingual Training of Neural Machine Translation Systems,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{232222906,
    title = {Learning Policies for Multilingual Training of Neural Machine Translation Systems},
    author = {{G. Kumar} and {Philipp Koehn} and {S. Khudanpur}},
    year = 2021,
    month = {3},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/04bc96a2380bccb884cf2568e06d6e726247032b},
    }

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

  587. Chen Wei, Kihyuk Sohn, Clayton Mellina, A. Yuille, and Fan Yang, “CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning,” in Computer Vision and Pattern Recognition, 2021.
    [BibTeX] [Link]
    @inproceedings{231951327,
    title = {CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning},
    author = {{Chen Wei} and {Kihyuk Sohn} and {Clayton Mellina} and {A. Yuille} and {Fan Yang}},
    year = 2021,
    month = {2},
    booktitle = {Computer Vision and Pattern Recognition},
    url = {https://www.semanticscholar.org/paper/09acced5fcb49322f5a26ac7a4cbe9f1308657c4},
    }

  588. J. Villalba, Sonal Joshi, Piotr Żelasko, and N. Dehak, “Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems,” in Interspeech, 2021.
    [BibTeX] [Link]
    @inproceedings{235790692,
    title = {Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems},
    author = {{J. Villalba} and {Sonal Joshi} and {Piotr Żelasko} and {N. Dehak}},
    year = 2021,
    month = {7},
    booktitle = {Interspeech},
    url = {https://www.semanticscholar.org/paper/8abbc820db608654c4ba10203245c191566e7286},
    }

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

  590. Jeya Maria Jose Valanarasu and Vishal M. Patel, “Fine-Context Shadow Detection using Shadow Removal,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021.
    [BibTeX] [Link]
    @inproceedings{244728304,
    title = {Fine-Context Shadow Detection using Shadow Removal},
    author = {{Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
    year = 2021,
    month = {9},
    booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
    url = {https://www.semanticscholar.org/paper/cfb6d14bf4609617c76f0deacad737d1d03925d2},
    }

  591. 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 CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{239885435,
    title = {A Light-weight Interpretable CompositionalNetwork 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,
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/e901638f1839f1e9ee92163193561e77921d524c},
    }

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

  593. Kevin Duh and Francisco Guzmán, “Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track),” in Machine Translation Summit, 2021.
    [BibTeX] [Link]
    @inproceedings{237206731,
    title = {Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)},
    author = {{Kevin Duh} and {Francisco Guzmán}},
    year = 2021,
    booktitle = {Machine Translation Summit},
    url = {https://www.semanticscholar.org/paper/a693afc22d8cf7cbdf824a774c1c17195ae4c371},
    }

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

  595. Rajeev Yasarla and Vishal M. Patel, “Learning to Restore Images Degraded by Atmospheric Turbulence Using Uncertainty,” in International Conference on Information Photonics, 2021.
    [BibTeX] [Link]
    @inproceedings{238660258,
    title = {Learning to Restore Images Degraded by Atmospheric Turbulence Using Uncertainty},
    author = {{Rajeev Yasarla} and {Vishal M. Patel}},
    year = 2021,
    month = {9},
    booktitle = {International Conference on Information Photonics},
    url = {https://www.semanticscholar.org/paper/8b2ca25e60e41f5d61be73e9611ba59bf951a100},
    }

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

  597. Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Leibny Paola García-Perera, and Kenji Nagamatsu, “Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{231662389,
    title = {Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers},
    author = {{Yawen Xue} and {Shota Horiguchi} and {Yusuke Fujita} and {Yuki Takashima} and {Shinji Watanabe} and {Leibny Paola García-Perera} and {Kenji Nagamatsu}},
    year = 2021,
    month = {1},
    booktitle = {arXiv.org},
    url = {https://www.semanticscholar.org/paper/8ca58f3f6e59a6d243f3da6c196e9f730e6e9993},
    }

  598. Enayat Ullah, Tung Mai, Anup B. Rao, Ryan A. Rossi, and R. Arora, “Machine Unlearning via Algorithmic Stability,” in Annual Conference Computational Learning Theory, 2021.
    [BibTeX] [Link]
    @inproceedings{232068763,
    title = {Machine Unlearning via Algorithmic Stability},
    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},
    }

  599. 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.
    [BibTeX] [Link]
    @inproceedings{233333189,
    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},
    }

  600. Michelle Yuan, Patrick Xia, Benjamin Van Durme, and Jordan L. Boyd-Graber, “Adaptive Active Learning for Coreference Resolution,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @inproceedings{233241036,
    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},
    }

  601. Jiefu Ou, Nathaniel Weir, Anton Belyy, Felix Yu, and Benjamin Van Durme, “InFillmore: Neural Frame Lexicalization for Narrative Text Infilling,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @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},
    }

  602. Seyoun Park, E. Fishman, and A. Yuille, “Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations,” in arXiv.org, 2021.
    [BibTeX] [Link]
    @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},
    }

  603. 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.
    [BibTeX] [Link]
    @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},
    }

  604. 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.
    [BibTeX] [Link]
    @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},
    }

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

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

  607. 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.
    [BibTeX] [Link]
    @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},
    }

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

  609. 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},
    }

  610. 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},
    }

  611. 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.",
    }

  612. 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},
    }

  613. 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.",
    }

  614. 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},
    }

  615. 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},
    }

  616. 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},
    }

  617. 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},
    }

  618. 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},
    }

  619. 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},
    }

  620. 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},
    }

  621. 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},
    }

  622. 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},
    }

  623. 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},
    }

  624. 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},
    }

  625. 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},
    }

  626. 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},
    }

  627. 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.",
    }

  628. 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},
    }

  629. 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",
    }

  630. 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.",
    }

  631. 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.",
    }

  632. 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},
    }

  633. 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.",
    }

  634. 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},
    }

  635. 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},
    }

  636. 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.",
    }

  637. 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.",
    }

  638. 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},
    }

  639. 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},
    }

  640. 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.",
    }

  641. 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 prefe