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. 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},
    }

  2. Marc Marone and Benjamin Van Durme, “Data Portraits: Recording Foundation Model Training Data,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/572b92972eff7501ca2b109b8998cdcb69aa1958},
    }

  3. 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,
    booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    url = {https://www.semanticscholar.org/paper/edcf374466f791118acf3bbd8430d4fd73e4ea79},
    }

  4. Qihao Liu, Adam Kortylewski, and A. Yuille, “PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/85fcce7ef6f5eec2d5e5bce82fc7246e8a90696c},
    }

  5. W. G. C. Bandara and Vishal M. Patel, “Deep Metric Learning for Unsupervised Remote Sensing Change Detection,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/30d02457a38374398deca536682c193f0f0b1a24},
    }

  6. 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},
    }

  7. 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, 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},
    url = {https://www.semanticscholar.org/paper/c12d7ef434266e4df411623c910e8d9bdf7d0b74},
    }

  8. Annapurna 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 = {{Annapurna Kala} and {E. McCollum} and {Mounya Elhilali}},
    year = 2023,
    booktitle = {Biomedical Signal Processing and Control},
    url = {https://www.semanticscholar.org/paper/4276e26be8c196ba4b496b4a0acc4102d32c0bd8},
    }

  9. 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.” 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 = {},
    url = {https://www.semanticscholar.org/paper/15c2b3ecdf1b9af2f94a2b106fddcfc89cb336cb},
    }

  10. 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},
    }

  11. 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},
    }

  12. N. Higgins, Alexandra N Scurry, Fang Jiang, David F. Little, Claude Alain, M. 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 {M. Elhilali} and {J. Snyder}},
    year = 2023,
    month = {1},
    booktitle = {Neuroscience of Consciousness},
    url = {https://www.semanticscholar.org/paper/c1c4a48270174de06f609bb2dc98c8e896ce78a3},
    }

  13. 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},
    }

  14. 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},
    }

  15. Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Diffuse-Denoise-Count: Accurate Crowd-Counting with Diffusion Models.” 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 = {},
    url = {https://www.semanticscholar.org/paper/46d82cae8a450c24db32f51ba0ee39a670274a09},
    }

  16. 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, Philip Heller, Daniel Jamieson, K. Jarvis, John Kalantari, K. Khezeli, Svetlana V. Komarova, M. Komorowski, Prachi Kothiyal, A. Mahabal, U. Manor, Hector Garcia Martin, 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 {Philip 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 {Hector Garcia Martin} 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},
    }

  17. 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, 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},
    url = {https://www.semanticscholar.org/paper/095138d9207da38bce4914c569e2f312927213b5},
    }

  18. Thanh Nguyen-Tang and R. Arora, “Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards,” in ArXiv, 2023.
    [BibTeX] [Link]
    @inproceedings{257206027,
    title = {Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards},
    author = {{Thanh Nguyen-Tang} and {R. Arora}},
    year = 2023,
    month = {2},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/a8dac0d0837ac4800f4462a121c59a98a05531ee},
    }

  19. Zihao Xiao, A. Yuille, and Yi-Ting Chen, “Learning Road Scene-level Representations via Semantic Region Prediction,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/11b29ca1a235d80a2e55f6eb7711d2aa5785bb8c},
    }

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

  21. Deepti Hegde, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/2eca4016fdcee5222f0c159db6f4f31cf9d3b37a},
    }

  22. 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},
    }

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

  24. Alicia M. Braxton, A. Kiemen, Mia P. Grahn, André Forjaz, Jaanvi Mahesh Babu, Lily Zheng, Li-yu Jiang, Haixia 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 {Haixia 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},
    }

  25. 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, 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},
    url = {https://www.semanticscholar.org/paper/4796e0d511ba8646d295f561f9b2dfa145352ce8},
    }

  26. 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.” 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 = {},
    url = {https://www.semanticscholar.org/paper/47cd9158e970329355a575ed992d4452ac498784},
    }

  27. Andrew Blair-Stanek, Nils Holzenberger, and Benjamin Van Durme, “Can GPT-3 Perform Statutory Reasoning?,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/4c0f95df93ac75f9ab06b0104e4196a6e8fda25e},
    }

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

  29. 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, 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},
    url = {https://www.semanticscholar.org/paper/46fd16213979b00e741b926539ad4ba7a1acd1cf},
    }

  30. Chen Wang, Angtian Wang, Junbo Li, A. Yuille, and Cihang Xie, “Benchmarking Robustness in Neural Radiance Fields,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/eaf0c04e9784d6efc8f9ce16d1d9c3ae43506ad9},
    }

  31. David Mueller, Nicholas Andrews, and Mark Dredze, “Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/2843661ee0d5fa159165beba50c345566cc44c57},
    }

  32. 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.",
    }

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

  34. 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},
    }

  35. 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, 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},
    url = {https://www.semanticscholar.org/paper/5f696247d384af650d07d3de30bd023a6128f048},
    }

  36. Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{254125357,
    title = {Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models},
    author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
    year = 2022,
    month = {12},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/89d794843eadb7eca6889e24f9fb374334fd85f7},
    }

  37. 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},
    }

  38. 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, 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},
    url = {https://www.semanticscholar.org/paper/1358ad196c4e300612fb3b65a2f3578836941384},
    }

  39. Orion Weller, Aleem Khan, Nathaniel Weir, Dawn J Lawrie, and Benjamin Van Durme, “Defending Against Poisoning Attacks in Open-Domain Question Answering,” in ArXiv, 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,
    month = {12},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/7e44002c4f78458987a90dc7a0408d60dd5cdb7c},
    }

  40. 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.",
    }

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

  42. Kangfu Mei and Vishal M. Patel, “VIDM: Video Implicit Diffusion Models,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{254125713,
    title = {VIDM: Video Implicit Diffusion Models},
    author = {{Kangfu Mei} and {Vishal M. Patel}},
    year = 2022,
    month = {12},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/13c7b29a100f67d285eb3625c160d06882d4c092},
    }

  43. 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.",
    }

  44. 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, 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},
    url = {https://www.semanticscholar.org/paper/7786825fd653b398c3975c3ff876459307d871f4},
    }

  45. 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).",
    }

  46. 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 ArXiv, 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 = {12},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/6934bd40d21e3bddce5328d29a7e1083e21d0aad},
    }

  47. 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, 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},
    url = {https://www.semanticscholar.org/paper/970a8ed9de244b080aa69dbf5996a37057909ca6},
    }

  48. 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.",
    }

  49. 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, 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},
    url = {https://www.semanticscholar.org/paper/756322a91fb19bb21d292da1d0918854b519e7fa},
    }

  50. 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.",
    }

  51. 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},
    }

  52. 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},
    }

  53. 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},
    }

  54. Kangfu Mei, Nithin Gopalakrishnan Nair, and Vishal M. Patel, “Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/e5fec7e9103c9edbc4c6b4bb1a47e53593c667bb},
    }

  55. 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},
    }

  56. 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.",
    }

  57. 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.",
    }

  58. 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, 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},
    url = {https://www.semanticscholar.org/paper/bbe93c90b7b87939cd064c805858feca61a3234d},
    }

  59. 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, 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},
    url = {https://www.semanticscholar.org/paper/7b0f98f51040700aae3cd9f0e3432dedcd69fb30},
    }

  60. 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},
    }

  61. 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.",
    }

  62. 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.
    [BibTeX] [Link]
    @InProceedings{svete-et-al-2022,
    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",
    year = "2022",
    month = dec,
    address = "Abu Dhabi",
    URL = "http://cs.jhu.edu/~jason/papers/#svete-et-al-2022",
    }

  63. 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.
    [BibTeX] [Link]
    @InProceedings{stengeleskin-et-al-2022,
    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",
    year = "2022",
    month = dec,
    address = "Abu Dhabi",
    URL = "http://cs.jhu.edu/~jason/papers/#stengeleskin-et-al-2022",
    }

  64. W. G. C. Bandara, Naman Patel, A. Gholami, M. Nikkhah, M. Agrawal, and Vishal M. Patel, “AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders,” in ArXiv, 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 {M. Nikkhah} and {M. Agrawal} and {Vishal M. Patel}},
    year = 2022,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/a135632a05cc1f3311859fdebcd1350b4e9e1ee7},
    }

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

  66. Zili Huang, Desh Raj, Paola Garc’ia, and S. Khudanpur, “Adapting self-supervised models to multi-talker speech recognition using speaker embeddings,” in ArXiv, 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 {Paola Garc'ia} and {S. Khudanpur}},
    year = 2022,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/2226b25c6656e1d7c99667b4e685cd01348e8577},
    }

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

  68. Vikas Raunak, Matt Post, and Arul Menezes, “Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/ad2149957cd288a5626adcce48f9981a2ab59184},
    }

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

  70. V. Rennoll, I. McLane, M. 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 {M. Elhilali} and {James E. West}},
    year = 2022,
    month = {11},
    booktitle = {Italian National Conference on Sensors},
    url = {https://www.semanticscholar.org/paper/0d7b6b5a15b47c1cd1d688f043fd06ff6822d5a1},
    }

  71. Shuyang Sun, Jie-Neng Chen, Ruifei He, A. Yuille, Philip H. S. Torr, and Song Bai, “LUMix: Improving Mixup by Better Modelling Label Uncertainty,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{254069733,
    title = {LUMix: Improving Mixup by Better Modelling Label Uncertainty},
    author = {{Shuyang Sun} and {Jie-Neng Chen} and {Ruifei He} and {A. Yuille} and {Philip H. S. Torr} and {Song Bai}},
    year = 2022,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/62ce349a6dbc58f64ae02d7203c2f9a06cf6f6d4},
    }

  72. 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, 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},
    url = {https://www.semanticscholar.org/paper/b61a3d718a192e39a437d32a6ed4037b8c29cc41},
    }

  73. 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 ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/92302ab168429c7c3a8f699b35ba8302916c6e7c},
    }

  74. Bardia Safaei, V. Vibashan, Celso M. de Melo, Shuowen Hu, and Vishal M. Patel, “Open-Set Automatic Target Recognition,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/878d61661e35c80c0b981fe4512fbad6c55ab037},
    }

  75. Dongji Gao, Jiatong Shi, Shun-Po Chuang, Leibny Paola Garcia, Hung-yi Lee, Shinji Watanabe, and S. Khudanpur, “EURO: ESPnet Unsupervised ASR Open-source Toolkit,” in ArXiv, 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 Garcia} and {Hung-yi Lee} and {Shinji Watanabe} and {S. Khudanpur}},
    year = 2022,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/4aba0dfb74c98c559f7d0012abd0111b464e07aa},
    }

  76. Shuyue Stella Li and Kenton Murray, “Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/96fdfc1ba9588d1fab990d504aa590233216326a},
    }

  77. 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, 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},
    url = {https://www.semanticscholar.org/paper/8536182e4379687e10517fd8ab587679641f983b},
    }

  78. Qixing Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jieneng Chen, A. Yuille, and Zongwei Zhou, “Synthetic Tumors Make AI Segment Tumors Better,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/0077f46c9cf3de56319aad65e419131e2466b848},
    }

  79. Yukun Feng, Patrick Xia, Benjamin Van Durme, and João Sedoc, “Automatic Document Selection for Efficient Encoder Pretraining,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{253018582,
    title = {Automatic Document Selection for Efficient Encoder Pretraining},
    author = {{Yukun Feng} and {Patrick Xia} and {Benjamin Van Durme} and {João Sedoc}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/4bef9d46209ac8988ea5ab83547149760d4af65e},
    }

  80. Yuxiang Guo, Cheng Peng, Chun Pong Lau, and R. Chellappa, “Multi-Modal Human Authentication Using Silhouettes, Gait and RGB,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/e89d9b5c7b5d9c4b490ba1d5fdbbca423920c3e1},
    }

  81. 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, 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},
    url = {https://www.semanticscholar.org/paper/a8a2a8229f99c291bf71ec92b801a073854c52e2},
    }

  82. Nupoor Gandhi, Anjalie Field, and Emma Strubell, “Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/43f09be116b87046334d395a71919ab423b204a1},
    }

  83. 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, 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},
    url = {https://www.semanticscholar.org/paper/a4af00f50f0b397b14ae5dc22e0e766c31adaaa8},
    }

  84. Kelly Marchisio, Neha Verma, Kevin Duh, and Philipp Koehn, “IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{252815609,
    title = {IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces},
    author = {{Kelly Marchisio} and {Neha Verma} and {Kevin Duh} and {Philipp Koehn}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/1d2d23adf5d468288d034f845ed15fe34883dfcd},
    }

  85. Junfei Xiao, Yutong Bai, A. Yuille, and Zongwei Zhou, “Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/249e00445585586214e27d1f4ade032533132d0a},
    }

  86. Vibashan Vs, 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 = {{Vibashan Vs} 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},
    }

  87. Weiwei Gu, Boyuan Zheng, Yunmo Chen, Tongfei Chen, and Benjamin Van Durme, “An Empirical Study on Finding Spans,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{252873563,
    title = {An Empirical Study on Finding Spans},
    author = {{Weiwei Gu} and {Boyuan Zheng} and {Yunmo Chen} and {Tongfei Chen} and {Benjamin Van Durme}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/6dac365d3ff10de1a7fe464c5c5007e0aa644184},
    }

  88. 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},
    }

  89. Kate Sanders, Reno Kriz, Anqi Liu, and Benjamin Van Durme, “Ambiguous Images With Human Judgments for Robust Visual Event Classification,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/2a55f57716576fdd5840252d673aabe9a676fced},
    }

  90. Weiting Tan, Kevin Heffernan, Holger Schwenk, and Philipp Koehn, “Multilingual Representation Distillation with Contrastive Learning,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{252816029,
    title = {Multilingual Representation Distillation with Contrastive Learning},
    author = {{Weiting Tan} and {Kevin Heffernan} and {Holger Schwenk} and {Philipp Koehn}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/560263c83671b831ed61bf3c7a31436d3a4bb446},
    }

  91. Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme, “Iterative Document-level Information Extraction via Imitation Learning,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{252873525,
    title = {Iterative Document-level Information Extraction via Imitation Learning},
    author = {{Yunmo Chen} and {William Gantt} and {Weiwei Gu} and {Tongfei Chen} and {Aaron Steven White} and {Benjamin Van Durme}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/60e98c3fedfde46cbd8b90ba6fb182f2e5879ed8},
    }

  92. Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey E. Priebe, and Philipp Koehn, “Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{253116758,
    title = {Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport},
    author = {{Kelly Marchisio} and {Ali Saad-Eldin} and {Kevin Duh} and {Carey E. Priebe} and {Philipp Koehn}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/0d1bceb56cfb765b46886ee83f1d6a498d6ea61a},
    }

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

  94. Elijah Matthew Rippeth and Matt Post, “Additive Interventions Yield Robust Multi-Domain Machine Translation Models,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{253098232,
    title = {Additive Interventions Yield Robust Multi-Domain Machine Translation Models},
    author = {{Elijah Matthew Rippeth} and {Matt Post}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/aea945f1e42b4c695995f28feb7cc0fbbce25aa9},
    }

  95. 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},
    }

  96. 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 ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/1fab5a425ad712bb8245741c5abec00aa80fc123},
    }

  97. 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.",
    }

  98. 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, 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},
    url = {https://www.semanticscholar.org/paper/747d3a8d6c7beff00377795c696f198b2c12ecff},
    }

  99. Hexin Liu, Leibny Paola Garcia 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 Garcia 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},
    }

  100. 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, 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},
    url = {https://www.semanticscholar.org/paper/17a6bee0ef616822d8a883f6bc373dd676242793},
    }

  101. 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.",
    }

  102. 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},
    }

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

  104. 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.",
    }

  105. Inna Wanyin Lin, Lucille Njoo, Anjalie Field, Ashish Sharma, K. Reinecke, Tim Althoff, and Yulia Tsvetkov, “Gendered Mental Health Stigma in Masked Language Models,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{253157444,
    title = {Gendered Mental Health Stigma in Masked Language Models},
    author = {{Inna Wanyin Lin} and {Lucille Njoo} and {Anjalie Field} and {Ashish Sharma} and {K. Reinecke} and {Tim Althoff} and {Yulia Tsvetkov}},
    year = 2022,
    month = {10},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/4bf6f629eda8301b2e1339401678af952dd9bfb1},
    }

  106. 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.",
    }

  107. 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.",
    }

  108. 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.",
    }

  109. 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.",
    }

  110. 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.",
    }

  111. 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.",
    }

  112. 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.",
    }

  113. A. Blair-stanek and B. Van Durme, “Improved Induction of Narrative Chains via Cross-Document Relations,” in Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, Seattle, Washington, 2022, p. 208–212. doi:10.18653/v1/2022.starsem-1.18
    [BibTeX] [Abstract] [Link]

    The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.

    @inproceedings{blair-stanek-van-durme-2022-improved,
    title = "Improved Induction of Narrative Chains via Cross-Document Relations",
    author = "Blair-stanek, Andrew and
    Van Durme, Benjamin",
    booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.starsem-1.18",
    doi = "10.18653/v1/2022.starsem-1.18",
    pages = "208--212",
    abstract = "The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58{\%} better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.",
    }

  114. 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.",
    }

  115. 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.",
    }

  116. 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.",
    }

  117. 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.",
    }

  118. 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.",
    }

  119. 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.",
    }

  120. 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.",
    }

  121. 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.",
    }

  122. 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.",
    }

  123. 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.",
    }

  124. 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.",
    }

  125. 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.",
    }

  126. 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.",
    }

  127. 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.",
    }

  128. 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.",
    }

  129. 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. 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)",
    year = "2022",
    month = may,
    address = "Dublin",
    URL = "http://cs.jhu.edu/~jason/papers/#zhou-et-al-2022",
    }

  130. 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",
    }

  131. 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",
    }

  132. 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},
    }

  133. Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, J. Eisner, and Yu Su, “When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems,” in ArXiv, 2022.
    [BibTeX] [Link]
    @inproceedings{249017588,
    title = {When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems},
    author = {{Elias Stengel-Eskin} and {Emmanouil Antonios Platanios} and {Adam Pauls} and {Sam Thomson} and {Hao Fang} and {Benjamin Van Durme} and {J. Eisner} and {Yu Su}},
    year = 2022,
    month = {5},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/1a5fcd44ebba0aaecc0397b26957fcc5e5476033},
    }

  134. 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},
    }

  135. 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},
    }

  136. 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.",
    }

  137. 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.",
    }

  138. Sandeep Reddy Kothinti and M. 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 {M. Elhilali}},
    year = 2022,
    month = {5},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/34fe9aa0f5e26768d196087ed146e2b3a576d73e},
    }

  139. 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/574bcb4aba88cd7a296d584a5bcb99bd769705d8},
    }

  140. 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, Elizabeth P. 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, Paul Pu 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, 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 {Elizabeth P. 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. 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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},
    url = {https://www.semanticscholar.org/paper/34503c0b6a615124eaf82cb0e4a1dab2866e8980},
    }

  141. 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).",
    }

  142. Jaejin Cho, J. Villalba, L. Moro-Velázquez, and N. Dehak, “Non-Contrastive Self-Supervised Learning for Utterance-Level Information Extraction From Speech,” in IEEE Journal on Selected Topics in Signal Processing, 2022.
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    @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},
    }

  143. Shao-Yuan Lo, Wei Wang, Jim Thomas, Jingjing Zheng, Vishal M. Patel, and Cheng-Hao Kuo, “Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation,” in IEEE/RJS International Conference on Intelligent RObots and Systems, 2022.
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    @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},
    }

  144. J. Villalba, B. J. Borgstrom, Saurabh Kataria, Jaejin Cho, P. Torres-Carrasquillo, and N. Dehak, “Advances in Speaker Recognition for Multilingual Conversational Telephone Speech: The JHU-MIT System for NIST SRE20 CTS Challenge,” in The Speaker and Language Recognition Workshop, 2022.
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    @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},
    }

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

  146. 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, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/ec64e324ce1210fe5245dfd0fb5a92058732e5b9},
    }

  147. Chan Young Park, Julia Mendelsohn, Anjalie Field, and Yulia Tsvetkov, “Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media.” 2022.
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    @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 = {},
    url = {https://www.semanticscholar.org/paper/b616154578751e156b21561e1a5d5ed833a3506f},
    }

  148. 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},
    }

  149. 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},
    }

  150. 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.",
    }

  151. Pengfei Guo, Yiqun Mei, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, “ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/92a574d34837b970e6c0610226362e801ca83442},
    }

  152. Nils Holzenberger, Yunmo Chen, and Benjamin Van Durme, “Asking the Right Questions in Low Resource Template Extraction,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/196b71b4e8465dd632954cf499f0467754cbd9d4},
    }

  153. B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, G. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, Bart 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, Pete Wheatstone, and P. McCulloch, “Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” in BMJ, 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 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 {Pete Wheatstone} and {P. McCulloch}},
    year = 2022,
    month = {5},
    booktitle = {BMJ},
    url = {https://www.semanticscholar.org/paper/3a8c344f67d5081ead5f7dd5ebf0f760d69fc01d},
    }

  154. 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},
    }

  155. 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},
    }

  156. Yunjuan Wang, Enayat Ullah, Poorya Mianjy, and R. Arora, “Adversarial Robustness is at Odds with Lazy Training,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/e2100da66c556f6ce3fbe904696fb0cec2aca2bf},
    }

  157. 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.
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    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},
    }

  158. 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, 2022.
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    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},
    url = {https://www.semanticscholar.org/paper/215a6f2b4c206975f59d81c0c9f45cfe935a85e9},
    }

  159. Saksham Suri, Saketh Rambhatla, R. Chellappa, and Abhinav Shrivastava, “R-SSL: Region based Semi-Supervised Learning for Sparsely Annotated Object Detection.” 2022.
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    @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},
    }

  160. W. G. C. Bandara and Vishal M. Patel, “A Transformer-Based Siamese Network for Change Detection,” in IEEE International Geoscience and Remote Sensing Symposium, 2022.
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    @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},
    }

  161. Boyuan Zheng, Patrick Xia, M. Yarmohammadi, and Benjamin Van Durme, “Multilingual Coreference Resolution in Multiparty Dialogue,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/840945dddcfaca56f8cfb42dc890a6185212eae2},
    }

  162. Xiaolei Huang, Franck Dernoncourt, and Mark Dredze, “Enriching Unsupervised User Embedding via Medical Concepts,” in ACM Conference on Health, Inference, and Learning, 2022.
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    @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},
    }

  163. Nithin Gopalakrishnan Nair and Vishal M. Patel, “T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models,” in ArXiv, 2022.
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    @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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/fc49634e80ab31929799786a97b7ea63834bbdb1},
    }

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

  165. Nithin Gopalakrishnan Nair, Rajeev Yasarla, and Vishal M. Patel, “NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images,” in International Conference on Information Photonics, 2022.
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    @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},
    }

  166. V. Vibashan, Jeya Maria Jose Valanarasu, and Vishal M. Patel, “Target and Task specific Source-Free Domain Adaptive Image Segmentation,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/db37fdfed1260f94ffb08a174e3e19f28dd8835e},
    }

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

  168. Daniel E Park, Nora L. Watson, Christopher Focht, D. Feikin, Laura L Hammit, W. A. Brooks, S. Howie, K. Kotloff, O. Levine, S. Madhi, D. Murdoch, K. O’Brien, J. Scott, D. Thea, Tussanee Amorninthapichet, Juliet O. Awori, C. Bunthi, B. Ebruke, M. Elhilali, M. Higdon, L. Hossain, Y. Jahan, D. Moore, Justin M. 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.
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    title = {Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study},
    author = {{Daniel E Park} and {Nora L. Watson} and {Christopher Focht} and {D. Feikin} and {Laura L Hammit} and {W. A. Brooks} and {S. Howie} and {K. Kotloff} and {O. Levine} and {S. Madhi} and {D. Murdoch} and {K. O'Brien} and {J. Scott} and {D. Thea} and {Tussanee Amorninthapichet} and {Juliet O. Awori} and {C. Bunthi} and {B. Ebruke} and {M. Elhilali} and {M. Higdon} and {L. Hossain} and {Y. Jahan} and {D. Moore} and {Justin M. 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},
    }

  169. B. Vasey, M. Nagendran, Bruce Campbell, D. Clifton, G. Collins, Spiros C. Denaxas, A. Denniston, L. Faes, Bart 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, Pete Wheatstone, and P. McCulloch, “Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.,” in Nature Network Boston, 2022.
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    @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 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 {Pete Wheatstone} and {P. McCulloch}},
    year = 2022,
    month = {5},
    booktitle = {Nature Network Boston},
    url = {https://www.semanticscholar.org/paper/83b6a76ba5112d27bdbfca3efd2ed918d8e73db5},
    }

  170. 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.",
    }

  171. 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.
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    @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},
    }

  172. Annapurna Kala, E. McCollum, and M. Elhilali, “Implications of clinical variability on computer-aided lung auscultation classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022.
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    @inproceedings{252165718,
    title = {Implications of clinical variability on computer-aided lung auscultation classification},
    author = {{Annapurna Kala} and {E. McCollum} and {M. 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},
    }

  173. 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.
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    @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},
    }

  174. Jaejin Cho, R. Pappagari, Piotr Żelasko, L. Moro-Velázquez, J. Villalba, and N. Dehak, “Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations,” in Interspeech, 2022.
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    @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},
    }

  175. Jeya Maria Jose Valanarasu, Pengfei Guo, V. Vibashan, and Vishal M. Patel, “On-the-Fly Test-time Adaptation for Medical Image Segmentation,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/3b8c4a2a005df6dc7e9fb0b9e2e81a887ace5a6c},
    }

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

  177. R. Adams, K. Henry, A. Sridharan, Hossein Soleimani, A. Zhan, Nishi Rawat, Lauren Johnson, D. Hager, S. Cosgrove, Andrew Markowski, E. Klein, E. Chen, M. Saheed, Maureen Henley, S. Miranda, Katrina Houston, Robert C. Linton, Anushree R Ahluwalia, Albert W Wu, and S. Saria, “Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.,” in Nature Network Boston, 2022.
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    @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,
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    url = {https://www.semanticscholar.org/paper/9ad55e7b87e1557983bdef0e9fe7eb0f4254dd94},
    }

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

  179. 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.
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    @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},
    }

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

  181. Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, Elvira Stellato, G. Carrafiello, Vishal M. Patel, and H. Roth, “Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation,” in European Conference on Computer Vision, 2022.
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    @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},
    }

  182. Jared Markowitz, Ryan W. Gardner, A. Llorens, R. Arora, and I.-J. Wang, “A Risk-Sensitive Approach to Policy Optimization,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/d37ca9aa15d6f34d942180752552132c51fe27e5},
    }

  183. Samik Sadhu and H. Hermansky, “Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives,” in ArXiv, 2022.
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    @inproceedings{247922750,
    title = {Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives},
    author = {{Samik Sadhu} and {H. Hermansky}},
    year = 2022,
    month = {3},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/3ce501d4d81d9a78c2e506df7f6de0d79ca91a5b},
    }

  184. A. Shelton, E. Davis, Cathryn S. Cortesa, Jonathan D. Jones, Gregory Hager, S. Khudanpur, and B. Landau, “Characterizing the Details of Spatial Construction: Cognitive Constraints and Variability,” in Cognitive Sciences, 2022.
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    url = {https://www.semanticscholar.org/paper/a8144dbb8481cb78e08fc34e452603984bb5aa01},
    }

  218. Xiangyu Zhang, Zhanhong He, Shuyu Li, R. Togneri, and L. P. García-Perera, “Investigating self-supervised learning for lyrics recognition,” in ArXiv, 2022.
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    @inproceedings{252531266,
    title = {Investigating self-supervised learning for lyrics recognition},
    author = {{Xiangyu Zhang} and {Zhanhong He} and {Shuyu Li} and {R. Togneri} and {L. P. García-Perera}},
    year = 2022,
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/6632436fd0a465c7b1399c503396233eb9d88b0e},
    }

  219. 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},
    }

  220. 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},
    }

  221. Weiting Tan and Philipp Koehn, “Bitext Mining for Low-Resource Languages via Contrastive Learning,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/767853fdd964e043c485ebb92afdcdf3ee8457e8},
    }

  222. 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.
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    @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},
    }

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

  224. 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},
    }

  225. 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.",
    }

  226. 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},
    }

  227. 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},
    }

  228. 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.",
    }

  229. 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},
    }

  230. 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},
    }

  231. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Predictive Inference Under Covariate Shift,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/e9b0db3dae9050413e3eda2861acf82bff41624b},
    }

  232. Lianhui Qin, S. Welleck, Daniel Khashabi, and Yejin Choi, “COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics,” in ArXiv, 2022.
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    @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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/4a6a65968a8eb8c09ffb57a7774ddabb596565b1},
    }

  233. Malsha V. Perera, Nithin Gopalakrishnan Nair, W. G. C. Bandara, and Vishal M. Patel, “SAR Despeckling using a Denoising Diffusion Probabilistic Model,” in ArXiv, 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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/d49713b2126f4b224a75b3bfea3e00c63c7e51e3},
    }

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

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

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

  237. Rajeev Yasarla, C. Priebe, and Vishal M. Patel, “ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images,” in European Conference on Computer Vision, 2022.
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    @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},
    }

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

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

  240. Tasnim Mohiuddin, Philipp Koehn, Vishrav Chaudhary, James Cross, Shruti Bhosale, and Shafiq R. Joty, “Data Selection Curriculum for Neural Machine Translation,” in ArXiv, 2022.
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    @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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/d6c4b31958fe9e4ff4f83e049ed5c6881653eb03},
    }

  241. Saurabh Kataria, J. Villalba, Laureano Moro-Vel’azquez, Piotr Żelasko, and N. Dehak, “Time-domain speech super-resolution with GAN based modeling for telephony speaker verification.” 2022.
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    @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},
    }

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

  243. 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 ArXiv, 2022.
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    @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 = {ArXiv},
    url = {https://www.semanticscholar.org/paper/1d41a0ddda57caa6c8d268dd1703e4c9b35db18b},
    }

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

  245. V. Vibashan, Poojan Oza, and Vishal M. Patel, “Towards Online Domain Adaptive Object Detection,” in ArXiv, 2022.
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    @inproceedings{248085083,
    title = {Towards Online Domain Adaptive Object Detection},
    author = {{V. Vibashan} and {Poojan Oza} and {Vishal M. Patel}},
    year = 2022,
    month = {4},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/ae1a767e40ce43b3cdcc2440a91dfe4a77cad901},
    }

  246. Drew Prinster, Anqi Liu, and S. Saria, “JAWS: Auditing Predictive Uncertainty Under Covariate Shift.” 2022.
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    title = {JAWS: Auditing Predictive Uncertainty Under Covariate Shift},
    author = {{Drew Prinster} and {Anqi Liu} and {S. Saria}},
    year = 2022,
    month = {7},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/4fb13897dad166844ca020e3cef1563b8dc81775},
    }

  247. Kangfu Mei, Yiqun Mei, and Vishal M. Patel, “Thermal to Visible Image Synthesis under Atmospheric Turbulence,” in International Conference on Information Photonics, 2022.
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    @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},
    }

  248. Cheng Peng and R. Chellappa, “PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/c900f690fdab5d17b0253d4362e7f1a7d9d2d495},
    }

  249. Pirazh Khorramshahi, V. Shenoy, and R. Chellappa, “Scalable Vehicle Re-Identification via Self-Supervision,” in ArXiv, 2022.
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    @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},
    url = {https://www.semanticscholar.org/paper/9d69f0b6c916ac36e2bf28491d27c653eae245cd},
    }

  250. W. G. C. Bandara and Vishal M. Patel, “HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening,” in Computer Vision and Pattern Recognition, 2022.
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    @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 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 {D. 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 {Stanford School of Medicine} and {Stanford} and {Bay Area Environmental Research Institute} and {Mortality ResearchConsulting} and {Inc.} and {Universities Space Research Association} and {UC Space Health} and {D. Surgery} and {U. California} and {San Francisco} and {AI CenterforHealth} and {D. Biochemistry} and {Molecular Genetics} and {U. S. N. A. O. 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 {D. 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 {Stanford School of 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},
    url = {https://www.semanticscholar.org/paper/0d6d142dc49cf7537ece045d8d469fd014a5d3b6},
    }

  301. 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},
    }

  302. Tiange Xiang, Yixiao Zhang, Yongyi Lu, A. Yuille, Chaoyi Zhang, Weidong 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 Cai} and {Zongwei Zhou}},
    year = 2021,
    month = {11},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/e2977c67f55b8a2a58ff1c232c96bed25002f8a2},
    }

  303. 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.",
    }

  304. 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.",
    }

  305. 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},
    }

  306. 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.",
    }

  307. 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},
    }

  308. 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.",
    }

  309. 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},
    }

  310. 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, 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},
    url = {https://www.semanticscholar.org/paper/9653c070724e44f023e8cc3ec79f0b9e6d59480d},
    }

  311. 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.",
    }

  312. 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.",
    }

  313. 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.",
    }

  314. 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},
    }

  315. 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},
    }

  316. 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.",
    }

  317. 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.",
    }

  318. 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.",
    }

  319. Zili Huang, Marc Delcroix, Leibny Paola Garcia, 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 Garcia} 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},
    }

  320. 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.",
    }

  321. 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},
    }

  322. 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},
    }

  323. Pengfei Guo and Vishal M. Patel, “Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/7bab95180b52749d2b018d120d8f04bba520ee0f},
    }

  324. 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},
    }

  325. 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.",
    }

  326. 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.",
    }

  327. 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},
    }

  328. 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",
    }

  329. 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. 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",
    year = "2021",
    month = nov,
    address = "Punta Cana",
    URL = "http://cs.jhu.edu/~jason/papers/#semanticmachines-2021-emnlp",
    }

  330. 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},
    }

  331. 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},
    }

  332. Sandeep Reddy Kothinti, Nicholas Huang, and M. 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 {M. Elhilali}},
    year = 2021,
    month = {10},
    booktitle = {Journal of the Acoustical Society of America},
    url = {https://www.semanticscholar.org/paper/06ae11378419c01df4297c03d962459aefb3c054},
    }

  333. 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},
    }

  334. 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},
    }

  335. 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},
    }

  336. 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},
    }

  337. 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.
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    @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},
    }

  338. 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.
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    @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},
    }

  339. 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},
    }

  340. 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.
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    @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},
    }

  341. M. Sophocleous, J. Georgiou, A. Andreou, Yosi Shacham-Diamand, Theerawit Wilaiprasitporn, J. Atkinson, P. 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 {P. French} and {E. García-Breijo} and {Mohammad Russel}},
    year = 2021,
    month = {10},
    booktitle = {IEEE Sensors Journal},
    url = {https://www.semanticscholar.org/paper/72e190cfe76cde934943ae35908bff346d4c970d},
    }

  342. A. Hamad, Alan Finn, A. Fahmy, Atsushi Irie, Baihua Xiao, Changping Liu, Chenglin 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 {Chenglin 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},
    }

  343. 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},
    }

  344. Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, B. Green, Elizabeth L. Engle, Guillermo Almodovar, Ryan Walk, Sigfredo 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 {Sigfredo Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
    year = 2021,
    month = {10},
    booktitle = {},
    url = {https://www.semanticscholar.org/paper/36b9a20c24bb33ac66feccd9dd8e1dc472f791b6},
    }

  345. Piotr Żelasko, Daniel Povey, J. Trmal, and S. Khudanpur, “Lhotse: a speech data representation library for the modern deep learning ecosystem,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/18394264fe8b4c05527117c5d15a1d19e52c2687},
    }

  346. Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, and R. Chellappa, “Identification of Attack-Specific Signatures in Adversarial Examples,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/7cfeca9f831e4f2d31114215abaa5078a98d1656},
    }

  347. 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},
    }

  348. K. Allen, Angeles Salles, Sa-Keun Park, M. 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 {M. Elhilali} and {C. Moss}},
    year = 2021,
    month = {10},
    booktitle = {Journal of Neurophysiology},
    url = {https://www.semanticscholar.org/paper/1652bdf2674f195b97aee0f1f32926f1c7b9aced},
    }

  349. 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},
    }

  350. 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.",
    }

  351. 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",
    }

  352. 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.",
    }

  353. 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.",
    }

  354. 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.",
    }

  355. 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.",
    }

  356. 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.",
    }

  357. 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.",
    }

  358. 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.",
    }

  359. 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.",
    }

  360. 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.",
    }

  361. 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",
    }

  362. 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.",
    }

  363. 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.",
    }

  364. 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.",
    }

  365. 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.",
    }

  366. 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.",
    }

  367. 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",
    }

  368. 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",
    }

  369. 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.",
    }

  370. 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.",
    }

  371. 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.",
    }

  372. 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.",
    }

  373. 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.",
    }

  374. 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},
    }

  375. Seyoun Park, E. Fishman, and A. Yuille, “Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/20ac864f361512c85577eab83115a7cfa48dc0d7},
    }

  376. Yuval Pinter, A. Stent, Mark Dredze, and Jacob Eisenstein, “Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/9c2e4e5ee224c20a45c37244924138b50f3fe603},
    }

  377. 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},
    }

  378. 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, 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},
    url = {https://www.semanticscholar.org/paper/e9edc2d44af422cb6b8d8ce494161c7779ba0895},
    }

  379. 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},
    }

  380. 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},
    }

  381. 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},
    }

  382. Sonal Joshi, J. Villalba, Piotr Żelasko, Laureano Moro-Vel’azquez, and N. Dehak, “Adversarial Attacks and Defenses for Speaker Identification Systems,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/b595a080a4376bab6edd2e8b8c4bfa3cede54f3b},
    }

  383. 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.",
    }

  384. 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},
    }

  385. 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},
    }

  386. 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},
    }

  387. Benjamin Skerritt-Davis and M. 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 {M. Elhilali}},
    year = 2021,
    month = {6},
    booktitle = {Journal of Neuroscience},
    url = {https://www.semanticscholar.org/paper/77543e1c8dc684e5a5343ee8001e5cc41d72ddd6},
    }

  388. 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 ML4H@NeurIPS, 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 = {ML4H@NeurIPS},
    url = {https://www.semanticscholar.org/paper/c59c0ea24987b63df440fe9a7c8838874d948a02},
    }

  389. 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},
    }

  390. 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},
    }

  391. 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},
    }

  392. 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},
    }

  393. 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},
    }

  394. 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},
    }

  395. 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},
    }

  396. 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},
    }

  397. 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},
    }

  398. 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",
    }

  399. 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},
    }

  400. 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},
    }

  401. 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, 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},
    url = {https://www.semanticscholar.org/paper/8abd724b770348bd21b16b9aaf2ba0a77596b2ed},
    }

  402. 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},
    }

  403. 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},
    }

  404. Jiefu Ou, Nathaniel Weir, Anton Belyy, Felix Yu, and Benjamin Van Durme, “InFillmore: Neural Frame Lexicalization for Narrative Text Infilling,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/f21e78a3f3e55fc081358176fe908910ef4571ea},
    }

  405. 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},
    }

  406. 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},
    }

  407. 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},
    }

  408. 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},
    }

  409. 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},
    }

  410. Benjamin Skerritt-Davis and M. 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 {M. Elhilali}},
    year = 2021,
    month = {4},
    booktitle = {Journal of Neuroscience Methods},
    url = {https://www.semanticscholar.org/paper/8046a293f376cce9d17b77d26cd04742019c50a3},
    }

  411. 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.",
    }

  412. I. McLane, Dimitra Emmanouilidou, James E. West, and M. 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 {M. Elhilali}},
    year = 2021,
    month = {2},
    booktitle = {IEEE journal of biomedical and health informatics},
    url = {https://www.semanticscholar.org/paper/84d283da84a56296c925a6c383bad6e4cb345376},
    }

  413. Shota Horiguchi, Nelson Yalta, Paola García, 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, 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 {Paola García} 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},
    url = {https://www.semanticscholar.org/paper/7a737872a6693ba3f0c99651191b93dad0dadcee},
    }

  414. 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},
    }

  415. Pramuditha Perera, Poojan Oza, and Vishal M. Patel, “One-Class Classification: A Survey,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/bd6262ebdd1a865e8e6859ab7dd8dc576d2a90e6},
    }

  416. Ju He, Adam Kortylewski, and A. Yuille, “CORL: Compositional Representation Learning for Few-Shot Classification.” 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 = {},
    url = {https://www.semanticscholar.org/paper/0f10d0f5355a3f7ce371008e26419172d258bf77},
    }

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

  418. 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 ASMUS@MICCAI, 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 = {ASMUS@MICCAI},
    url = {https://www.semanticscholar.org/paper/764ad2c50a028fa7e9b60f0d45fd6d9037a21696},
    }

  419. 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, 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},
    url = {https://www.semanticscholar.org/paper/8ca58f3f6e59a6d243f3da6c196e9f730e6e9993},
    }

  420. 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},
    }

  421. 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, 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},
    url = {https://www.semanticscholar.org/paper/fd3bee898ae69bd956af9f4aabd3f7b478de2cbd},
    }

  422. 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},
    }

  423. 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},
    }

  424. 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},
    }

  425. Jejin Cho, J. Villalba, and N. Dehak, “The JHU submission to VoxSRC-21: Track 3,” in ArXiv, 2021.
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    @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},
    url = {https://www.semanticscholar.org/paper/ed2065a9cb6f31806aba9a70a4148b99225782a3},
    }

  426. V. Rennoll, I. McLane, Adebayo A. Eisape, M. 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 {M. Elhilali} and {James E. West}},
    year = 2021,
    month = {6},
    booktitle = {Journal of the Acoustical Society of America},
    url = {https://www.semanticscholar.org/paper/ea047ae6955b4f0343c48e3b9066efbc9d5e7d20},
    }

  427. 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},
    }

  428. 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},
    }

  429. 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},
    }

  430. 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},
    }

  431. 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},
    }

  432. 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.
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    @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}},
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    url = {https://www.semanticscholar.org/paper/2dd4b5e8633a5587ce2ebf73284134f21d1bc6a9},
    }

  565. 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},
    }

  566. 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},
    }

  567. 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},
    }

  568. 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},
    }

  569. 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.",
    }

  570. 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},
    }

  571. Mengqi Guo, Yutong Bai, Zhishuai Zhang, Adam Kortylewski, and A. Yuille, “Unsupervised Part Discovery via Feature Alignment,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/2f2879a07875a94e0e04bc59068807924ea17f97},
    }

  572. 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.",
    }

  573. 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},
    }

  574. 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},
    }

  575. 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},
    }

  576. 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},
    }

  577. 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.",
    }

  578. 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},
    }

  579. 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},
    }

  580. 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},
    }

  581. 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},
    }

  582. Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zhe L. Lin, and A. Yuille, “Meticulous Object Segmentation,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/67b4298db52b5082e851ff6bbd7fbcebeb1c33fc},
    }

  583. 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, 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},
    url = {https://www.semanticscholar.org/paper/53ae52ef49a8c2ffa4d893332fa0ea9ca7b20805},
    }

  584. 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},
    }

  585. 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",
    }

  586. 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.",
    }

  587. Jesús Antonio Villalba López, D. Garcia-Romero, Nanxin Chen, Gregory Sell, Jonas Borgstrom, A. McCree, L. P. 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 {L. P. 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},
    }

  588. K. Harrigian, C. Aguirre, and M. Dredze, “Do Models of Mental Health Based on Social Media Data Generalize?,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 3774–3788. doi:10.18653/v1/2020.findings-emnlp.337
    [BibTeX] [Abstract] [Link]

    Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.

    @inproceedings{harrigian-etal-2020-models,
    title = "Do Models of Mental Health Based on Social Media Data Generalize?",
    author = "Harrigian, Keith and
    Aguirre, Carlos and
    Dredze, Mark",
    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.337",
    doi = "10.18653/v1/2020.findings-emnlp.337",
    pages = "3774--3788",
    abstract = "Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.",
    }

  589. Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, V. Chandra, and A. Yuille, “Can Temporal Information Help with Contrastive Self-Supervised Learning?,” in ArXiv, 2020.
    [BibTeX] [Link]
    @inproceedings{227209513,
    title = {Can Temporal Information Help with Contrastive Self-Supervised Learning?},
    author = {{Yutong Bai} and {Haoqi Fan} and {Ishan Misra} and {Ganesh Venkatesh} and {Yongyi Lu} and {Yuyin Zhou} and {Qihang Yu} and {V. Chandra} and {A. Yuille}},
    year = 2020,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/c8993a95dac7a0bf86fb96ee30cf653a57755783},
    }

  590. B. Thompson and M. Post, “Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 90–121. doi:10.18653/v1/2020.emnlp-main.8
    [BibTeX] [Abstract] [Link]

    We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser{‘}s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric{–-}conditioning on the source instead of the reference{–-}and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.

    @inproceedings{thompson-post-2020-automatic,
    title = "Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing",
    author = "Thompson, Brian and
    Post, Matt",
    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.8",
    doi = "10.18653/v1/2020.emnlp-main.8",
    pages = "90--121",
    abstract = "We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser{'}s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric{---}conditioning on the source instead of the reference{---}and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.",
    }

  591. K. Marchisio, K. Duh, and P. Koehn, “When Does Unsupervised Machine Translation Work?,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 571–583.
    [BibTeX] [Abstract] [Link]

    Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.

    @inproceedings{marchisio-etal-2020-unsupervised,
    title = "When Does Unsupervised Machine Translation Work?",
    author = "Marchisio, Kelly and
    Duh, Kevin and
    Koehn, Philipp",
    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-1.68",
    pages = "571--583",
    abstract = "Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.",
    }

  592. 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.",
    }

  593. Yuhui Xu, Lingxi Xie, Cihang Xie, Jieru Mei, Siyuan Qiao, Wei Shen, H. Xiong, and A. Yuille, “Batch Normalization with Enhanced Linear Transformation,” in ArXiv, 2020.
    [BibTeX] [Link]
    @inproceedings{227228087,
    title = {Batch Normalization with Enhanced Linear Transformation},
    author = {{Yuhui Xu} and {Lingxi Xie} and {Cihang Xie} and {Jieru Mei} and {Siyuan Qiao} and {Wei Shen} and {H. Xiong} and {A. Yuille}},
    year = 2020,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/95824133679061448b57ea746456f36f14796aa0},
    }

  594. 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.",
    }

  595. 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 preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.

    @inproceedings{bremerman-etal-2020-evaluation,
    title = "On the Evaluation of Machine Translation n-best Lists",
    author = "Bremerman, Jacob and
    Khayrallah, Huda and
    Oard, Douglas and
    Post, Matt",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.eval4nlp-1.7",
    doi = "10.18653/v1/2020.eval4nlp-1.7",
    pages = "60--68",
    abstract = "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 preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.",
    }

  596. Desh Raj, L. P. 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 {L. P. 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},
    }

  597. Rachel Dorn, A. Nobles, Masoud Rouhizadeh, and Mark Dredze, “Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data,” in ArXiv, 2020.
    [BibTeX] [Link]
    @inproceedings{226975724,
    title = {Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data},
    author = {{Rachel Dorn} and {A. Nobles} and {Masoud Rouhizadeh} and {Mark Dredze}},
    year = 2020,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/3d35c0aec777f6c180d4bf61a2443ec35230bfd2},
    }

  598. 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.",
    }

  599. Desh Raj, Zili Huang, and S. Khudanpur, “Multi-Class Spectral Clustering with Overlaps for Speaker Diarization,” in Spoken Language Technology Workshop, 2020.
    [BibTeX] [Link]
    @inproceedings{226254048,
    title = {Multi-Class Spectral Clustering with Overlaps for Speaker Diarization},
    author = {{Desh Raj} and {Zili Huang} and {S. Khudanpur}},
    year = 2020,
    month = {11},
    booktitle = {Spoken Language Technology Workshop},
    url = {https://www.semanticscholar.org/paper/43dadc5a85b3b6203f9b78d6eb985dd1f65b2dfc},
    }

  600. B. Thompson and M. Post, “Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 561–570.
    [BibTeX] [Abstract] [Link]

    Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.

    @inproceedings{thompson-post-2020-paraphrase,
    title = "Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity",
    author = "Thompson, Brian and
    Post, Matt",
    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-1.67",
    pages = "561--570",
    abstract = "Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.",
    }

  601. R. Bawden, B. Zhang, A. Tättar, and M. Post, “ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 887–894.
    [BibTeX] [Abstract] [Link]

    We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.

    @inproceedings{bawden-etal-2020-parbleu,
    title = "{P}ar{BLEU}: Augmenting Metrics with Automatic Paraphrases for the {WMT}{'}20 Metrics Shared Task",
    author = {Bawden, Rachel and
    Zhang, Biao and
    T{\"a}ttar, Andre and
    Post, Matt},
    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-1.98",
    pages = "887--894",
    abstract = "We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.",
    }

  602. P. Koehn, V. Chaudhary, A. El-Kishky, N. Goyal, P. Chen, and F. Guzmán, “Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 726–742.
    [BibTeX] [Abstract] [Link]

    Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto{–}English and Khmer{–}English and also included the challenge of sentence alignment from document pairs.

    @inproceedings{koehn-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Task on Parallel Corpus Filtering and Alignment",
    author = "Koehn, Philipp and
    Chaudhary, Vishrav and
    El-Kishky, Ahmed and
    Goyal, Naman and
    Chen, Peng-Jen and
    Guzm{\'a}n, Francisco",
    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-1.78",
    pages = "726--742",
    abstract = "Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto{--}English and Khmer{--}English and also included the challenge of sentence alignment from document pairs.",
    }

  603. Jeya Maria Jose Valanarasu and Vishal M. Patel, “Overcomplete Deep Subspace Clustering Networks,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020.
    [BibTeX] [Link]
    @inproceedings{226975634,
    title = {Overcomplete Deep Subspace Clustering Networks},
    author = {{Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
    year = 2020,
    month = {11},
    booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
    url = {https://www.semanticscholar.org/paper/ace30204c77e5aecf28fc26d2775b89e839cbe7e},
    }

  604. H. Khayrallah, B. Thompson, M. Post, and P. Koehn, “Simulated multiple reference training improves low-resource machine translation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 82–89. doi:10.18653/v1/2020.emnlp-main.7
    [BibTeX] [Abstract] [Link]

    Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser{‘}s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.

    @inproceedings{khayrallah-etal-2020-simulated,
    title = "Simulated multiple reference training improves low-resource machine translation",
    author = "Khayrallah, Huda and
    Thompson, Brian and
    Post, Matt and
    Koehn, Philipp",
    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.7",
    doi = "10.18653/v1/2020.emnlp-main.7",
    pages = "82--89",
    abstract = "Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser{'}s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.",
    }

  605. J. D. Arias-Londoño, J. Gómez-García, L. Moro-Velázquez, and J. I. Godino-Llorente, “Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach,” in IEEE Access, 2020.
    [BibTeX] [Link]
    @inproceedings{227228735,
    title = {Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach},
    author = {{J. D. Arias-Londoño} and {J. Gómez-García} and {L. Moro-Velázquez} and {J. I. Godino-Llorente}},
    year = 2020,
    month = {11},
    booktitle = {IEEE Access},
    url = {https://www.semanticscholar.org/paper/325a462076363f59ad76daff579666adfd1af3ea},
    }

  606. 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.",
    }

  607. B. Thompson and P. Koehn, “Exploiting Sentence Order in Document Alignment,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5997–6007. doi:10.18653/v1/2020.emnlp-main.483
    [BibTeX] [Abstract] [Link]

    We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61{\%} relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala{–}English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.

    @inproceedings{thompson-koehn-2020-exploiting,
    title = "Exploiting Sentence Order in Document Alignment",
    author = "Thompson, Brian and
    Koehn, Philipp",
    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.483",
    doi = "10.18653/v1/2020.emnlp-main.483",
    pages = "5997--6007",
    abstract = "We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61{\%} relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala{--}English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.",
    }

  608. N. Weir, J. Sedoc, and B. Van Durme, “COD3S: Diverse Generation with Discrete Semantic Signatures,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5199–5211. doi:10.18653/v1/2020.emnlp-main.421
    [BibTeX] [Abstract] [Link]

    We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition{‘}s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.

    @inproceedings{weir-etal-2020-cod3s,
    title = "{COD3S}: Diverse Generation with Discrete Semantic Signatures",
    author = "Weir, Nathaniel and
    Sedoc, Jo{\~a}o and
    Van Durme, Benjamin",
    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.421",
    doi = "10.18653/v1/2020.emnlp-main.421",
    pages = "5199--5211",
    abstract = "We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition{'}s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.",
    }

  609. A. Kejriwal and P. Koehn, “An exploratory approach to the Parallel Corpus Filtering shared task WMT20,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 959–965.
    [BibTeX] [Abstract] [Link]

    In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.

    @inproceedings{kejriwal-koehn-2020-exploratory,
    title = "An exploratory approach to the Parallel Corpus Filtering shared task {WMT}20",
    author = "Kejriwal, Ankur and
    Koehn, Philipp",
    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-1.108",
    pages = "959--965",
    abstract = "In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.",
    }

  610. Yu Zeng, Zhe L. Lin, Huchuan Lu, and Vishal M. Patel, “Image Inpainting with Contextual Reconstruction Loss,” in ArXiv, 2020.
    [BibTeX] [Link]
    @inproceedings{227162482,
    title = {Image Inpainting with Contextual Reconstruction Loss},
    author = {{Yu Zeng} and {Zhe L. Lin} and {Huchuan Lu} and {Vishal M. Patel}},
    year = 2020,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/590c81fe445551cca14e6e7b66a64534fdb454f8},
    }

  611. P. Xia, S. Wu, and B. Van Durme, “Which *BERT? A Survey Organizing Contextualized Encoders,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 7516–7533. doi:10.18653/v1/2020.emnlp-main.608
    [BibTeX] [Abstract] [Link]

    Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.

    @inproceedings{xia-etal-2020-bert,
    title = "Which *{BERT}? {A} Survey Organizing Contextualized Encoders",
    author = "Xia, Patrick and
    Wu, Shijie and
    Van Durme, Benjamin",
    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.608",
    doi = "10.18653/v1/2020.emnlp-main.608",
    pages = "7516--7533",
    abstract = "Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.",
    }

  612. 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.",
    }

  613. R. Bawden, B. Zhang, L. Yankovskaya, A. Tättar, and M. Post, “A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online, 2020, p. 918–932. doi:10.18653/v1/2020.findings-emnlp.82
    [BibTeX] [Abstract] [Link]

    We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU{‘}s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.

    @inproceedings{bawden-etal-2020-study,
    title = "A Study in Improving {BLEU} Reference Coverage with Diverse Automatic Paraphrasing",
    author = {Bawden, Rachel and
    Zhang, Biao and
    Yankovskaya, Lisa and
    T{\"a}ttar, Andre and
    Post, Matt},
    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.82",
    doi = "10.18653/v1/2020.findings-emnlp.82",
    pages = "918--932",
    abstract = "We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU{'}s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.",
    }

  614. A. Singh, P. Xia, G. Qin, M. Yarmohammadi, and B. Van Durme, “CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models,” in Proceedings of the Fourth Workshop on Structured Prediction for NLP, Online, 2020, p. 11–16. doi:10.18653/v1/2020.spnlp-1.2
    [BibTeX] [Abstract] [Link]

    Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.

    @inproceedings{singh-etal-2020-copynext,
    title = "{C}opy{N}ext: Explicit Span Copying and Alignment in Sequence to Sequence Models",
    author = "Singh, Abhinav and
    Xia, Patrick and
    Qin, Guanghui and
    Yarmohammadi, Mahsa and
    Van Durme, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.spnlp-1.2",
    doi = "10.18653/v1/2020.spnlp-1.2",
    pages = "11--16",
    abstract = "Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.",
    }

  615. Nanxin Chen, Piotr Żelasko, J. Villalba, and N. Dehak, “Focus on the Present: A Regularization Method for the ASR Source-Target Attention Layer,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
    [BibTeX] [Link]
    @inproceedings{226236802,
    title = {Focus on the Present: A Regularization Method for the ASR Source-Target Attention Layer},
    author = {{Nanxin Chen} and {Piotr Żelasko} and {J. Villalba} and {N. Dehak}},
    year = 2020,
    month = {11},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/f90f383a3f027bfa48fea68790d3cb77f7634b92},
    }

  616. N. Weber, R. Rudinger, and B. Van Durme, “Causal Inference of Script Knowledge,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 7583–7596. doi:10.18653/v1/2020.emnlp-main.612
    [BibTeX] [Abstract] [Link]

    When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.

    @inproceedings{weber-etal-2020-causal,
    title = "Causal Inference of Script Knowledge",
    author = "Weber, Noah and
    Rudinger, Rachel and
    Van Durme, Benjamin",
    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.612",
    doi = "10.18653/v1/2020.emnlp-main.612",
    pages = "7583--7596",
    abstract = "When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.",
    }

  617. Yvette Graham, B. Haddow, and Philipp Koehn, “Statistical Power and Translationese in Machine Translation Evaluation,” in Conference on Empirical Methods in Natural Language Processing, 2020.
    [BibTeX] [Link]
    @inproceedings{227034354,
    title = {Statistical Power and Translationese in Machine Translation Evaluation},
    author = {{Yvette Graham} and {B. Haddow} and {Philipp Koehn}},
    year = 2020,
    month = {11},
    booktitle = {Conference on Empirical Methods in Natural Language Processing},
    url = {https://www.semanticscholar.org/paper/5efdee89bd8a0dc703a79df4f0698bb5bbd04228},
    }

  618. He Zhang, Jianming Zhang, Federico Perazzi, Zhe L. Lin, and Vishal M. Patel, “Deep Image Compositing,” in IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020.
    [BibTeX] [Link]
    @inproceedings{226246291,
    title = {Deep Image Compositing},
    author = {{He Zhang} and {Jianming Zhang} and {Federico Perazzi} and {Zhe L. Lin} and {Vishal M. Patel}},
    year = 2020,
    month = {11},
    booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
    url = {https://www.semanticscholar.org/paper/313f77fec4a2a18e84eea1d9923bd94b732ec2b2},
    }

  619. Desh Raj, J. Villalba, Daniel Povey, and S. Khudanpur, “Frustratingly Easy Noise-aware Training of Acoustic Models,” in ArXiv, 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},
    url = {https://www.semanticscholar.org/paper/3b2eb1a573dcdb5a27103b857d32bd0c4d5ef60a},
    }

  620. L. Barrault, M. Biesialska, O. Bojar, M. R. Costa-jussà, C. Federmann, Y. Graham, R. Grundkiewicz, B. Haddow, M. Huck, E. Joanis, T. Kocmi, P. Koehn, C. Lo, N. Ljubešić, C. Monz, M. Morishita, M. Nagata, T. Nakazawa, S. Pal, M. Post, and M. Zampieri, “Findings of the 2020 Conference on Machine Translation (WMT20),” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 1–55.
    [BibTeX] [Abstract] [Link]

    This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

    @inproceedings{barrault-etal-2020-findings,
    title = "Findings of the 2020 Conference on Machine Translation ({WMT}20)",
    author = {Barrault, Lo{\"\i}c and
    Biesialska, Magdalena and
    Bojar, Ond{\v{r}}ej and
    Costa-juss{\`a}, Marta R. and
    Federmann, Christian and
    Graham, Yvette and
    Grundkiewicz, Roman and
    Haddow, Barry and
    Huck, Matthias and
    Joanis, Eric and
    Kocmi, Tom and
    Koehn, Philipp and
    Lo, Chi-kiu and
    Ljube{\v{s}}i{\'c}, Nikola and
    Monz, Christof and
    Morishita, Makoto and
    Nagata, Masaaki and
    Nakazawa, Toshiaki and
    Pal, Santanu and
    Post, Matt and
    Zampieri, Marcos},
    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-1.1",
    pages = "1--55",
    abstract = "This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.",
    }

  621. P. Xia, J. Sedoc, and B. Van Durme, “Incremental Neural Coreference Resolution in Constant Memory,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 8617–8624. doi:10.18653/v1/2020.emnlp-main.695
    [BibTeX] [Abstract] [Link]

    We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity{‘}s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3{\%} relative loss in F1 on OntoNotes 5.0.

    @inproceedings{xia-etal-2020-incremental,
    title = "Incremental Neural Coreference Resolution in Constant Memory",
    author = "Xia, Patrick and
    Sedoc, Jo{\~a}o and
    Van Durme, Benjamin",
    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.695",
    doi = "10.18653/v1/2020.emnlp-main.695",
    pages = "8617--8624",
    abstract = "We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity{'}s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3{\%} relative loss in F1 on OntoNotes 5.0.",
    }

  622. Y. Chen, T. Chen, and B. Van Durme, “Joint Modeling of Arguments for Event Understanding,” in Proceedings of the First Workshop on Computational Approaches to Discourse, Online, 2020, p. 96–101. doi:10.18653/v1/2020.codi-1.10
    [BibTeX] [Abstract] [Link]

    We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.

    @inproceedings{chen-etal-2020-joint-modeling,
    title = "Joint Modeling of Arguments for Event Understanding",
    author = "Chen, Yunmo and
    Chen, Tongfei and
    Van Durme, Benjamin",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.codi-1.10",
    doi = "10.18653/v1/2020.codi-1.10",
    pages = "96--101",
    abstract = "We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.",
    }

  623. D. Dreizin, Yuyin Zhou, Shuhao Fu, Yan Wang, Guang Li, Kathryn Champ, E. Siegel, Ze Wang, Tina Chen, and A. Yuille, “A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.,” in Radiology: Artificial Intelligence, 2020.
    [BibTeX] [Link]
    @inproceedings{228930077,
    title = {A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.},
    author = {{D. Dreizin} and {Yuyin Zhou} and {Shuhao Fu} and {Yan Wang} and {Guang Li} and {Kathryn Champ} and {E. Siegel} and {Ze Wang} and {Tina Chen} and {A. Yuille}},
    year = 2020,
    month = {11},
    booktitle = {Radiology: Artificial Intelligence},
    url = {https://www.semanticscholar.org/paper/c194d760641dc8333dca3d5819e6664c25b5b53b},
    }

  624. 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},
    }

  625. K. Kelly, A. Fine, and G. Coppersmith, “Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,” in Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, Online, 2020, p. 184–192. doi:10.18653/v1/2020.nlpcss-1.20
    [BibTeX] [Abstract] [Link]

    In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans{‘} issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.

    @inproceedings{kelly-etal-2020-social,
    title = "Social media data as a lens onto care-seeking behavior among women veterans of the {US} armed forces",
    author = "Kelly, Kacie and
    Fine, Alex 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-1.20",
    doi = "10.18653/v1/2020.nlpcss-1.20",
    pages = "184--192",
    abstract = "In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans{'} issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.",
    }

  626. F. Koerner and P. Koehn, “Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task,” in Proceedings of the Fifth Conference on Machine Translation, Online, 2020, p. 966–971.
    [BibTeX] [Abstract] [Link]

    This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year{‘}s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER{_}LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF{_}DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.

    @inproceedings{koerner-koehn-2020-dual,
    title = "Dual Conditional Cross Entropy Scores and {LASER} Similarity Scores for the {WMT}20 Parallel Corpus Filtering Shared Task",
    author = "Koerner, Felicia and
    Koehn, Philipp",
    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-1.109",
    pages = "966--971",
    abstract = "This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year{'}s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER{\_}LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF{\_}DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.",
    }

  627. Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory Hager, and A. Yuille, “Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation,” in ArXiv, 2020.
    [BibTeX] [Link]
    @inproceedings{227239264,
    title = {Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation},
    author = {{Qihao Liu} and {Weichao Qiu} and {Weiyao Wang} and {Gregory Hager} and {A. Yuille}},
    year = 2020,
    month = {11},
    booktitle = {ArXiv},
    url = {https://www.semanticscholar.org/paper/287a96966040d6dd5aa84d329cb08929de624135},
    }

  628. 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},
    }

  629. Y. Graham, B. Haddow, and P. Koehn, “Statistical Power and Translationese in Machine Translation Evaluation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 72–81. doi:10.18653/v1/2020.emnlp-main.6
    [BibTeX] [Abstract] [Link]

    The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.

    @inproceedings{graham-etal-2020-statistical,
    title = "Statistical Power and Translationese in Machine Translation Evaluation",
    author = "Graham, Yvette and
    Haddow, Barry and
    Koehn, Philipp",
    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.6",
    doi = "10.18653/v1/2020.emnlp-main.6",
    pages = "72--81",
    abstract = "The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.",
    }

  630. Y. Chen, T. Chen, S. Ebner, A. S. White, and B. Van Durme, “Reading the Manual: Event Extraction as Definition Comprehension,” in Proceedings of the Fourth Workshop on Structured Prediction for NLP, Online, 2020, p. 74–83. doi:10.18653/v1/2020.spnlp-1.9
    [BibTeX] [Abstract] [Link]

    We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, {“}Some person was born in some location at some time.{”} We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.

    @inproceedings{chen-etal-2020-reading,
    title = "Reading the Manual: Event Extraction as Definition Comprehension",
    author = "Chen, Yunmo and
    Chen, Tongfei and
    Ebner, Seth and
    White, Aaron Steven and
    Van Durme, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.spnlp-1.9",
    doi = "10.18653/v1/2020.spnlp-1.9",
    pages = "74--83",
    abstract = "We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, {``}Some person was born in some location at some time.{''} We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.",
    }

  631. A. El-Kishky, V. Chaudhary, F. Guzmán, and P. Koehn, “CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5960–5969. doi:10.18653/v1/2020.emnlp-main.480
    [BibTeX] [Abstract] [Link]

    Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.

    @inproceedings{el-kishky-etal-2020-ccaligned,
    title = "{CCA}ligned: A Massive Collection of Cross-Lingual Web-Document Pairs",
    author = "El-Kishky, Ahmed and
    Chaudhary, Vishrav and
    Guzm{\'a}n, Francisco and
    Koehn, Philipp",
    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.480",
    doi = "10.18653/v1/2020.emnlp-main.480",
    pages = "5960--5969",
    abstract = "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.",
    }

  632. M. Yuan, M. Zhang, B. Van Durme, L. Findlater, and J. Boyd-Graber, “Interactive Refinement of Cross-Lingual Word Embeddings,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, p. 5984–5996. doi:10.18653/v1/2020.emnlp-main.482
    [BibTeX] [Abstract] [Link]

    Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.

    @inproceedings{yuan-etal-2020-interactive,
    title = "Interactive Refinement of Cross-Lingual Word Embeddings",
    author = "Yuan, Michelle and
    Zhang, Mozhi and
    Van Durme, Benjamin and
    Findlater, Leah and
    Boyd-Graber, Jordan",
    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.482",
    doi = "10.18653/v1/2020.emnlp-main.482",
    pages = "5984--5996",
    abstract = "Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.",
    }

  633. Saurabh Kataria, J. Villalba, and N. Dehak, “Perceptual Loss Based Speech Denoising with an Ensemble of Audio Pattern Recognition and Self-Supervised Models,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020.
    [BibTeX] [Link]
    @inproceedings{225039829,
    title = {Perceptual Loss Based Speech Denoising with an Ensemble of Audio Pattern Recognition and Self-Supervised Models},
    author = {{Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
    year = 2020,
    month = {10},
    booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
    url = {https://www.semanticscholar.org/paper/af803a305d5f1b079bb55a9f0ceeb5acf3726a1a},
    }

  634. E. Leas, A. Nobles, Theodore L. Caputi, Mark Dredze, Shu-Hong Zhu, Joanna E Cohen, and J. Ayers, “News coverage of the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak and internet searches for vaping cessation,” in Tobacco Control, 2020.
    [BibTeX] [Link]