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].
@inproceedings{274515167,
title = {CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing},
author = {{Yen-Ju Lu} and {Jing Liu} and {Thomas Thebaud} and {L. Moro-Velázquez} and {A. Rastrow} and {N. Dehak} and {J. Villalba}},
year = 2024,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/daf0aba5a88ce8461062165249baba14f723c151},
}
@inproceedings{274655916,
title = {GenEx: Generating an Explorable World},
author = {{Taiming Lu} and {Tianmin Shu} and {Junfei Xiao} and {Luoxin Ye} and {Jiahao Wang} and {Cheng Peng} and {Chen Wei} and {Daniel Khashabi} and {Rama Chellappa} and {Alan Yuille} and {Jieneng Chen}},
year = 2024,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/f3ddf43a719befeb657de7444040140245db7295},
}
@inproceedings{274598076,
title = {Are Clinical T5 Models Better for Clinical Text?},
author = {{Yahan Li} and {Keith Harrigian} and {Ayah Zirikly} and {Mark Dredze}},
year = 2024,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/88d0b0189e6d33ce0a4b9315bd8fe82bad326824},
}
@inproceedings{274606149,
title = {Decoding contextual influences on auditory perception from primary auditory cortex},
author = {{B. Englitz} and {S. Akram} and {Mounya Elhilali} and {S. Shamma}},
year = 2024,
month = {12},
booktitle = {eLife},
url = {https://www.semanticscholar.org/paper/67535ffb91c1f43a979f686f66551000013f83de},
}
@inproceedings{274529945,
title = {Sustained EEG responses to rapidly unfolding stochastic sounds reflect Bayesian inferred reliability tracking},
author = {{Sijia Zhao} and {Benjamin Skerritt-Davis} and {Mounya Elhilali} and {Frederic Dick} and {M. Chait}},
year = 2024,
month = {12},
booktitle = {Progress in neurobiology},
url = {https://www.semanticscholar.org/paper/f0e4df68fb354046903eda159be1495b02db49fc},
}
@inproceedings{274635947,
title = {Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.},
author = {{P. R. Payne} and {Kevin B. Johnson} and {Thomas M Maddox} and {Peter J Embi} and {Kenneth D. Mandl} and {Deven McGraw} and {S. Saria} and {Laura Adams}},
year = 2024,
month = {12},
booktitle = {JAMIA Journal of the American Medical Informatics Association},
url = {https://www.semanticscholar.org/paper/da62bc07ba3ae939ed1f0edd591c5b200c6dd910},
}
@inproceedings{274178323,
title = {A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform},
author = {{Adarsh Subbaswamy} and {B. Sahiner} and {Nicholas Petrick} and {Vinay Pai} and {Roy Adams} and {Matthew C. Diamond} and {S. Saria}},
year = 2024,
month = {11},
booktitle = {npj Digital Medicine},
url = {https://www.semanticscholar.org/paper/54ec15ff209d42b8540da5786b4262f74e869151},
}
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what _valid decompositional entailment_ is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.
@inproceedings{weir-etal-2024-enhancing,
title = "Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic",
author = "Weir, Nathaniel and
Sanders, Kate and
Weller, Orion and
Sharma, Shreya and
Jiang, Dongwei and
Jiang, Zhengping and
Dalvi Mishra, Bhavana and
Tafjord, Oyvind and
Jansen, Peter and
Clark, Peter and
Van Durme, Benjamin",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.531/",
doi = "10.18653/v1/2024.emnlp-main.531",
pages = "9458--9482",
abstract = "Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what \_valid decompositional entailment\_ is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic entailment engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference."
}
@inproceedings{273745499,
title = {Multimodal Emotion Recognition Harnessing the Complementarity of Speech, Language, and Vision},
author = {{Thomas Thebaud} and {A. Favaro} and {Yaohan Guan} and {Yuchen Yang} and {Prabhav Singh} and {J. Villalba} and {Laureano Mono-Velazquez} and {N. Dehak}},
year = 2024,
month = {11},
booktitle = {International Conference on Multimodel Interaction},
url = {https://www.semanticscholar.org/paper/acf86833b29d724bf3a2134f60a2cf4497e013e0},
}
It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by searching for trees of entailment relationships between simple text-video evidence and higher-level conclusions that prove question-answer pairs. We also introduce the task of multimodal entailment tree generation to evaluate reasoning quality. Our method’s performance on the challenging TVQA benchmark demonstrates interpretable, state-of-the-art zero-shot performance on full clips, illustrating that multimodal entailment tree generation can be a best-of-both-worlds alternative to black-box systems.
@inproceedings{sanders-etal-2024-tv,
title = "{TV}-{TREES}: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning",
author = "Sanders, Kate and
Weir, Nathaniel and
Van Durme, Benjamin",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1059/",
doi = "10.18653/v1/2024.emnlp-main.1059",
pages = "19009--19028",
abstract = "It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by searching for trees of entailment relationships between simple text-video evidence and higher-level conclusions that prove question-answer pairs. We also introduce the task of multimodal entailment tree generation to evaluate reasoning quality. Our method's performance on the challenging TVQA benchmark demonstrates interpretable, state-of-the-art zero-shot performance on full clips, illustrating that multimodal entailment tree generation can be a best-of-both-worlds alternative to black-box systems."
}
@inproceedings{274131239,
title = {Generative World Explorer},
author = {{Taiming Lu} and {Tianmin Shu} and {Alan Yuille} and {Daniel Khashabi} and {Jieneng Chen}},
year = 2024,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/13279a99e7a2b6c8aaac9026cef53034e6cfa1de},
}
While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.
@inproceedings{bafna-etal-2024-evaluating,
title = "Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization",
author = "Bafna, Niyati and
Murray, Kenton and
Yarowsky, David",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1044/",
doi = "10.18653/v1/2024.emnlp-main.1044",
pages = "18742--18762",
abstract = "While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation."
}
@inproceedings{274139049,
title = {Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.},
author = {{Drew Prinster} and {Amama Mahmood} and {S. Saria} and {Jean Jeudy} and {Cheng Ting Lin} and {Paul H Yi} and {Chien-Ming Huang}},
year = 2024,
month = {11},
booktitle = {Radiology},
url = {https://www.semanticscholar.org/paper/71e974f5a49bcdfa72aad21b64158a9f5c176b2e},
}
Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We collect a set of 340 high quality, human written analogies for use in our benchmark, which constitutes the largest such collection to date. We then test a broad collection of models consisting of 12 open source and 3 proprietary in various sizes and architectures. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field.
@inproceedings{ye-etal-2024-analobench,
title = "{A}nalo{B}ench: Benchmarking the Identification of Abstract and Long-context Analogies",
author = "Ye, Xiao and
Wang, Andrew and
Choi, Jacob and
Lu, Yining and
Sharma, Shreya and
Shen, Lingfeng and
Tiyyala, Vijay Murari and
Andrews, Nicholas and
Khashabi, Daniel",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.725/",
doi = "10.18653/v1/2024.emnlp-main.725",
pages = "13060--13082",
abstract = "Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We collect a set of 340 high quality, human written analogies for use in our benchmark, which constitutes the largest such collection to date. We then test a broad collection of models consisting of 12 open source and 3 proprietary in various sizes and architectures. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field."
}
@inproceedings{274287296,
title = {Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.},
author = {{Natalie Wang} and {Sukrit Treewaree} and {Ayah Zirikly} and {Yuzhi L. Lu} and {M. Nguyen} and {Bhavik Agarwal} and {Jash Shah} and {J. M. Stevenson} and {Casey Overby Taylor}},
year = 2024,
month = {11},
booktitle = {Journal of Biomedical Informatics},
url = {https://www.semanticscholar.org/paper/aceb915df57e8f1708fa54cb7f828da00c76c42f},
}
@inproceedings{274444639,
title = {Deep Stroop: Integrating eye tracking and speech processing to characterize people with neurodegenerative disorders while performing neuropsychological tests.},
author = {{Trevor Meyer} and {A. Favaro} and {Esther S. Oh} and {A. Butala} and {C. Motley} and {Pedro P. Irazoqui} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2024,
month = {11},
booktitle = {Computers in Biology and Medicine},
url = {https://www.semanticscholar.org/paper/528e2a1618feeb267fc7fc4873207266ef5c01a3},
}
We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of examples that lawyers and paralegals would expect LLMs to handle zero-shot, such as looking up the text at a line of a witness deposition or at a subsection of a contract. LLMs’ poor performance on this benchmark casts into doubt their reliability as-is for legal practice. However, fine-tuning on our training set brings even a small model to near-perfect performance. This benchmark will be useful for fine-tuning LLMs for downstream legal tasks, as well as for tracking LLMs’ reliability as-is for basic legal tasks.
@inproceedings{blair-stanek-etal-2024-blt,
title = "{BLT}: Can Large Language Models Handle Basic Legal Text?",
author = "Blair-Stanek, Andrew and
Holzenberger, Nils and
Van Durme, Benjamin",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan\textcommabelow t\u a, C\u at\u alina and
Preo\textcommabelow tiuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nllp-1.18/",
doi = "10.18653/v1/2024.nllp-1.18",
pages = "216--232",
abstract = "We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of examples that lawyers and paralegals would expect LLMs to handle zero-shot, such as looking up the text at a line of a witness deposition or at a subsection of a contract. LLMs' poor performance on this benchmark casts into doubt their reliability as-is for legal practice. However, fine-tuning on our training set brings even a small model to near-perfect performance. This benchmark will be useful for fine-tuning LLMs for downstream legal tasks, as well as for tracking LLMs' reliability as-is for basic legal tasks."
}
We introduce a language generation dataset grounded in a popular video game. KNUDGE (**KN**owledge Constrained **U**ser-NPC **D**ialogue **GE**neration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment’s _The Outer Worlds_, leading to real-world complexities in generation: (1) utterances must remain faithful to the game lore, including character personas and backstories; (2) a dialogue must accurately reveal new quest details to the human player; and (3) dialogues are large trees as opposed to linear chains of utterances. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.
@inproceedings{weir-etal-2024-ontologically,
title = "Ontologically Faithful Generation of Non-Player Character Dialogues",
author = "Weir, Nathaniel and
Thomas, Ryan and
d'Amore, Randolph and
Hill, Kellie and
Van Durme, Benjamin and
Jhamtani, Harsh",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.520/",
doi = "10.18653/v1/2024.emnlp-main.520",
pages = "9212--9242",
abstract = "We introduce a language generation dataset grounded in a popular video game. KNUDGE (**KN**owledge Constrained **U**ser-NPC **D**ialogue **GE**neration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment's \_The Outer Worlds\_, leading to real-world complexities in generation: (1) utterances must remain faithful to the game lore, including character personas and backstories; (2) a dialogue must accurately reveal new quest details to the human player; and (3) dialogues are large trees as opposed to linear chains of utterances. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues."
}
@InProceedings{chen-et-al-2024,
aclid = "2024.emnlp-main.406",
author = "Yunmo Chen and Tongfei Chen and Harsh Jhamtani and
Patrick Xia and Richard Shin and Jason Eisner and
Benjamin Van Durme",
title = "Learning to Retrieve Iteratively for In-Context
Learning",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "7156--7168",
year = "2024",
month = nov,
URL = "http://cs.jhu.edu/~jason/papers/#chen-et-al-2024",
}
@InProceedings{bostrom-et-al-2024,
aclid = "2024.emnlp-main.462",
author = "Kaj Bostrom and Harsh Jhamtani and Hao Fang and Sam
Thomson and Richard Shin and Patrick Xia and Benjamin
Van Durme and Jason Eisner and Jacob Andreas",
title = "Language-to-Code Translation with a Single Labeled
Example",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "8101--8112",
year = "2024",
month = nov,
URL = "http://cs.jhu.edu/~jason/papers/#bostrom-et-al-2024",
}
@inproceedings{273636715,
title = {Temporal coherence shapes cortical responses to speech mixtures in a ferret cocktail party},
author = {{Neha Joshi} and {Wing Yiu Ng} and {Karan Thakkar} and {Daniel Duque} and {Pingbo Yin} and {Jonathan Fritz} and {Mounya Elhilali} and {S. Shamma}},
year = 2024,
month = {10},
booktitle = {Communications Biology},
url = {https://www.semanticscholar.org/paper/8afe9f8e4a9485e20070c362067d3a06f3653311},
}
@inproceedings{273691413,
title = {Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes},
author = {{E. Hatef} and {C. Kitchen} and {Geoffrey M Gray} and {Ayah Zirikly} and {Thomas M Richards} and {Luis M Ahumada} and {Jonathan P. Weiner}},
year = 2024,
month = {10},
booktitle = {JAMIA Open},
url = {https://www.semanticscholar.org/paper/d5013b3593f9a1e25a3b0bb84150158911d752f0},
}
@inproceedings{273648380,
title = {Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings},
author = {{A. Favaro} and {A. Butala} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2024,
month = {10},
booktitle = {npj Parkinson's Disease},
url = {https://www.semanticscholar.org/paper/4daac9614c0979ec790583db1a2c86c07456f32a},
}
@inproceedings{273323322,
title = {Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains},
author = {{Krithika Ramesh} and {Nupoor Gandhi} and {Pulkit Madaan} and {Lisa Bauer} and {Charith Peris} and {Anjalie Field}},
year = 2024,
month = {10},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/d1bc6e53f2cc088406ab8bb4d8b63d26944c15c8},
}
@inproceedings{273163058,
title = {On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions},
author = {{Huy Nguyen} and {Xing Han} and {C. Harris} and {S. Saria} and {Nhat Ho}},
year = 2024,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8fa041c2762bd3ad22dc673d89059e27daf01881},
}
@InProceedings{hashemi-et-al-2024,
aclid = "2024.acl-long.745",
author = "Helia Hashemi and Jason Eisner and Corby Rosset and
Benjamin Van Durme and Chris Kedzie",
title = "{LLM-Rubric}: {A} Multidimensional, Calibrated
Approach to Automated Evaluation of Natural Language
Texts",
booktitle = "Proceedings of the 62nd Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "13806--13834",
year = "2024",
month = aug,
URL = "http://cs.jhu.edu/~jason/papers/#hashemi-et-al-2024",
}
@InProceedings{wang-et-al-2024-tools,
aclid = "2024.acl-long.570",
author = "Boshi Wang and Hao Fang and Jason Eisner and Benjamin
Van Durme and Yu Su",
title = "{LLMs} in the {I}maginarium: Tool Learning through
Simulated Trial and Error",
booktitle = "Proceedings of the 62nd Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "10583--10604",
year = "2024",
month = aug,
URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-tools",
}
@InProceedings{wang-et-al-2024-hallucination,
aclid = "2024.findings-acl.260",
author = "Sky CH-Wang and Benjamin Van Durme and Jason Eisner
and Chris Kedzie",
title = "Do Androids Know They’re Only Dreaming of Electric
Sheep?",
booktitle = "Findings of the 62nd Annual Meeting of the Association
for Computational Linguistics (ACL)",
pages = "4401--4420",
year = "2024",
month = aug,
URL = "http://cs.jhu.edu/~jason/papers/#wang-et-al-2024-hallucination",
}
@InProceedings{monea-et-al-2024,
aclid = "2024.acl-long.369",
author = "Giovanni Monea and Maxime Peyrard and Martin Josifoski
and Vishrav Chaudhary and Jason Eisner and Emre
K{\i}c{\i}man and Hamid Palangi and Barun Patra and
Robert West",
title = "A Glitch in the {M}atrix? {L}ocating and Detecting
Language Model Grounding with {F}akepedia",
booktitle = "Proceedings of the 62nd Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "6828--6844",
year = "2024",
month = aug,
URL = "http://cs.jhu.edu/~jason/papers/#monea-et-al-2024",
}
@InProceedings{du-et-al-2024-tight,
aclid = "2024.findings-acl.659",
author = "Li Du and Jason Eisner and Holden Lee and Ryan
Cotterell",
title = "When is a Language Process a Language Model?",
booktitle = "Findings of the 62nd Annual Meeting of the Association
for Computational Linguistics (ACL)",
pages = "11083--11094",
year = "2024",
month = aug,
URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-tight",
}
@InProceedings{du-et-al-2024-mcmc,
author = "Li Du and Afra Amini and Lucas Torroba Hennigen and
Xinyan Velocity Yu and Holden Lee and Jason Eisner and
Ryan Cotterell",
title = "Principled Gradient-Based {MCMC} for Conditional
Sampling of Text",
booktitle = "Proceedings of the 41st International Conference on
Machine Learning (ICML)",
year = "2024",
month = jul,
URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2024-mcmc",
}
While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural “shortcuts”, such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.
@inproceedings{bafna-etal-2024-pointer,
title = "Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!",
author = "Bafna, Niyati and
Koehn, Philipp and
Yarowsky, David",
editor = "Tafreshi, Shabnam and
Akula, Arjun and
Sedoc, Jo\~ao and
Drozd, Aleksandr and
Rogers, Anna and
Rumshisky, Anna",
booktitle = "Proceedings of the Fifth Workshop on Insights from Negative Results in NLP",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.insights-1.9/",
doi = "10.18653/v1/2024.insights-1.9",
pages = "60--72",
abstract = "While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural ``shortcuts'', such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field."
}
Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a “bigram paraphrase” attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation.
@inproceedings{hou-etal-2024-semstamp,
title = "{S}em{S}tamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation",
author = "Hou, Abe and
Zhang, Jingyu and
He, Tianxing and
Wang, Yichen and
Chuang, Yung-Sung and
Wang, Hongwei and
Shen, Lingfeng and
Van Durme, Benjamin and
Khashabi, Daniel and
Tsvetkov, Yulia",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.226/",
doi = "10.18653/v1/2024.naacl-long.226",
pages = "4067--4082",
abstract = "Existing watermarked generation algorithms employ token-level designs and therefore, are vulnerable to paraphrase attacks. To address this issue, we introduce watermarking on the semantic representation of sentences. We propose SemStamp, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence generated by a language model, and conducts rejection sampling until the sampled sentence falls in watermarked partitions in the semantic embedding space. To test the paraphrastic robustness of watermarking algorithms, we propose a ``bigram paraphrase'' attack that produces paraphrases with small bigram overlap with the original sentence. This attack is shown to be effective against existing token-level watermark algorithms, while posing only minor degradations to SemStamp. Experimental results show that our novel semantic watermark algorithm is not only more robust than the previous state-of-the-art method on various paraphrasers and domains, but also better at preserving the quality of generation."
}
A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations–-11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages–-the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions.
@inproceedings{robinson-etal-2024-kreyol,
title = "Krey\`ol-{MT}: Building {MT} for {L}atin {A}merican, {C}aribbean and Colonial {A}frican Creole Languages",
author = {Robinson, Nathaniel and
Dabre, Raj and
Shurtz, Ammon and
Dent, Rasul and
Onesi, Onenamiyi and
Monroc, Claire and
Grobol, Lo\"\i c and
Muhammad, Hasan and
Garg, Ashi and
Etori, Naome and
Tiyyala, Vijay Murari and
Samuel, Olanrewaju and
Stutzman, Matthew and
Odoom, Bismarck and
Khudanpur, Sanjeev and
Richardson, Stephen and
Murray, Kenton},
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.170/",
doi = "10.18653/v1/2024.naacl-long.170",
pages = "3083--3110",
abstract = "A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations---11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages---the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity then ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions."
}
Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations.
@inproceedings{raunak-etal-2024-slide,
title = "{SLIDE}: Reference-free Evaluation for Machine Translation using a Sliding Document Window",
author = "Raunak, Vikas and
Kocmi, Tom and
Post, Matt",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.18/",
doi = "10.18653/v1/2024.naacl-short.18",
pages = "205--211",
abstract = "Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output.This is unsurprising, since references resolve ambiguities that may be present in the source.In this paper, we investigate whether additional source context can effectively substitute for a reference.We present a metric named SLIDE (SLIding Document Evaluator), which operates on blocks of sentences. SLIDE leverages a moving window that slides over each document in the test set, feeding each chunk of sentences into an unmodified, off-the-shelf quality estimation model.We find that SLIDE obtains significantly higher pairwise system accuracy than its sentence-level baseline, in some cases even eliminating the gap with reference-base metrics.This suggests that source context may provide the same information as a human reference in disambiguating source ambiguities. This finding is especially pertinent for reference-free document-level evaluation, wherein SLIDE could provide higher-quality pairwise system assessments while only requiring document boundary annotations."
}
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation–-determining whether a document is a valid source for a target report event–-and cross-document argument extraction–-full-document argument extraction for a target event from both its report and the correct source article.
@inproceedings{vashishtha-etal-2024-famus,
title = "{FAM}u{S}: Frames Across Multiple Sources",
author = "Vashishtha, Siddharth and
Martin, Alexander and
Gantt, William and
Van Durme, Benjamin and
White, Aaron",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.457/",
doi = "10.18653/v1/2024.naacl-long.457",
pages = "8250--8273",
abstract = "Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation---determining whether a document is a valid source for a target report event---and cross-document argument extraction---full-document argument extraction for a target event from both its report and the correct source article."
}
@InProceedings{moghe-et-al-2024,
aclid = "2024.findings-naacl.255",
author = "Nikita Moghe and Patrick Xia and Jacob Andreas and
Jason Eisner and Benjamin Van Durme and Harsh
Jhamtani",
title = "Interpreting User Requests in the Context of Natural
Language Standing Instructions",
booktitle = "Findings of the North American Conference on
Cmputational Linguistics (NAACL)",
volume = "arXiv:2311.09796",
pages = "4043--4060",
year = "2024",
month = jun,
URL = "http://cs.jhu.edu/~jason/papers/#moghe-et-al-2024",
}
Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases –- such as the speaker’s contacts or recent song playlists –- during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus (“ConEC”) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants’ names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology
@inproceedings{huang-etal-2024-conec,
title = "{C}on{EC}: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition",
author = "Huang, Ruizhe and
Yarmohammadi, Mahsa and
Trmal, Jan and
Liu, Jing and
Raj, Desh and
Garcia, Leibny Paola and
Ivanov, Alexei V. and
Ehlen, Patrick and
Yu, Mingzhi and
Povey, Dan and
Khudanpur, Sanjeev",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.328/",
pages = "3700--3706",
abstract = "Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases --- such as the speaker's contacts or recent song playlists --- during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus (``ConEC'') and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants' names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology"
}
Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity.
@inproceedings{verma-etal-2024-exploring,
title = "Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models",
author = "Verma, Neha and
Murray, Kenton and
Duh, Kevin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.604/",
pages = "6909--6921",
abstract = "Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sharing. However, some language pairs in multilingual models can see worse performance than in bilingual models, especially in the one-to-many translation setting. Motivated by their empirical differences, we examine the geometric differences in representations from bilingual models versus those from one-to-many multilingual models. Specifically, we compute the isotropy of these representations using intrinsic dimensionality and IsoScore, in order to measure how the representations utilize the dimensions in their underlying vector space. Using the same evaluation data in both models, we find that for a given language pair, its multilingual model decoder representations are consistently less isotropic and occupy fewer dimensions than comparable bilingual model decoder representations. Additionally, we show that much of the anisotropy in multilingual decoder representations can be attributed to modeling language-specific information, therefore limiting remaining representational capacity."
}
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.
@inproceedings{gantt-etal-2024-multimuc,
title = "{M}ulti{MUC}: Multilingual Template Filling on {MUC}-4",
author = "Gantt, William and
Behzad, Shabnam and
An, Hannah and
Chen, Yunmo and
White, Aaron and
Van Durme, Benjamin and
Yarmohammadi, Mahsa",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.21/",
pages = "349--368",
abstract = "We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings."
}
In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document.
@inproceedings{sirin-etal-2024-detecting,
title = "Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with {F}ourier Analysis",
author = "Sirin, Hale and
Li, Sabrina and
Lippincott, Thomas",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.6/",
pages = "46--50",
abstract = "In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish. We introduce the task of detecting distinct patterns of multilinguality based on the frequency of structured language alternations within a document."
}
We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level (`Low’, `Mod- erate’ or `High’) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.
@inproceedings{chim-etal-2024-overview,
title = "Overview of the {CLP}sych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts",
author = "Chim, Jenny and
Tsakalidis, Adam and
Gkoumas, Dimitris and
Atzil-Slonim, Dana and
Ophir, Yaakov and
Zirikly, Ayah and
Resnik, Philip and
Liakata, Maria",
editor = "Yates, Andrew and
Desmet, Bart and
Prud'hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.15/",
pages = "177--190",
abstract = "We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level (`Low', `Mod- erate' or `High') and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings."
}
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.
@inproceedings{weller-etal-2024-nevir,
title = "{N}ev{IR}: Negation in Neural Information Retrieval",
author = "Weller, Orion and
Lawrie, Dawn and
Van Durme, Benjamin",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.139/",
pages = "2274--2287",
abstract = "Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance."
}
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of “according to sources”, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.
@inproceedings{weller-etal-2024-according,
title = "``According to . . . '': Prompting Language Models Improves Quoting from Pre-Training Data",
author = "Weller, Orion and
Marone, Marc and
Weir, Nathaniel and
Lawrie, Dawn and
Khashabi, Daniel and
Van Durme, Benjamin",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.140/",
pages = "2288--2301",
abstract = "Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of ``according to sources'', we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request."
}
Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use.
@inproceedings{lu-etal-2024-gear,
title = "{GEAR}: Augmenting Language Models with Generalizable and Efficient Tool Resolution",
author = "Lu, Yining and
Yu, Haoping and
Khashabi, Daniel",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.7/",
pages = "112--138",
abstract = "Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrations. GEAR achieves better efficiency by delegating tool grounding and execution to small language models (SLM) and LLM, respectively; while leveraging semantic and pattern-based evaluation at both question and answer levels for generalizable tool grounding. We evaluate GEAR on 14 datasets across 6 downstream tasks, demonstrating its strong generalizability to novel tasks, tools and different SLMs. Despite offering more efficiency, GEAR achieves higher precision in tool grounding compared to prior strategies using LLM prompting, thus improving downstream accuracy at a reduced computational cost. For example, we demonstrate that GEAR-augmented GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of better tool use."
}
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.
@inproceedings{weller-etal-2024-generative,
title = "When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets",
author = "Weller, Orion and
Lo, Kyle and
Wadden, David and
Lawrie, Dawn and
Van Durme, Benjamin and
Cohan, Arman and
Soldaini, Luca",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.134/",
pages = "1987--2003",
abstract = "Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear."
}
Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
@inproceedings{li-etal-2024-localization,
title = "Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models",
author = "Li, Zhuowan and
Xie, Cihang and
Van Durme, Benjamin and
Yuille, Alan",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.146/",
pages = "2378--2390",
abstract = "Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether multi-modal learning can help understand each individual modality. In this work, we conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models by probing on a broad range of tasks. Five probing tasks are evaluated in order to assess the quality of the learned representations in a nuanced manner. Our results on five probing tasks suggest vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models."
}
Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20\% exact match across varying levels of data poisoning/knowledge conflicts.
@inproceedings{weller-etal-2024-defending,
title = "Defending Against Disinformation Attacks in Open-Domain Question Answering",
author = "Weller, Orion and
Khan, Aleem and
Weir, Nathaniel and
Lawrie, Dawn and
Van Durme, Benjamin",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-short.35/",
pages = "402--417",
abstract = "Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call Confidence from Answer Redundancy, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20\% exact match across varying levels of data poisoning/knowledge conflicts."
}
@inproceedings{271162279,
title = {Benchmarking Language Model Creativity: A Case Study on Code Generation},
author = {{Yining Lu} and {Dixuan Wang} and {Tianjian Li} and {Dongwei Jiang} and {Daniel Khashabi}},
year = 2024,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/20be3ba3f361d9630cc0c17442a0d0132873e63d},
}
@inproceedings{272654115,
title = {Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis},
author = {{Thomas Thebaud} and {A. Favaro} and {Casey Chen} and {Gabrielle Chavez} and {L. Moro-Velázquez} and {A. Butala} and {N. Dehak}},
year = 2024,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c8851ccf781b66eb487ca7a77784baf168d9ef23},
}
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.
@inproceedings{sirin-lippincott-2024-dynamic,
title = "Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses",
author = "Sirin, Hale and
Lippincott, Thomas",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.22/",
pages = "231--236",
abstract = "We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold."
}
@inproceedings{267028540,
title = {Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
author = {{Haoran Xu} and {Amr Sharaf} and {Yunmo Chen} and {Weiting Tan} and {Lingfeng Shen} and {Benjamin Van Durme} and {Kenton Murray} and {Young Jin Kim}},
year = 2024,
month = {1},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/ebd1c04c61f73f46def3305ca11d038c46665b65},
}
@inproceedings{267144330,
title = {Slowness Regularized Contrastive Predictive Coding for Acoustic Unit Discovery},
author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2024,
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/1748de2018438a1015f557ed72424602b144f5ba},
}
@inproceedings{272694526,
title = {EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer},
author = {{Jiarui Hai} and {Yong Xu} and {Hao Zhang} and {Chenxing Li} and {Helin Wang} and {Mounya Elhilali} and {Dong Yu}},
year = 2024,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8e146956594a7580148dad5ccdb2c0c791abd386},
}
@inproceedings{267311651,
title = {On Speaker Attribution with SURT},
author = {{Desh Raj} and {Matthew Wiesner} and {Matthew Maciejewski} and {Leibny Paola García-Perera} and {Daniel Povey} and {S. Khudanpur}},
year = 2024,
month = {1},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/924d189fbe7aa43ab9de9989ce45d4e4936f533d},
}
@inproceedings{274826287,
title = {Computer-Aided Lung Auscultation Screening and Radiographic Evaluation of Pediatric Pneumonia},
author = {{A. Kala} and {Daniel Chong} and {Abdullah H. Baqui} and {Salahuddin Ahmed} and {A. A. Islam} and {N. Chowdhury} and {A. Roy} and {Eric D. McCollum} and {Mounya Elhilali}},
year = 2024,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/1207bfef4c72a1fc30fbacfa219751638ccf4aec},
}
@inproceedings{271894946,
title = {Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment},
author = {{Qiming Xie} and {Zengzhi Wang} and {Yihao Feng} and {Rui Xia} and {Neel Guha} and {Tatsunori Hashimoto} and {Peter Henderson} and {John Hewitt} and {Daniel E. Ho} and {Jenny Hong} and {Kyle Hsu} and {Jing Huang} and {Thomas Icard} and {Saahil Jain} and {Dan Juraf-sky} and {Pratyusha Kalluri} and {Siddharth Karamcheti} and {Geoff Keeling} and {Fereshte Khani} and {Pang Omar Khattab} and {Wei Koh} and {M. Krass} and {Ranjay Krishna} and {Tom Brown} and {Benjamin Mann} and {Nick Ryder} and {Melanie Subbiah} and {Jared Kaplan} and {Prafulla Dhariwal} and {Arvind Neelakantan} and {Pranav Shyam} and {Girish Sastry} and {Amanda Askell} and {Sandhini Agarwal} and {Ariel Herbert-Voss} and {Gretchen Krueger} and {T. Henighan} and {R. Child} and {Aditya Ramesh} and {Daniel M. Ziegler} and {Jeffrey Wu} and {Clemens Winter} and {Chris Hesse} and {Mark Chen} and {Eric Sigler} and {Ma-teusz Litwin} and {Scott Gray} and {B. Chess} and {J. Clark} and {Christopher Berner} and {Sam McCandlish} and {Wei-Lin Chiang} and {Zhuohan Li} and {Zi Lin} and {Ying Sheng} and {Zhanghao Wu} and {Hao Zhang} and {Lianmin Zheng} and {Siyuan Zhuang} and {Yonghao Zhuang} and {Joseph E. Gonzalez} and {Aakanksha Chowdhery} and {Sharan Narang} and {Jacob Devlin} and {Maarten Bosma} and {Gaurav Mishra} and {Adam Roberts} and {Hyung Paul Barham} and {Won Chung} and {Charles Sutton} and {Sebastian Gehrmann} and {Parker Schuh} and {Kensen Shi} and {Sasha Tsvyashchenko} and {Joshua Maynez} and {Abhishek Rao} and {Parker Barnes} and {Yi Tay} and {Noam M. Shazeer} and {Vinodkumar Prabhakaran} and {Emily Reif} and {Nan Du} and {Ben Hutchinson} and {Reiner Pope} and {James Bradbury} and {Jacob Austin} and {M. Isard} and {Guy Gur-Ari} and {Pengcheng Yin} and {Toju Duke} and {Anselm Levskaya} and {Sanjay Ghe-mawat} and {Sunipa Dev} and {H. Michalewski} and {Xavier Garcia} and {Vedant Misra} and {Kevin Robinson} and {Liam Fe-dus} and {Denny Zhou} and {Daphne Ippolito} and {D. Luan} and {Hyeontaek Lim} and {Barret Zoph} and {A. Spiridonov} and {Ryan Sepassi} and {David Dohan} and {Shivani Agrawal} and {Mark Omernick} and {Andrew M. Dai} and {Thanumalayan Sankaranarayana Pillai} and {Marie Pellat} and {Aitor Lewkowycz} and {Erica Moreira} and {Oleksandr Polozov} and {Katherine Lee} and {Zongwei Zhou} and {Xuezhi Wang} and {Brennan Saeta} and {Mark Díaz} and {Orhan Firat} and {M. Catasta} and {Jason Wei} and {K. Meier-Hellstern} and {K. Cobbe} and {Vineet Kosaraju} and {Mo Bavarian} and {Heewoo Jun} and {Lukasz Kaiser} and {Matthias Plappert} and {Jerry Tworek} and {Jacob Hilton} and {Reiichiro Nakano} and {L. D. Angelis} and {F. Baglivo} and {G. Arzilli} and {Gaetano Pierpaolo} and {P. Privitera} and {Alberto Eugenio Ferrag-ina} and {Tozzi Caterina} and {Rizzo} and {Chatgpt} and {Deep Ganguli} and {Liane Lovitt} and {John Kernion} and {Yuntao Bai} and {Saurav Kadavath} and {Ethan Perez} and {Nicholas Schiefer} and {Kamal Ndousse} and {Andy Jones} and {Sam Bowman} and {Anna Chen} and {Tom Con-erly} and {Nova Dassarma} and {Dawn Drain} and {Sheer Nelson El-hage} and {El Showk} and {Stanislav Fort} and {Zac Hatfield-Dodds} and {Danny Hernandez} and {Tristan Hume} and {J. Jacobson} and {Scott Johnston} and {Shauna Kravec} and {Catherine Olsson} and {Sam Ringer} and {Eli Tran-Johnson} and {Dario Amodei} and {Nicholas Joseph} and {C. Olah} and {Mor Geva} and {Daniel Khashabi} and {Elad Segal} and {Tushar Khot} and {Dan Roth} and {Jonathan Berant. 2021} and {Did Aristo-tle} and {Kai Greshake} and {Sahar Abdelnabi} and {Shailesh Mishra} and {Christoph Endres} and {Thorsten Holz} and {Dan Hendrycks} and {Collin Burns} and {Steven Basart} and {Andy Zou} and {Mantas Mazeika} and {Mohammad Hosseini} and {Catherine A Gao} and {David M. Liebovitz} and {Faraz Alexandre M Carvalho} and {S. Ahmad} and {Yuan Luo} and {N. MacDonald} and {Kristi L. Holmes} and {Abel Kho. 2023} and {An} and {Edward J. Hu} and {Yelong Shen} and {Zeyuan Phillip Wallis} and {Kevin B. Johnson} and {Wei-Qi Wei} and {D. Weeraratne} and {M. Frisse} and {K. Misulis} and {Kyu Rhee} and {Juan Zhao} and {Tom Conerly} and {Nelson Elhage} and {Tristan Hume} and {Kamal Ndousse} and {Stephanie Lin} and {Owain Evans. 2022} and {Yao Lu} and {Max Bartolo} and {Alastair Moore} and {Sewon Min} and {Xinxi Lyu} and {Ari Holtzman} and {Mikel Artetxe} and {Ouyang Long} and {Xu Jiang} and {Diogo Almeida} and {Carroll L. Wainwright} and {Pamela Mishkin} and {Chong Zhang} and {Katarina Slama} and {Alex Ray} and {John Schulman} and {Fraser Kelton} and {Luke Miller} and {Maddie Simens} and {P. Welinder} and {Paul F. Christiano} and {Jan Leike} and {Ryan Lowe. 2022} and {Kamilė Lukošiūtė} and {Karina Nguyen} and {Edwin Chen} and {Scott Heiner} and {Craig Pettit} and {Sandipan Kundu} and {Saurav Kada-vath} and {Brian Israel} and {Bryan Seethor} and {C. McKinnon} and {Da Yan} and {D. Amodei} and {Dustin Li} and {Guro Khundadze} and {James Landis} and {Jamie Kerr} and {J. Mueller} and {Jeeyoon Hyun} and {Joshua Landau} and {Landon Goldberg} and {Martin Lucas} and {M. Sellitto} and {Miranda Zhang} and {Neerav Kingsland} and {Noem'i Mercado} and {Oliver Rausch} and {Robin Larson} and {Tamera Lanham} and {Timothy Telleen-Lawton} and {Roger Grosse} and {Evan Hubinger} and {Ansh Radhakrishnan} and {Carol Chen} and {Carson E. Denison} and {Esin Durmus} and {Newton Cheng} and {Sheer Sam McCan-dlish} and {Tamera Lanham} and {Tim Maxwell} and {Venkatesa Chandrasekaran}},
year = 2024,
booktitle = {Volume 1},
url = {https://www.semanticscholar.org/paper/7925f16e76cf274c257570ed3aa0ab97638be7d1},
}
@inproceedings{267781862,
title = {Large language models for science and medicine},
author = {{Amalio Telenti} and {Michael Auli} and {B. Hie} and {Cyrus Maher} and {S. Saria} and {J. P. Ioannidis}},
year = 2024,
month = {2},
booktitle = {European Journal of Clinical Investigation},
url = {https://www.semanticscholar.org/paper/6dcb9cb40c948aa6033c0ae2228542c14acf0ff7},
}
@inproceedings{269983348,
title = {DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation},
author = {{Weiting Tan} and {Jingyu (Jack) Zhang} and {Lingfeng Shen} and {Daniel Khashabi} and {Philipp Koehn}},
year = 2024,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f2b48782b86f4f8bc0b537d1d91bae6cf642c8c4},
}
To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method–-an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people’s portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale.
@inproceedings{samir-etal-2024-locating,
title = "Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of {LGBT} People Portrayals on {W}ikipedia",
author = "Samir, Farhan and
Park, Chan Young and
Field, Anjalie and
Shwartz, Vered and
Tsvetkov, Yulia",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.384/",
doi = "10.18653/v1/2024.emnlp-main.384",
pages = "6747--6762",
abstract = "To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method---an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people's portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale."
}
@inproceedings{271776874,
title = {End-to-End Neural Speaker Diarization With Non-Autoregressive Attractors},
author = {{Magdalena Rybicka} and {J. Villalba} and {Thomas Thebaud} and {N. Dehak} and {Konrad Kowalczyk}},
year = 2024,
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/3eb78f245370801e1ed5a80972c8d88d275b0852},
}
Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM’s maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes’ theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master
@inproceedings{su-etal-2024-naive,
title = "Naive {B}ayes-based Context Extension for Large Language Models",
author = "Su, Jianlin and
Ahmed, Murtadha and
Wen, Bo and
Ao, Luo and
Zhu, Mingren and
Liu, Yunfeng",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.431/",
doi = "10.18653/v1/2024.naacl-long.431",
pages = "7791--7807",
abstract = "Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM's maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes' theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master"
}
@inproceedings{274826397,
title = {Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson’s disease},
author = {{Sophia A. Watkinson} and {Anthony Anderson} and {Michael Caiola} and {D. Eguren} and {Michael Gonzalez} and {Laureano Moro Velázquez} and {N. Dehak} and {Chelsea Motley} and {Emile Moukheiber} and {Kelly Mills} and {Brittney Muir} and {A. Butala} and {K. Kontson}},
year = 2024,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/cce84da1077ee0f5d0b3ff474982c2d44af0552b},
}
@inproceedings{273770555,
title = {The SHADOW team submission to the ASVSpoof 2024 Challenge},
author = {{J. Villalba} and {Tiantian Feng} and {Thomas Thebaud} and {Jihwan Lee} and {Shrikanth S. Narayanan} and {N. Dehak}},
year = 2024,
month = {8},
booktitle = {The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024)},
url = {https://www.semanticscholar.org/paper/20ab192366ccf71e3d1feda7c8b040030d58266f},
}
@inproceedings{271207138,
title = {Discovering Invariant Patterns of Cognitive Decline Via an Automated Analysis of the Cookie Thief Picture Description Task},
author = {{A. Favaro} and {N. Dehak} and {Thomas Thebaud} and {J. Villalba} and {Esther S Oh} and {L. Moro-Velázquez}},
year = 2024,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/99dec8ab1d7aa47117062e1daf36dcbcce4aece2},
}
@inproceedings{270688653,
title = {Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell},
author = {{Taiming Lu} and {Muhan Gao} and {Kuai Yu} and {Adam Byerly} and {Daniel Khashabi}},
year = 2024,
month = {6},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/2138841e6e5ecf59d881a0108e0d9551484cfe32},
}
@inproceedings{269005582,
title = {SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses},
author = {{Dongwei Jiang} and {Jingyu (Jack) Zhang} and {Orion Weller} and {Nathaniel Weir} and {Benjamin Van Durme} and {Daniel Khashabi}},
year = 2024,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/9e3d14e4697a325fcabc6d952232a3ad7e9fa809},
}
@inproceedings{272593614,
title = {WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson's Disease and Age-Matched Controls},
author = {{Anthony Anderson} and {D. Eguren} and {Michael Gonzalez} and {Naima Khan} and {Sophia A. Watkinson} and {Michael Caiola} and {Siegfried S Hirczy} and {Cyrus P. Zabetian} and {Kelly Mills} and {Emile Moukheiber} and {L. Moro-Velázquez} and {N. Dehak} and {Chelsie Motely} and {B. Muir} and {A. Butala} and {K. Kontson}},
year = 2024,
month = {9},
booktitle = {medRxiv},
url = {https://www.semanticscholar.org/paper/74a0ee0f0a4fb6f63c903768d002413e58458269},
}
@inproceedings{271967099,
title = {Iterative alignment discovery of speech-associated neural activity},
author = {{Qinwan Rabbani} and {Samyak Shah} and {Griffin W. Milsap} and {M. Fifer} and {H. Hermansky} and {N. Crone}},
year = 2024,
month = {8},
booktitle = {Journal of Neural Engineering},
url = {https://www.semanticscholar.org/paper/d182dc65e550bb6cf2eb956e5c2278a45c983572},
}
@inproceedings{268531391,
title = {Tur[k]ingBench: A Challenge Benchmark for Web Agents},
author = {{Kevin Xu} and {Yeganeh Kordi} and {Kate Sanders} and {Yizhong Wang} and {Adam Byerly} and {Jingyu (Jack) Zhang} and {Benjamin Van Durme} and {Daniel Khashabi}},
year = 2024,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/341da3f8af6edd31edd8f5a3d9452957aeaaa744},
}
@inproceedings{268527006,
title = {Biomimetic Mappings for Active Sonar Object Recognition in Clutter},
author = {{Sangwook Park} and {Angeles Salles} and {Kathryne Allen} and {Cynthia Moss} and {Mounya Elhilali}},
year = 2024,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/aba6e97d493b8b593eac929b49ee8d1c7bf9c953},
}
@inproceedings{269987122,
title = {Temporal-Coherence Induces Binding of Responses to Sound Sequences in Ferret Auditory Cortex},
author = {{Kai Lu} and {Kelsey Dutta} and {Mounya Elhilali} and {S. Shamma}},
year = 2024,
month = {5},
booktitle = {bioRxiv},
url = {https://www.semanticscholar.org/paper/ad2b79189ddfee6af017d1569bfebde37e587aea},
}
@inproceedings{272345914,
title = {Exploring the Complementary Nature of Speech and Eye Movements for Profiling Neurological Disorders},
author = {{Yuzhe Wang} and {A. Favaro} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2024,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/06c6bbd302ec67def5ccd2df13d8563e2d4a5e31},
}
@inproceedings{270702574,
title = {DreamVoice: Text-Guided Voice Conversion},
author = {{Jiarui Hai} and {Karan Thakkar} and {Helin Wang} and {Zengyi Qin} and {Mounya Elhilali}},
year = 2024,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/fe5cccd11f8cdcfa6584b25b3762d06253bdd813},
}
@inproceedings{268877286,
title = {Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception},
author = {{Margaret A. McMullin} and {Rohit Kumar} and {Nathan C. Higgins} and {Brian Gygi} and {Mounya Elhilali} and {J. Snyder}},
year = 2024,
month = {3},
booktitle = {Open Mind},
url = {https://www.semanticscholar.org/paper/e46542680689e1358d6ed072560f1ec7eefce069},
}
@inproceedings{271211023,
title = {A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients},
author = {{Thomas Thebaud} and {Gabriel Hernández} and {Sarah Flora Samson Juan} and {Marie Tahon}},
year = 2024,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/098e0d7c2f273bc052a74f1bd91db8cf2b57cd1f},
}
@inproceedings{269459473,
title = {Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results},
author = {{Lucas Goncalves} and {Ali N. Salman} and {Abinay Reddy Naini} and {Laureano Moro Velázquez} and {Thomas Thebaud} and {Leibny Paola} and {Najim Garcia} and {Berrak Dehak} and {Carlos Sisman} and {Busso}},
year = 2024,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/44a30157f437065fd0672b1327edaa32a9239ce5},
}
The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI’s GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4’s performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93\%. GPT-4’s tagging performance showed a trade-off between precision and recall, where the latter got as high as 97\%, but precision never exceeded 45\%. Our approach reduces the time required for the filtering and tagging tasks by 95\% and 80\%, respectively. We also present an in-depth error analysis of GPT-4’s performance.
@inproceedings{jahan-etal-2024-finding,
title = "Finding Spoken Identifications: Using {GPT}-4 Annotation for an Efficient and Fast Dataset Creation Pipeline",
author = "Jahan, Maliha and
Wang, Helin and
Thebaud, Thomas and
Sun, Yinglun and
Le, Giang Ha and
Fagyal, Zsuzsanna and
Scharenborg, Odette and
Hasegawa-Johnson, Mark and
Moro Velazquez, Laureano and
Dehak, Najim",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.641/",
pages = "7296--7306",
abstract = "The growing emphasis on fairness in speech-processing tasks requires datasets with speakers from diverse subgroups that allow training and evaluating fair speech technology systems. However, creating such datasets through manual annotation can be costly. To address this challenge, we present a semi-automated dataset creation pipeline that leverages large language models. We use this pipeline to generate a dataset of speakers identifying themself or another speaker as belonging to a particular race, ethnicity, or national origin group. We use OpenaAI's GPT-4 to perform two complex annotation tasks- separating files relevant to our intended dataset from the irrelevant ones (filtering) and finding and extracting information on identifications within a transcript (tagging). By evaluating GPT-4's performance using human annotations as ground truths, we show that it can reduce resources required by dataset annotation while barely losing any important information. For the filtering task, GPT-4 had a very low miss rate of 6.93\%. GPT-4's tagging performance showed a trade-off between precision and recall, where the latter got as high as 97\%, but precision never exceeded 45\%. Our approach reduces the time required for the filtering and tagging tasks by 95\% and 80\%, respectively. We also present an in-depth error analysis of GPT-4's performance."
}
@inproceedings{272236956,
title = {Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence},
author = {{Aamir Javaid} and {Sruthika Baviriseaty} and {Rehan Javaid} and {Ayah Zirikly} and {Harshita Kukreja} and {Chang H. Kim} and {Michael J. Blaha} and {Roger S. Blumenthal} and {Seth S. Martin} and {F. Marvel}},
year = 2024,
month = {8},
booktitle = {JACC: Advances},
url = {https://www.semanticscholar.org/paper/fa5702a10f5b9b5d53cd7e82e06658bfde95c77f},
}
@inproceedings{269741223,
title = {Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)},
author = {{Drew Prinster} and {Samuel Stanton} and {Anqi Liu} and {S. Saria}},
year = 2024,
month = {5},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/bb86b50363e9d100606a534c6a877dacbf8b0e25},
}
@inproceedings{269006160,
title = {Use of artificial intelligence in critical care: opportunities and obstacles},
author = {{Michael R. Pinsky} and {Armando Bedoya} and {A. Bihorac} and {L. Celi} and {Matthew Churpek} and {Nicoleta J. Economou-Zavlanos} and {Paul Elbers} and {S. Saria} and {Vincent Liu} and {Patrick G. Lyons} and {B. Shickel} and {Patrick Toral} and {D. Tscholl} and {Gilles Clermont}},
year = 2024,
month = {4},
booktitle = {Critical Care},
url = {https://www.semanticscholar.org/paper/a386424f2f61647ebec3dd27e33c6db92c1c07ac},
}
@inproceedings{272357972,
title = {Enhancing Neural Transducer for Multilingual ASR with Synchronized Language Diarization},
author = {{Amir Hussein} and {Desh Raj} and {Matthew Wiesner} and {Dan Povey} and {Paola Garcia} and {S. Khudanpur}},
year = 2024,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/8f6b595c7e09a7bec11726e8e1520c3eb693fec4},
}
@inproceedings{268987627,
title = {Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data},
author = {{Jingyu (Jack) Zhang} and {Marc Marone} and {Tianjian Li} and {Benjamin Van Durme} and {Daniel Khashabi}},
year = 2024,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/9689b5fdb0d3a1bad802d03d348bd32aa5a4c2df},
}
@inproceedings{272340732,
title = {Leveraging Universal Speech Representations for Detecting and Assessing the Severity of Mild Cognitive Impairment Across Languages},
author = {{A. Favaro} and {Tianyu Cao} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2024,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/a01c088d9cd7523e36c21353cc972690f0de307d},
}
@inproceedings{272653901,
title = {HLTCOE JHU Submission to the Voice Privacy Challenge 2024},
author = {{Henry Li Xinyuan} and {Zexin Cai} and {Ashi Garg} and {Kevin Duh} and {Leibny Paola Garc'ia-Perera} and {S. Khudanpur} and {Nicholas Andrews} and {Matthew Wiesner}},
year = 2024,
month = {9},
booktitle = {4th Symposium on Security and Privacy in Speech Communication},
url = {https://www.semanticscholar.org/paper/d20a9b6e43042214017716d06e7cd7f21da9371c},
}
@inproceedings{271088562,
title = {WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment},
author = {{Jiefu Ou} and {Arda Uzunouglu} and {Benjamin Van Durme} and {Daniel Khashabi}},
year = 2024,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/143ff789f500307bab6725c466da97492cb5c771},
}
@inproceedings{268091248,
title = {Unraveling Adversarial Examples against Speaker Identification - Techniques for Attack Detection and Victim Model Classification},
author = {{Sonal Joshi} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak}},
year = 2024,
month = {2},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/af87c6786c1e7f8345f3c5768668617df6cc2771},
}
@inproceedings{266977175,
title = {Reframing Tax Law Entailment as Analogical Reasoning},
author = {{Xinrui Zou} and {Ming Zhang} and {Nathaniel Weir} and {Benjamin Van Durme} and {Nils Holzenberger}},
year = 2024,
month = {1},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e993e7971fa8b96514fd63699c12bec68e83b6d1},
}
@inproceedings{268532373,
title = {FaceXFormer: A Unified Transformer for Facial Analysis},
author = {{Kartik Narayan} and {VS Vibashan} and {R. Chellappa} and {Vishal M. Patel}},
year = 2024,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/cf9ea0a2ae56bce6d2fdbc9f81633ef8ce9df59c},
}
@inproceedings{270380183,
title = {Noise-robust Speech Separation with Fast Generative Correction},
author = {{Helin Wang} and {J. Villalba} and {L. Moro-Velázquez} and {Jiarui Hai} and {Thomas Thebaud} and {N. Dehak}},
year = 2024,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/6983f5ef3a136df52cbaf0b623c16a7983e99d59},
}
@inproceedings{269408252,
title = {Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS},
author = {{Miguel Angrick} and {Shiyu Luo} and {Qinwan Rabbani} and {Daniel N Candrea} and {Samyak Shah} and {Griffin W. Milsap} and {William S Anderson} and {Chad R Gordon} and {Kathryn R Rosenblatt} and {Lora Clawson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {F. Tenore} and {M. Fifer} and {H. Hermansky} and {Nick F Ramsey} and {N. Crone}},
year = 2024,
month = {4},
booktitle = {Scientific Reports},
url = {https://www.semanticscholar.org/paper/fa58f21ecda2053d2c1c9360e682b1140b6ff4c1},
}
@inproceedings{270521572,
title = {Multi-Channel Multi-Speaker ASR Using Target Speaker’s Solo Segment},
author = {{Yiwen Shao} and {Shizhong Zhang} and {Yong Xu} and {Meng Yu} and {Dong Yu} and {Dan Povey} and {S. Khudanpur}},
year = 2024,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/18eadf3b7cf2ceb7b3c4e034b948af90ceec7219},
}
@inproceedings{270845380,
title = {Efficient Large Multi-modal Models via Visual Context Compression},
author = {{Jieneng Chen} and {Luoxin Ye} and {Ju He} and {Zhao-Yang Wang} and {Daniel Khashabi} and {Alan L. Yuille}},
year = 2024,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/227c2f542f7848c16af712ea417ea2177a6450b9},
}
Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.
@inproceedings{romney-robinson-etal-2024-jhu,
title = "{JHU} {IWSLT} 2024 Dialectal and Low-resource System Description",
author = "Romney Robinson, Nathaniel and
Sun, Kaiser and
Xiao, Cihan and
Bafna, Niyati and
Tan, Weiting and
Xu, Haoran and
Li Xinyuan, Henry and
Kejriwal, Ankur and
Khudanpur, Sanjeev and
Murray, Kenton and
McNamee, Paul",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.19/",
doi = "10.18653/v1/2024.iwslt-1.19",
pages = "140--153",
abstract = "Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning."
}
@inproceedings{267522236,
title = {Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care},
author = {{Peter M Abadir} and {Esther S Oh} and {Rama Chellappa} and {N. Choudhry} and {George Demiris} and {Deepak Ganesan} and {Jason Karlawish} and {Benjamin M. Marlin} and {Rose M Li} and {N. Dehak} and {Alicia Arbaje} and {Mathias Unberath} and {Thomas K. M. Cudjoe} and {Christopher Chute} and {Jason H Moore} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn} and {Alexis Battle} and {Jeremy D Walston}},
year = 2024,
month = {2},
booktitle = {Alzheimer's & Dementia},
url = {https://www.semanticscholar.org/paper/893d9dc2d86b71e3ba67490decd96f91954e47ce},
}
@inproceedings{272087268,
title = {Time Scale Network: An Efficient Shallow Neural Network For Time Series Data in Biomedical Applications},
author = {{Trevor Meyer} and {Camden Shultz} and {N. Dehak} and {L. Moro-Velázquez} and {Pedro P. Irazoqui}},
year = 2024,
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/4e2f72bf6970fdbafb922f26d80b1d5626fa6939},
}
@inproceedings{267200158,
title = {The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts},
author = {{Lingfeng Shen} and {Weiting Tan} and {Sihao Chen} and {Yunmo Chen} and {Jingyu (Jack) Zhang} and {Haoran Xu} and {Boyuan Zheng} and {Philipp Koehn} and {Daniel Khashabi}},
year = 2024,
month = {1},
booktitle = {Annual Meeting of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/3cd81b0123b5f8477f6b5777681030ef6b05dd46},
}
@inproceedings{272600353,
title = {SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis},
author = {{Helin Wang} and {Meng Yu} and {Jiarui Hai} and {Chen Chen} and {Yuchen Hu} and {Rilin Chen} and {N. Dehak} and {Dong Yu}},
year = 2024,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a96b6a7d9d8146cb28643672a5013fec19ad4cbb},
}
@inproceedings{267406764,
title = {Streaming Sequence Transduction through Dynamic Compression},
author = {{Weiting Tan} and {Yunmo Chen} and {Tongfei Chen} and {Guanghui Qin} and {Haoran Xu} and {Heidi C. Zhang} and {Benjamin Van Durme} and {Philipp Koehn}},
year = 2024,
month = {2},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/50b2a8c4c8a0d4b26621e84e3e0b5b300775e030},
}
@inproceedings{272423555,
title = {Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization},
author = {{Zexin Cai} and {Henry Li Xinyuan} and {Ashi Garg} and {Leibny Paola Garc'ia-Perera} and {Kevin Duh} and {S. Khudanpur} and {Nicholas Andrews} and {Matthew Wiesner}},
year = 2024,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3c2d94cfa24b979eb13ca06d8a2bce79b5ab2321},
}
@inproceedings{272653828,
title = {SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer},
author = {{Helin Wang} and {Jiarui Hai} and {Yen-Ju Lu} and {Karan Thakkar} and {Mounya Elhilali} and {N. Dehak}},
year = 2024,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7f38e38bcdf7312cad3534211d40f4a86431ff7d},
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}
@InProceedings{tan-et-al-2023,
author = "Weiting Tan and Chu-Cheng Lin and Jason Eisner",
title = "Structure-Aware Path Inference for Neural Finite State
Transducers",
booktitle = "Proceedings of the {NeurIPS} 2023 Workshop ``{I}
Can’t Believe It’s Not Better: Failure Modes in the
Age of Foundation Models''",
year = "2023",
month = dec,
URL = "http://cs.jhu.edu/~jason/papers/#tan-et-al-2023",
}
@InProceedings{roy-et-al-2023,
author = "Subhro Roy and Sam Thomson and Tongfei Chen and
Richard Shin and Adam Pauls and Jason Eisner and
Benjamin Van Durme",
title = "{BenchCLAMP}: {A} Benchmark for Evaluating Language
Models on Syntactic and Semantic Parsing",
booktitle = "Proceedings of the Thirty-Seventh Conference on Neural
Information Processing Systems",
note = "Datasets and Benchmarks Track",
year = "2023",
month = dec,
URL = "http://cs.jhu.edu/~jason/papers/#roy-et-al-2023",
}
@InProceedings{zhong-et-al-2023,
aclid = "2023.emnlp-main.312",
author = "Ruiqi Zhong and Charlie Snell and Dan Klein and Jason
Eisner",
title = "Non-Programmers Can Label Programs Indirectly via
Active Examples: {A} Case Study with Text-to-{SQL}",
booktitle = "Proceedings of the 2023 Conference on Empirical
Methods in Natural Language Processing",
pages = "5126--5152",
year = "2023",
month = dec,
URL = "http://cs.jhu.edu/~jason/papers/#zhong-et-al-2023",
}
@inproceedings{265213263,
title = {Toucan: Token-Aware Character Level Language Modeling},
author = {{William Fleshman} and {Benjamin Van Durme}},
year = 2023,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/28c75ab7f6951f1c2bb349b34abc3204ba8b9498},
}
@inproceedings{265220995,
title = {Interpreting User Requests in the Context of Natural Language Standing Instructions},
author = {{Nikita Moghe} and {Patrick Xia} and {Jacob Andreas} and {J. Eisner} and {Benjamin Van Durme} and {Harsh Jhamtani}},
year = 2023,
month = {11},
booktitle = {NAACL-HLT},
url = {https://www.semanticscholar.org/paper/f2abeec1256f80970827d60f0151c7a19f2dbe7a},
}
@inproceedings{265033117,
title = {Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles},
author = {{Weiting Tan} and {Haoran Xu} and {Lingfeng Shen} and {Shuyue Stella Li} and {Kenton Murray} and {Philipp Koehn} and {Benjamin Van Durme} and {Yunmo Chen}},
year = 2023,
month = {11},
booktitle = {NAACL-HLT},
url = {https://www.semanticscholar.org/paper/bac133a9cb14eadea94c55ec15ea3bb866bf6c03},
}
@inproceedings{265539658,
title = {Are acoustics enough? Semantic effects on auditory salience in natural scenes},
author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}},
year = 2023,
month = {11},
booktitle = {Frontiers in Psychology},
url = {https://www.semanticscholar.org/paper/cdb491d1d121a7461fac00c4c71dfc45b9c8ae7a},
}
@inproceedings{264935647,
title = {Investigating Self-Supervised Deep Representations for EEG-Based Auditory Attention Decoding},
author = {{Karan Thakkar} and {Jiarui Hai} and {Mounya Elhilali}},
year = 2023,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/57b903a709dc9ab64b3bd81378f33547eabb01bd},
}
@inproceedings{265281100,
title = {Improving fairness for spoken language understanding in atypical speech with Text-to-Speech},
author = {{Helin Wang} and {Venkatesh Ravichandran} and {Milind Rao} and {Becky Lammers} and {Myra Sydnor} and {Nicholas J Maragakis} and {A. Butala} and {Jayne Zhang} and {Lora Clawson} and {Victoria Chovaz} and {L. Moro-Velázquez}},
year = 2023,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/45e3115df40de802f9c4095f329ea374aac56825},
}
@inproceedings{265128667,
title = {Time Scale Network: A Shallow Neural Network For Time Series Data},
author = {{Trevor Meyer} and {Camden Shultz} and {N. Dehak} and {L. Moro-Velázquez} and {Pedro P. Irazoqui}},
year = 2023,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/deacbb4906e1d2e597602a65b434a8132953ad8d},
}
@inproceedings{263620438,
title = {Dodo: Dynamic Contextual Compression for Decoder-only LMs},
author = {{Guanghui Qin} and {Corby Rosset} and {Ethan C. Chau} and {Nikhil Rao} and {Benjamin Van Durme}},
year = 2023,
month = {10},
booktitle = {Annual Meeting of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/883187d0bacf57238ac95e2749bcc601baf2c212},
}
@inproceedings{260957214,
title = {Nugget: Neural Agglomerative Embeddings of Text},
author = {{Guanghui Qin} and {Benjamin Van Durme}},
year = 2023,
month = {10},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/531b37c44c7e39539f617fb1a4149ef8cce8f4ec},
}
@inproceedings{264448311,
title = {Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months},
author = {{Shiyu Luo} and {Miguel Angrick} and {Christopher Coogan} and {Daniel N Candrea} and {Kimberley Wyse-Sookoo} and {Samyak Shah} and {Qinwan Rabbani} and {Griffin W. Milsap} and {Alexander R Weiss} and {William S Anderson} and {Donna C. Tippett} and {Nicholas J Maragakis} and {Lora Clawson} and {M. Vansteensel} and {Brock Andrew Wester} and {F. Tenore} and {H. Hermansky} and {M. Fifer} and {Nick F Ramsey} and {N. Crone}},
year = 2023,
month = {10},
booktitle = {Advancement of science},
url = {https://www.semanticscholar.org/paper/dee851d6c5652ee423118132e1483bc0af9f30fc},
}
@inproceedings{263827534,
title = {A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.},
author = {{Alex Walker} and {Ayah Zirikly} and {Melissa D. Stockbridge} and {H. C. Wilcox}},
year = 2023,
month = {10},
booktitle = {Crisis},
url = {https://www.semanticscholar.org/paper/a0ee01acead1ccb6064f603f75186f8aa25d2562},
}
@inproceedings{263909464,
title = {Do pretrained Transformers Learn In-Context by Gradient Descent?},
author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}},
year = 2023,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/f30ddf0c7455f89f016c540564e235b191c503db},
}
@inproceedings{264426523,
title = {A Unified View of Evaluation Metrics for Structured Prediction},
author = {{Yunmo Chen} and {William Gantt Walden} and {Tongfei Chen} and {Aaron Steven White} and {Benjamin Van Durme}},
year = 2023,
month = {10},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/a0df8169889043dae6ac111136a61162a5185a77},
}
@inproceedings{263669151,
title = {Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system},
author = {{Geoffrey M Gray} and {Ayah Zirikly} and {Luis M Ahumada} and {Masoud Rouhizadeh} and {Thomas M Richards} and {C. Kitchen} and {Iman Foroughmand} and {E. Hatef}},
year = 2023,
month = {10},
booktitle = {JAMIA Open},
url = {https://www.semanticscholar.org/paper/1be931a9ebfeaa018e47abc582b1a9760ced4710},
}
@inproceedings{264426857,
title = {InstructExcel: A Benchmark for Natural Language Instruction in Excel},
author = {{Justin Payan} and {Swaroop Mishra} and {Mukul Singh} and {Carina Negreanu} and {Christian Poelitz} and {Chitta Baral} and {Subhro Roy} and {Rasika Chakravarthy} and {Benjamin Van Durme} and {E. Nouri}},
year = 2023,
month = {10},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/706fc333fc951f48ea26169d88478b5e4c36e82c},
}
@inproceedings{263830793,
title = {DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction},
author = {{Jiarui Hai} and {Helin Wang} and {Dongchao Yang} and {Karan Thakkar} and {N. Dehak} and {Mounya Elhilali}},
year = 2023,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/70aec6486668cc5ca25d45240c68de223a8deda7},
}
@inproceedings{264305897,
title = {Data Augmentations for Improved (Large) Language Model Generalization},
author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}},
year = 2023,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/23f96db82ae02c5c3c0a861571e7aa8d27c91bc9},
}
@inproceedings{261899586,
title = {Diff-Pitcher: Diffusion-Based Singing Voice Pitch Correction},
author = {{Jiarui Hai} and {Mounya Elhilali}},
year = 2023,
month = {10},
booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
url = {https://www.semanticscholar.org/paper/377ffdc7cf16822e8aa12ea28ab16d0f5bc8f0c2},
}
@inproceedings{263605981,
title = {Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models},
author = {{Tianjian Li} and {Haoran Xu} and {Philipp Koehn} and {Daniel Khashabi} and {Kenton Murray}},
year = 2023,
month = {10},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/d15021d750dbe9cee120b562acea857ca02d9104},
}
@inproceedings{272144679,
title = {SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation},
author = {{A. Hou} and {Jingyu (Jack) Zhang} and {Tianxing He} and {Yichen Wang} and {Yung-Sung Chuang} and {Hongwei Wang} and {Lingfeng Shen} and {Benjamin Van Durme} and {Daniel Khashabi} and {Yulia Tsvetkov}},
year = 2023,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f81faa4f130c287a293c9d21eb27b325be9b5ee9},
}
Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.
@inproceedings{delucia-etal-2023-multi,
title = "A Multi-instance Learning Approach to Civil Unrest Event Detection on {T}witter",
author = "DeLucia, Alexandra and
Dredze, Mark and
Buczak, Anna L.",
editor = {H\"urriyeto\u glu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Yeniterzi, Reyyan and
Y\"or\"uk, Erdem and
Slavcheva, Milena},
booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.case-1.3/",
pages = "18--33",
abstract = "Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset."
}
@InProceedings{chi-et-al-2023,
author = "Jie Chi and Brian Lu and Jason Eisner and Peter Bell
and Preethi Jyothi and Ahmed M. Ali",
title = "Unsupervised Code-Switched Text Generation from
Parallel Text",
booktitle = "Proceedings of INTERSPEECH",
year = "2023",
month = aug,
address = "Dublin",
URL = "http://cs.jhu.edu/~jason/papers/#chi-et-al-2023",
}
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P – that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may “over-generalize”, in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies.
@inproceedings{zhang-etal-2023-mixce,
title = "{M}ix{CE}: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies",
author = "Zhang, Shiyue and
Wu, Shijie and
Irsoy, Ozan and
Lu, Steven and
Bansal, Mohit and
Dredze, Mark and
Rosenberg, David",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.502/",
doi = "10.18653/v1/2023.acl-long.502",
pages = "9027--9050",
abstract = "Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may ``over-generalize'', in the sense that they produce non-human-like text. Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P relative to Q, is a better reflection of how a human would evaluate text generated by a model. Hence, we propose learning with MixCE, an objective that mixes the forward and reverse cross-entropies. We evaluate models trained with this objective on synthetic data settings (where P is known) and real data, and show that the resulting models yield better generated text without complex decoding strategies."
}
This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.
@inproceedings{agrawal-etal-2023-findings,
title = "{FINDINGS} {OF} {THE} {IWSLT} 2023 {EVALUATION} {CAMPAIGN}",
author = {Agarwal, Milind and
Agrawal, Sweta and
Anastasopoulos, Antonios and
Bentivogli, Luisa and
Bojar, Ond\v rej and
Borg, Claudia and
Carpuat, Marine and
Cattoni, Roldano and
Cettolo, Mauro and
Chen, Mingda and
Chen, William and
Choukri, Khalid and
Chronopoulou, Alexandra and
Currey, Anna and
Declerck, Thierry and
Dong, Qianqian and
Duh, Kevin and
Est\`eve, Yannick and
Federico, Marcello and
Gahbiche, Souhir and
Haddow, Barry and
Hsu, Benjamin and
Mon Htut, Phu and
Inaguma, Hirofumi and
Javorsk\'y, D\'avid and
Judge, John and
Kano, Yasumasa and
Ko, Tom and
Kumar, Rishu and
Li, Pengwei and
Ma, Xutai and
Mathur, Prashant and
Matusov, Evgeny and
McNamee, Paul and
P. McCrae, John and
Murray, Kenton and
Nadejde, Maria and
Nakamura, Satoshi and
Negri, Matteo and
Nguyen, Ha and
Niehues, Jan and
Niu, Xing and
Kr. Ojha, Atul and
E. Ortega, John and
Pal, Proyag and
Pino, Juan and
van der Plas, Lonneke and
Pol\'ak, Peter and
Rippeth, Elijah and
Salesky, Elizabeth and
Shi, Jiatong and
Sperber, Matthias and
St\"uker, Sebastian and
Sudoh, Katsuhito and
Tang, Yun and
Thompson, Brian and
Tran, Kevin and
Turchi, Marco and
Waibel, Alex and
Wang, Mingxuan and
Watanabe, Shinji and
Zevallos, Rodolfo},
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.1/",
doi = "10.18653/v1/2023.iwslt-1.1",
pages = "1--61",
abstract = "This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia."
}
Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL.
@inproceedings{spaulding-etal-2023-joint,
title = "Joint End-to-end Semantic Proto-role Labeling",
author = "Spaulding, Elizabeth and
Kazantsev, Gary and
Dredze, Mark",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.63/",
doi = "10.18653/v1/2023.acl-short.63",
pages = "723--736",
abstract = "Semantic proto-role labeling (SPRL) assigns properties to arguments based on a series of binary labels. While multiple studies have evaluated various approaches to SPRL, it has only been studied in-depth as a standalone task using gold predicate/argument pairs. How do SPRL systems perform as part of an information extraction pipeline? We model SPRL jointly with predicate-argument extraction using a deep transformer model. We find that proto-role labeling is surprisingly robust in this setting, with only a small decrease when using predicted arguments. We include a detailed analysis of each component of the joint system, and an error analysis to understand correlations in errors between system stages. Finally, we study the effects of annotation errors on SPRL."
}
We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area.
@inproceedings{xinyuan-etal-2023-jhu,
title = "{JHU} {IWSLT} 2023 Multilingual Speech Translation System Description",
author = "Xinyuan, Henry Li and
Verma, Neha and
Bamfo Odoom, Bismarck and
Pradeep, Ujvala and
Wiesner, Matthew and
Khudanpur, Sanjeev",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.28/",
doi = "10.18653/v1/2023.iwslt-1.28",
pages = "302--310",
abstract = "We describe the Johns Hopkins ACL 60-60 Speech Translation systems submitted to the IWSLT 2023 Multilingual track, where we were tasked to translate ACL presentations from English into 10 languages. We developed cascaded speech translation systems for both the constrained and unconstrained subtracks. Our systems make use of pre-trained models as well as domain-specific corpora for this highly technical evaluation-only task. We find that the specific technical domain which ACL presentations fall into presents a unique challenge for both ASR and MT, and we present an error analysis and an ACL-specific corpus we produced to enable further work in this area."
}
This paper presents JHU’s submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively.
@inproceedings{hussein-etal-2023-jhu,
title = "{JHU} {IWSLT} 2023 Dialect Speech Translation System Description",
author = "Hussein, Amir and
Xiao, Cihan and
Verma, Neha and
Thebaud, Thomas and
Wiesner, Matthew and
Khudanpur, Sanjeev",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.26/",
doi = "10.18653/v1/2023.iwslt-1.26",
pages = "283--290",
abstract = "This paper presents JHU's submissions to the IWSLT 2023 dialectal and low-resource track of Tunisian Arabic to English speech translation. The Tunisian dialect lacks formal orthography and abundant training data, making it challenging to develop effective speech translation (ST) systems. To address these challenges, we explore the integration of large pre-trained machine translation (MT) models, such as mBART and NLLB-200 in both end-to-end (E2E) and cascaded speech translation (ST) systems. We also improve the performance of automatic speech recognition (ASR) through the use of pseudo-labeling data augmentation and channel matching on telephone data. Finally, we combine our E2E and cascaded ST systems with Minimum Bayes-Risk decoding. Our combined system achieves a BLEU score of 21.6 and 19.1 on test2 and test3, respectively."
}
Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.
@inproceedings{raunak-etal-2023-gpts,
title = "Do {GPT}s Produce Less Literal Translations?",
author = "Raunak, Vikas and
Menezes, Arul and
Post, Matt and
Hassan, Hany",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.90/",
doi = "10.18653/v1/2023.acl-short.90",
pages = "1041--1050",
abstract = "Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions."
}
We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset.
@inproceedings{gwinnup-etal-2023-enhancing,
title = "Enhancing Video Translation Context with Object Labels",
author = "Gwinnup, Jeremy and
Anderson, Tim and
Ore, Brian and
Hansen, Eric and
Duh, Kevin",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.8/",
doi = "10.18653/v1/2023.iwslt-1.8",
pages = "130--137",
abstract = "We present a simple yet efficient method to enhance the quality of machine translation models trained on multimodal corpora by augmenting the training text with labels of detected objects in the corresponding video segments. We then test the effects of label augmentation in both baseline and two automatic speech recognition (ASR) conditions. In contrast with multimodal techniques that merge visual and textual features, our modular method is easy to implement and the results are more interpretable. Comparisons are made with Transformer translation architectures trained with baseline and augmented labels, showing improvements of up to +1.0 BLEU on the How2 dataset."
}
For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation.
@inproceedings{maillard-etal-2023-small,
title = "Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation",
author = "Maillard, Jean and
Gao, Cynthia and
Kalbassi, Elahe and
Sadagopan, Kaushik Ram and
Goswami, Vedanuj and
Koehn, Philipp and
Fan, Angela and
Guzman, Francisco",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.154/",
doi = "10.18653/v1/2023.acl-long.154",
pages = "2740--2756",
abstract = "For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does. We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation."
}
Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes.
@inproceedings{portillo-wightman-etal-2023-strength,
title = "Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement",
author = "Portillo Wightman, Gwenyth and
Delucia, Alexandra and
Dredze, Mark",
editor = "Ovalle, Anaelia and
Chang, Kai-Wei and
Mehrabi, Ninareh and
Pruksachatkun, Yada and
Galystan, Aram and
Dhamala, Jwala and
Verma, Apurv and
Cao, Trista and
Kumar, Anoop and
Gupta, Rahul",
booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.trustnlp-1.28/",
doi = "10.18653/v1/2023.trustnlp-1.28",
pages = "326--362",
abstract = "Large language models have achieved impressive few-shot performance on a wide variety of tasks. However, in many settings, users require confidence estimates for model predictions. While traditional classifiers produce scores for each label, language models instead produce scores for the generation which may not be well calibrated. We compare generations across diverse prompts and show that these can be used to create confidence scores. By utilizing more prompts we can get more precise confidence estimates and use response diversity as a proxy for confidence. We evaluate this approach across ten multiple-choice question-answering datasets using three models: T0, FLAN-T5, and GPT-3. In addition to analyzing multiple human written prompts, we automatically generate more prompts using a language model in order to produce finer-grained confidence estimates. Our method produces more calibrated confidence estimates compared to the log probability of the answer to a single prompt. These improvements could benefit users who rely on prediction confidence for integration into a larger system or in decision-making processes."
}
We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models’ predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16\% of paraphrased examples, indicating that there is still room for improvement.
@inproceedings{verma-etal-2023-evaluating,
title = "Evaluating Paraphrastic Robustness in Textual Entailment Models",
author = "Verma, Dhruv and
Lal, Yash Kumar and
Sinha, Shreyashee and
Van Durme, Benjamin and
Poliak, Adam",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.76/",
doi = "10.18653/v1/2023.acl-short.76",
pages = "880--892",
abstract = "We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We use the evaluation set to determine if RTE models' predictions change when examples are paraphrased. In our experiments, contemporary models change their predictions on 8-16\% of paraphrased examples, indicating that there is still room for improvement."
}
Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}
@inproceedings{zhang-etal-2023-hyperparameter,
title = "A Hyperparameter Optimization Toolkit for Neural Machine Translation Research",
author = "Zhang, Xuan and
Duh, Kevin and
McNamee, Paul",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.15/",
doi = "10.18653/v1/2023.acl-demo.15",
pages = "161--168",
abstract = "Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies. To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm. Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models. We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane. The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. Code: \url{https://github.com/kevinduh/sockeye-recipes3Video} demo: \url{https://cs.jhu.edu/kevinduh/j/demo.mp4}"
}
Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality–-automatic, manual, and mixed–-on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.
@inproceedings{behzad-etal-2023-effect,
title = "The Effect of Alignment Correction on Cross-Lingual Annotation Projection",
author = "Behzad, Shabnam and
Ebner, Seth and
Marone, Marc and
Van Durme, Benjamin and
Yarmohammadi, Mahsa",
editor = "Prange, Jakob and
Friedrich, Annemarie",
booktitle = "Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.law-1.24/",
doi = "10.18653/v1/2023.law-1.24",
pages = "244--251",
abstract = "Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality---automatic, manual, and mixed---on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance."
}
Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models.
@inproceedings{schumacher-etal-2023-surprising,
title = "On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking",
author = "Schumacher, Elliot and
Mayfield, James and
Dredze, Mark",
editor = "Hruschka, Estevam and
Mitchell, Tom and
Rahman, Sajjadur and
Mladeni\'c, Dunja and
Grobelnik, Marko",
booktitle = "Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)",
month = jul,
year = "2023",
address = "Toronto, ON, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.matching-1.6/",
doi = "10.18653/v1/2023.matching-1.6",
pages = "58--69",
abstract = "Fifteen years of work on entity linking has established the importance of different information sources in making linking decisions: mention and entity name similarity, contextual relevance, and features of the knowledge base. Modern state-of-the-art systems build on these features, including through neural representations (Wu et al., 2020). In contrast to this trend, the autoregressive language model GENRE (De Cao et al., 2021) generates normalized entity names for mentions and beats many other entity linking systems, despite making no use of knowledge base (KB) information. How is this possible? We analyze the behavior of GENRE on several entity linking datasets and demonstrate that its performance stems from memorization of name patterns. In contrast, it fails in cases that might benefit from using the KB. We experiment with a modification to the model to enable it to utilize KB information, highlighting challenges to incorporating traditional entity linking information sources into autoregressive models."
}
Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.
@inproceedings{harrigian-etal-2023-characterization,
title = "Characterization of Stigmatizing Language in Medical Records",
author = "Harrigian, Keith and
Zirikly, Ayah and
Chee, Brant and
Ahmad, Alya and
Links, Anne and
Saha, Somnath and
Beach, Mary Catherine and
Dredze, Mark",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.28/",
doi = "10.18653/v1/2023.acl-short.28",
pages = "312--329",
abstract = "Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models."
}
Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision.
@inproceedings{stengel-eskin-etal-2023-chicken,
title = "Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in {VQA}",
author = "Stengel-Eskin, Elias and
Guallar-Blasco, Jimena and
Zhou, Yi and
Van Durme, Benjamin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.569/",
doi = "10.18653/v1/2023.acl-long.569",
pages = "10220--10237",
abstract = "Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. Our analysis reveals a linguistically-aligned ontology of reasons for ambiguity in visual questions. We then develop an English question-generation model which we demonstrate via automatic and human evaluation produces less ambiguous questions. We further show that the question generation objective we use allows the model to integrate answer group information without any direct supervision."
}
Large “instruction-tuned” language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33\% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5\% absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.
@inproceedings{wang-etal-2023-self-instruct,
title = "Self-Instruct: Aligning Language Models with Self-Generated Instructions",
author = "Wang, Yizhong and
Kordi, Yeganeh and
Mishra, Swaroop and
Liu, Alisa and
Smith, Noah A. and
Khashabi, Daniel and
Hajishirzi, Hannaneh",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.754/",
doi = "10.18653/v1/2023.acl-long.754",
pages = "13484--13508",
abstract = "Large ``instruction-tuned'' language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33\% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5\% absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning."
}
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs’ strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary.
@inproceedings{mallen-etal-2023-trust,
title = "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories",
author = "Mallen, Alex and
Asai, Akari and
Zhong, Victor and
Das, Rajarshi and
Khashabi, Daniel and
Hajishirzi, Hannaneh",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.546/",
doi = "10.18653/v1/2023.acl-long.546",
pages = "9802--9822",
abstract = "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary."
}
How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases.
@inproceedings{selvam-etal-2023-tail,
title = "The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks",
author = "Selvam, Nikil and
Dev, Sunipa and
Khashabi, Daniel and
Khot, Tushar and
Chang, Kai-Wei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.118/",
doi = "10.18653/v1/2023.acl-short.118",
pages = "1373--1386",
abstract = "How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases."
}
Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14\% improvement in average F1 without increasing annotator time.
@inproceedings{gandhi-etal-2023-annotating,
title = "Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution",
author = "Gandhi, Nupoor and
Field, Anjalie and
Strubell, Emma",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.588/",
doi = "10.18653/v1/2023.acl-long.588",
pages = "10543--10558",
abstract = "Although recent neural models for coreference resolution have led to substantial improvements on benchmark datasets, it remains a challenge to successfully transfer these models to new target domains containing many out-of-vocabulary spans and requiring differing annotation schemes. Typical approaches involve continued training on annotated target-domain data, but obtaining annotations is costly and time-consuming. In this work, we show that adapting mention detection is the key component to successful domain adaptation of coreference models, rather than antecedent linking. We also show annotating mentions alone is nearly twice as fast as annotating full coreference chains. Based on these insights, we propose a method for efficiently adapting coreference models, which includes a high-precision mention detection objective and requires only mention annotations in the target domain. Extensive evaluation across three English coreference datasets: CoNLL-2012 (news/conversation), i2b2/VA (medical notes), and child welfare notes, reveals that our approach facilitates annotation-efficient transfer and results in a 7-14\% improvement in average F1 without increasing annotator time."
}
@InProceedings{fang-et-al-2023,
author = "Hao Fang and Anusha Balakrishnan and Harsh Jhamtani
and John Bufe and Jean Crawford and Jayant
Krishnamurthy and Adam Pauls and Jason Eisner and Jacob
Andreas and Dan Klein",
title = "The Whole Truth and Nothing But the Truth: Faithful
and Controllable Dialogue Response Generation with
Dataflow Transduction and Constrained Decoding",
booktitle = "Findings of the Association for Computational
Linguistics: ACL 2023",
year = "2023",
month = jul,
pages = "5682--5700",
URL = "http://cs.jhu.edu/~jason/papers/#fang-et-al-2023",
}
@InProceedings{li-et-al-2023-dictation,
author = "Belinda Z. Li and Jason Eisner and Adam Pauls and Sam
Thomson",
title = "Toward Interactive Dictation",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2023",
month = jul,
pages = "15319--15338",
URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2023-dictation",
}
@InProceedings{mireshghallah-et-al-2023,
author = "Fatemehsadat Mireshghallah and Yu Su and Tatsunori
Hashimoto and Jason Eisner and Richard Shin",
title = "Privacy-Preserving Domain Adaptation of Semantic
Parsers",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2023",
month = jul,
pages = "4950--4970",
URL = "http://cs.jhu.edu/~jason/papers/#mireshghallah-et-al-2023",
}
@InProceedings{li-et-al-2023-cd,
author = "Xiang Lisa Li and Ari Holtzman and Daniel Fried and
Percy Liang and Jason Eisner and Tatsunori Hashimoto
and Luke Zettlemoyer and Mike Lewis",
title = "Contrastive Decoding: Open-ended Text Generation as
Optimization",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2023",
month = jul,
pages = "12286--12312",
URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2023-cd",
}
@InProceedings{du-et-al-2023,
author = "Li Du and Lucas Torroba Hennigen and Tiago Pimentel
and Clara Meister and Jason Eisner and Ryan Cotterell",
title = "A Measure-Theoretic Characterization of Tight Language
Models",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2023",
month = jul,
pages = "9744--9770",
URL = "http://cs.jhu.edu/~jason/papers/#du-et-al-2023",
}
@InProceedings{opedal-et-al-2023,
author = "Andreas Opedal and Ran Zmigrod and Tim Vieira and Ryan
Cotterell and Jason Eisner",
title = "Efficient Semiring-Weighted {E}arley Parsing",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2023",
month = jul,
pages = "3687--3713",
URL = "http://cs.jhu.edu/~jason/papers/#opedal-et-al-2023",
}
Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process.
@inproceedings{duh-zhang-2023-automl,
title = "{A}uto{ML} for {NLP}",
author = "Duh, Kevin and
Zhang, Xuan",
editor = "Zanzotto, Fabio Massimo and
Pradhan, Sameer",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-tutorials.5/",
doi = "10.18653/v1/2023.eacl-tutorials.5",
pages = "25--26",
abstract = "Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. As an umbrella term that includes topics like hyperparameter optimization and neural architecture search, AutoML has recently become mainstream at major conferences such as NeurIPS, ICML, and ICLR. What does this mean to NLP? Currently, models are often built in an ad hoc process: we might borrow default hyperparameters from previous work and try a few variant architectures, but it is never guaranteed that final trained model is optimal. Automation can introduce rigor in this model-building process. This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process."
}
Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.
@inproceedings{tan-etal-2023-multilingual,
title = "Multilingual Representation Distillation with Contrastive Learning",
author = "Tan, Weiting and
Heffernan, Kevin and
Schwenk, Holger and
Koehn, Philipp",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.108/",
doi = "10.18653/v1/2023.eacl-main.108",
pages = "1477--1490",
abstract = "Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE."
}
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks – 4-ary relation extraction on SciREX and template extraction on MUC-4 – as well as a strong baseline on the new BETTER Granular task.
@inproceedings{chen-etal-2023-iterative,
title = "Iterative Document-level Information Extraction via Imitation Learning",
author = "Chen, Yunmo and
Gantt, William and
Gu, Weiwei and
Chen, Tongfei and
White, Aaron and
Van Durme, Benjamin",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.136/",
doi = "10.18653/v1/2023.eacl-main.136",
pages = "1858--1874",
abstract = "We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template's slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks -- 4-ary relation extraction on SciREX and template extraction on MUC-4 -- as well as a strong baseline on the new BETTER Granular task."
}
Transformer models cannot easily scale to long sequences due to their O(N\^{}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.
@inproceedings{qin-etal-2023-nlp,
title = "The {NLP} Task Effectiveness of Long-Range Transformers",
author = "Qin, Guanghui and
Feng, Yukun and
Van Durme, Benjamin",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.273/",
doi = "10.18653/v1/2023.eacl-main.273",
pages = "3774--3790",
abstract = "Transformer models cannot easily scale to long sequences due to their O(N\^{}2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error."
}
@inproceedings{262185166,
title = {Using a quality improvement tool, Plan-Do-Study-Act cycle, to boost TB notification in India post-Covid-19 pandemic.},
author = {{Manoj Jain} and {Salil Bhargava} and {R. Arora} and {Rajendra P. Joshi} and {Ravinder Kumar} and {Deepak Saxena} and {Kiran Rade} and {Rebecca Martin}},
year = 2023,
month = {9},
booktitle = {Indian Journal of Tuberculosis},
url = {https://www.semanticscholar.org/paper/acbaffb72d4c3bd7c9a12d6c756a4a207dea3703},
}
@inproceedings{261076475,
title = {Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape},
author = {{Jiacong Xu} and {Yi Zhang} and {Jia-Xiong Peng} and {Wufei Ma} and {Artur Jesslen} and {Pengliang Ji} and {Qixing Hu} and {Jiehua Zhang} and {Qihao Liu} and {Jiahao Wang} and {Wei Ji} and {Chen Wang} and {Xiaoding Yuan} and {Prakhar Kaushik} and {Guofeng Zhang} and {Jie Liu} and {Yushan Xie} and {Yawen Cui} and {A. Yuille} and {Adam Kortylewski}},
year = 2023,
month = {8},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/cf6f0b77e006083e74d5f08bae59bd207d0e4ac6},
}
@inproceedings{258841479,
title = {Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer},
author = {{Elizabeth Salesky} and {Neha Verma} and {Philipp Koehn} and {Matt Post}},
year = 2023,
month = {5},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/c60736e61f8961ec535ecfdc6f0398925d34d0b8},
}
@inproceedings{258686491,
title = {Natural Language Decomposition and Interpretation of Complex Utterances},
author = {{Harsh Jhamtani} and {Hao Fang} and {Patrick Xia} and {Eran Levy} and {Jacob Andreas} and {Benjamin Van Durme}},
year = 2023,
month = {5},
booktitle = {International Joint Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/68040213e9a83408cdc491ed3e235b52b537eed1},
}
@inproceedings{256604402,
title = {Adaptation in the sensory cortex drives bistable switching during auditory stream segregation},
author = {{Nathan C Higgins} and {Alexandra N. Scurry} and {Fang Jiang} and {David F. Little} and {Claude Alain} and {Mounya Elhilali} and {J. Snyder}},
year = 2023,
month = {1},
booktitle = {Neuroscience of Consciousness},
url = {https://www.semanticscholar.org/paper/c1c4a48270174de06f609bb2dc98c8e896ce78a3},
}
@inproceedings{261637808,
title = {Editorial: Artificial intelligence for human function and disability},
author = {{Denis Newman-Griffis} and {Bart Desmet} and {Ayah Zirikly} and {Suzanne Tamang} and {Chih-Hung Chang}},
year = 2023,
month = {9},
booktitle = {Frontiers Digit. Health},
url = {https://www.semanticscholar.org/paper/251b6ab5f8aa64447bcab84b2078a7198afc4ac3},
}
Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.
@inproceedings{antoniak-etal-2023-riveter,
title = "Riveter: Measuring Power and Social Dynamics Between Entities",
author = "Antoniak, Maria and
Field, Anjalie and
Mun, Jimin and
Walsh, Melanie and
Klein, Lauren and
Sap, Maarten",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.36/",
doi = "10.18653/v1/2023.acl-demo.36",
pages = "377--388",
abstract = "Riveter provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, Riveter greatly improves the accessibility of verb lexica and can facilitate a broad range of future research."
}
@inproceedings{258999294,
title = {Zero and Few-shot Semantic Parsing with Ambiguous Inputs},
author = {{Elias Stengel-Eskin} and {Kyle Rawlins} and {Benjamin Van Durme}},
year = 2023,
month = {6},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/b8e49068441a43aaf039527c6063a033368dd357},
}
@inproceedings{261065732,
title = {Crosslingual Handwritten Text Generation Using GANs},
author = {{Chun-Chieh Chang} and {Leibny Paola García-Perera} and {S. Khudanpur}},
year = 2023,
booktitle = {ICDAR Workshops},
url = {https://www.semanticscholar.org/paper/48abc94b0eaf32dac2573dc5cdbe5dcfa897b7f0},
}
@inproceedings{259373518,
title = {Making Your First Choice: To Address Cold Start Problem in Medical Active Learning},
author = {{Liangyu Chen} and {Yutong Bai} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
booktitle = {International Conference on Medical Imaging with Deep Learning},
url = {https://www.semanticscholar.org/paper/251516c1549fc4566b801788c932ef1f18f343b3},
}
@inproceedings{268083188,
title = {Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis},
author = {{H. E. Wang} and {Jonathan P. Weiner} and {S. Saria} and {Hadi Kharrazi}},
year = 2023,
month = {3},
booktitle = {Journal of Medical Internet Research},
url = {https://www.semanticscholar.org/paper/ed42741178d7d119acb440ae5a6d6f9f87fc1523},
}
@inproceedings{257697867,
title = {Biological research and self-driving labs in deep space supported by artificial intelligence},
author = {{Lauren M. Sanders} and {Ryan T. Scott} and {Jason H. Yang} and {Amina Ann Qutub} and {Héctor García Martín} and {D. Berrios} and {Jaden J. A. Hastings} and {J. Rask} and {Graham Mackintosh} and {A. Hoarfrost} and {Stuart Chalk} and {John Kalantari} and {Kia Khezeli} and {E. Antonsen} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {Afshin Beheshti} and {Guillermo M. Delgado-Aparicio} and {B. Glicksberg} and {Casey S. Greene} and {Melissa Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {Svetlana V. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {Jack M. Miller} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {Seung-min Park} and {P. Parsons-Wingerter} and {R. K. Prabhu} and {R. Reynolds} and {Amanda M. Saravia-Butler} and {S. Saria} and {A. Sawyer} and {N. Singh} and {M. Snyder} and {Frank Soboczenski} and {Karthik Soman} and {C. Theriot} and {David Van Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {M. Zitnik} and {S. Costes}},
year = 2023,
month = {3},
booktitle = {Nature Machine Intelligence},
url = {https://www.semanticscholar.org/paper/880e7f45c1952189e350545dd98a73ef47465cba},
}
@inproceedings{258987867,
title = {Examining risks of racial biases in NLP tools for child protective services},
author = {{Anjalie Field} and {Amanda Coston} and {Nupoor Gandhi} and {A. Chouldechova} and {Emily Putnam-Hornstein} and {David Steier} and {Yulia Tsvetkov}},
year = 2023,
month = {5},
booktitle = {Conference on Fairness, Accountability and Transparency},
url = {https://www.semanticscholar.org/paper/346e4f35a5a81ef893792133ec1fec18f23c1768},
}
@inproceedings{258383693,
title = {Leveraging synthetic data for robust gesture recognition},
author = {{Kapil D. Katyal} and {R. Chellappa} and {Ketul Shah} and {Arun V. Reddy} and {Judy Hoffman} and {William Paul} and {Rohita Mocharla} and {D. Handelman} and {Celso de Melo}},
year = 2023,
month = {6},
booktitle = {Defense + Commercial Sensing},
url = {https://www.semanticscholar.org/paper/922198774621861436721bd923dc0f0028872a84},
}
@inproceedings{257378503,
title = {Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition},
author = {{Saurabh Kataria} and {J. Villalba} and {Laureano Moro-Vel'azquez} and {Thomas Thebaud} and {N. Dehak}},
year = 2023,
month = {3},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/03e266795339008e9366daabfd2a2db2fbd51151},
}
@inproceedings{259203410,
title = {HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation},
author = {{Cihan Xiao} and {Henry Li Xinyuan} and {Jinyi Yang} and {Dongji Gao} and {Matthew Wiesner} and {Kevin Duh} and {S. Khudanpur}},
year = 2023,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/74173dec94055d7f4051aa2e80be31ccd2bde596},
}
@inproceedings{257505032,
title = {InstMove: Instance Motion for Object-centric Video Segmentation},
author = {{Qihao Liu} and {Junfeng Wu} and {Yi Jiang} and {Xiang Bai} and {A. Yuille} and {S. Bai}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/0e60c1229d7963b605b83cb10a90ed6a8cf79149},
}
@inproceedings{258967838,
title = {MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization},
author = {{Victoria Y. H. Chua} and {Hexin Liu} and {Leibny Paola García Perera} and {Fei Ting Woon} and {Jinyi Wong} and {Xiangyu Zhang} and {S. Khudanpur} and {Andy W. H. Khong} and {J. Dauwels} and {S. Styles}},
year = 2023,
month = {5},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/ea701359c6e2b2ba5c36c4849d4144d318171418},
}
@inproceedings{255569926,
title = {Benchmarking Robustness in Neural Radiance Fields},
author = {{Chen Wang} and {Angtian Wang} and {Junbo Li} and {A. Yuille} and {Cihang Xie}},
year = 2023,
month = {1},
booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/eaf0c04e9784d6efc8f9ce16d1d9c3ae43506ad9},
}
@inproceedings{257698009,
title = {Biomonitoring and precision health in deep space supported by artificial intelligence},
author = {{Ryan T. Scott} and {Lauren M. Sanders} and {E. Antonsen} and {Jaden J. A. Hastings} and {Seung-min Park} and {Graham Mackintosh} and {R. Reynolds} and {A. Hoarfrost} and {A. Sawyer} and {Casey S. Greene} and {Benjamin S. Glicksberg} and {C. Theriot} and {D. Berrios} and {Jack Miller} and {Joel Babdor} and {Richard Barker} and {S. Baranzini} and {Afshin Beheshti} and {Stuart Chalk} and {Guillermo M. Delgado-Aparicio} and {Melissa Haendel} and {Arif A. Hamid} and {P. Heller} and {Daniel Jamieson} and {K. Jarvis} and {John Kalantari} and {Kia Khezeli} and {Svetlana V. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {Héctor García Martín} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {J. Myers} and {Charlotte A. Nelson} and {Jonathan Oribello} and {P. Parsons-Wingerter} and {R. K. Prabhu} and {A. Qutub} and {J. Rask} and {Amanda M. Saravia-Butler} and {S. Saria} and {N. Singh} and {M. Snyder} and {Frank Soboczenski} and {Karthik Soman} and {David Van Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {Jason H. Yang} and {M. Zitnik} and {S. Costes}},
year = 2023,
month = {3},
booktitle = {Nature Machine Intelligence},
url = {https://www.semanticscholar.org/paper/275a42c374d6381406a5da16dfa52fa939817a15},
}
@inproceedings{260334568,
title = {Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training},
author = {{Jeya Maria Jose Valanarasu} and {Yucheng Tang} and {Dong Yang} and {Ziyue Xu} and {Can Zhao} and {Wenqi Li} and {Vishal M. Patel} and {Bennett A. Landman} and {Daguang Xu} and {Yufan He} and {V. Nath}},
year = 2023,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/21510551199cc8ebfba5252575133de607448fa9},
}
@inproceedings{262056669,
title = {OpenAI Cribbed Our Tax Example, But Can GPT-4 Really Do Tax?},
author = {{Andrew Blair-Stanek} and {Nils Holzenberger} and {Benjamin Van Durme}},
year = 2023,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a77de1f2e2dc253798e5ca9bee71e4d651dc30cd},
}
@inproceedings{258887517,
title = {Robust Category-Level 3D Pose Estimation from Synthetic Data},
author = {{Jiahao Yang} and {Wufei Ma} and {Angtian Wang} and {Xiaoding Yuan} and {A. Yuille} and {Adam Kortylewski}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/55aa226650e6eeed51e181195b7b7a9b87102bc5},
}
@inproceedings{259129355,
title = {Developing Speech Processing Pipelines for Police Accountability},
author = {{Anjalie Field} and {Prateek Verma} and {Nay San} and {J. Eberhardt} and {Dan Jurafsky}},
year = 2023,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/38c2e4e54a50ea5027c7a06ab325c22ece7b6c40},
}
@inproceedings{254853589,
title = {Reference free auscultation quality metric and its trends},
author = {{A. Kala} and {Eric D. McCollum} and {Mounya Elhilali}},
year = 2023,
month = {8},
booktitle = {Biomedical Signal Processing and Control},
url = {https://www.semanticscholar.org/paper/4276e26be8c196ba4b496b4a0acc4102d32c0bd8},
}
@inproceedings{257766789,
title = {Label-Free Liver Tumor Segmentation},
author = {{Qixing Hu} and {Yixiong Chen} and {Junfei Xiao} and {Shuwen Sun} and {Jieneng Chen} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/74fc777becc43b9e94c2fb59ed3ee78d212ca01e},
}
@inproceedings{259893511,
title = {Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era},
author = {{M. Marín-Jiménez} and {Shiqi Yu} and {Yasushi Makihara} and {Vishal M. Patel} and {Maneet Singh} and {M. De Marsico}},
year = 2023,
month = {5},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/df958104e921170b592e27798e18de9b9c892cbd},
}
@inproceedings{259251557,
title = {The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios},
author = {{Samuele Cornell} and {Matthew Wiesner} and {Shinji Watanabe} and {Desh Raj} and {Xuankai Chang} and {Paola García} and {Yoshiki Masuyama} and {Zhong-Qiu Wang} and {S. Squartini} and {S. Khudanpur}},
year = 2023,
month = {6},
booktitle = {7th International Workshop on Speech Processing in Everyday Environments (CHiME 2023)},
url = {https://www.semanticscholar.org/paper/d4d5fe4a35e9de845877015075f727415e83d18f},
}
@inproceedings{266199727,
title = {Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds},
author = {{A. Kala} and {Mounya Elhilali}},
year = 2023,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/d463716d4860ccc8a42190b9d90bc94af45db1ac},
}
@inproceedings{257206027,
title = {Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards},
author = {{Thanh Nguyen-Tang} and {R. Arora}},
year = 2023,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a8dac0d0837ac4800f4462a121c59a98a05531ee},
}
@inproceedings{258309151,
title = {Escaping the sentence-level paradigm in machine translation},
author = {{Matt Post} and {Marcin Junczys-Dowmunt}},
year = 2023,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/59e2cfbb1395a4a02e9efecadd4f4005af462c1b},
}
@inproceedings{259991385,
title = {GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos},
author = {{Nisarg A. Shah} and {S. Sikder} and {S. Vedula} and {Vishal M. Patel}},
year = 2023,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/330bf5c3162606581ebfba1f744e1f7da90c5de4},
}
@inproceedings{259202526,
title = {SURT 2.0: Advances in Transducer-Based Multi-Talker Speech Recognition},
author = {{Desh Raj} and {Daniel Povey} and {S. Khudanpur}},
year = 2023,
month = {6},
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/a6fffd418fabef307cba5e70324a3ba89c7ffc39},
}
@inproceedings{258865247,
title = {T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities},
author = {{Kangfu Mei} and {Mo Zhou} and {Vishal M. Patel}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/95dd7685e9b4e6195d80b22167d980be4379da44},
}
@inproceedings{260202959,
title = {Distillation-guided Representation Learning for Unconstrained Gait Recognition},
author = {{Yuxiang Guo} and {Cheng-Fang Peng} and {R. Prabhakar} and {Chun Pong Lau} and {R. Chellappa}},
year = 2023,
month = {7},
booktitle = {2024 IEEE International Joint Conference on Biometrics (IJCB)},
url = {https://www.semanticscholar.org/paper/5d66c8c86d4ecc2b4ee38f2aa98a470e1e6ea48f},
}
@inproceedings{265244446,
title = {Genre Classification of Books on Spanish},
author = {{J. Nolazco-Flores} and {Ana Verónica Guerrero-Galván} and {C. del-Valle-Soto} and {Leibny Paola García-Perera}},
year = 2023,
booktitle = {IEEE Access},
url = {https://www.semanticscholar.org/paper/0895c353c61f4ef6452ba892e4608d45742d455b},
}
@inproceedings{255372874,
title = {Learning Road Scene-level Representations via Semantic Region Prediction},
author = {{Zihao Xiao} and {A. Yuille} and {Yi-Ting Chen}},
year = 2023,
month = {1},
booktitle = {Conference on Robot Learning},
url = {https://www.semanticscholar.org/paper/11b29ca1a235d80a2e55f6eb7711d2aa5785bb8c},
}
@inproceedings{258444556,
title = {Evaluation of a Novel Digital Stethoscope Prototype in a Low-resource Setting: Expert Listening Panel Agreement With Conventional Auscultation in Hospitalized Malawian Children With Severe Pneumonia},
author = {{Z. Smith} and {N. Hoekstra} and {T. Mvalo} and {I. McLane} and {A. Kala} and {M. Chiume} and {C. Verwey} and {D. Olson} and {C. Buck} and {J. Mulindwa} and {E. Fitzgerald} and {M. Chagomerana} and {Mounya Elhilali} and {M. Hosseinipour} and {E. McCollum}},
year = 2023,
month = {5},
booktitle = {C25. OPPORTUNITIES AND ADVANCES IN PEDIATRIC GLOBAL HEALTH},
url = {https://www.semanticscholar.org/paper/00ff74d263d80498ea78cca8850c565b66057476},
}
@inproceedings{258959429,
title = {Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution},
author = {{Tianjian Li} and {Kenton Murray}},
year = 2023,
month = {5},
booktitle = {Annual Meeting of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/aa933e27c470eeecbe7bbec5debdd8c5d2faa4be},
}
@inproceedings{258997975,
title = {Automating analysis of eye movement and feature extraction for different neurodegenerative disorders},
author = {{D. Li} and {A. Butala} and {T. Meyer} and {E. Oh} and {C. Motley} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2023,
month = {6},
booktitle = {medRxiv},
url = {https://www.semanticscholar.org/paper/2b5f1cfc2b507561bd463b0a5ac14fd92d75dc50},
}
@inproceedings{259923580,
title = {MULTIMEDIA CURRICULUM LEARNING FOR LANGUAGE ACQUISITION},
author = {{Pengfei Yu} and {Heng Ji} and {Shih-Fu Chang} and {Kevin Duh}},
year = 2023,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7c7d8f106f8cd1bdadfd3b46f6ebb1509cb1be42},
}
@inproceedings{257833842,
title = {BloombergGPT: A Large Language Model for Finance},
author = {{Shijie Wu} and {Ozan Irsoy} and {Steven Lu} and {Vadim Dabravolski} and {Mark Dredze} and {Sebastian Gehrmann} and {P. Kambadur} and {D. Rosenberg} and {Gideon Mann}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/83edcfbb206ddad38a971d605da09390604248ea},
}
@inproceedings{258999203,
title = {Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search},
author = {{Qihao Liu} and {Adam Kortylewski} and {Yutong Bai} and {Song Bai} and {A. Yuille}},
year = 2023,
month = {6},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/b469d5e906eee4f682726fb8b7f899fd96bcd8a3},
}
@inproceedings{258766137,
title = {Generalization bounds for Kernel Canonical Correlation Analysis},
author = {{Enayat Ullah} and {R. Arora}},
year = 2023,
booktitle = {Trans. Mach. Learn. Res.},
url = {https://www.semanticscholar.org/paper/4a55079d0145870461cbe2a48f53e40e64b7db3d},
}
@inproceedings{260681299,
title = {Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis},
author = {{Bowen Li} and {Yu-Cheng Chou} and {Shuwen Sun} and {Hualin Qiao} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/d556aaf4ac55b6cebb9b889ec7b89b086cb41bff},
}
Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.
@inproceedings{post-etal-2023-sotastream,
title = "{SOTASTREAM}: A Streaming Approach to Machine Translation Training",
author = "Post, Matt and
Gowda, Thamme and
Grundkiewicz, Roman and
Khayrallah, Huda and
Jain, Rohit and
Junczys-Dowmunt, Marcin",
editor = "Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah",
booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlposs-1.13/",
doi = "10.18653/v1/2023.nlposs-1.13",
pages = "110--119",
abstract = "Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: https://github.com/marian-nmt/sotastream. We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models."
}
@inproceedings{257687311,
title = {ReBotNet: Fast Real-time Video Enhancement},
author = {{Jeya Maria Jose Valanarasu} and {Rahul Garg} and {Andeep S. Toor} and {Xin Tong} and {Weijuan Xi} and {Andreas Lugmayr} and {Vishal M. Patel} and {A. Menini}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/15c2b3ecdf1b9af2f94a2b106fddcfc89cb336cb},
}
@inproceedings{259202766,
title = {DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model},
author = {{Helin Wang} and {Thomas Thebaud} and {J. Villalba} and {Myra Sydnor} and {Becky Lammers} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2023,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/bf6339e920466f2dc7dc0da5edde5b3187cf9d0d},
}
@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},
}
@inproceedings{258967563,
title = {Investigating model performance in language identification: beyond simple error statistics},
author = {{S. Styles} and {Victoria Y. H. Chua} and {Fei Ting Woon} and {Hexin Liu} and {Leibny Paola García Perera} and {S. Khudanpur} and {Andy W. H. Khong} and {J. Dauwels}},
year = 2023,
month = {5},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/5550d0db7030aa77480bfb10c4ec00862bb233eb},
}
@inproceedings{260909100,
title = {Segmental SpeechCLIP: Utilizing Pretrained Image-text Models for Audio-Visual Learning},
author = {{Saurabhchand Bhati} and {J. Villalba} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak}},
year = 2023,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/1617d389b7947161f2943e2d30afeb1856052b14},
}
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title = {Regularizing Contrastive Predictive Coding for Speech Applications},
author = {{Saurabhchand Bhati} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2023,
month = {4},
booktitle = {},
url = {https://www.semanticscholar.org/paper/47ac48e7ee37e7cf4d3bb183477e42d6c5632b64},
}
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title = {Speech Collage: Code-Switched Audio Generation by Collaging Monolingual Corpora},
author = {{A. Hussein} and {Dorsa Zeinali} and {Ondrej Klejch} and {Matthew Wiesner} and {Brian Yan} and {Shammur A. Chowdhury} and {Ahmed Ali} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2023,
month = {9},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/fa5ebb425c57f6c4f1c36a7200ef1da867346e8c},
}
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title = {Condensing Multilingual Knowledge with Lightweight Language-Specific Modules},
author = {{Haoran Xu} and {Weiting Tan} and {Shuyue Stella Li} and {Yunmo Chen} and {Benjamin Van Durme} and {Philipp Koehn} and {Kenton Murray}},
year = 2023,
month = {5},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/cf99558d35891d35ba5429cfce5ca2e2ae383b71},
}
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title = {A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation},
author = {{Jeremy Gwinnup} and {Kevin Duh}},
year = 2023,
month = {6},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c581d2ad3b092a2cc152d0c6f55fd6320f78eb3a},
}
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title = {A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders},
author = {{A. Favaro} and {C. Motley} and {Tianyu Cao} and {Miguel Iglesias} and {A. Butala} and {E. Oh} and {R. Stevens} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2023,
month = {1},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/40eb935374d67b7b9979e0c9333c291d188c472b},
}
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title = {Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.},
author = {{V. Rennoll} and {Ian McLane} and {Adebayo A. Eisape} and {D. Grant} and {Helena Hahn} and {Mounya Elhilali} and {James E. West}},
year = 2023,
month = {7},
booktitle = {ACS Applied Bio Materials},
url = {https://www.semanticscholar.org/paper/bf5172b246adb601b731618108ba8ce5d1367177},
}
@inproceedings{258762744,
title = {Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency},
author = {{Lingfeng Shen} and {Weiting Tan} and {Boyuan Zheng} and {Daniel Khashabi}},
year = 2023,
month = {5},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/b8ba16a107621f760e7830ddaab8c3d5c5ff06b0},
}
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title = {Compositor: Bottom-Up Clustering and Compositing for Robust Part and Object Segmentation},
author = {{Ju He} and {Jieneng Chen} and {Ming-Xian Lin} and {Qihang Yu} and {A. Yuille}},
year = 2023,
month = {6},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/c49666de550031cd63514dacc74b5c4a632da6a6},
}
@inproceedings{259840157,
title = {Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline},
author = {{Wei Ji} and {Jingjing Li} and {Cheng Bian} and {Zongwei Zhou} and {Jiaying Zhao} and {A. Yuille} and {Li Cheng}},
year = 2023,
month = {6},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/fa0215c456d1957e131e43702e50e4c8f3e9477d},
}
@inproceedings{257985028,
title = {Diffusion Models as Masked Autoencoders},
author = {{Chen Wei} and {K. Mangalam} and {Po-Yao (Bernie) Huang} and {Yanghao Li} and {Haoqi Fan} and {Hu Xu} and {Huiyu Wang} and {Cihang Xie} and {A. Yuille} and {Christoph Feichtenhofer}},
year = 2023,
month = {4},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/b032f324a0d4a24fd917551345bd100dc368e41a},
}
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title = {MegaWika: Millions of reports and their sources across 50 diverse languages},
author = {{Samuel Barham} and {Orion Weller} and {Michelle Yuan} and {Kenton Murray} and {M. Yarmohammadi} and {Zhengping Jiang} and {Siddharth Vashishtha} and {Alexander Martin} and {Anqi Liu} and {Aaron Steven White} and {Jordan L. Boyd-Graber} and {Benjamin Van Durme}},
year = 2023,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c8e2b9f68bd585759d31741193516f58b5619584},
}
@inproceedings{261048725,
title = {3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation},
author = {{Yi Zhang} and {Pengliang Ji} and {Angtian Wang} and {Jieru Mei} and {Adam Kortylewski} and {A. Yuille}},
year = 2023,
month = {8},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/8a46058e5353ae5cc66dd8fc67e635aaad3a5f17},
}
@inproceedings{257404851,
title = {Stabilized training of joint energy-based models and their practical applications},
author = {{Martin Sustek} and {Samik Sadhu} and {L. Burget} and {H. Hermansky} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/46fd16213979b00e741b926539ad4ba7a1acd1cf},
}
@inproceedings{258570384,
title = {Explicit-memory multiresolution adaptive framework for speech and music separation},
author = {{Ashwin Bellur} and {Karan Thakkar} and {Mounya Elhilali}},
year = 2023,
month = {5},
booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
url = {https://www.semanticscholar.org/paper/237ea0d3b14b924f12693a29de6fa903a3ae54ed},
}
@inproceedings{260870320,
title = {Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection},
author = {{Yoav Wald} and {S. Saria}},
year = 2023,
booktitle = {Conference on Uncertainty in Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/90869ce11a670ca4fb568a56a38f3ffe37161121},
}
@inproceedings{259436318,
title = {The unsung hero: how synthetic data has helped computer vision, machine learning, and AI},
author = {{R. Chellappa}},
year = 2023,
month = {6},
booktitle = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications},
url = {https://www.semanticscholar.org/paper/c061dd875146aa8d87b5bfe45eea73df8da3c373},
}
@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 = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/125632627bfad80c2c688bcbed7f3ee915de7359},
}
@inproceedings{259063779,
title = {Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts},
author = {{Dongji Gao} and {Matthew Wiesner} and {Hainan Xu} and {Leibny Paola García} and {Daniel Povey} and {S. Khudanpur}},
year = 2023,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/b620d46668d62930e41393168434118bb9a2bfcb},
}
@inproceedings{256389683,
title = {CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans},
author = {{Jieneng Chen} and {Yingda Xia} and {Jiawen Yao} and {K. Yan} and {Jianpeng Zhang} and {Le Lu} and {Fakai Wang} and {Bo Zhou} and {Mingyan Qiu} and {Qihang Yu} and {Ming Yuan} and {Wei Fang} and {Yuxing Tang} and {Minfeng Xu} and {Jian Zhou} and {Yuqian Zhao} and {Qifeng Wang} and {X. Ye} and {Xiaoli Yin} and {Yu Shi} and {Xin Chen} and {Jingren Zhou} and {A. Yuille} and {Zai-De Liu} and {Ling Zhang}},
year = 2023,
month = {1},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/30be551bfce603f76ba526f047ec4e610b0c2376},
}
@inproceedings{257804958,
title = {Mask-Free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations},
author = {{VS Vibashan} and {Ning Yu} and {Chen Xing} and {Can Qin} and {Mingfei Gao} and {Juan Carlos Niebles} and {Vishal M. Patel} and {Ran Xu}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/7aa528b8732033cfb7a6d130bb321723a4e49700},
}
@inproceedings{258460982,
title = {Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity},
author = {{Haoran Xu} and {Maha Elbayad} and {Kenton Murray} and {Jean Maillard} and {Vedanuj Goswami}},
year = 2023,
month = {5},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/09d3a354d22d75deddb0dcc99870f307aae2fd3c},
}
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title = {SCREWS: A Modular Framework for Reasoning with Revisions},
author = {{K. Shridhar} and {Harsh Jhamtani} and {Hao Fang} and {Benjamin Van Durme} and {Jason Eisner} and {Patrick Xia}},
year = 2023,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/5d5dbba58aaf5b3fa4044cc3ffc71a3fe2b8c654},
}
@inproceedings{257632366,
title = {CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition},
author = {{Deepti Hegde} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
year = 2023,
month = {3},
booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
url = {https://www.semanticscholar.org/paper/b460a263abec8b1aaa039963be9b371a581e7b21},
}
@inproceedings{258615767,
title = {Analyzing Bias in Diffusion-based Face Generation Models},
author = {{Malsha V. Perera} and {Vishal M. Patel}},
year = 2023,
month = {5},
booktitle = {2023 IEEE International Joint Conference on Biometrics (IJCB)},
url = {https://www.semanticscholar.org/paper/94831cbd104369092b08f3711e6ac95c5f5f2c7b},
}
@inproceedings{257766699,
title = {Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation},
author = {{Shao-Yuan Lo} and {Poojan Oza} and {Sumanth Chennupati} and {Alejandro Galindo} and {Vishal M. Patel}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/47cd9158e970329355a575ed992d4452ac498784},
}
@inproceedings{259164709,
title = {Generating Images with 3D Annotations Using Diffusion Models},
author = {{Wufei Ma} and {Qihao Liu} and {Jiahao Wang} and {Angtian Wang} and {Yaoyao Liu} and {Adam Kortylewski} and {A. Yuille}},
year = 2023,
month = {6},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/1036f39069a1fe3d5f818f1d7bc07286ad3f1363},
}
@inproceedings{257687723,
title = {Self-supervised Learning with Speech Modulation Dropout},
author = {{Samik Sadhu} and {H. Hermansky}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/eb6358eca5f4ee632f929cb384d07b6a5f04e0ef},
}
@inproceedings{259300311,
title = {JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift},
author = {{Drew Prinster} and {S. Saria} and {Anqi Liu}},
year = 2023,
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/6faf60347b9ec3672a4d191cfe9fe0076191e9a0},
}
@inproceedings{257557771,
title = {Deep Metric Learning for Unsupervised Remote Sensing Change Detection},
author = {{W. G. C. Bandara} and {Vishal M. Patel}},
year = 2023,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/30d02457a38374398deca536682c193f0f0b1a24},
}
@inproceedings{260918551,
title = {Advances in Language Recognition in Low Resource African Languages: The JHU-MIT Submission for NIST LRE22},
author = {{J. Villalba} and {Jonas Borgstrom} and {Maliha Jahan} and {Saurabh Kataria} and {Leibny Paola García} and {P. Torres-Carrasquillo} and {N. Dehak}},
year = 2023,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/51aca07d500c44ebde896b8df3b0388dd3ade489},
}
@inproceedings{259298670,
title = {Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks},
author = {{Yixiong Chen} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/ca195fc6d5879060135a5cf6ff571b243f0c6156},
}
@inproceedings{258766141,
title = {Clustering using Approximate Nearest Neighbour Oracles},
author = {{Enayat Ullah} and {Harry Lang} and {R. Arora} and {V. Braverman}},
year = 2023,
booktitle = {Trans. Mach. Learn. Res.},
url = {https://www.semanticscholar.org/paper/2e864475d80f551d97232f9a6cba079dd128c54d},
}
@inproceedings{257366012,
title = {VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation},
author = {{Thanh Nguyen-Tang} and {R. Arora}},
year = 2023,
month = {2},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/7d200b868cb92657a68ac64c112a2cd0a4045f87},
}
@inproceedings{263152210,
title = {Learning From Flawed Data: Weakly Supervised Automatic Speech Recognition},
author = {{Dongji Gao} and {Hainan Xu} and {Desh Raj} and {Leibny Paola García Perera} and {Daniel Povey} and {S. Khudanpur}},
year = 2023,
month = {9},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/4df2d56e2c81d315e8ead7c3eaf840064ea3665e},
}
@inproceedings{260091661,
title = {From Adaptive Query Release to Machine Unlearning},
author = {{Enayat Ullah} and {R. Arora}},
year = 2023,
month = {7},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/ea3eff68041f3a22b984578e8da8533aa3f766de},
}
@inproceedings{258074434,
title = {Retinomorphic Channel Design and Considerations},
author = {{Jonah P. Sengupta} and {A. Andreou}},
year = 2023,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/7f97effeed913a6089ca98d576d585401e251f9b},
}
@inproceedings{258865888,
title = {InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction},
author = {{Ishani Mondal} and {Michelle Yuan} and {N. Anandhavelu} and {Aparna Garimella} and {Francis Ferraro} and {Andrew Blair-Stanek} and {Benjamin Van Durme} and {Jordan L. Boyd-Graber}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/21e0a1324522b39e5cec94885501e906942c43d0},
}
@inproceedings{255669634,
title = {BNET: Batch Normalization With Enhanced Linear Transformation},
author = {{Yuhui Xu} and {Lingxi Xie} and {Cihang Xie} and {Wenrui Dai} and {Jieru Mei} and {Siyuan Qiao} and {Wei Shen} and {H. Xiong} and {A. Yuille}},
year = 2023,
month = {1},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/edcf374466f791118acf3bbd8430d4fd73e4ea79},
}
@inproceedings{259361037,
title = {MultiVENT: Multilingual Videos of Events with Aligned Natural Text},
author = {{Kate Sanders} and {David Etter} and {Reno Kriz} and {Benjamin Van Durme}},
year = 2023,
month = {7},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/d6592cd383da0ebae9f4ff78a4df96ab635acfa2},
}
@inproceedings{248397783,
title = {Conference on Health, Inference, and Learning, CHIL 2023, Broad Institute of MIT and Harvard (Merkin Building), 415 Main Street, Cambridge, MA, USA},
author = {{Gerardo Flores} and {George H. Chen} and {T. Pollard} and {Ayah Zirikly} and {Michael C. Hughes} and {Tasmie Sarker} and {Joyce Ho} and {Tristan Naumann}},
year = 2023,
booktitle = {ACM Conference on Health, Inference, and Learning},
url = {https://www.semanticscholar.org/paper/1cfe0feed33d2452f951afa7304d017131dc4520},
}
The phenomena of in-context learning has typically been thought of as “learning from examples”. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation.
@inproceedings{sia-duh-2023-context,
title = "In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models",
author = "Sia, Suzanna and
Duh, Kevin",
editor = "Utiyama, Masao and
Wang, Rui",
booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track",
month = sep,
year = "2023",
address = "Macau SAR, China",
publisher = "Asia-Pacific Association for Machine Translation",
url = "https://aclanthology.org/2023.mtsummit-research.15/",
pages = "173--185",
abstract = "The phenomena of in-context learning has typically been thought of as ``learning from examples''. In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (en$\rightarrow${pt, de, fr}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of in-context Machine Translation for on-the-fly adaptation."
}
@inproceedings{258909084,
title = {The promise of AI and technology to improve quality of life and care for older adults},
author = {{P. Abadir} and {Ramalingam Chellappa} and {N. Choudhry} and {G. Demiris} and {Deepak Ganesan} and {Jason Karlawish} and {Rose M Li} and {Jason H. Moore} and {J. Walston} and {Benjamin Najim Alicia I. Mathias Thomas K. M. Suchi Esther Marlin Dehak Arbaje Unberath Cudjoe Saria Oh Lunde} and {Benjamin M Marlin} and {N. Dehak} and {A. Arbaje} and {M. Unberath} and {Thomas K. M. Cudjoe} and {S. Saria} and {Esther Oh} and {N. Lundebjerg} and {C. Chute} and {Phillip Phan} and {Quincy M. Samus} and {Nancy L. Schoenborn}},
year = 2023,
month = {5},
booktitle = {Nature Aging},
url = {https://www.semanticscholar.org/paper/24eafaf005bd6d73870b66525e8978b760e7b3ad},
}
@inproceedings{257848515,
title = {AC Transit Fuel Cell Electric Bus Progress Report (Data Period Focus: Jan. 2020 through Dec. 2020)},
author = {{L. Eudy} and {Matt Post}},
year = 2023,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/69cf8aae9aa20f261b91ad67636fc064a2376e7a},
}
@inproceedings{258060050,
title = {MOST: Multiple Object localization with Self-supervised Transformers for object discovery},
author = {{Sai Saketh Rambhatla} and {Ishan Misra} and {R. Chellappa} and {Abhinav Shrivastava}},
year = 2023,
month = {4},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/3be837073f08eecc01e1bc742c541c5f0e644946},
}
@inproceedings{258865176,
title = {NOVUM: Neural Object Volumes for Robust Object Classification},
author = {{Artur Jesslen} and {Guofeng Zhang} and {Angtian Wang} and {Wufei Ma} and {A. Yuille} and {Adam Kortylewski}},
year = 2023,
month = {5},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/7f6b686b1a9ae3983dd4facfb23038d49f16dcc4},
}
@inproceedings{257495961,
title = {PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation},
author = {{Qihao Liu} and {Adam Kortylewski} and {A. Yuille}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/85fcce7ef6f5eec2d5e5bce82fc7246e8a90696c},
}
@inproceedings{258888240,
title = {Securing Deep Generative Models with Universal Adversarial Signature},
author = {{Yu Zeng} and {Mo Zhou} and {Yuan Xue} and {Vishal M. Patel}},
year = 2023,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/98e87cd4c19dad7018270b4561dc64b0109ee360},
}
@inproceedings{257663507,
title = {CrowdDiff: Multi-Hypothesis Crowd Density Estimation Using Diffusion Models},
author = {{Y. Ranasinghe} and {Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
year = 2023,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/315d4007e3cc0384c9e340160c44fd698a9ec052},
}
@inproceedings{257378087,
title = {Data Portraits: Recording Foundation Model Training Data},
author = {{Marc Marone} and {Benjamin Van Durme}},
year = 2023,
month = {3},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/572b92972eff7501ca2b109b8998cdcb69aa1958},
}
@inproceedings{268499126,
title = {Do pretrained Transformers Really Learn In-context by Gradient Descent?},
author = {{Lingfeng Shen} and {Aayush Mishra} and {Daniel Khashabi}},
year = 2023,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a9d460f8eb9001b1bed11b7fb2af555185c70fcf},
}
@inproceedings{257323163,
title = {Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease},
author = {{A. Favaro} and {L. Moro-Velázquez} and {A. Butala} and {C. Motley} and {Tianyu Cao} and {R. Stevens} and {J. Villalba} and {N. Dehak}},
year = 2023,
month = {3},
booktitle = {Frontiers in Neurology},
url = {https://www.semanticscholar.org/paper/3ed2d557a323c9fc39dbdd64e0ffab064b35a7f9},
}
@inproceedings{258997982,
title = {Deep Stroop: Using eye tracking and speech processing to characterize people with neurodegenerative disorders while performing the Stroop Test},
author = {{T. Meyer} and {A. Favaro} and {Tianyu Cao} and {A. Butala} and {E. Oh} and {C. Motley} and {P. Irazoqui} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2023,
month = {6},
booktitle = {medRxiv},
url = {https://www.semanticscholar.org/paper/172e04d89d89109626cba6a5b2d4d8a736bd145d},
}
@inproceedings{261682358,
title = {Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning},
author = {{Saurabhchand Bhati} and {J. Villalba} and {L. Moro-Velázquez} and {Thomas Thebaud} and {N. Dehak}},
year = 2023,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ada7b279876196a283a8379729212338386c7eba},
}
@inproceedings{258535938,
title = {Building Keyword Search System from End-To-End Asr Systems},
author = {{Ruizhe Huang} and {Matthew Wiesner} and {Leibny Paola García-Perera} and {Daniel Povey} and {J. Trmal} and {S. Khudanpur}},
year = 2023,
month = {6},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/1b610ce986449cbef77d0f6bdd28421fd8495268},
}
@inproceedings{258714648,
title = {AbdomenAtlas-8K: Annotating 8, 000 CT Volumes for Multi-Organ Segmentation in Three Weeks},
author = {{Chongyu Qu} and {Tiezheng Zhang} and {Hualin Qiao} and {Jie Liu} and {Yucheng Tang} and {A. Yuille} and {Zongwei Zhou}},
year = 2023,
month = {5},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/40927a6916f00f8e7ad43ad7009decb25a861767},
}
@inproceedings{258374319,
title = {Perception of global properties, objects, and settings in natural auditory scenes},
author = {{Margaret A. McMullin} and {Nathan C Higgins} and {Brian Gygi} and {Rohit Kumar} and {Mounya Elhilali} and {J. Snyder}},
year = 2023,
month = {3},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/8890c3afc837ab0871d11f6ed17bfb6109943bff},
}
@inproceedings{263152687,
title = {Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization},
author = {{A. Hussein} and {Brian Yan} and {Antonios Anastasopoulos} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2023,
month = {9},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/c2745e86ecc9bec372690cced53ccfdf44f407f8},
}
@inproceedings{259047417,
title = {Interpretable Speech Features vs. DNN Embeddings: What to Use in the Automatic Assessment of Parkinson's Disease in Multi-lingual Scenarios},
author = {{A. Favaro} and {Yi-Ting Tsai} and {A. Butala} and {Thomas Thebaud} and {J. Villalba} and {N. Dehak} and {L. Moro-Velázquez}},
year = 2023,
month = {6},
booktitle = {medRxiv},
url = {https://www.semanticscholar.org/paper/8d18efe22ad66b53a0a13fc71c9b57c41b7790d0},
}
@inproceedings{268064142,
title = {Causal-structure Driven Augmentations for Text OOD Generalization},
author = {{Amir Feder} and {Yoav Wald} and {Claudia Shi} and {S. Saria} and {David M. Blei}},
year = 2023,
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/dce7db550d6edda246d7848a37f777ba3b9bbf2f},
}
@inproceedings{260125020,
title = {SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation},
author = {{Yiqing Wang} and {Zihan Li} and {Jieru Mei} and {Zi-Ying Wei} and {Li Liu} and {Chen Wang} and {Shengtian Sang} and {A. Yuille} and {Cihang Xie} and {Yuyin Zhou}},
year = 2023,
month = {7},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/c84e03c8ba7feaa2679a90b1637f3b079be15aa9},
}
@inproceedings{263310495,
title = {Enhancing Code-Switching Speech Recognition With Interactive Language Biases},
author = {{Hexin Liu} and {Leibny Paola García} and {Xiangyu Zhang} and {Andy W. H. Khong} and {S. Khudanpur}},
year = 2023,
month = {9},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/47c14df80f649488c64f5659fa49ad356ff59470},
}
@inproceedings{260003954,
title = {Asynchronous, Spatiotemporal Filtering using an Analog Cellular Neural Network Processor},
author = {{Jonah P. Sengupta} and {M. A. Tomlinson} and {Daniel R. Mendat} and {M. Villemur} and {A. Andreou}},
year = 2023,
month = {5},
booktitle = {International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/30446a1b3ca0fc61c3b672d5a284e0dcb761fe6d},
}
@inproceedings{259267429,
title = {What are Adapters Really Efficient At?},
author = {{Alexis Conneau} and {Kartikay Khandelwal} and {Naman Goyal} and {Mitesh M. Khapra} and {Pratyush Kumar} and {V. Rudra} and {Murthy Anoop} and {Kunchukuttan. 2022} and {Naama-677} and {Adam Roberts} and {Stella Biderman} and {Teven Le Scao} and {Saiful Bari} and {Sheng Shen} and {Zheng-Xin Yong} and {Hai-682 ley Schoelkopf} and {Xiangru Tang} and {Dragomir R. Radev} and {Al-683 ham} and {Fikri Aji} and {Khalid Almubarak} and {Samuel Albanie} and {Zaid Alyafeai} and {Albert Webson} and {Edward Raff} and {Jonas Pfeiffer} and {Aishwarya Kamath} and {Andreas Rücklé} and {Kyunghyun Cho} and {Iryna Gurevych} and {Clifton Poth} and {Aishwarya} and {Ivan Kamath} and {Sebastian Vuli´c} and {Kyunghyun Ruder} and {Gregor Geigle} and {Max Glockner} and {Jonas Beck} and {Nils Pfeiffer} and {Reimers Iryna} and {Victor Sanh} and {Colin Raffel} and {Lintang Bach} and {Zaid Sutawika} and {Antoine Alyafeai} and {Arnaud Chaffin} and {Arun Stiegler} and {Manan Raja} and {Dey Saiful} and {Canwen Bari} and {Urmish Xu} and {Thakker} and {Shanya Sharma} and {Eliza Szczechla} and {Taewoon} and {Gunjan Kim} and {Nihal Chhablani} and {Nayak} and {Debajyoti} and {Jonathan Datta} and {Mike Tian-Jian Chang} and {Han Jiang} and {Matteo Wang} and {S. Mânica} and {Zheng Xin Shen} and {Yong} and {Harshit Pandey} and {Rachel Bawden} and {Thomas Wang} and {Tripathi Neeraj} and {Jos Rozen} and {Abheesht Sharma} and {A. Santilli} and {Thibault Févry} and {Jason Alan Fries} and {Maarten Sap} and {Hannah Rashkin} and {Derek Chen} and {Ronan} and {Aarohi Srivastava} and {Abhinav Rastogi} and {Abhishek Rao} and {Adam R. Brown} and {Adam Santoro} and {Adrià Gupta} and {Agnieszka Garriga-Alonso} and {Kluska} and {Aitor Lewkowycz} and {Akshat Agarwal} and {Alethea Power} and {Alex Ray} and {Alex Warstadt} and {Alexander W. Kocurek} and {Ali Safaya} and {Ali Tazarv} and {Alice Xiang} and {Alicia Par-765} and {Allen Nie} and {Aman Hussain} and {Amanda Askell} and {Anantharaman S. Iyer} and {Anders Andreassen} and {Andrea Madotto} and {A. Santilli} and {Andreas Stuhlmüller} and {Andrew M. Dai} and {Andrew La} and {Andrew K. 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title = {Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial},
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title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
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title = {Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions},
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year = 2023,
booktitle = {International Symposium on Conformal and Probabilistic Prediction with Applications},
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year = 2023,
month = {9},
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year = 2023,
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@inproceedings{258999199,
title = {Continual Learning for Abdominal Multi-Organ and Tumor Segmentation},
author = {{Yixiao Zhang} and {Xinyi Li} and {Huimiao Chen} and {A. Yuille} and {Yaoyao Liu} and {Zongwei Zhou}},
year = 2023,
month = {6},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/7f29e3cb212df207146c567a420999cba6d9fff8},
}
@inproceedings{260715129,
title = {Best Paper Section IEEE International Conference on Automatic Face and Gesture Recognition 2021},
author = {{Rachael E. Jack} and {Vishal M. Patel} and {P. Turaga} and {Mayank Vatsa} and {Ramalingam Chellappa} and {A. Pentland} and {Richa Singh}},
year = 2023,
month = {7},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/51bdfb0e1834a150abd52ad73b63e80ad690aa80},
}
@inproceedings{257805227,
title = {Did You Mean...? Confidence-based Trade-offs in Semantic Parsing},
author = {{Elias Stengel-Eskin} and {Benjamin Van Durme}},
year = 2023,
month = {3},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/695a24f4bd79293d7c4dc41ce3f86c66d601f930},
}
@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},
}
We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases.
@inproceedings{gu-etal-2022-empirical,
title = "An Empirical Study on Finding Spans",
author = "Gu, Weiwei and
Zheng, Boyuan and
Chen, Yunmo and
Chen, Tongfei and
Van Durme, Benjamin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.264/",
doi = "10.18653/v1/2022.emnlp-main.264",
pages = "3976--3983",
abstract = "We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases."
}
@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},
}
@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},
}
Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores.
@inproceedings{feng-etal-2022-toward,
title = "Toward the Limitation of Code-Switching in Cross-Lingual Transfer",
author = "Feng, Yukun and
Li, Feng and
Koehn, Philipp",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.400/",
doi = "10.18653/v1/2022.emnlp-main.400",
pages = "5966--5971",
abstract = "Multilingual pretrained models have shown strong cross-lingual transfer ability. Some works used code-switching sentences, which consist of tokens from multiple languages, to enhance the cross-lingual representation further, and have shown success in many zero-shot cross-lingual tasks. However, code-switched tokens are likely to cause grammatical incoherence in newly substituted sentences, and negatively affect the performance on token-sensitive tasks, such as Part-of-Speech (POS) tagging and Named-Entity-Recognition (NER). This paper mitigates the limitation of the code-switching method by not only making the token replacement but considering the similarity between the context and the switched tokens so that the newly substituted sentences are grammatically consistent during both training and inference. We conduct experiments on cross-lingual POS and NER over 30+ languages, and demonstrate the effectiveness of our method by outperforming the mBERT by 0.95 and original code-switching method by 1.67 on F1 scores."
}
@inproceedings{254636282,
title = {Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors},
author = {{Kangfu Mei} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e5fec7e9103c9edbc4c6b4bb1a47e53593c667bb},
}
For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, “does segmentation matter?” and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing.
@inproceedings{wicks-post-2022-sentence,
title = "Does Sentence Segmentation Matter for Machine Translation?",
author = "Wicks, Rachel and
Post, Matt",
editor = {Koehn, Philipp and
Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.78/",
pages = "843--854",
abstract = "For the most part, NLP applications operate at the sentence level. Since sentences occur most naturally in documents, they must be extracted and segmented via the use of a segmenter, of which there are a handful of options. There has been some work evaluating the performance of segmenters on intrinsic metrics, that look at their ability to recover human-segmented sentence boundaries, but there has been no work looking at the effect of segmenters on downstream tasks. We ask the question, ``does segmentation matter?'' and attempt to answer it on the task of machine translation. We consider two settings: the application of segmenters to a black-box system whose training segmentation is mostly unknown, as well as the variation in performance when segmenters are applied to the training process, too. We find that the choice of segmenter largely does not matter, so long as its behavior is not one of extreme under- or over-segmentation. For such settings, we provide some qualitative analysis examining their harms, and point the way towards document-level processing."
}
@inproceedings{254879636,
title = {Automatic Extraction of Oculographic Signals as Digital Biomarkers for Alzheimer's Disease},
author = {{Trevor Meyer} and {L. Moro-Velázquez} and {Seneca Motley} and {A. Butala} and {Ashley M Paul} and {Quincy M. Samus} and {Pedro P. Irazoqui} and {N. Dehak} and {Esther S. Oh}},
year = 2022,
month = {12},
booktitle = {Alzheimer's & Dementia},
url = {https://www.semanticscholar.org/paper/e5a0988cdd73b981611be9fe06e0b7328ff1c0d0},
}
Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder’s representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings.
@inproceedings{rippeth-post-2022-additive,
title = "Additive Interventions Yield Robust Multi-Domain Machine Translation Models",
author = "Rippeth, Elijah and
Post, Matt",
editor = {Koehn, Philipp and
Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.14/",
pages = "220--232",
abstract = "Additive interventions are a recently-proposed mechanism for controlling target-side attributes in neural machine translation by modulating the encoder's representation of a source sequence as opposed to manipulating the raw source sequence as seen in most previous tag-based approaches. In this work we examine the role of additive interventions in a large-scale multi-domain machine translation setting and compare its performance in various inference scenarios. We find that while the performance difference is small between intervention-based systems and tag-based systems when the domain label matches the test domain, intervention-based systems are robust to label error, making them an attractive choice under label uncertainty. Further, we find that the superiority of single-domain fine-tuning comes under question when training data is scaled, contradicting previous findings."
}
We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration.
@inproceedings{jiang-etal-2022-calibrating,
title = "Calibrating Zero-shot Cross-lingual (Un-)structured Predictions",
author = "Jiang, Zhengping and
Liu, Anqi and
Van Durme, Benjamin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.170/",
doi = "10.18653/v1/2022.emnlp-main.170",
pages = "2648--2674",
abstract = "We investigate model calibration in the setting of zero-shot cross-lingual transfer with large-scale pre-trained language models. The level of model calibration is an important metric for evaluating the trustworthiness of predictive models. There exists an essential need for model calibration when natural language models are deployed in critical tasks. We study different post-training calibration methods in structured and unstructured prediction tasks. We find that models trained with data from the source language become less calibrated when applied to the target language and that calibration errors increase with intrinsic task difficulty and relative sparsity of training data. Moreover, we observe a potential connection between the level of calibration error and an earlier proposed measure of the distance from English to other languages. Finally, our comparison demonstrates that among other methods Temperature Scaling (TS) generalizes well to distant languages, but TS fails to calibrate more complex confidence estimation in structured predictions compared to more expressive alternatives like Gaussian Process Calibration."
}
@inproceedings{254563914,
title = {GPU-accelerated Guided Source Separation for Meeting Transcription},
author = {{Desh Raj} and {Daniel Povey} and {S. Khudanpur}},
year = 2022,
month = {12},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/ad9199a35c5ce4738c39bb4af8abf7caee418365},
}
Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions.
@inproceedings{deb-etal-2022-post,
title = "Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machines",
author = "Deb, Kiron and
Zhang, Xuan and
Duh, Kevin",
editor = "Bastings, Jasmijn and
Belinkov, Yonatan and
Elazar, Yanai and
Hupkes, Dieuwke and
Saphra, Naomi and
Wiegreffe, Sarah",
booktitle = "Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.blackboxnlp-1.5/",
doi = "10.18653/v1/2022.blackboxnlp-1.5",
pages = "51--61",
abstract = "Hyperparameter tuning is important for achieving high accuracy in deep learning models, yet little interpretability work has focused on hyperparameters. We propose to use the Explainable Boosting Machine (EBM), a glassbox method, as a post-hoc analysis tool for understanding how hyperparameters influence model accuracy. We present a case study on Transformer models in machine translation to illustrate the kinds of insights that may be gleaned, and perform extensive analysis to test the robustness of EBM under different data conditions."
}
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions–-training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9\% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.
@inproceedings{wang-etal-2022-super,
title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks",
author = "Wang, Yizhong and
Mishra, Swaroop and
Alipoormolabashi, Pegah and
Kordi, Yeganeh and
Mirzaei, Amirreza and
Naik, Atharva and
Ashok, Arjun and
Dhanasekaran, Arut Selvan and
Arunkumar, Anjana and
Stap, David and
Pathak, Eshaan and
Karamanolakis, Giannis and
Lai, Haizhi and
Purohit, Ishan and
Mondal, Ishani and
Anderson, Jacob and
Kuznia, Kirby and
Doshi, Krima and
Pal, Kuntal Kumar and
Patel, Maitreya and
Moradshahi, Mehrad and
Parmar, Mihir and
Purohit, Mirali and
Varshney, Neeraj and
Kaza, Phani Rohitha and
Verma, Pulkit and
Puri, Ravsehaj Singh and
Karia, Rushang and
Doshi, Savan and
Sampat, Shailaja Keyur and
Mishra, Siddhartha and
Reddy A, Sujan and
Patro, Sumanta and
Dixit, Tanay and
Shen, Xudong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.340/",
doi = "10.18653/v1/2022.emnlp-main.340",
pages = "5085--5109",
abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions---training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9\% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
}
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.
@inproceedings{kim-etal-2022-prosocialdialog,
title = "{P}rosocial{D}ialog: A Prosocial Backbone for Conversational Agents",
author = "Kim, Hyunwoo and
Yu, Youngjae and
Jiang, Liwei and
Lu, Ximing and
Khashabi, Daniel and
Kim, Gunhee and
Choi, Yejin and
Sap, Maarten",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.267/",
doi = "10.18653/v1/2022.emnlp-main.267",
pages = "4005--4029",
abstract = "Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible."
}
@inproceedings{254877586,
title = {When Do Decompositions Help for Machine Reading?},
author = {{Kangda Wei} and {Dawn J Lawrie} and {Benjamin Van Durme} and {Yunmo Chen} and {Orion Weller}},
year = 2022,
month = {12},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/624ea7bdaf7e8e3f7bd76f72aa665b562f0dd70a},
}
@inproceedings{254880773,
title = {Artificial Intelligence Tools to Evaluate Language and Speech Patterns in Alzheimer's Disease},
author = {{A. Favaro} and {Seneca Motley} and {Quincy M. Samus} and {A. Butala} and {N. Dehak} and {Esther S. Oh} and {L. Moro-Velázquez}},
year = 2022,
month = {12},
booktitle = {Alzheimer's & Dementia},
url = {https://www.semanticscholar.org/paper/e8f74514d4b195230ddd7dd6b60cabbc7ed240b1},
}
Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision.
@inproceedings{marchisio-etal-2022-bilingual,
title = "Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport",
author = "Marchisio, Kelly and
Saad-Eldin, Ali and
Duh, Kevin and
Priebe, Carey and
Koehn, Philipp",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.164/",
doi = "10.18653/v1/2022.emnlp-main.164",
pages = "2545--2561",
abstract = "Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. In this work, we improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision."
}
@inproceedings{254125164,
title = {Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning},
author = {{Zhuowan Li} and {Xingrui Wang} and {Elias Stengel-Eskin} and {Adam Kortylewski} and {Wufei Ma} and {Benjamin Van Durme} and {Alan Yuille Johns Hopkins University} and {U. California} and {Max Planck Institute for Informatics} and {U. Freiburg}},
year = 2022,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/cb6365a1aa3133318ce7fa2461b6d1d48cd8152e},
}
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 rej 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\'ak, Michal and
Popel, Martin and
Popovi\'c, Maja",
editor = {Koehn, Philipp and
Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.1/",
pages = "1--45",
abstract = "This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM)."
}
@inproceedings{254125357,
title = {Unite and Conquer: Cross Dataset Multimodal Synthesis using Diffusion Models},
author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/24e6c62fd28da4ecf748620e1f25eae7337bad40},
}
Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers’ ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse–dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix.
@inproceedings{ogundepo-etal-2022-africlirmatrix,
title = "{A}fri{CLIRM}atrix: Enabling Cross-Lingual Information Retrieval for {A}frican Languages",
author = "Ogundepo, Odunayo and
Zhang, Xinyu and
Sun, Shuo and
Duh, Kevin and
Lin, Jimmy",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.597/",
doi = "10.18653/v1/2022.emnlp-main.597",
pages = "8721--8728",
abstract = "Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers' ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparse--dense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.AfriCLIRMatrix can be downloaded at https://github.com/castorini/africlirmatrix."
}
@inproceedings{254591386,
title = {Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?},
author = {{David Mueller} and {Nicholas Andrews} and {Mark Dredze}},
year = 2022,
month = {12},
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/2843661ee0d5fa159165beba50c345566cc44c57},
}
@inproceedings{254366617,
title = {AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation},
author = {{Si-Jia Yang} and {Longlong Jing} and {Junfei Xiao} and {Hang Zhao} and {A. Yuille} and {Yingwei Li}},
year = 2022,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1358ad196c4e300612fb3b65a2f3578836941384},
}
@inproceedings{262383173,
title = {A prospective birth cohort study of maternal prenatal cigarette smoking assessed by self-report and biomarkers on childhood risk of overweight or obesity.},
author = {{Wenpin Hou} and {Mingyu Zhang} and {Yuelong Ji} and {X. Hong} and {Guoying Wang} and {Richard Xu} and {Liming Liang} and {S. Saria} and {Hongkai Ji}},
year = 2022,
month = {12},
booktitle = {Precision Nutrition},
url = {https://www.semanticscholar.org/paper/e898ce790bbb170c93ff44e139e83c3448b590ab},
}
Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.
@inproceedings{casacuberta-etal-2022-findings,
title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2022",
author = "Casacuberta, Francisco and
Foster, George and
Huang, Guoping and
Koehn, Philipp and
Kovacs, Geza and
Liu, Lemao and
Shi, Shuming and
Watanabe, Taro and
Zong, Chengqing",
editor = {Koehn, Philipp and
Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.75/",
pages = "812--820",
abstract = "Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task."
}
@inproceedings{254125713,
title = {VIDM: Video Implicit Diffusion Models},
author = {{Kangfu Mei} and {Vishal M. Patel}},
year = 2022,
month = {12},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/13c7b29a100f67d285eb3625c160d06882d4c092},
}
In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.
@inproceedings{sia-etal-2022-offer,
title = "Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates",
author = "Sia, Suzanna and
Jaidka, Kokil and
Ahuja, Hansin and
Chhaya, Niyati and
Duh, Kevin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.818/",
doi = "10.18653/v1/2022.emnlp-main.818",
pages = "11939--11950",
abstract = "In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed."
}
Expressing natural language descriptions of structured facts or relations – data-to-text generation (D2T) – increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.
@inproceedings{keymanesh-etal-2022-makes,
title = "What Makes Data-to-Text Generation Hard for Pretrained Language Models?",
author = "Keymanesh, Moniba and
Benton, Adrian and
Dredze, Mark",
editor = "Bosselut, Antoine and
Chandu, Khyathi and
Dhole, Kaustubh and
Gangal, Varun and
Gehrmann, Sebastian and
Jernite, Yacine and
Novikova, Jekaterina and
Perez-Beltrachini, Laura",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.50/",
doi = "10.18653/v1/2022.gem-1.50",
pages = "539--554",
abstract = "Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation."
}
Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.
@inproceedings{stengel-eskin-van-durme-2022-curious,
title = "The Curious Case of Control",
author = "Stengel-Eskin, Elias and
Van Durme, Benjamin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.760/",
doi = "10.18653/v1/2022.emnlp-main.760",
pages = "11065--11076",
abstract = "Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients."
}
@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},
}
@inproceedings{254877370,
title = {Unleashing the Power of Visual Prompting At the Pixel Level},
author = {{Junyang Wu} and {Xianhang Li} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
year = 2022,
month = {12},
booktitle = {Trans. Mach. Learn. Res.},
url = {https://www.semanticscholar.org/paper/7786825fd653b398c3975c3ff876459307d871f4},
}
The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces–-their degree of “isomorphism.” We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec.
@inproceedings{marchisio-etal-2022-isovec,
title = "{I}so{V}ec: Controlling the Relative Isomorphism of Word Embedding Spaces",
author = "Marchisio, Kelly and
Verma, Neha and
Duh, Kevin and
Koehn, Philipp",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.404/",
doi = "10.18653/v1/2022.emnlp-main.404",
pages = "6019--6033",
abstract = "The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces---their degree of ``isomorphism.'' We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec."
}
In natural language understanding (NLU) production systems, users’ evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues.
@inproceedings{stengel-eskin-etal-2022-data,
title = "When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems",
author = "Stengel-Eskin, Elias and
Platanios, Emmanouil Antonios and
Pauls, Adam and
Thomson, Sam and
Fang, Hao and
Van Durme, Benjamin and
Eisner, Jason and
Su, Yu",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.789/",
doi = "10.18653/v1/2022.emnlp-main.789",
pages = "11473--11487",
abstract = "In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation into this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on a small set of new symbols often decreases. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues."
}
Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models’ propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32\% vs. 19\%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models’ gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models’ social biases.
@inproceedings{lin-etal-2022-gendered,
title = "Gendered Mental Health Stigma in Masked Language Models",
author = "Lin, Inna and
Njoo, Lucille and
Field, Anjalie and
Sharma, Ashish and
Reinecke, Katharina and
Althoff, Tim and
Tsvetkov, Yulia",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.139/",
doi = "10.18653/v1/2022.emnlp-main.139",
pages = "2152--2170",
abstract = "Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models' propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32\% vs. 19\%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models' gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models' social biases."
}
Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT’14 dataset.
@inproceedings{xu-etal-2022-importance,
title = "The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains",
author = "Xu, Haoran and
Koehn, Philipp and
Murray, Kenton",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.13/",
doi = "10.18653/v1/2022.emnlp-main.13",
pages = "170--183",
abstract = "Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT'14 dataset."
}
Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining.
@inproceedings{feng-etal-2022-automatic,
title = "Automatic Document Selection for Efficient Encoder Pretraining",
author = "Feng, Yukun and
Xia, Patrick and
Van Durme, Benjamin and
Sedoc, Jo\~ao",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.647/",
doi = "10.18653/v1/2022.emnlp-main.647",
pages = "9522--9530",
abstract = "Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying smaller yet domain-representative subsets. We extend Cynical Data Selection, a statistical sentence scoring method that conditions on a representative target domain corpus. As an example, we treat the OntoNotes corpus as a target domain and pretrain a RoBERTa-like encoder from a cynically selected subset of the Pile. On both perplexity and across several downstream tasks in the target domain, it consistently outperforms random selection with 20x less data, 3x fewer training iterations, and 2x less estimated cloud compute cost, validating the recipe of automatic document selection for LM pretraining."
}
@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},
}
Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting.
@inproceedings{schumacher-etal-2022-zero,
title = "Zero-shot Cross-Language Transfer of Monolingual Entity Linking Models",
author = "Schumacher, Elliot and
Mayfield, James and
Dredze, Mark",
editor = {Ataman, Duygu and
Gonen, Hila and
Ruder, Sebastian and
Firat, Orhan and
G\"ul Sahin, G\"ozde and
Mirzakhalov, Jamshidbek},
booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mrl-1.4/",
doi = "10.18653/v1/2022.mrl-1.4",
pages = "38--51",
abstract = "Most entity linking systems, whether mono or multilingual, link mentions to a single English knowledge base. Few have considered linking non-English text to a non-English KB, and therefore, transferring an English entity linking model to both a new document and KB language. We consider the task of zero-shot cross-language transfer of entity linking systems to a new language and KB. We find that a system trained with multilingual representations does reasonably well, and propose improvements to system training that lead to improved recall in most datasets, often matching the in-language performance. We further conduct a detailed evaluation to elucidate the challenges of this setting."
}
@InProceedings{svete-et-al-2022,
aclid = "2022.emnlp-main.567",
author = "Anej Svete and Benjamin Dayan and Ryan Cotterell and
Tim Vieira and Jason Eisner",
title = "Acyclic Weighted Finite-State Automata with Failure
Transitions",
booktitle = "Proceedings of the 2022 Conference on Empirical
Methods in Natural Language Processing",
pages = "8289--8305",
year = "2022",
month = dec,
address = "Abu Dhabi",
URL = "http://cs.jhu.edu/~jason/papers/#svete-et-al-2022",
}
@InProceedings{stengeleskin-et-al-2022,
aclid = "2022.emnlp-main.789",
author = "Elias Stengel-Eskin and Emmanouil Antonios Platanios
and Adam Pauls and Sam Thomson and Hao Fang and
Benjamin Van Durme and Jason Eisner and Yu Su",
title = "When More Data Hurts: {A} Troubling Quirk in
Developing Broad-Coverage Natural Language
Understanding Systems",
booktitle = "Proceedings of the 2022 Conference on Empirical
Methods in Natural Language Processing",
pages = "11473--11487",
year = "2022",
month = dec,
address = "Abu Dhabi",
URL = "http://cs.jhu.edu/~jason/papers/#stengeleskin-et-al-2022",
}
@inproceedings{253903852,
title = {Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors},
author = {{V. Rennoll} and {Ian McLane} and {Mounya Elhilali} and {James E. West}},
year = 2022,
month = {11},
booktitle = {Italian National Conference on Sensors},
url = {https://www.semanticscholar.org/paper/0d7b6b5a15b47c1cd1d688f043fd06ff6822d5a1},
}
@inproceedings{253510862,
title = {Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns},
author = {{Shuyue Stella Li} and {Kenton Murray}},
year = 2022,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/96fdfc1ba9588d1fab990d504aa590233216326a},
}
@inproceedings{253510101,
title = {Calibrated Interpretation: Confidence Estimation in Semantic Parsing},
author = {{Elias Stengel-Eskin} and {Benjamin Van Durme}},
year = 2022,
month = {11},
booktitle = {Transactions of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/c428f1621f79925311082d8d7425dd4d50cd64ed},
}
@inproceedings{254069733,
title = {LUMix: Improving Mixup by Better Modelling Label Uncertainty},
author = {{Shuyang Sun} and {Jieneng Chen} and {Ruifei He} and {A. Yuille} and {Philip H. S. Torr} and {Song Bai}},
year = 2022,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/62ce349a6dbc58f64ae02d7203c2f9a06cf6f6d4},
}
@inproceedings{254095971,
title = {Euro: Espnet Unsupervised ASR Open-Source Toolkit},
author = {{Dongji Gao} and {Jiatong Shi} and {Shun-Po Chuang} and {Leibny Paola García-Perera} and {Hung-yi Lee} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2022,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/012771aa3a8d59401d22fade9416dbaad22f42b1},
}
@inproceedings{253244355,
title = {Adapting Self-Supervised Models to Multi-Talker Speech Recognition Using Speaker Embeddings},
author = {{Zili Huang} and {Desh Raj} and {Leibny Paola García-Perera} and {S. Khudanpur}},
year = 2022,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/2226b25c6656e1d7c99667b4e685cd01348e8577},
}
@inproceedings{253383773,
title = {Bridging Speech and Textual Pre-Trained Models With Unsupervised ASR},
author = {{Jiatong Shi} and {Chan-Jan Hsu} and {Ho-Lam Chung} and {Dongji Gao} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Ann Lee} and {Hung-yi Lee}},
year = 2022,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/92302ab168429c7c3a8f699b35ba8302916c6e7c},
}
@inproceedings{253735003,
title = {SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training},
author = {{Yuanze Lin} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Cihang Xie}},
year = 2022,
month = {11},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/210f6ffbed4bf3a0f020cfcb48dab9d6a9939cdb},
}
Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models either translating from or into many languages. The model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. The location and content of the prefix can vary and many use different approaches without much justification towards one approach or another. As a guidance to future researchers and directions for future work, we present a series of experiments that show how the positioning and type of a target language prefix token effects translation performance. We show that source side prefixes improve performance. Further, we find that the best language information to denote via tokens depends on the supported language set.
@inproceedings{wicks-duh-2022-effects,
title = "The Effects of Language Token Prefixing for Multilingual Machine Translation",
author = "Wicks, Rachel and
Duh, Kevin",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.19/",
doi = "10.18653/v1/2022.aacl-short.19",
pages = "148--153",
abstract = "Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models either translating from or into many languages. The model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. The location and content of the prefix can vary and many use different approaches without much justification towards one approach or another. As a guidance to future researchers and directions for future work, we present a series of experiments that show how the positioning and type of a target language prefix token effects translation performance. We show that source side prefixes improve performance. Further, we find that the best language information to denote via tokens depends on the supported language set."
}
@inproceedings{253734941,
title = {SceneComposer: Any-Level Semantic Image Synthesis},
author = {{Yu Zeng} and {Zhe Lin} and {Jianming Zhang} and {Qing Liu} and {J. Collomosse} and {Jason Kuen} and {Vishal M. Patel}},
year = 2022,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/4cc5266166478592ec8539a2b940740b8d380cdd},
}
@inproceedings{253553494,
title = {AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders},
author = {{W. G. C. Bandara} and {Naman Patel} and {A. Gholami} and {Mehdi Nikkhah} and {M. Agrawal} and {Vishal M. Patel}},
year = 2022,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/a135632a05cc1f3311859fdebcd1350b4e9e1ee7},
}
@inproceedings{253801674,
title = {On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation},
author = {{Thanh Nguyen-Tang} and {Ming Yin} and {Sunil Gupta} and {S. Venkatesh} and {R. Arora}},
year = 2022,
month = {11},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/b61a3d718a192e39a437d32a6ed4037b8c29cc41},
}
@inproceedings{253499210,
title = {Open-Set Automatic Target Recognition},
author = {{Bardia Safaei} and {VS Vibashan} and {Celso M. de Melo} and {Shuowen Hu} and {Vishal M. Patel}},
year = 2022,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/878d61661e35c80c0b981fe4512fbad6c55ab037},
}
@inproceedings{254125113,
title = {Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models},
author = {{Vikas Raunak} and {Matt Post} and {Arul Menezes}},
year = 2022,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ad2149957cd288a5626adcce48f9981a2ab59184},
}
@inproceedings{252780362,
title = {Multi-Modal Human Authentication Using Silhouettes, Gait and RGB},
author = {{Yuxiang Guo} and {Cheng Peng} and {Chun Pong Lau} and {R. Chellappa}},
year = 2022,
month = {10},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/e89d9b5c7b5d9c4b490ba1d5fdbbca423920c3e1},
}
@inproceedings{251829168,
title = {Efficient Self-Supervised Learning Representations for Spoken Language Identification},
author = {{Hexin Liu} and {Leibny Paola García-Perera} and {Andy W. H. Khong} and {E. Chng} and {S. Styles} and {S. Khudanpur}},
year = 2022,
month = {10},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/130693386f2f7b7c1a98c4298c4ed27b9a56f79e},
}
@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},
}
@inproceedings{253116576,
title = {Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization},
author = {{Hexin Liu} and {Haihua Xu} and {Leibny Paola García} and {Andy W. H. Khong} and {Yi He} and {S. Khudanpur}},
year = 2022,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/1fab5a425ad712bb8245741c5abec00aa80fc123},
}
Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.
@inproceedings{xia-van-durme-2022-online,
title = "Online Neural Coreference Resolution with Rollback",
author = "Xia, Patrick and
Van Durme, Benjamin",
editor = "Ogrodniczuk, Maciej and
Pradhan, Sameer and
Nedoluzhko, Anna and
Ng, Vincent and
Poesio, Massimo",
booktitle = "Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.crac-1.2/",
pages = "13--21",
abstract = "Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1."
}
@inproceedings{253098023,
title = {Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification},
author = {{Junfei Xiao} and {Yutong Bai} and {A. Yuille} and {Zongwei Zhou}},
year = 2022,
month = {10},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/249e00445585586214e27d1f4ade032533132d0a},
}
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."
}
@inproceedings{253098673,
title = {1st Place Solution of The Robust Vision Challenge (RVC) 2022 Semantic Segmentation Track},
author = {{Junfei Xiao} and {Zhichao Xu} and {Shiyi Lan} and {Zhiding Yu} and {A. Yuille} and {Anima Anandkumar}},
year = 2022,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/17a6bee0ef616822d8a883f6bc373dd676242793},
}
@inproceedings{252715847,
title = {Making Your First Choice: To Address Cold Start Problem in Vision Active Learning},
author = {{Liangyu Chen} and {Yutong Bai} and {Siyu Huang} and {Yongyi Lu} and {B. Wen} and {A. Yuille} and {Zongwei Zhou}},
year = 2022,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a4af00f50f0b397b14ae5dc22e0e766c31adaaa8},
}
@inproceedings{252735237,
title = {Ambiguous Images With Human Judgments for Robust Visual Event Classification},
author = {{Kate Sanders} and {Reno Kriz} and {Anqi Liu} and {Benjamin Van Durme}},
year = 2022,
month = {10},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/2a55f57716576fdd5840252d673aabe9a676fced},
}
@inproceedings{253347117,
title = {Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection},
author = {{VS Vibashan} and {Poojan Oza} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2022,
month = {10},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/96a609d83a2aaf739fedc4cbfa3f96b14ae234cb},
}
@inproceedings{252715598,
title = {MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models},
author = {{Chenglin Yang} and {Siyuan Qiao} and {Qihang Yu} and {Xiaoding Yuan} and {Yukun Zhu} and {A. Yuille} and {Hartwig Adam} and {Liang-Chieh Chen}},
year = 2022,
month = {10},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/a8a2a8229f99c291bf71ec92b801a073854c52e2},
}
@inproceedings{253244506,
title = {Generating Sequences by Learning to Self-Correct},
author = {{S. Welleck} and {Ximing Lu} and {Peter West} and {Faeze Brahman} and {T. Shen} and {Daniel Khashabi} and {Yejin Choi}},
year = 2022,
month = {10},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/538288d24bdad73d831dfed44b706958287ed318},
}
@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},
}
@inproceedings{253117124,
title = {Synthetic Tumors Make AI Segment Tumors Better},
author = {{Qixing Hu} and {Junfei Xiao} and {Yixiong Chen} and {Shuwen Sun} and {Jieneng Chen} and {A. Yuille} and {Zongwei Zhou}},
year = 2022,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0077f46c9cf3de56319aad65e419131e2466b848},
}
@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},
}
@inproceedings{254853697,
title = {A Brief Survey on Person Recognition at a Distance},
author = {{Chris Nalty} and {Neehar Peri} and {Joshua Gleason} and {C. Castillo} and {Shuowen Hu} and {T. Bourlai} and {R. Chellappa}},
year = 2022,
month = {10},
booktitle = {Asilomar Conference on Signals, Systems and Computers},
url = {https://www.semanticscholar.org/paper/6934bd40d21e3bddce5328d29a7e1083e21d0aad},
}
Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.
@inproceedings{wu-yarowsky-2022-known,
title = "Known Words Will Do: Unknown Concept Translation via Lexical Relations",
author = "Wu, Winston and
Yarowsky, David",
editor = "Ojha, Atul Kr. and
Liu, Chao-Hong and
Vylomova, Ekaterina and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Pirinen, Tommi A and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.loresmt-1.3/",
pages = "15--22",
abstract = "Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach."
}
Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART’s pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data.
@inproceedings{verma-etal-2022-strategies,
title = "Strategies for Adapting Multilingual Pre-training for Domain-Specific Machine Translation",
author = "Verma, Neha and
Murray, Kenton and
Duh, Kevin",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.3/",
pages = "31--44",
abstract = "Pretrained multilingual sequence-to-sequence models have been successful in improving translation performance for mid- and lower-resourced languages. However, it is unclear if these models are helpful in the domain adaptation setting, and if so, how to best adapt them to both the domain and translation language pair. Therefore, in this work, we propose two major fine-tuning strategies: our language-first approach first learns the translation language pair via general bitext, followed by the domain via in-domain bitext, and our domain-first approach first learns the domain via multilingual in-domain bitext, followed by the language pair via language pair-specific in-domain bitext. We test our approach on 3 domains at different levels of data availability, and 5 language pairs. We find that models using an mBART initialization generally outperform those using a random Transformer initialization. This holds for languages even outside of mBART's pretraining set, and can result in improvements of over +10 BLEU. Additionally, we find that via our domain-first approach, fine-tuning across multilingual in-domain corpora can lead to stark improvements in domain adaptation without sourcing additional out-of-domain bitext. In larger domain availability settings, our domain-first approach can be competitive with our language-first approach, even when using over 50X less data."
}
Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001\% of the model parameters, the simple method improves \textasciitilde 0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings’ training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence.
@inproceedings{sia-duh-2022-prefix,
title = "Prefix Embeddings for In-context Machine Translation",
author = "Sia, Suzanna and
Duh, Kevin",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.4/",
pages = "45--57",
abstract = "Very large language models have been shown to translate with few-shot in-context examples. However, they have not achieved state-of-art results for translating out of English. In this work, we investigate an extremely lightweight fixed-parameter method for conditioning a large language model to better translate into the target language. Our method introduces additional embeddings, known as prefix embeddings which do not interfere with the existing weights of the model. Using unsupervised and weakly semi-supervised methods that train only 0.0001\% of the model parameters, the simple method improves \textasciitilde 0.2-1.3 BLEU points across 3 domains and 3 languages. We analyze the resulting embeddings' training dynamics, and where they lie in the embedding space, and show that our trained embeddings can be used for both in-context translation, and diverse generation of the target sentence."
}
Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.
@inproceedings{licht-etal-2022-consistent,
title = "Consistent Human Evaluation of Machine Translation across Language Pairs",
author = "Licht, Daniel and
Gao, Cynthia and
Lam, Janice and
Guzman, Francisco and
Diab, Mona and
Koehn, Philipp",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.24/",
pages = "309--321",
abstract = "Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English."
}
Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.
@inproceedings{tan-etal-2022-doubly,
title = "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation",
author = "Tan, Weiting and
Ding, Shuoyang and
Khayrallah, Huda and
Koehn, Philipp",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.12/",
pages = "157--174",
abstract = "Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness."
}
Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players’ interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.
@inproceedings{volum-etal-2022-craft,
title = "Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code",
author = "Volum, Ryan and
Rao, Sudha and
Xu, Michael and
DesGarennes, Gabriel and
Brockett, Chris and
Van Durme, Benjamin and
Deng, Olivia and
Malhotra, Akanksha and
Dolan, Bill",
editor = "C\^ot\'e, Marc-Alexandre and
Yuan, Xingdi and
Ammanabrolu, Prithviraj",
booktitle = "Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wordplay-1.3/",
doi = "10.18653/v1/2022.wordplay-1.3",
pages = "25--43",
abstract = "Non-Player Characters (NPCs) significantly enhance the player experience in many games. Historically, players' interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state. In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code. This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive. We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed. We show that with a few example prompts, a Codex-based agent can generate novel code, hold multi-turn conversations and answer questions about structured data. We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions."
}
Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.
@inproceedings{zirikly-dredze-2022-explaining,
title = "Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks",
author = "Zirikly, Ayah and
Dredze, Mark",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.3/",
doi = "10.18653/v1/2022.clpsych-1.3",
pages = "30--39",
abstract = "Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations."
}
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of `Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-`Switches’- and gradual changes -`Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).
@inproceedings{tsakalidis-etal-2022-overview,
title = "Overview of the {CLP}sych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts",
author = "Tsakalidis, Adam and
Chim, Jenny and
Bilal, Iman Munire and
Zirikly, Ayah and
Atzil-Slonim, Dana and
Nanni, Federico and
Resnik, Philip and
Gaur, Manas and
Roy, Kaushik and
Inkster, Becky and
Leintz, Jeff and
Liakata, Maria",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.16/",
doi = "10.18653/v1/2022.clpsych-1.16",
pages = "184--198",
abstract = "We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of `Moments of Change' in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year's task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual's mood (dras- tic changes-`Switches'- and gradual changes -`Escalations'- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual -- a continuation of the CLPsych 2019 Shared Task-- where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b)."
}
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.
@inproceedings{weller-etal-2022-pretrained,
title = "Pretrained Models for Multilingual Federated Learning",
author = "Weller, Orion and
Marone, Marc and
Braverman, Vladimir and
Lawrie, Dawn and
Van Durme, Benjamin",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.101/",
doi = "10.18653/v1/2022.naacl-main.101",
pages = "1413--1421",
abstract = "Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous works have studied how multilingual text impacts FL algorithms. Furthermore, multilingual text provides an interesting avenue to examine the impact of non-IID text (e.g. different languages) on FL in naturally occurring data. We explore three multilingual language tasks, language modeling, machine translation, and text classification using differing federated and non-federated learning algorithms. Our results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning."
}
Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data.
@inproceedings{zhang-etal-2022-visual,
title = "Visual Commonsense in Pretrained Unimodal and Multimodal Models",
author = "Zhang, Chenyu and
Van Durme, Benjamin and
Li, Zhuowan and
Stengel-Eskin, Elias",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.390/",
doi = "10.18653/v1/2022.naacl-main.390",
pages = "5321--5335",
abstract = "Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (image and language) models capture a broad range of visually salient attributes. To that end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects. We validate this dataset by showing that our grounded color data correlates much better than ungrounded text-only data with crowdsourced color judgments provided by Paik et al. (2021). We then use our dataset to evaluate pretrained unimodal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does not enhance performance, suggesting that the key to visual commonsense lies in the data."
}
The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58\% better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.
@inproceedings{blair-stanek-van-durme-2022-improved,
title = "Improved Induction of Narrative Chains via Cross-Document Relations",
author = "Blair-stanek, Andrew and
Van Durme, Benjamin",
editor = "Nastase, Vivi and
Pavlick, Ellie and
Pilehvar, Mohammad Taher and
Camacho-Collados, Jose and
Raganato, Alessandro",
booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.starsem-1.18/",
doi = "10.18653/v1/2022.starsem-1.18",
pages = "208--212",
abstract = "The standard approach for inducing narrative chains considers statistics gathered per individual document. We consider whether statistics gathered using cross-document relations can lead to improved chain induction. Our study is motivated by legal narratives, where cases typically cite thematically similar cases. We consider four novel variations on pointwise mutual information (PMI), each accounting for cross-document relations in a different way. One proposed PMI variation performs 58\% better relative to standard PMI on recall@50 and induces qualitatively better narrative chains."
}
Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.
@inproceedings{shin-van-durme-2022-shot,
title = "Few-Shot Semantic Parsing with Language Models Trained on Code",
author = "Shin, Richard and
Van Durme, Benjamin",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.396/",
doi = "10.18653/v1/2022.naacl-main.396",
pages = "5417--5425",
abstract = "Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets."
}
Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.
@inproceedings{zirikly-etal-2022-whole,
title = "A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach",
author = "Zirikly, Ayah and
Desmet, Bart and
Porcino, Julia and
Camacho Maldonado, Jonathan and
Ho, Pei-Shu and
Jimenez Silva, Rafael and
Sacco, Maryanne",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.305/",
pages = "2850--2855",
abstract = "Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches."
}
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\'abor 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\'opez Francis, Didier and
Oncevay, Arturo and
L\'opez 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 ga, Ritv\'an and
Markantonatou, Stella and
Pavlidis, George and
Plugaryov, Matvey and
Klyachko, Elena and
Salehi, Ali and
Angulo, Candy and
Baxi, Jatayu and
Krizhanovsky, Andrew and
Krizhanovskaya, Natalia and
Salesky, Elizabeth and
Vania, Clara and
Ivanova, Sardana and
White, Jennifer and
Maudslay, Rowan Hall and
Valvoda, Josef and
Zmigrod, Ran and
Czarnowska, Paula and
Nikkarinen, Irene and
Salchak, Aelita and
Bhatt, Brijesh and
Straughn, Christopher and
Liu, Zoey and
Washington, Jonathan North and
Pinter, Yuval and
Ataman, Duygu and
Wolinski, Marcin and
Suhardijanto, Totok and
Yablonskaya, Anna and
Stoehr, Niklas and
Dolatian, Hossep and
Nuriah, Zahroh and
Ratan, Shyam and
Tyers, Francis M. and
Ponti, Edoardo M. and
Aiton, Grant and
Arora, Aryaman and
Hatcher, Richard J. and
Kumar, Ritesh and
Young, Jeremiah and
Rodionova, Daria and
Yemelina, Anastasia and
Andrushko, Taras and
Marchenko, Igor and
Mashkovtseva, Polina and
Serova, Alexandra and
Prud'hommeaux, Emily and
Nepomniashchaya, Maria and
Giunchiglia, Fausto and
Chodroff, Eleanor and
Hulden, Mans and
Silfverberg, Miikka and
McCarthy, Arya D. and
Yarowsky, David and
Cotterell, Ryan and
Tsarfaty, Reut and
Vylomova, Ekaterina",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.89/",
pages = "840--855",
abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet."
}
We evaluate two popular neural cognate generation models’ robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models’ results with respect to each error type to explain how and why these models perform as they do.
@inproceedings{wu-yarowsky-2022-robustness,
title = "On the Robustness of Cognate Generation Models",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.458/",
pages = "4299--4305",
abstract = "We evaluate two popular neural cognate generation models' robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models' results with respect to each error type to explain how and why these models perform as they do."
}
Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21\% was observed, demonstrating the utility of the corpus.
@inproceedings{mcnamee-duh-2022-multilingual,
title = "The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text",
author = "McNamee, Paul and
Duh, Kevin",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.96/",
pages = "910--918",
abstract = "Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21\% was observed, demonstrating the utility of the corpus."
}
We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.
@inproceedings{kanashiro-pereira-2022-attention,
title = "Attention-Focused Adversarial Training for Robust Temporal Reasoning",
author = "Kanashiro Pereira, Lis",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.800/",
pages = "7352--7359",
abstract = "We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results."
}
Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to “program” complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.
@inproceedings{weber-etal-2022-human,
title = "Human Schema Curation via Causal Association Rule Mining",
author = "Weber, Noah and
Belyy, Anton and
Holzenberger, Nils and
Rudinger, Rachel and
Van Durme, Benjamin",
editor = "Pradhan, Sameer and
Kuebler, Sandra",
booktitle = "Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.law-1.17/",
pages = "139--150",
abstract = "Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to ``program'' complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online."
}
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 rej and
Cattoni, Roldano and
Currey, Anna and
Dinu, Georgiana and
Duh, Kevin and
Elbayad, Maha and
Emmanuel, Clara and
Est\`eve, 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\'avid and
Kloudov\'a, V\u era and
Lakew, Surafel and
Ma, Xutai and
Mathur, Prashant and
McNamee, Paul and
Murray, Kenton and
N\v adejde, 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\"uker, Sebastian and
Sudoh, Katsuhito and
Turchi, Marco and
Virkar, Yogesh and
Waibel, Alexander and
Wang, Changhan and
Watanabe, Shinji},
editor = "Salesky, Elizabeth and
Federico, Marcello and
Costa-juss\`a, Marta",
booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.iwslt-1.10/",
doi = "10.18653/v1/2022.iwslt-1.10",
pages = "98--157",
abstract = "The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved."
}
We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task.
@inproceedings{panthaplackel-etal-2022-updated,
title = "Updated Headline Generation: Creating Updated Summaries for Evolving News Stories",
author = "Panthaplackel, Sheena and
Benton, Adrian and
Dredze, Mark",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.446/",
doi = "10.18653/v1/2022.acl-long.446",
pages = "6438--6461",
abstract = "We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using the NewsEdits corpus by automatically identifying contiguous article versions that are likely to require a substantive headline update. We find that models conditioned on the prior headline and body revisions produce headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. Our experiments establish benchmarks for this new contextual summarization task."
}
This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects.
@inproceedings{yang-etal-2022-jhu,
title = "{JHU} {IWSLT} 2022 Dialect Speech Translation System Description",
author = "Yang, Jinyi and
Hussein, Amir and
Wiesner, Matthew and
Khudanpur, Sanjeev",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Costa-juss\`a, Marta",
booktitle = "Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.iwslt-1.29/",
doi = "10.18653/v1/2022.iwslt-1.29",
pages = "319--326",
abstract = "This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We use a Conformer model for ASR systems and a Transformer model for machine translation. Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly. We also describe an approach, similar to back-translation, for improving performance using synthetic dialectal source text produced from source sentences in mismatched dialects."
}
Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9’s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.
@inproceedings{nguyen-etal-2022-improving,
title = "Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires",
author = "Nguyen, Thong and
Yates, Andrew and
Zirikly, Ayah and
Desmet, Bart and
Cohan, Arman",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.578/",
doi = "10.18653/v1/2022.acl-long.578",
pages = "8446--8459",
abstract = "Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect."
}
Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance.
@inproceedings{wu-etal-2022-zero,
title = "Zero-shot Cross-lingual Transfer is Under-specified Optimization",
author = "Wu, Shijie and
Van Durme, Benjamin and
Dredze, Mark",
editor = "Gella, Spandana and
He, He and
Majumder, Bodhisattwa Prasad and
Can, Burcu and
Giunchiglia, Eleonora and
Cahyawijaya, Samuel and
Min, Sewon and
Mozes, Maximilian and
Li, Xiang Lorraine and
Augenstein, Isabelle and
Rogers, Anna and
Cho, Kyunghyun and
Grefenstette, Edward and
Rimell, Laura and
Dyer, Chris",
booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.repl4nlp-1.25/",
doi = "10.18653/v1/2022.repl4nlp-1.25",
pages = "236--248",
abstract = "Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that any linear-interpolated model between the source language monolingual model and source + target bilingual model has equally low source language generalization error, yet the target language generalization error reduces smoothly and linearly as we move from the monolingual to bilingual model, suggesting that the model struggles to identify good solutions for both source and target languages using the source language alone. Additionally, we show that zero-shot solution lies in non-flat region of target language error generalization surface, causing the high variance."
}
Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation.
@inproceedings{belyy-etal-2022-guided,
title = "Guided K-best Selection for Semantic Parsing Annotation",
author = "Belyy, Anton and
Huang, Chieh-yang and
Andreas, Jacob and
Platanios, Emmanouil Antonios and
Thomson, Sam and
Shin, Richard and
Roy, Subhro and
Nisnevich, Aleksandr and
Chen, Charles and
Van Durme, Benjamin",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.11/",
doi = "10.18653/v1/2022.acl-demo.11",
pages = "114--126",
abstract = "Collecting data for conversational semantic parsing is a time-consuming and demanding process. In this paper we consider, given an incomplete dataset with only a small amount of data, how to build an AI-powered human-in-the-loop process to enable efficient data collection. A guided K-best selection process is proposed, which (i) generates a set of possible valid candidates; (ii) allows users to quickly traverse the set and filter incorrect parses; and (iii) asks users to select the correct parse, with minimal modification when necessary. We investigate how to best support users in efficiently traversing the candidate set and locating the correct parse, in terms of speed and accuracy. In our user study, consisting of five annotators labeling 300 instances each, we find that combining keyword searching, where keywords can be used to query relevant candidates, and keyword suggestion, where representative keywords are automatically generated, enables fast and accurate annotation."
}
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33\% relative improvement over a non-data-augmented baseline in top-1 match.
@inproceedings{yang-etal-2022-addressing,
title = "Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation",
author = "Yang, Kevin and
Deng, Olivia and
Chen, Charles and
Shin, Richard and
Roy, Subhro and
Van Durme, Benjamin",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.291/",
doi = "10.18653/v1/2022.findings-acl.291",
pages = "3685--3695",
abstract = "We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al. 2020), we observe 33\% relative improvement over a non-data-augmented baseline in top-1 match."
}
Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.
@inproceedings{yuan-etal-2022-adapting,
title = "Adapting Coreference Resolution Models through Active Learning",
author = "Yuan, Michelle and
Xia, Patrick and
May, Chandler and
Van Durme, Benjamin and
Boyd-Graber, Jordan",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.519/",
doi = "10.18653/v1/2022.acl-long.519",
pages = "7533--7549",
abstract = "Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models."
}
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\'an, Francisco",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.363/",
doi = "10.18653/v1/2022.acl-long.363",
pages = "5291--5305",
abstract = "Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble -- training a model on a mixture of various signals and ensembling the outputs of the same model fed with different signals during inference, outperforms strong ensemble baselines by 1.3 BLEU points on both language families. Further, we find that incorporating alternative inputs via self-ensemble can be particularly effective when training set is small, leading to +5 BLEU when only 5\% of the total training data is accessible. Finally, our analysis demonstrates that including alternative signals yields more consistency and translates named entities more accurately, which is crucial for increased factuality of automated systems."
}
@InProceedings{zhou-et-al-2022,
aclid = "2022.acl-long.110",
doi = "10.18653/v1/2022.acl-long.110",
author = "Jiawei Zhou and Jason Eisner and Michael Newman and
Emmanouil Anthony Platanios and Sam Thomson",
title = "Online Semantic Parsing for Latency Reduction in
Task-Oriented Dialogue",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
pages = "1554--1576",
year = "2022",
month = may,
address = "Dublin",
URL = "http://cs.jhu.edu/~jason/papers/#zhou-et-al-2022",
}
@InProceedings{cotterell-eisner-2022,
author = "Ryan Cotterell and Jason Eisner",
title = "A Functionalist Account of Vowel System Typology",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
year = "2022",
month = may,
address = "Dublin",
note = "Paper was accepted, but we withdrew it in order to add
more experiments and analysis before publication.",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2022",
}
@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",
}
@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},
}
@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},
}
@inproceedings{260444131,
title = {Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution},
author = {{Nupoor Gandhi} and {Anjalie Field} and {Emma Strubell}},
year = 2022,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/43f09be116b87046334d395a71919ab423b204a1},
}
@inproceedings{248239720,
title = {A Comparison of Different Atmospheric Turbulence Simulation Methods for Image Restoration},
author = {{Nithin Gopalakrishnan Nair} and {Kangfu Mei} and {Vishal M. Patel}},
year = 2022,
month = {4},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/be3eb6827c645f176e204dffb5d740e5281dd67c},
}
@inproceedings{252186400,
title = {Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings},
author = {{Kelly Marchisio} and {Conghao Xiong} and {Philipp Koehn}},
year = 2022,
booktitle = {Conference of the Association for Machine Translation in the Americas},
url = {https://www.semanticscholar.org/paper/4768c7f83f1c4fbb4fd98d9b4237ab483a8bc4b2},
}
@inproceedings{248887310,
title = {On Trace of PGD-Like Adversarial Attacks},
author = {{Mo Zhou} and {Vishal M. Patel}},
year = 2022,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/90d02089aaf88b621880a036a2cc4c5924f7102c},
}
@inproceedings{251762249,
title = {Morphological, Object Detection Framework for Embedded, Event-based Sensing},
author = {{M. Villemur} and {Jonah P. Sengupta} and {P. Julián} and {A. Andreou}},
year = 2022,
month = {6},
booktitle = {International Conference on Event-Based Control, Communication, and Signal Processing},
url = {https://www.semanticscholar.org/paper/dc774c02c8260a15a0098b2a193b7b5db7e3fdb1},
}
@inproceedings{252368348,
title = {T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models},
author = {{Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
year = 2022,
month = {9},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/fc49634e80ab31929799786a97b7ea63834bbdb1},
}
@inproceedings{252337295,
title = {Temporal coding with magnitude-phase regularization for sound event detection},
author = {{Sangwook Park} and {Sandeep Reddy Kothinti} and {Mounya Elhilali}},
year = 2022,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/2c502d4f5eeb29bbf282d78d111cab0ed5d4cc00},
}
@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},
}
@inproceedings{251800257,
title = {Masked Autoencoders Enable Efficient Knowledge Distillers},
author = {{Yutong Bai} and {Zeyu Wang} and {Junfei Xiao} and {Chen Wei} and {Huiyu Wang} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
year = 2022,
month = {8},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/a7cd547c539d69f99f17855242cb07bd80047f9a},
}
@inproceedings{249437588,
title = {Temporal Contrastive-Loss for Audio Event Detection},
author = {{Sandeep Reddy Kothinti} and {Mounya Elhilali}},
year = 2022,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/34fe9aa0f5e26768d196087ed146e2b3a576d73e},
}
@inproceedings{249929578,
title = {CNN-Based Restoration of a Single Face Image Degraded by Atmospheric Turbulence},
author = {{R. Yasarla} and {Vishal M. Patel}},
year = 2022,
month = {4},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/59f5937d4d7a81185a7a0501059c42cee271432f},
}
@inproceedings{249827199,
title = {Advances in Cross-Lingual and Cross-Source Audio-Visual Speaker Recognition: The JHU-MIT System for NIST SRE21},
author = {{J. Villalba} and {B. J. Borgstrom} and {Saurabh Kataria} and {Magdalena Rybicka} and {C. Castillo} and {Jaejin Cho} and {Leibny Paola García-Perera} and {P. Torres-Carrasquillo} and {N. Dehak}},
year = 2022,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/9d9b5b782cbaf98bfb198b120c343d813c99ecf5},
}
@inproceedings{247450789,
title = {Investigating Self-Supervised Learning for Speech Enhancement and Separation},
author = {{Zili Huang} and {Shinji Watanabe} and {Shu-Wen Yang} and {Leibny Paola García-Perera} and {S. Khudanpur}},
year = 2022,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d5634a21b3727258822b78f5c5ababf7261a5c79},
}
@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},
}
This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.
@inproceedings{botev-etal-2022-deciphering,
title = "Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages",
author = "Botev, Georgie and
McCarthy, Arya D. and
Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.472/",
pages = "5309--5326",
abstract = "This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples."
}
@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 {Gary S. Collins} and {Spiros C. Denaxas} and {A. Denniston} and {L. Faes} and {B. Geerts} and {Mudathir Ibrahim} and {Xiaoxuan Liu} and {B. Mateen} and {P. Mathur} and {M. Mccradden} and {L. Morgan} and {Johan Ordish} and {Campbell Rogers} and {S. Saria} and {D. Ting} and {P. Watkinson} and {W. Weber} and {P. Wheatstone} and {P. McCulloch}},
year = 2022,
month = {5},
booktitle = {British medical journal},
url = {https://www.semanticscholar.org/paper/3a8c344f67d5081ead5f7dd5ebf0f760d69fc01d},
}
@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 = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/c900f690fdab5d17b0253d4362e7f1a7d9d2d495},
}
@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},
}
@inproceedings{248069341,
title = {Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser},
author = {{Sonal Joshi} and {Saurabh Kataria} and {Yiwen Shao} and {Piotr Żelasko} and {J. Villalba} and {S. Khudanpur} and {N. Dehak}},
year = 2022,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/49011d1b139bbb65fe273fd9e4b2197cee237385},
}
@inproceedings{258762900,
title = {NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning},
author = {{Nathaniel Weir} and {Benjamin Van Durme}},
year = 2022,
month = {9},
booktitle = {International Joint Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/52aa0f1347459ab9dec1655fc8fa29866919f624},
}
@inproceedings{251765371,
title = {AT-DDPM: Restoring Faces Degraded by Atmospheric Turbulence Using Denoising Diffusion Probabilistic Models},
author = {{Nithin Gopalakrishnan Nair} and {Kangfu Mei} and {Vishal M. Patel}},
year = 2022,
month = {8},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/dad4a46e1fe0e8317bd6734ffdf5609d1f577559},
}
@inproceedings{249953553,
title = {DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection},
author = {{W. G. C. Bandara} and {Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
year = 2022,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/f278eaffb6ee792858ecdcb7209986542d018269},
}
@inproceedings{255595965,
title = {Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition},
author = {{A. Hussein} and {S. A. Chowdhury} and {Ahmed Abdelali} and {N. Dehak} and {Ahmed M. Ali} and {S. Khudanpur}},
year = 2022,
month = {1},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/3c00e6cc82b49f046b5f36e5d5f8aa4af68cad5a},
}
@inproceedings{253461961,
title = {Embedded Processing Pipeline Exploration For Neuromorphic Event Based Perceptual Systems},
author = {{Jonah P. Sengupta} and {M. Villemur} and {P. Pouliquen} and {P. Julián} and {A. Andreou}},
year = 2022,
month = {5},
booktitle = {International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/42845a69a8efd8e8dc7b697c3ce0a4a8f6dfae86},
}
@inproceedings{248512944,
title = {In Defense of Image Pre-Training for Spatiotemporal Recognition},
author = {{Xianhang Li} and {Huiyu Wang} and {Chen Wei} and {Jieru Mei} and {A. Yuille} and {Yuyin Zhou} and {Cihang Xie}},
year = 2022,
month = {5},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/c0c0139333b9c642fe7789f4fe8f27bc647c280d},
}
@inproceedings{216914509,
title = {The 6th AI City Challenge},
author = {{M. Naphade} and {Shuo Wang} and {D. Anastasiu} and {Zheng Tang} and {Ming-Ching Chang} and {Xiaodong Yang} and {Liang Zheng} and {Anuj Sharma} and {R. Chellappa} and {Pranamesh Chakraborty}},
year = 2022,
month = {4},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/7f489232a16a54fa2b11d5758101f078f9db797c},
}
@inproceedings{246805990,
title = {Trade-Offs in Sensor Systems Design: A Tutorial},
author = {{Christos Sapsanis} and {M. Sophocleous} and {A. Andreou} and {J. Georgiou}},
year = 2022,
month = {6},
booktitle = {IEEE Sensors Journal},
url = {https://www.semanticscholar.org/paper/07cfa0c80e6ef73a2aa5fab377c2f698ed476341},
}
@inproceedings{251223772,
title = {Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation},
author = {{Qihao Liu} and {Yi Zhang} and {S. Bai} and {A. Yuille}},
year = 2022,
month = {7},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/79743618fd9bc1e249e8b9df6ddde77b5e29e84f},
}
@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},
}
@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},
}
@inproceedings{247596632,
title = {CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation},
author = {{Feng Wang} and {Huiyu Wang} and {Chen Wei} and {A. Yuille} and {Wei Shen}},
year = 2022,
month = {3},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/3eb748f6279de5cfc582b3179bd1012bbd95614e},
}
@inproceedings{250918749,
title = {In Defense of Online Models for Video Instance Segmentation},
author = {{Junfeng Wu} and {Qihao Liu} and {Yi Jiang} and {S. Bai} and {A. Yuille} and {Xiang Bai}},
year = 2022,
month = {7},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/65edfa85e5e665d51540a2c7ae1bcb6381793f68},
}
@inproceedings{248300043,
title = {Fast AdvProp},
author = {{Jieru Mei} and {Yucheng Han} and {Yutong Bai} and {Yixiao Zhang} and {Yingwei Li} and {Xianhang Li} and {A. Yuille} and {Cihang Xie}},
year = 2022,
month = {4},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/f2eaaf8afc89a86035fd7127305f2bb9d2169495},
}
@inproceedings{249605363,
title = {Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models},
author = {{Nithin Gopalakrishnan Nair} and {W. G. C. Bandara} and {Vishal M. Patel}},
year = 2022,
month = {6},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c6480d46777da8f0e5fa6e65760f0adec31e4bff},
}
@inproceedings{249282662,
title = {Faster Rates of Convergence to Stationary Points in Differentially Private Optimization},
author = {{R. Arora} and {Raef Bassily} and {Tom'as Gonz'alez} and {Crist'obal Guzm'an} and {Michael Menart} and {Enayat Ullah}},
year = 2022,
month = {6},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/6f85ad4e04fc157ed5b499e348972f188a39cd10},
}
@inproceedings{249890221,
title = {CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation},
author = {{Qihang Yu} and {Huiyu Wang} and {Dahun Kim} and {Siyuan Qiao} and {Maxwell D. Collins} and {Yukun Zhu} and {Hartwig Adam} and {A. Yuille} and {Liang-Chieh Chen}},
year = 2022,
month = {6},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/31a9744bd5421b3fbbad2ab38ce33bb2f352c77a},
}
@inproceedings{251710281,
title = {A Risk-Sensitive Approach to Policy Optimization},
author = {{Jared Markowitz} and {Ryan W. Gardner} and {Ashley J. Llorens} and {R. Arora} and {I-J. Wang}},
year = 2022,
month = {8},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/2a1b41221def527e17eb1ca04f4f32442fa09ba7},
}
@inproceedings{249848080,
title = {Orientation-guided Graph Convolutional Network for Bone Surface Segmentation},
author = {{Aimon Rahman} and {W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {I. Hacihaliloglu} and {Vishal M. Patel}},
year = 2022,
month = {6},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/bdcd82545a729552d83ed920bd117718c9f6948f},
}
@inproceedings{250953863,
title = {Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing},
author = {{K. Henry} and {R. Adams} and {Cassandra Parent} and {Hossein Soleimani} and {A. Sridharan} and {Lauren Johnson} and {D. Hager} and {S. Cosgrove} and {Andrew Markowski} and {E. Klein} and {E. Chen} and {M. Saheed} and {Maureen Henley} and {S. Miranda} and {Katrina Houston} and {Robert C. Linton} and {Anushree R Ahluwalia} and {Albert W. Wu} and {S. Saria}},
year = 2022,
month = {7},
booktitle = {Nature Network Boston},
url = {https://www.semanticscholar.org/paper/4cf1afc1e27d26a77aca58d7a5ec7fe3d6b7ffad},
}
@inproceedings{248069457,
title = {AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification},
author = {{Sonal Joshi} and {Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
year = 2022,
month = {4},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/a8144dbb8481cb78e08fc34e452603984bb5aa01},
}
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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 {Afshin 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 {Kia Khezeli} and {S. Komarova} and {M. Komorowski} and {Prachi Kothiyal} and {A. Mahabal} and {U. Manor} and {H. Martín} and {Christopher E. Mason} and {Mona Matar} and {G. Mias} and {J. Myers} and {Jr.} and {Charlotte A. Nelson} and {Jonathan Oribello} and {P. Parsons-Wingerter} and {R. Prabhu} and {A. Qutub} and {J. Rask} and {Amanda M. Saravia-Butler} and {S. Saria} and {N. Singh} and {Frank Soboczenski} and {M. Snyder} and {Karthik Soman} and {D. V. Valen} and {K. Venkateswaran} and {L. Warren} and {Liz Worthey} and {Jason H. Yang} and {M. Zitnik} and {S. V. C. Kbr} and {Space Biosciences Division} and {N. R. Center} and {M. Field} and {Ca} and {USA.} and {Department of Preventive Medicine} and {Center for Individualized Medicine} and {Baylor College of Medicine} and {Houston} and {Tx} and {Blue Marble Space Institute of Science} and {D. Physiology} and {Biophysics} and {Weill Cornell Medicine} and {New York.} and {Ny} and {D. Urology} and {D. Radiology} and {S. Medicine} and {Stanford} and {Bay Area Environmental Research Institute} and {Mortality ResearchConsulting} and {Inc.} and {Universities Space Research Association} and {UC Space Health} and {Department of Orthopaedic Surgery} and {U. California} and {San Francisco} and {AI CenterforHealth} and {D. Biochemistry} and {Molecular Genetics} and {U. Medicine} and {Anschutz Medical Campus} and {Aurora} and {Co} and {Hasso Plattner Institute for Digital Health at Mount Sinai} and {Department of Genetics} and {Genomic Sciences} and {I. A. Sinai} and {Department of Preventive Medicine} and {C. Health} and {Utmb} and {Galveston} and {Human Health} and {Performance Directorate} and {N. J. S. Center} and {D. Microbiology} and {Immunology} and {Department of Otolaryngology} and {Head} and {N. Surgery} and {University of California San Francisco} and {The Gilroy AstroBiology Research Group} and {The University of Wisconsin} and {Madison} and {Wi} and {Weill Institute for Neurosciences} and {D. Neurology} and {D. Chemistry} and {U. Florida} and {Jacksonville} and {Fl} and {D. Analytics} and {G. I. O. Technology} and {Lima} and {Perú} and {Department of Neuroscience} and {U. Minnesota} and {Minneapolis} and {Mn} and {Department of Materials Science} and {College of Materials Science} and {San Diego State University} and {San José} and {Biorelate} and {Manchester} and {United Kingdom.} and {Center for Individualized Medicine} and {Department of Orthopaedic Surgery} and {Department of Mathematical Sciences} and {Mayo Clinic} and {Rochester} and {Faculty of Veterinary Medicine} and {Oral Health Sciences} and {McGill 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 {C. I. O. 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 Quantum 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 Biomedical Engineering} and {U. Texas} and {San Antonio} and {UT Health Sciences} and {Office of the Director} and {Logyx} and {Computer Science} and {Statistics} and {Health Policy} and {J. University} and {Baltimore.} and {Md} and {Ml} and {Ai} and {H. Lab} and {B. Health} and {Biotechnology} and {Planetary Protection Group} and {Jet propulsion Laboratory} and {Sphes} and {Medical Faculty} and {King’s College London} and {S. Medicine} and {Department of Medical Biology} and {Iss National Laboratory} and {Center for Space} and {Melbourne} and {Uab Center for Computational Biology} and {Data Science} and {U. Alabama} and {Birmingham} and {Al} and {Center for Emerging} and {Re-Emerging Pathogens} and {Biochemistry} and {Rutgers New Jersey Medical School} and {Newark} and {Nj} and {Department of Biomedical Informatics} and {H. School} and {Harvard Data Science} and {Broad Institute of Mit} and {Harvard} and {Harvard University} and {Boston} and {Ma.}},
year = 2021,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0d6d142dc49cf7537ece045d8d469fd014a5d3b6},
}
@inproceedings{245353696,
title = {Lite Vision Transformer with Enhanced Self-Attention},
author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zijun Wei} and {Zhe L. Lin} and {A. Yuille}},
year = 2021,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/72e81bc41ffae1d414836169107910025aaacb75},
}
@inproceedings{246291268,
title = {Proxy Model Explanations for Time Series RNNs},
author = {{Zach Wood-Doughty} and {Isabel Cachola} and {Mark Dredze}},
year = 2021,
month = {12},
booktitle = {International Conference on Machine Learning and Applications},
url = {https://www.semanticscholar.org/paper/9e031c15797f9e41598a6c7ebe583e3bb72dceb0},
}
@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 {Serge J. 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},
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
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.",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
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."
}
@inproceedings{244107471,
title = {Joint speaker diarization and speech recognition based on region proposal networks},
author = {{Zili Huang} and {Marc Delcroix} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Desh Raj} and {S. Khudanpur}},
year = 2021,
month = {11},
booktitle = {Computer Speech and Language},
url = {https://www.semanticscholar.org/paper/9bb9b23823b45ba7521d872bb3e970ede4aafb8a},
}
@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},
}
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 rej and
Chatterjee, Rajen and
Chaudhary, Vishrav and
Costa-jussa, Marta R. and
Espa\~na-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",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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."
}
@inproceedings{244345634,
title = {Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme},
author = {{Pengfei Guo} and {Vishal M. Patel}},
year = 2021,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7bab95180b52749d2b018d120d8f04bba520ee0f},
}
@inproceedings{244117494,
title = {iBOT: Image BERT Pre-Training with Online Tokenizer},
author = {{Jinghao Zhou} and {Chen Wei} and {Huiyu Wang} and {Wei Shen} and {Cihang Xie} and {A. Yuille} and {Tao Kong}},
year = 2021,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/9653c070724e44f023e8cc3ec79f0b9e6d59480d},
}
@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},
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
@inproceedings{244714491,
title = {TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions},
author = {{Jeya Maria Jose Valanarasu} and {R. Yasarla} and {Vishal M. Patel}},
year = 2021,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/b27d3be4264dcd06f990b44968f4382526f24f1e},
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
@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},
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
@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},
}
@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},
}
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",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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."
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
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",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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."
}
@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},
}
@inproceedings{257766829,
title = {SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection},
author = {{Tiange Xiang} and {Yixiao Zhang} and {Yongyi Lu} and {A. Yuille} and {Chaoyi Zhang} and {Weidong (Tom) Cai} and {Zongwei Zhou}},
year = 2021,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/e2977c67f55b8a2a58ff1c232c96bed25002f8a2},
}
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",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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."
}
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$\rightarrow$De and 38.61 for De$\rightarrow$En on the IWSLT’14 dataset, and 31.26 for En$\rightarrow$De and 34.94 for De$\rightarrow$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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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$\rightarrow$De and 38.61 for De$\rightarrow$En on the IWSLT'14 dataset, and 31.26 for En$\rightarrow$De and 34.94 for De$\rightarrow$En on the WMT'14 dataset, which exceeds all published numbers."
}
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",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
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."
}
@inproceedings{244096848,
title = {Crystal Cube: Forecasting Disruptive Events},
author = {{A. Buczak} and {Benjamin D. Baugher} and {Christine S. Martin} and {Meg W. Keiley-Listermann} and {J. Howard} and {Nathan H. Parrish} and {Anton Q. Stalick} and {D. S. Berman} and {Mark Dredze}},
year = 2021,
month = {11},
booktitle = {Applied Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/3168dec5c6a5c1441f258c14d05f8520f20ecbaf},
}
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",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
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."
}
@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",
}
@InProceedings{semanticmachines-2021-emnlp,
aclid = "2021.emnlp-main.608",
doi = "10.18653/v1/2021.emnlp-main.608",
author = "Richard Shin and Christopher H. Lin and Sam Thomson
and Charles Chen and Subhro Roy and Emmanouil Antonios
Platanios and Adam Pauls and Dan Klein and Jason Eisner
and Benjamin Van Durme",
title = "Constrained Language Models Yield Few-Shot Semantic
Parsers",
booktitle = "Proceedings of the 2021 Conference on Empirical
Methods in Natural Language Processing",
pages = "7699--7715",
year = "2021",
month = nov,
address = "Punta Cana",
URL = "http://cs.jhu.edu/~jason/papers/#semanticmachines-2021-emnlp",
}
@inproceedings{239454688,
title = {Auditory salience using natural scenes: An online study},
author = {{Sandeep Reddy Kothinti} and {Nicholas Huang} and {Mounya Elhilali}},
year = 2021,
month = {10},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/06ae11378419c01df4297c03d962459aefb3c054},
}
@inproceedings{252440016,
title = {A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation},
author = {{Yixiao Zhang} and {Adam Kortylewski} and {Qing Liu} and {Seyoun Park} and {B. Green} and {E. Engle} and {Guillermo Almodovar} and {Ryan Walk} and {Sigfredo Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
year = 2021,
month = {10},
booktitle = {MOVI@MICCAI},
url = {https://www.semanticscholar.org/paper/4795bf843f77bfd891e34729609c194b85b72a4d},
}
@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},
}
@inproceedings{238743967,
title = {Identification of Attack-Specific Signatures in Adversarial Examples},
author = {{Hossein Souri} and {Pirazh Khorramshahi} and {Chun Pong Lau} and {Micah Goldblum} and {R. Chellappa}},
year = 2021,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7cfeca9f831e4f2d31114215abaa5078a98d1656},
}
@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},
}
@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},
}
@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},
}
@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},
}
@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},
}
@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},
}
@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},
}
@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},
}
@inproceedings{239051966,
title = {Effect of background clutter on neural discrimination in the bat auditory midbrain.},
author = {{K. Allen} and {Angeles Salles} and {Sanwook Park} and {Mounya Elhilali} and {C. Moss}},
year = 2021,
month = {10},
booktitle = {Journal of Neurophysiology},
url = {https://www.semanticscholar.org/paper/1652bdf2674f195b97aee0f1f32926f1c7b9aced},
}
@inproceedings{239768221,
title = {Lhotse: a speech data representation library for the modern deep learning ecosystem},
author = {{Piotr Żelasko} and {Daniel Povey} and {J. Trmal} and {S. Khudanpur}},
year = 2021,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/18394264fe8b4c05527117c5d15a1d19e52c2687},
}
@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},
}
@inproceedings{245002248,
title = {Guest Editorial Special Issue on Sensors Tutorials: A Vigorous Dive Into the Vast Sea of Sensor- Related Knowledge—Part I},
author = {{M. Sophocleous} and {J. Georgiou} and {A. Andreou} and {Yosi Shacham-Diamand} and {Theerawit Wilaiprasitporn} and {J. Atkinson} and {Paddy J. French} and {E. García-Breijo} and {Mohammad Russel}},
year = 2021,
month = {10},
booktitle = {IEEE Sensors Journal},
url = {https://www.semanticscholar.org/paper/72e190cfe76cde934943ae35908bff346d4c970d},
}
@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},
}
@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},
}
@inproceedings{238419143,
title = {Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning},
author = {{VS Vibashan} and {Domenick Poster} and {Suya You} and {Shuowen Hu} and {Vishal M. Patel}},
year = 2021,
month = {10},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/b9e3bd4e032adcdb4093a0cad5ae21d9eabbcee9},
}
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",
editor = "Rapp, Reinhard and
Sharoff, Serge and
Zweigenbaum, Pierre",
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."
}
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",
editor = "Roth, Michael and
Tsarfaty, Reut and
Goldberg, Yoav",
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."
}
@inproceedings{wu-etal-2021-sequence,
title = "Sequence Models for Computational Etymology of Borrowings",
author = "Wu, Winston and
Duh, Kevin and
Yarowsky, David",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
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"
}
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",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
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."
}
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",
editor = "Shterionov, Dimitar",
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."
}
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",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
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."
}
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",
editor = "Federico, Marcello and
Waibel, Alex and
Costa-juss\`a, Marta R. and
Niehues, Jan and
Stuker, Sebastian and
Salesky, Elizabeth",
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."
}
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",
editor = "Federico, Marcello and
Waibel, Alex and
Costa-juss\`a, Marta R. and
Niehues, Jan and
Stuker, Sebastian and
Salesky, Elizabeth",
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."
}
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",
editor = "Ku, Lun-Wei and
Nastase, Vivi and
Vuli\'c, Ivan",
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."
}
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",
editor = "Federico, Marcello and
Waibel, Alex and
Costa-juss\`a, Marta R. and
Niehues, Jan and
Stuker, Sebastian and
Salesky, Elizabeth",
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."
}
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",
editor = "Duh, Kevin and
Guzm\'an, Francisco",
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."
}
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",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
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."
}
@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",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
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"
}
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",
editor = "Dragut, Eduard and
Li, Yunyao and
Popa, Lucian and
Vucetic, Slobodan",
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."
}
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",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
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."
}
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",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
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."
}
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",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
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."
}
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",
editor = "Ku, Lun-Wei and
Li, Cheng-Te",
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."
}
@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",
}
@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",
}
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",
editor = "Blodgett, Su Lin and
Madaio, Michael and
O'Connor, Brendan and
Wallach, Hanna and
Yang, Qian",
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."
}
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",
editor = "Ben-David, Eyal and
Cohen, Shay and
McDonald, Ryan and
Plank, Barbara and
Reichart, Roi and
Rotman, Guy and
Ziser, Yftah",
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."
}
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",
editor = "Gkatzia, Dimitra and
Seddah, Djam\'e",
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."
}
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",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
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."
}
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",
editor = "Ben-David, Eyal and
Cohen, Shay and
McDonald, Ryan and
Plank, Barbara and
Reichart, Roi and
Rotman, Guy and
Ziser, Yftah",
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."
}
@inproceedings{237940275,
title = {Data, Assemble: Leveraging Multiple Datasets with Heterogeneous and Partial Labels},
author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b269e5ae28f360b7ea159135a63ae1f82d9effbf},
}
@inproceedings{235367664,
title = {Simulated Adversarial Testing of Face Recognition Models},
author = {{Nataniel Ruiz} and {Adam Kortylewski} and {Weichao Qiu} and {Cihang Xie} and {Sarah Adel Bargal} and {A. Yuille} and {S. Sclaroff}},
year = 2021,
month = {6},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/1f5ec5c5bc69ee850d31d70281322e8026c5bd52},
}
@inproceedings{239039731,
title = {Invariant Representation Learning for Robust Far-Field Speaker Recognition},
author = {{Aviad Shtrosberg} and {J. Villalba} and {N. Dehak} and {Azaria Cohen} and {Bar Ben-Yair}},
year = 2021,
booktitle = {International Conference on Statistical Language and Speech Processing},
url = {https://www.semanticscholar.org/paper/f157b429553c4a6165856783ec879cd8d0f6a4cd},
}
@inproceedings{233231566,
title = {Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN},
author = {{Rakhil Immidisetti} and {Shuowen Hu} and {Vishal M. Patel}},
year = 2021,
month = {4},
booktitle = {2021 IEEE International Joint Conference on Biometrics (IJCB)},
url = {https://www.semanticscholar.org/paper/d27eac86c86a953a5b1ad13f7c7bc9d5fb127837},
}
@inproceedings{232379984,
title = {CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models},
author = {{Qing Liu} and {Adam Kortylewski} and {Zhishuai Zhang} and {Zizhang Li} and {Mengqi Guo} and {Qihao Liu} and {Xiaoding Yuan} and {Jiteng Mu} and {Weichao Qiu} and {A. Yuille}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/88923b7b455e3ebe63810ebf8dbd1c0c47e79a3c},
}
@inproceedings{231951327,
title = {CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning},
author = {{Chen Wei} and {Kihyuk Sohn} and {Clayton Mellina} and {A. Yuille} and {Fan Yang}},
year = 2021,
month = {2},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/09acced5fcb49322f5a26ac7a4cbe9f1308657c4},
}
@inproceedings{237492280,
title = {Beyond Isolated Utterances: Conversational Emotion Recognition},
author = {{R. Pappagari} and {Piotr Żelasko} and {J. Villalba} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2021,
month = {9},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/6b39bd717627d97c7e69e46801fdbb38ef4eb946},
}
@inproceedings{235743099,
title = {Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction},
author = {{W. G. C. Bandara} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
year = 2021,
month = {7},
booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
url = {https://www.semanticscholar.org/paper/6dffdd9ad229900de79646f53cc73715ad261508},
}
@inproceedings{233333189,
title = {A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering},
author = {{Jonah P. Sengupta} and {M. Villemur} and {A. Andreou}},
year = 2021,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/76791fe786d8fd412ee15ca19b65c8e5b3103bc1},
}
@inproceedings{239704028,
title = {Speaker Verification-Based Evaluation of Single-Channel Speech Separation},
author = {{Matthew Maciejewski} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2021,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/39c5740304b5f4072f92e4e012a4b57e7bc2e817},
}
@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},
}
@inproceedings{235657291,
title = {Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems},
author = {{Pirazh Khorramshahi} and {Sai Saketh Rambhatla} and {R. Chellappa}},
year = 2021,
month = {6},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/8be99c2d0802d6222e233dd67d2927c75a0bed24},
}
“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\'oxochitl {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",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
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\'ochitl 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{235223299,
title = {Align or attend? Toward More Efficient and Accurate Spoken Word Discovery Using Speech-to-Image Retrieval},
author = {{Liming Wang} and {Xinsheng Wang} and {M. Hasegawa-Johnson} and {O. Scharenborg} and {N. Dehak}},
year = 2021,
month = {6},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/70c65b5b0b2debec53c631ab99f0f6a01a86602c},
}
@inproceedings{232168928,
title = {A Parallelizable Lattice Rescoring Strategy with Neural Language Models},
author = {{Ke Li} and {Daniel Povey} and {S. Khudanpur}},
year = 2021,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/736f0404a5352ea100d9a81f4b0b3af10a14b4fe},
}
@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},
}
@inproceedings{232380365,
title = {FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition},
author = {{Samik Sadhu} and {H. Hermansky}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/e0a963ee0038b6cbe3e2aa90770080056d8555e6},
}
@inproceedings{232104977,
title = {Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning},
author = {{Pengfei Guo} and {Puyang Wang} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
year = 2021,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/245df7d8e53b3860107edc76b467e055eb80744d},
}
@inproceedings{237571374,
title = {R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection},
author = {{Jeya Maria Jose Valanarasu} and {Christina Chen} and {Vishal M. Patel}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/6336d19526c1028ec9d9317fdbec68c8ca901eaa},
}
@inproceedings{231698354,
title = {A Joint Representation Learning and Feature Modeling Approach for One-class Recognition},
author = {{Pramuditha Perera} and {Vishal M. Patel}},
year = 2021,
month = {1},
booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/370f4722d2fe2a3ea9ae9198ecaf5047685be904},
}
@inproceedings{233025093,
title = {Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem},
author = {{Desh Raj} and {S. Khudanpur}},
year = 2021,
month = {4},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/88dfc766aeff22a4e5fbdb81ce6161994c745039},
}
@inproceedings{235726458,
title = {Deeply Shape-guided Cascade for Instance Segmentation},
author = {{Hao Ding} and {Siyuan Qiao} and {A. Yuille} and {Wei Shen}},
year = 2021,
month = {6},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/c14e9e74519a2a791f99e2dd8723b9b4f6bfef0e},
}
@inproceedings{233333658,
title = {Compositional Generative Networks and Robustness to Perceptible Image Changes},
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title = {Sound Event Detection with Cross-Referencing Self-Training},
author = {{Sangwook Park} and {Woohyun Choi} and {Mounya Elhilali}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/14d161c0665ce7508c64270707ef62216b4e19a9},
}
@inproceedings{232068763,
title = {Machine Unlearning via Algorithmic Stability},
author = {{Enayat Ullah} and {Tung Mai} and {Anup B. Rao} and {Ryan A. Rossi} and {R. Arora}},
year = 2021,
month = {2},
booktitle = {Annual Conference Computational Learning Theory},
url = {https://www.semanticscholar.org/paper/0fa360d5bb8ce649155c6816fd19e5bffac4e07c},
}
@inproceedings{238681785,
title = {Confidence Guided Network For Atmospheric Turbulence Mitigation},
author = {{Nithin Gopalakrishnan Nair} and {Vishal M. Patel}},
year = 2021,
month = {9},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/34ff864bcef1d3f8bbacc3c241ee65cc6b13b84e},
}
@inproceedings{235691818,
title = {Supplementary: Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions},
author = {{Navaneeth Bodla} and {G. Shrivastava} and {R. Chellappa} and {Abhinav Shrivastava}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/302e4537b277384542d7f0b5cdc4db33abbaa1db},
}
@inproceedings{232222906,
title = {Learning Policies for Multilingual Training of Neural Machine Translation Systems},
author = {{G. Kumar} and {Philipp Koehn} and {S. Khudanpur}},
year = 2021,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/04bc96a2380bccb884cf2568e06d6e726247032b},
}
@inproceedings{239885435,
title = {A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation},
author = {{Yixiao Zhang} and {Adam Kortylewski} and {Qing Liu} and {Seyoun Park} and {B. Green} and {Elizabeth L Engle} and {Guillermo Almodovar} and {Ryan Walk} and {Sigfredo Soto-Diaz} and {J. Taube} and {A. Szalay} and {A. Yuille}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e901638f1839f1e9ee92163193561e77921d524c},
}
@inproceedings{244043942,
title = {Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses},
author = {{Alycen Wiacek} and {N. Dehak} and {M. L. Lediju Bell}},
year = 2021,
month = {9},
booktitle = {IUS},
url = {https://www.semanticscholar.org/paper/36a66d1519a846b05d014858fa611f8e9d500747},
}
@inproceedings{247245043,
title = {Label-Assemble: Leveraging Multiple Datasets with Partial Labels},
author = {{Mintong Kang} and {Yongyi Lu} and {A. Yuille} and {Zongwei Zhou}},
year = 2021,
month = {9},
booktitle = {IEEE International Symposium on Biomedical Imaging},
url = {https://www.semanticscholar.org/paper/ace00da928797186bf3c6e48e79149f4b8886418},
}
@inproceedings{237266533,
title = {Exploring Simple 3D Multi-Object Tracking for Autonomous Driving},
author = {{Chenxu Luo} and {Xiaodong Yang} and {A. Yuille}},
year = 2021,
month = {8},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/31115520c75bb9eb11ff2aee37c7605684d039f5},
}
@inproceedings{237291787,
title = {Adversarially Robust One-Class Novelty Detection},
author = {{Shao-Yuan Lo} and {Poojan Oza} and {Vishal M. Patel}},
year = 2021,
month = {8},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/75892061a60651e9f5db894ea0b5a4b1f6c272c1},
}
@inproceedings{232373611,
title = {ATFaceGAN: Single Face Semantic Aware Image Restoration and Recognition From Atmospheric Turbulence},
author = {{Chun Pong Lau} and {C. Castillo} and {R. Chellappa}},
year = 2021,
month = {4},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/d5ef84d04a6f527158d22304ff0bf73990d6563d},
}
@inproceedings{236087620,
title = {Image Fusion Transformer},
author = {{VS Vibashan} and {Jeya Maria Jose Valanarasu} and {Poojan Oza} and {Vishal M. Patel}},
year = 2021,
month = {7},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/48ec7f1bcf8953ac472384bcea88bc38774508f0},
}
@inproceedings{260435370,
title = {Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry},
author = {{Kelly Marchisio} and {Conghao Xiong} and {Philipp Koehn}},
year = 2021,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f7bb9e4c44356182bff28bfca99404c7d9c04d2b},
}
@inproceedings{233204538,
title = {Multimodal Face Synthesis From Visual Attributes},
author = {{Xing Di} and {Vishal M. Patel}},
year = 2021,
month = {4},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/b3b4be784e92a78ac4987faa0d9d39f113807efc},
}
@inproceedings{244909446,
title = {T HE S TABILITY AND A CCURACY T RADEOFF U NDER D ATASET S HIFT : A C AUSAL G RAPHICAL A NALYSIS},
author = {{Adarsh Subbaswamy} and {Bryant Chen} and {S. Saria}},
year = 2021,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7353a679102d49f5a66265c56f74a338edbeed16},
}
@inproceedings{239711135,
title = {Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition},
author = {{Matthew Wiesner} and {Mousmita Sarma} and {Ashish Arora} and {Desh Raj} and {Dongji Gao} and {Ruizhe Huang} and {Supreet Preet} and {Moris Johnson} and {Zikra Iqbal} and {N. Goel} and {J. Trmal} and {Leibny Paola García Perera} and {S. Khudanpur}},
year = 2021,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/dc6c49acca0d3d6f3fad0971d0962f0990c45a7d},
}
@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},
}
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",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
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/",
doi = "10.18653/v1/2020.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."
}
@inproceedings{227239366,
title = {Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks},
author = {{Christian Cosgrove} and {Adam Kortylewski} and {Chenglin Yang} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/53ae52ef49a8c2ffa4d893332fa0ea9ca7b20805},
}
@inproceedings{229173551,
title = {Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets},
author = {{Denis R. Newman-Griffis} and {Guy 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},
}
@inproceedings{227745021,
title = {Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization},
author = {{Joshua C. Chang} and {P. Fletcher} and {Ju Han} and {Ted L.Chang} and {Shashaank 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},
}
@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},
}
@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},
}
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",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
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."
}
@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},
}
@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},
}
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",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
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."
}
@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},
}
@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},
}
@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},
}
@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},
}
@inproceedings{227239052,
title = {Unsupervised Part Discovery via Feature Alignment},
author = {{Mengqi Guo} and {Yutong Bai} and {Zhishuai Zhang} and {Adam Kortylewski} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/2f2879a07875a94e0e04bc59068807924ea17f97},
}
@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},
}
@inproceedings{229687263,
title = {Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs},
author = {{Pengfei Guo} and {Puyang Wang} and {R. 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},
}
@inproceedings{229153995,
title = {Meticulous Object Segmentation},
author = {{Chenglin Yang} and {Yilin Wang} and {Jianming Zhang} and {He Zhang} and {Zhe L. Lin} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/67b4298db52b5082e851ff6bbd7fbcebeb1c33fc},
}
@inproceedings{229156417,
title = {Mask Guided Matting via Progressive Refinement Network},
author = {{Qihang Yu} and {Jianming Zhang} and {He Zhang} and {Yilin Wang} and {Zhe L. Lin} and {N. Xu} and {Yutong Bai} and {A. Yuille}},
year = 2020,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/2dd4b5e8633a5587ce2ebf73284134f21d1bc6a9},
}
@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},
}
@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 {Denis R. 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},
}
@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",
}
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",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
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\"attar, Andre and
Post, Matt},
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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\~ao and
Van Durme, Benjamin",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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\~ao and
Van Durme, Benjamin",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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\'an, Francisco and
Neubig, Graham and
Durrani, Nadir and
Belinkov, Yonatan and
Koehn, Philipp and
Sajjad, Hassan and
Michel, Paul and
Li, Xian",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.4/",
pages = "76--91",
abstract = "We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs -- English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for ''catastrophic errors''. We received 59 submissions by 11 participating teams from a variety of types of institutions."
}
A 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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
@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.org},
url = {https://www.semanticscholar.org/paper/287a96966040d6dd5aa84d329cb08929de624135},
}
@inproceedings{219505334,
title = {Advances in Speaker Recognition for Telephone and Audio-Visual Data: the JHU-MIT Submission for NIST SRE19},
author = {{Jesús Antonio Villalba López} and {D. Garcia-Romero} and {Nanxin Chen} and {Gregory Sell} and {Jonas Borgstrom} and {A. McCree} and {Leibny Paola García-Perera} and {Saurabh Kataria} and {P. S. Nidadavolu} and {Pedro Torres-Carrasquiilo} and {N. Dehak}},
year = 2020,
month = {11},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/de00fffe4b64aef3797e05e74b5d3d07065b20ee},
}
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",
editor = "Braud, Chlo\'e and
Hardmeier, Christian and
Li, Junyi Jessy and
Louis, Annie and
Strube, Michael",
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
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."
}
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",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
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\"attar, Andre and
Post, Matt},
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
@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 {Juan Ignacio Godino-Llorente}},
year = 2020,
month = {11},
booktitle = {IEEE Access},
url = {https://www.semanticscholar.org/paper/325a462076363f59ad76daff579666adfd1af3ea},
}
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 rej 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 si\'c, Nikola and
Monz, Christof and
Morishita, Makoto and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Pal, Santanu and
Post, Matt and
Zampieri, Marcos},
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
@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.org},
url = {https://www.semanticscholar.org/paper/590c81fe445551cca14e6e7b66a64534fdb454f8},
}
@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},
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Eger, Steffen and
Gao, Yang and
Peyrard, Maxime and
Zhao, Wei and
Hovy, Eduard",
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."
}
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\'an, Francisco",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
@inproceedings{226246188,
title = {Frustratingly Easy Noise-aware Training of Acoustic Models},
author = {{Desh Raj} and {J. Villalba} and {Daniel Povey} and {S. Khudanpur}},
year = 2020,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3b2eb1a573dcdb5a27103b857d32bd0c4d5ef60a},
}
@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},
}
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",
editor = "Agrawal, Priyanka and
Kozareva, Zornitsa and
Kreutzer, Julia and
Lampouras, Gerasimos and
Martins, Andr\'e and
Ravi, Sujith and
Vlachos, Andreas",
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."
}
@inproceedings{227238661,
title = {The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.},
author = {{R. Adams} and {S. Saria} and {Michael Rosenblum}},
year = 2020,
month = {11},
booktitle = {arXiv: Methodology},
url = {https://www.semanticscholar.org/paper/4fbea743d7e81b8a1cd48376a264ea30df9ea6f2},
}
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",
editor = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
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."
}
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",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
@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.org},
url = {https://www.semanticscholar.org/paper/3d35c0aec777f6c180d4bf61a2443ec35230bfd2},
}
@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},
}
@inproceedings{226246280,
title = {DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs},
author = {{Desh Raj} and {Leibny Paola García-Perera} and {Zili Huang} and {Shinji Watanabe} and {Daniel Povey} and {A. Stolcke} and {S. Khudanpur}},
year = 2020,
month = {11},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/6c59a6ad00d82ca9f76fef92232ff3e2f3c1acc8},
}
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",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Volkova, Svitlana",
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."
}
@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.org},
url = {https://www.semanticscholar.org/paper/95824133679061448b57ea746456f36f14796aa0},
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
@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.org},
url = {https://www.semanticscholar.org/paper/c8993a95dac7a0bf86fb96ee30cf653a57755783},
}
@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},
}
@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},
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Agrawal, Priyanka and
Kozareva, Zornitsa and
Kreutzer, Julia and
Lampouras, Gerasimos and
Martins, Andr\'e and
Ravi, Sujith and
Vlachos, Andreas",
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."
}
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\'an, Francisco and
Koehn, Philipp",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
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",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
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."
}
@inproceedings{naradowsky-etal-2020-machine,
title = "Machine Translation System Selection from Bandit Feedback",
author = "Naradowsky, Jason and
Zhang, Xuan and
Duh, Kevin",
editor = "Denkowski, Michael and
Federmann, Christian",
booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = oct,
year = "2020",
address = "Virtual",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2020.amta-research.5/",
pages = "50--63"
}
@inproceedings{225068823,
title = {On Convergence and Generalization of Dropout Training},
author = {{Poorya Mianjy} and {R. Arora}},
year = 2020,
month = {10},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/2d9dc4b6228ca78f395bd55be79b26e02fcb608b},
}
@inproceedings{222154979,
title = {Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.},
author = {{David A. Broniatowski} and {Amelia M. Jamison} and {N. Johnson} and {N. Velásquez} and {R. Leahy} and {N. J. Restrepo} and {Mark Dredze} and {S. Quinn}},
year = 2020,
month = {10},
booktitle = {American Journal of Public Health},
url = {https://www.semanticscholar.org/paper/77f452950894994f55dae8a4cfbdf4cd1980fc59},
}
@inproceedings{226200412,
title = {x-Vectors Meet Adversarial Attacks: Benchmarking Adversarial Robustness in Speaker Verification},
author = {{J. Villalba} and {Yuekai Zhang} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/1b305dbfb789a19013d7ab8fa4f26ab33d99f6ed},
}
@inproceedings{222119577,
title = {Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls},
author = {{D. Kelly} and {Max Spaderna} and {Vedrana Hodzic} and {Suraj Nair} and {Christopher Kitchen} and {A. Werkheiser} and {Megan M. Powell} and {Fang Liu} and {Glen A. Coppersmith} and {Shuo Chen} and {P. Resnik}},
year = 2020,
month = {10},
booktitle = {Psychiatry Research},
url = {https://www.semanticscholar.org/paper/dbfbee8705b0f3f172963bc22d1b145cfdec0f55},
}
@inproceedings{222834475,
title = {Self-reported Cannabidiol (CBD) Use for Conditions With Proven Therapies},
author = {{E. Leas} and {E. M. Hendrickson} and {A. Nobles} and {R. Todd} and {Davey M. Smith} and {Mark Dredze} and {J. Ayers}},
year = 2020,
month = {10},
booktitle = {JAMA Network Open},
url = {https://www.semanticscholar.org/paper/43da600949c62a5cb2a54f427ddfa468167a3243},
}
@inproceedings{244728395,
title = {Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model},
author = {{Yihong Sun} and {Adam Kortylewski} and {A. Yuille}},
year = 2020,
month = {10},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/941e8b25be33bb961182c9ecbb95815d8e62eee6},
}
@inproceedings{229518417,
title = {Flexible electrostatic transducer with tuned acoustic impedance for improved sensing of body-and water-borne sounds},
author = {{Ian McLane} and {V. Rennoll} and {Adebayo A. Eisape} and {Mounya Elhilali} and {J. West}},
year = 2020,
month = {10},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/5b105c7c8ccef29c54b4f8b1f3d9e8ca8f8da7e4},
}
@inproceedings{226200343,
title = {Continual Learning in Automatic Speech Recognition},
author = {{Samik Sadhu} and {H. Hermansky}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/802731161ad5db91bac8569307d8e75019d9ffdd},
}
@inproceedings{222137553,
title = {GAN-Based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation},
author = {{Ahmed Z. Alsinan} and {Charles Rule} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
year = 2020,
month = {10},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/b77d0fd091ddc9b6d23f1b246ea5cbf333c6be24},
}
@inproceedings{226203271,
title = {Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer's Disease and Assess its Severity},
author = {{R. Pappagari} and {Jaejin Cho} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/4c25acf91e0b0b475e69cb9ab9f0041d16bc7c7d},
}
@inproceedings{222133837,
title = {KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation},
author = {{Jeya Maria Jose Valanarasu} and {Vishwanath A. Sindagi} and {I. Hacihaliloglu} and {Vishal M. Patel}},
year = 2020,
month = {10},
booktitle = {IEEE Transactions on Medical Imaging},
url = {https://www.semanticscholar.org/paper/380f9376e00ae9e56c79c1bef7e4e3a10ae75365},
}
@inproceedings{224829138,
title = {Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd’s Death},
author = {{J. Ayers} and {B. Althouse} and {Adam Poliak} and {E. Leas} and {A. Nobles} and {Mark Dredze} and {Davey M. Smith}},
year = 2020,
month = {10},
booktitle = {Journal of Medical Internet Research},
url = {https://www.semanticscholar.org/paper/53dfb4e46c47b98a11ca5fc94db5dc55c42243ee},
}
@inproceedings{226238222,
title = {Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing},
author = {{Jaime S. Cardoso} and {H. Nguyen} and {N. Heller} and {P. Abreu} and {I. Išgum} and {W. Silva} and {Ricardo Cruz} and {J. P. Amorim} and {Vishal M. Patel} and {B. Roysam} and {Kevin Zhou} and {Steve Jiang} and {Ngan T. H. Le} and {Khoa Luu} and {R. Sznitman} and {V. Cheplygina} and {D. Mateus} and {E. Trucco} and {Samaneh Abbasi-Sureshjani}},
year = 2020,
month = {10},
booktitle = {iMIMIC/MIL3ID/LABELS@MICCAI},
url = {https://www.semanticscholar.org/paper/ec323495610daede5d9fb1143be4b07c14971f7e},
}
@inproceedings{226037987,
title = {Predictors of the start of declining eGFR in patients with systemic lupus erythematosus},
author = {{T. Yip} and {S. Saria} and {M. Petri} and {L. Magder}},
year = 2020,
month = {10},
booktitle = {Lupus},
url = {https://www.semanticscholar.org/paper/dc049aa6be740ffbb5e03be04c9c7f3c8f56eb5b},
}
@inproceedings{225062469,
title = {How Phonotactics Affect Multilingual and Zero-Shot ASR Performance},
author = {{Siyuan Feng} and {Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {A. Abavisani} and {M. Hasegawa-Johnson} and {O. Scharenborg} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/2fb642dc5d724c32d3b4cfa2359432968d591287},
}
@inproceedings{222354379,
title = {News coverage of the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak and internet searches for vaping cessation},
author = {{E. Leas} and {A. Nobles} and {Theodore L. Caputi} and {Mark Dredze} and {Shu-Hong Zhu} and {Joanna E. Cohen} and {J. Ayers}},
year = 2020,
month = {10},
booktitle = {Tobacco Control},
url = {https://www.semanticscholar.org/paper/22c3117fc4fa28bef30d00843035d604ee1dc0c4},
}
@inproceedings{225039997,
title = {Learning Speaker Embedding from Text-to-Speech},
author = {{Jaejin Cho} and {Piotr Żelasko} and {J. Villalba} and {Shinji Watanabe} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/faf494d0aa25a17aa25930ffb4c750fa59c44849},
}
@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},
}
@inproceedings{222310549,
title = {Shape-Texture Debiased Neural Network Training},
author = {{Yingwei Li} and {Qihang Yu} and {Mingxing Tan} and {Jieru Mei} and {Peng Tang} and {Wei Shen} and {A. Yuille} and {Cihang Xie}},
year = 2020,
month = {10},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/33ccec80b42f624cec07f0ab485c04de14886fe5},
}
@inproceedings{226227140,
title = {Streaming Simultaneous Speech Translation with Augmented Memory Transformer},
author = {{Xutai Ma} and {Yongqiang Wang} and {M. Dousti} and {Philipp Koehn} and {J. Pino}},
year = 2020,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/0d85f33d43ef7dbac3e559b94aea2fd8f5e64f7f},
}
@inproceedings{224883209,
title = {Deep Multimodal Sparse Representation-Based Classification},
author = {{Mahdi Abavisani} and {Vishal M. Patel}},
year = 2020,
month = {10},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/3244860c13dbeef339a92a6a37f0975891c539ca},
}
@inproceedings{225069289,
title = {ORTHROS: non-autoregressive end-to-end speech translation With dual-decoder},
author = {{H. Inaguma} and {Yosuke Higuchi} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
year = 2020,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/589e651c69251ee20a89e075d015eb03b35cf17d},
}
@inproceedings{229510453,
title = {Characterizing the acoustic impedance and attenuation of biocompatible elastomers: An optimal design of experiments approach},
author = {{V. Rennoll} and {Ian McLane} and {Mounya Elhilali} and {J. West}},
year = 2020,
month = {10},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/7212311ad0f267e90414a56b95f4f4ead2753645},
}
@inproceedings{225062156,
title = {Adversarial Robustness of Supervised Sparse Coding},
author = {{Jeremias Sulam} and {Ramchandran Muthumukar} and {R. Arora}},
year = 2020,
month = {10},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/07cc4408d5fa28007db9135fceb73943a713a962},
}
@inproceedings{202760086,
title = {Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples},
author = {{Yingwei Li} and {Zhuotun Zhu} and {Yuyin Zhou} and {Yingda Xia} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2020,
month = {10},
booktitle = {Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics},
url = {https://www.semanticscholar.org/paper/71ec4e16c6a313dc04ece50aed94554aebe41b1f},
}
@inproceedings{222137241,
title = {Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images},
author = {{Puyang Wang} and {Pengfei Guo} and {Jianhua Lu} and {Jinyuan Zhou} and {Shanshan Jiang} and {Vishal M. Patel}},
year = 2020,
month = {10},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/7f010fac05da563967298618e663effdcafe3e9d},
}
@inproceedings{222140947,
title = {CO2: Consistent Contrast for Unsupervised Visual Representation Learning},
author = {{Chen Wei} and {Huiyu Wang} and {Wei Shen} and {A. Yuille}},
year = 2020,
month = {10},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/b6f54f6d3a0cb9d3f1244c63773c40b0f5a1e224},
}
@inproceedings{222152020,
title = {Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter.},
author = {{Amelia M. Jamison} and {David A. Broniatowski} and {Michael C. Smith} and {Kajal Parikh} and {Adeena Malik} and {Mark Dredze} and {S. Quinn}},
year = 2020,
month = {10},
booktitle = {American Journal of Public Health},
url = {https://www.semanticscholar.org/paper/8aceab6f7c62f65667094060b79b7ac735ae7f3a},
}
@inproceedings{222136412,
title = {Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition},
author = {{Puyang Wang} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
year = 2020,
month = {10},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/5a6acd6c8daf2468b27bca9042e94e337dfb4f1b},
}
@inproceedings{225094283,
title = {Evaluating Model Robustness to Dataset Shift},
author = {{Adarsh Subbaswamy} and {R. Adams} and {S. Saria}},
year = 2020,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/d814c4f5d1e5a0b134638344d43b30645144f9b4},
}
@inproceedings{225062331,
title = {Training Noisy Single-Channel Speech Separation with Noisy Oracle Sources: A Large Gap and a Small Step},
author = {{Matthew Maciejewski} and {Jing Shi} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2020,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/085072963b33367b842369b9ce81394d32ac8843},
}
@inproceedings{225067102,
title = {Weakly-Supervised Amodal Instance Segmentation with Compositional Priors},
author = {{Yihong Sun} and {Adam Kortylewski} and {A. Yuille}},
year = 2020,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f3edb5d5a1c825949578c2f9bfacc3141180ec43},
}
@inproceedings{225094487,
title = {CopyPaste: An Augmentation Method for Speech Emotion Recognition},
author = {{R. Pappagari} and {J. Villalba} and {Piotr Żelasko} and {L. Moro-Velázquez} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/f620d71fccdf3efad7be1748d40eaadea5c9d6dd},
}
@inproceedings{224814251,
title = {Exploring Overcomplete Representations for Single Image Deraining Using CNNs},
author = {{R. Yasarla} and {Jeya Maria Jose Valanarasu} and {Vishal M. Patel}},
year = 2020,
month = {10},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/051a024dcf5b7c81f22504dc317a8de9d1940020},
}
@inproceedings{226201790,
title = {An Alternative to MFCCs for ASR},
author = {{Pegah Ghahramani} and {Hossein Hadian} and {Daniel Povey} and {H. Hermansky} and {S. Khudanpur}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/ce6fca70a2e54733a501648f9f7f3b346a57096a},
}
@inproceedings{226202223,
title = {Black-Box Attacks on Spoofing Countermeasures Using Transferability of Adversarial Examples},
author = {{Yuekai Zhang} and {Ziyan Jiang} and {J. Villalba} and {N. Dehak}},
year = 2020,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/cf1e3bf91fa9989981e5ed3e00331ff0dbe3d56f},
}
Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider \textit{polyglot} named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are \textit{fine-tuned} on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.
@inproceedings{mueller-etal-2020-sources,
title = "Sources of Transfer in Multilingual Named Entity Recognition",
author = "Mueller, David and
Andrews, Nicholas and
Dredze, Mark",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.720/",
doi = "10.18653/v1/2020.acl-main.720",
pages = "8093--8104",
abstract = "Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider \textit{polyglot} named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are \textit{fine-tuned} on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters."
}
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40\%) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.
@inproceedings{gordon-etal-2020-compressing,
title = "Compressing {BERT}: Studying the Effects of Weight Pruning on Transfer Learning",
author = "Gordon, Mitchell and
Duh, Kevin and
Andrews, Nicholas",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.18/",
doi = "10.18653/v1/2020.repl4nlp-1.18",
pages = "143--155",
abstract = "Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40\%) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance."
}
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data.
@inproceedings{wu-dredze-2020-languages,
title = "Are All Languages Created Equal in Multilingual {BERT}?",
author = "Wu, Shijie and
Dredze, Mark",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.16/",
doi = "10.18653/v1/2020.repl4nlp-1.16",
pages = "120--130",
abstract = "Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data."
}
In traditional approaches to entity linking, linking decisions are based on three sources of information – the similarity of the mention string to an entity’s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.
@inproceedings{schumacher-etal-2020-clinical,
title = "Clinical Concept Linking with Contextualized Neural Representations",
author = "Schumacher, Elliot and
Mulyar, Andriy and
Dredze, Mark",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.760/",
doi = "10.18653/v1/2020.acl-main.760",
pages = "8585--8592",
abstract = "In traditional approaches to entity linking, linking decisions are based on three sources of information -- the similarity of the mention string to an entity's name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker."
}
We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.
@inproceedings{chen-etal-2020-hierarchical,
title = "Hierarchical Entity Typing via Multi-level Learning to Rank",
author = "Chen, Tongfei and
Chen, Yunmo and
Van Durme, Benjamin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.749/",
doi = "10.18653/v1/2020.acl-main.749",
pages = "8465--8475",
abstract = "We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method."
}
We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.
@inproceedings{belyy-van-durme-2020-script,
title = "Script Induction as Association Rule Mining",
author = "Belyy, Anton and
Van Durme, Benjamin",
editor = "Bonial, Claire and
Caselli, Tommaso and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Huang, Ruihong and
Iyyer, Mohit and
Jaimes, Alejandro and
Ji, Heng and
Martin, Lara J. and
Miller, Ben and
Mitamura, Teruko and
Peng, Nanyun and
Tetreault, Joel",
booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nuse-1.7/",
doi = "10.18653/v1/2020.nuse-1.7",
pages = "55--62",
abstract = "We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test."
}
We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.
@inproceedings{gordon-duh-2020-distill,
title = "Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation",
author = "Gordon, Mitchell and
Duh, Kevin",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Heafield, Kenneth and
Junczys-Dowmunt, Marcin and
Konstas, Ioannis and
Li, Xian and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ngt-1.12/",
doi = "10.18653/v1/2020.ngt-1.12",
pages = "110--118",
abstract = "We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher."
}
We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the “metrics shared task” of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at \url{https://github.com/ssun32/CLIReval}.
@inproceedings{sun-etal-2020-clireval,
title = "{CLIR}eval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task",
author = "Sun, Shuo and
Sia, Suzanna and
Duh, Kevin",
editor = "Celikyilmaz, Asli and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.18/",
doi = "10.18653/v1/2020.acl-demos.18",
pages = "134--141",
abstract = "We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the ``metrics shared task'' of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at \url{https://github.com/ssun32/CLIReval}."
}
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at \url{https://github.com/espnet/espnet}.
@inproceedings{inaguma-etal-2020-espnet,
title = "{ESP}net-{ST}: All-in-One Speech Translation Toolkit",
author = "Inaguma, Hirofumi and
Kiyono, Shun and
Duh, Kevin and
Karita, Shigeki and
Yalta, Nelson and
Hayashi, Tomoki and
Watanabe, Shinji",
editor = "Celikyilmaz, Asli and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.34/",
doi = "10.18653/v1/2020.acl-demos.34",
pages = "302--311",
abstract = "We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at \url{https://github.com/espnet/espnet}."
}
This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric–-weighted macro F1 of an n-best list.
@inproceedings{khayrallah-etal-2020-jhu,
title = "The {JHU} Submission to the 2020 {D}uolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education",
author = "Khayrallah, Huda and
Bremerman, Jacob and
McCarthy, Arya D. and
Murray, Kenton and
Wu, Winston and
Post, Matt",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Heafield, Kenneth and
Junczys-Dowmunt, Marcin and
Konstas, Ioannis and
Li, Xian and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ngt-1.22/",
doi = "10.18653/v1/2020.ngt-1.22",
pages = "188--197",
abstract = "This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric---weighted macro F1 of an n-best list."
}
We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.
@inproceedings{adams-etal-2020-induced,
title = "Induced Inflection-Set Keyword Search in Speech",
author = "Adams, Oliver and
Wiesner, Matthew and
Trmal, Jan and
Nicolai, Garrett and
Yarowsky, David",
editor = "Nicolai, Garrett and
Gorman, Kyle and
Cotterell, Ryan",
booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigmorphon-1.25/",
doi = "10.18653/v1/2020.sigmorphon-1.25",
pages = "210--216",
abstract = "We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches."
}
We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.
@inproceedings{stengel-eskin-etal-2020-universal,
title = "Universal Decompositional Semantic Parsing",
author = "Stengel-Eskin, Elias and
White, Aaron Steven and
Zhang, Sheng and
Van Durme, Benjamin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.746/",
doi = "10.18653/v1/2020.acl-main.746",
pages = "8427--8439",
abstract = "We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups."
}
We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.
@inproceedings{chen-etal-2020-uncertain,
title = "Uncertain Natural Language Inference",
author = "Chen, Tongfei and
Jiang, Zhengping and
Poliak, Adam and
Sakaguchi, Keisuke and
Van Durme, Benjamin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.774/",
doi = "10.18653/v1/2020.acl-main.774",
pages = "8772--8779",
abstract = "We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks."
}
We present a novel document-level model for finding argument spans that fill an event’s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.
@inproceedings{ebner-etal-2020-multi,
title = "Multi-Sentence Argument Linking",
author = "Ebner, Seth and
Xia, Patrick and
Culkin, Ryan and
Rawlins, Kyle and
Van Durme, Benjamin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.718/",
doi = "10.18653/v1/2020.acl-main.718",
pages = "8057--8077",
abstract = "We present a novel document-level model for finding argument spans that fill an event's roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets."
}
@InProceedings{mei-et-al-2020-icml,
author = "Hongyuan Mei and Guanghui Qin and Minjie Xu and Jason
Eisner",
title = "Neural {D}atalog Through Time: Informed Temporal
Modeling via Logical Specification",
booktitle = "Proceedings of the 37th International Conference on
Machine Learning (ICML)",
year = "2020",
month = jul,
URL = "http://cs.jhu.edu/~jason/papers/#mei-et-al-2020-icml",
}
@InProceedings{salesky-et-al-2020,
aclid = "2020.acl-main.415",
doi = "10.18653/v1/2020.acl-main.415",
author = "Elizabeth Salesky and Eleanor Chodroff and Tiago
Pimentel and Matthew Wiesner and Ryan Cotterell and
Alan W. Black and Jason Eisner",
title = "A Corpus for Large-Scale Phonetic Typology",
booktitle = "Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "2388--2397",
year = "2020",
month = jul,
URL = "http://cs.jhu.edu/~jason/papers/#salesky-et-al-2020",
}
We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field.
@inproceedings{wu-yarowsky-2020-computational,
title = "Computational Etymology and Word Emergence",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.397/",
pages = "3252--3259",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field."
}
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms 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. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.
@inproceedings{mccarthy-etal-2020-unimorph,
title = "{U}ni{M}orph 3.0: {U}niversal {M}orphology",
author = "McCarthy, Arya D. and
Kirov, Christo and
Grella, Matteo and
Nidhi, Amrit and
Xia, Patrick and
Gorman, Kyle and
Vylomova, Ekaterina and
Mielke, Sabrina J. and
Nicolai, Garrett and
Silfverberg, Miikka and
Arkhangelskiy, Timofey and
Krizhanovsky, Nataly and
Krizhanovsky, Andrew and
Klyachko, Elena and
Sorokin, Alexey and
Mansfield, John and
Ern\v streits, Valts and
Pinter, Yuval and
Jacobs, Cassandra L. and
Cotterell, Ryan and
Hulden, Mans and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.483/",
pages = "3922--3931",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms 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. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018."
}
Exploiting the broad translation of the Bible into the world’s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike
@inproceedings{nicolai-etal-2020-fine,
title = "Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages",
author = "Nicolai, Garrett and
Lewis, Dylan and
McCarthy, Arya D. and
Mueller, Aaron and
Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.488/",
pages = "3963--3972",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "Exploiting the broad translation of the Bible into the world's languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike"
}
We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world’s languages. We catalog this by showing highly similar proportions of representation of Ethnologue’s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.
@inproceedings{mccarthy-etal-2020-johns,
title = "The {J}ohns {H}opkins {U}niversity {B}ible Corpus: 1600+ Tongues for Typological Exploration",
author = "McCarthy, Arya D. and
Wicks, Rachel and
Lewis, Dylan and
Mueller, Aaron and
Wu, Winston and
Adams, Oliver and
Nicolai, Garrett and
Post, Matt and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.352/",
pages = "2884--2892",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world's languages. We catalog this by showing highly similar proportions of representation of Ethnologue's typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction."
}
We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high overlap with existing widely utilized core vocabulary lists targeted at applications such as first and second language learning or field linguistics. Our in-depth analysis illustrates multiple desirable properties of our newly proposed core vocabulary set, including their non-compositionality. We employ a cognate prediction method to recover missing coverage of this core vocabulary in massively multilingual dictionary construction, and we argue that this core vocabulary should be prioritized for elicitation when creating new dictionaries for low-resource languages for multiple downstream tasks including machine translation and language learning.
@inproceedings{wu-etal-2020-multilingual,
title = "Multilingual Dictionary Based Construction of Core Vocabulary",
author = "Wu, Winston and
Nicolai, Garrett and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.519/",
pages = "4211--4217",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high overlap with existing widely utilized core vocabulary lists targeted at applications such as first and second language learning or field linguistics. Our in-depth analysis illustrates multiple desirable properties of our newly proposed core vocabulary set, including their non-compositionality. We employ a cognate prediction method to recover missing coverage of this core vocabulary in massively multilingual dictionary construction, and we argue that this core vocabulary should be prioritized for elicitation when creating new dictionaries for low-resource languages for multiple downstream tasks including machine translation and language learning."
}
In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance–-the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one’s approach to the source language and its typology.
@inproceedings{mueller-etal-2020-analysis,
title = "An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages",
author = "Mueller, Aaron and
Nicolai, Garrett and
McCarthy, Arya D. and
Lewis, Dylan and
Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.458/",
pages = "3710--3718",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance---the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one's approach to the source language and its typology."
}
Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.
@inproceedings{duh-etal-2020-benchmarking,
title = "Benchmarking Neural and Statistical Machine Translation on Low-Resource {A}frican Languages",
author = "Duh, Kevin and
McNamee, Paul and
Post, Matt and
Thompson, Brian",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.325/",
pages = "2667--2675",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions."
}
The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA’s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.
@inproceedings{desmet-etal-2020-development,
title = "Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the {U}.{S}.",
author = "Desmet, Bart and
Porcino, Julia and
Zirikly, Ayah and
Newman-Griffis, Denis and
Divita, Guy and
Rasch, Elizabeth",
editor = "Samy, Doaa and
P\'erez-Fern\'andez, David and
Arenas-Garc\'\i a, Jer\'onimo",
booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lt4gov-1.1/",
pages = "1--6",
language = "eng",
ISBN = "979-10-95546-62-7",
abstract = "The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA's adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain."
}
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification–-with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at \url{http://decomp.io}.
@inproceedings{white-etal-2020-universal,
title = "The Universal Decompositional Semantics Dataset and Decomp Toolkit",
author = "White, Aaron Steven and
Stengel-Eskin, Elias and
Vashishtha, Siddharth and
Govindarajan, Venkata Subrahmanyan and
Reisinger, Dee Ann and
Vieira, Tim and
Sakaguchi, Keisuke and
Zhang, Sheng and
Ferraro, Francis and
Rudinger, Rachel and
Rawlins, Kyle and
Van Durme, Benjamin",
editor = "Calzolari, Nicoletta and
B\'echet, Fr\'ed\'eric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.699/",
pages = "5698--5707",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification---with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at \url{http://decomp.io}."
}
@InProceedings{francislandau-vieira-eisner-2020-wrla,
author = "Matthew Francis-Landau and Tim Vieira and Jason
Eisner",
title = "Evaluation of Logic Programs with Built-Ins and
Aggregation: {A} Calculus for Bag Relations",
booktitle = "13th International Workshop on Rewriting Logic and Its
Applications",
pages = "49--63",
year = "2020",
month = apr,
note = "Extended version (27 pages) available on arXiv,
October 2020.",
URL = "http://cs.jhu.edu/~jason/papers/#francislandau-vieira-eisner-2020-wrla",
}
@inproceedings{218674572,
title = {Single Channel Far Field Feature Enhancement For Speaker Verification In The Wild},
author = {{P. S. Nidadavolu} and {Saurabh Kataria} and {Leibny Paola García-Perera} and {J. Villalba} and {N. Dehak}},
year = 2020,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f1f072d88905a9d52cd759dd16ab42f2eedf4908},
}
@inproceedings{209832256,
title = {SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis},
author = {{Cheng Peng} and {Wei-An Lin} and {Haofu Liao} and {R. Chellappa} and {S. Zhou}},
year = 2020,
month = {1},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/cfbfee77c684bc5fa73ab7519f10f0b5ff5a82f5},
}
@inproceedings{218674480,
title = {That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages},
author = {{Piotr Żelasko} and {Laureano Moro-Vel'azquez} and {M. Hasegawa-Johnson} and {O. Scharenborg} and {N. Dehak}},
year = 2020,
month = {5},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/b10e212e462b48f21cc8d8a2ee23487ead0edf50},
}
@inproceedings{209898633,
title = {Neural Response Selectivity to Natural Sounds in the Bat Midbrain},
author = {{Angeles Salles} and {Sangwook Park} and {Harshavardhan Sundar} and {S. Macias} and {Mounya Elhilali} and {C. Moss}},
year = 2020,
month = {1},
booktitle = {Neuroscience},
url = {https://www.semanticscholar.org/paper/2ffd27a554f678d3ab4b81ceb6351624b525eb8b},
}
@inproceedings{226292033,
title = {Multiple Class Novelty Detection Under Data Distribution Shift},
author = {{Poojan Oza} and {H. Nguyen} and {Vishal M. Patel}},
year = 2020,
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/7b0ab21f58015f3baf5f0574fab23954a5d409af},
}
@inproceedings{13155467,
title = {Domain Adaptation},
author = {{R. Knowles} and {Matt Post}},
year = 2020,
month = {6},
booktitle = {Machine Learning for Speaker Recognition},
url = {https://www.semanticscholar.org/paper/ddf9f0ce76ddf31274ed150e2030a7bb2ccf85e0},
}
@inproceedings{215745555,
title = {PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning},
author = {{Chenglin Yang} and {Adam Kortylewski} and {Cihang Xie} and {Yinzhi Cao} and {A. Yuille}},
year = 2020,
month = {4},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/998d21a86459ee7c8365e1fd16d7c05cbcbf0105},
}
@inproceedings{215415929,
title = {JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method},
author = {{Vishwanath A. Sindagi} and {R. Yasarla} and {Vishal M. Patel}},
year = 2020,
month = {4},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/6eb6c3c4285983eb49e005a8b6b49da11ea19e5d},
}
@inproceedings{215415889,
title = {Context-Aware Group Captioning via Self-Attention and Contrastive Features},
author = {{Zhuowan Li} and {Quan Hung Tran} and {Long Mai} and {Zhe L. Lin} and {A. Yuille}},
year = 2020,
month = {4},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/477b70ed4753745e2700dd2791c3a5fb966f8b64},
}
@inproceedings{221654934,
title = {Completely Self-Supervised Crowd Counting via Distribution Matching},
author = {{Deepak Babu Sam} and {Abhinav Agarwalla} and {Jimmy Joseph} and {Vishwanath A. Sindagi} and {R. Venkatesh Babu} and {Vishal M. Patel}},
year = 2020,
month = {9},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/2244950fa43f0b7ce6a92de779b536942894dfa1},
}
@inproceedings{221150462,
title = {Very Deep Transformers for Neural Machine Translation},
author = {{Xiaodong Liu} and {Kevin Duh} and {Liyuan Liu} and {Jianfeng Gao}},
year = 2020,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b5271f4522fd72e335535c5f65d3afc01d1cb2bd},
}
@inproceedings{219793032,
title = {Is Network the Bottleneck of Distributed Training?},
author = {{Zhen Zhang} and {Chaokun Chang} and {Haibin Lin} and {Yida Wang} and {R. Arora} and {Xin Jin}},
year = 2020,
month = {6},
booktitle = {NetAI@SIGCOMM},
url = {https://www.semanticscholar.org/paper/cfa6e7ac8bef5b3aadcdc7a27d2a9e9d508b3322},
}
@inproceedings{211556990,
title = {Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.},
author = {{D. Dreizin} and {Yuyin Zhou} and {Tina Chen} and {Guang Li} and {A. Yuille} and {Ashley McLenithan} and {J. Morrison}},
year = 2020,
month = {3},
booktitle = {Journal of Trauma and Acute Care Surgery},
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month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3ec5d2fcf2b070401f1178326b66ab0f0c0059b1},
}
@inproceedings{208910107,
title = {cGANs with Multi-Hinge Loss},
author = {{Ilya Kavalerov} and {W. Czaja} and {R. Chellappa}},
year = 2019,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c1d58def0becaeaf368e124f2898ccd89f0c74eb},
}
@inproceedings{209439815,
title = {C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation},
author = {{Qihang Yu} and {Dong Yang} and {H. Roth} and {Yutong Bai} and {Yixiao Zhang} and {A. Yuille} and {Daguang Xu}},
year = 2019,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/eca36cc534f516db1c4ff94531ae458240141b9c},
}
@inproceedings{209439665,
title = {AtomNAS: Fine-Grained End-to-End Neural Architecture Search},
author = {{Jieru Mei} and {Yingwei Li} and {Xiaochen Lian} and {Xiaojie Jin} and {Linjie Yang} and {A. Yuille} and {Jianchao Yang}},
year = 2019,
month = {12},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/f5f35340893d550bd5d1a2711f04308525c6dcd2},
}
@inproceedings{208910258,
title = {Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation},
author = {{Mitchell A. Gordon} and {Kevin Duh}},
year = 2019,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/d1ba8c532b954ea8b3a66d9c155e769fc2081af6},
}
@inproceedings{209405066,
title = {Learning From Synthetic Animals},
author = {{Jiteng Mu} and {Weichao Qiu} and {Gregory Hager} and {A. Yuille}},
year = 2019,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/b56501802083d1e25f262f1e2d5ba3939b41cf20},
}
@inproceedings{227049234,
title = {DASZL: Dynamic Action Signatures for Zero-shot Learning},
author = {{Tae Soo Kim} and {Jonathan D. Jones} and {Michael Peven} and {Zihao Xiao} and {Jin Bai} and {Yi Zhang} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
year = 2019,
month = {12},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/707b023e6c25ed283f23625bec514da480fb90da},
}
@inproceedings{208910119,
title = {Zero-shot Recognition of Complex Action Sequences},
author = {{Jonathan D. Jones} and {Tae Soo Kim} and {Michael Peven} and {Jin Bai} and {Zihao Xiao} and {Yi Zhang} and {Weichao Qiu} and {A. Yuille} and {Gregory Hager}},
year = 2019,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3aa1c9750ccf9da321db7b893776c060a1d0a7b3},
}
@inproceedings{208910348,
title = {Deep Distance Transform for Tubular Structure Segmentation in CT Scans},
author = {{Yan Wang} and {Xu Wei} and {Fengze Liu} and {Jieneng Chen} and {Yuyin Zhou} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2019,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/5e47365ed87009e78eb53eae2f5660e1cfd1df1d},
}
@inproceedings{210706835,
title = {Incremental Lattice Determinization for WFST Decoders},
author = {{Zhehuai Chen} and {M. Yarmohammadi} and {Hainan Xu} and {Hang Lv} and {Lei Xie} and {Daniel Povey} and {S. Khudanpur}},
year = 2019,
month = {12},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/920238fedadcfb835c808c039a44d3ccf8ebab69},
}
@inproceedings{209515968,
title = {Multiview Representation Learning for a Union of Subspaces},
author = {{Nils Holzenberger} and {R. Arora}},
year = 2019,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/68941236b9ea941350180427fe60aa1f3644ae75},
}
@inproceedings{209405109,
title = {Learning to Segment Brain Anatomy From 2D Ultrasound With Less Data},
author = {{Jeya Maria Jose Valanarasu} and {R. Yasarla} and {Puyang Wang} and {I. Hacihaliloglu} and {Vishal M. Patel}},
year = 2019,
month = {12},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
url = {https://www.semanticscholar.org/paper/de9d268cb1c518717bf6b5df1d807867cbb4a9a6},
}
@inproceedings{209515687,
title = {Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots},
author = {{Qi Chen} and {Lin Sun} and {Zhixin Wang} and {K. Jia} and {A. Yuille}},
year = 2019,
month = {12},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/78ea7c49d28a904ec2a5af3326f0752949101983},
}
@inproceedings{209543410,
title = {General properties of auditory spectro-temporal receptive fields.},
author = {{Nagaraj R. Mahajan} and {N. Mesgarani} and {H. Hermansky}},
year = 2019,
month = {12},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/603bfa5b7d64978224862eda05135945af90c525},
}
@inproceedings{207863618,
title = {Non-Autoregressive Transformer Automatic Speech Recognition},
author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {N. Dehak}},
year = 2019,
month = {11},
booktitle = {arXiv: Audio and Speech Processing},
url = {https://www.semanticscholar.org/paper/49f657d704a1b80ce3dba0d8a9e5479ec1d703d4},
}
Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines – constrained decoding and continued training – and an improvement to continued training to address overfitting.
@inproceedings{thompson-etal-2019-hablex,
title = "{HABL}ex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation",
author = "Thompson, Brian and
Knowles, Rebecca and
Zhang, Xuan and
Khayrallah, Huda and
Duh, Kevin and
Koehn, Philipp",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1142/",
doi = "10.18653/v1/D19-1142",
pages = "1382--1387",
abstract = "Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines - constrained decoding and continued training - and an improvement to continued training to address overfitting."
}
@inproceedings{210972323,
title = {Bottom-Up Unsupervised Word Discovery via Acoustic Units},
author = {{Saurabhchand Bhati} and {Chunxi Liu} and {J. Villalba} and {J. Trmal} and {S. Khudanpur} and {N. Dehak}},
year = 2019,
month = {11},
booktitle = {IEEE Global Conference on Signal and Information Processing},
url = {https://www.semanticscholar.org/paper/2a626d33a9e7af638eac1660426a486288a489cc},
}
@inproceedings{207894590,
title = {Requests for Diagnoses of Sexually Transmitted Diseases on a Social Media Platform.},
author = {{A. Nobles} and {E. Leas} and {B. Althouse} and {Mark Dredze} and {C. Longhurst} and {Davey M. Smith} and {J. Ayers}},
year = 2019,
month = {11},
booktitle = {Journal of the American Medical Association (JAMA)},
url = {https://www.semanticscholar.org/paper/9d98c236bf7e729db2b31cace3328e335dc8a942},
}
@inproceedings{208201954,
title = {Adversarial Examples Improve Image Recognition},
author = {{Cihang Xie} and {Mingxing Tan} and {Boqing Gong} and {Jiang Wang} and {A. Yuille} and {Quoc V. Le}},
year = 2019,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/948839277bface5780896e8e8791906818aa41ac},
}
@inproceedings{215867646,
title = {Prior-Based Domain Adaptive Object Detection for Hazy and Rainy Conditions},
author = {{Vishwanath A. Sindagi} and {Poojan Oza} and {R. Yasarla} and {Vishal M. Patel}},
year = 2019,
month = {11},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/4586b801c3c72a7ccb081867a56b0dff92a9c6d9},
}
@inproceedings{208247947,
title = {Rethinking Normalization and Elimination Singularity in Neural Networks},
author = {{Siyuan Qiao} and {Huiyu Wang} and {Chenxi Liu} and {Wei Shen} and {A. Yuille}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/557c8597b1cfecc0fdaf8c070024c19a89c3c7dd},
}
@inproceedings{208291427,
title = {Generating Commit Messages from Git Diffs},
author = {{S.R.P. van Hal} and {Matt Post} and {K. Wendel}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ccff37de654ce48ce219239da0fe91ef23a8d7f3},
}
@inproceedings{208291180,
title = {Shape-aware Feature Extraction for Instance Segmentation},
author = {{Hao Ding} and {Siyuan Qiao} and {Wei Shen} and {A. Yuille}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e15761ef565af80f37e6ceddc62ef094c0af54fd},
}
@inproceedings{208268082,
title = {Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference},
author = {{Wei-An Lin} and {Y. Balaji} and {Pouya Samangouei} and {R. Chellappa}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/34adff99c6ce47057b24c1bd1305adf292403fa7},
}
@inproceedings{210971728,
title = {LSTM Siamese Network for Parkinson’s Disease Detection from Speech},
author = {{Saurabhchand Bhati} and {L. Moro-Velázquez} and {J. Villalba} and {N. Dehak}},
year = 2019,
month = {11},
booktitle = {IEEE Global Conference on Signal and Information Processing},
url = {https://www.semanticscholar.org/paper/e8c28555fe828a27a691a24608cd229c0359c8b1},
}
There is an extensive history of scholarship into what constitutes a “basic” color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design–-as well as their aggregation–-correlate strongly with both the Berlin and Kay basic/secondary color term partition ($\gamma$ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.
@inproceedings{mccarthy-etal-2019-modeling,
title = "Modeling Color Terminology Across Thousands of Languages",
author = "McCarthy, Arya D. and
Wu, Winston and
Mueller, Aaron and
Watson, William and
Yarowsky, David",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1229/",
doi = "10.18653/v1/D19-1229",
pages = "2241--2250",
abstract = "There is an extensive history of scholarship into what constitutes a ``basic'' color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design---as well as their aggregation---correlate strongly with both the Berlin and Kay basic/secondary color term partition ($\gamma$ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy."
}
@inproceedings{208527596,
title = {Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions},
author = {{Vishwanath A. Sindagi} and {Poojan Oza} and {R. Yasarla} and {Vishal M. Patel}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8428ac16108c6ffa2e4a71325939f361dd31d8ed},
}
@inproceedings{211033454,
title = {Learning unsupervised contextual representations for medical synonym discovery},
author = {{Elliot Schumacher} and {Mark Dredze}},
year = 2019,
month = {11},
booktitle = {JAMIA Open},
url = {https://www.semanticscholar.org/paper/cad8a6b9248227d041f35acbfb341ab870d8995f},
}
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.
@inproceedings{wu-dredze-2019-beto,
title = "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of {BERT}",
author = "Wu, Shijie and
Dredze, Mark",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1077/",
doi = "10.18653/v1/D19-1077",
pages = "833--844",
abstract = "Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer."
}
We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks – AMR, SDP and UCCA – demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.
@inproceedings{zhang-etal-2019-broad,
title = "Broad-Coverage Semantic Parsing as Transduction",
author = "Zhang, Sheng and
Ma, Xutai and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1392/",
doi = "10.18653/v1/D19-1392",
pages = "3786--3798",
abstract = "We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP."
}
@inproceedings{208170863,
title = {From development to deployment: dataset shift, causality, and shift-stable models in health AI.},
author = {{Adarsh Subbaswamy} and {S. Saria}},
year = 2019,
month = {11},
booktitle = {Biostatistics},
url = {https://www.semanticscholar.org/paper/9fb10e0ee0e200d4298f3a146f81c12dce441179},
}
Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.
@inproceedings{ebner-etal-2019-bag,
title = "Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning",
author = "Ebner, Seth and
Wang, Felicity and
Van Durme, Benjamin",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6105/",
doi = "10.18653/v1/D19-6105",
pages = "40--46",
abstract = "Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size."
}
Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering. We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.
@inproceedings{hu-etal-2019-large,
title = "Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering",
author = "Hu, J. Edward and
Singh, Abhinav and
Holzenberger, Nils and
Post, Matt and
Van Durme, Benjamin",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1005/",
doi = "10.18653/v1/K19-1005",
pages = "44--54",
abstract = "Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering. We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks."
}
@inproceedings{208063342,
title = {Vaccine-related advertising in the Facebook Ad Archive.},
author = {{Amelia M. Jamison} and {David A. Broniatowski} and {Mark Dredze} and {Zach Wood-Doughty} and {DureAden Khan} and {S. Quinn}},
year = 2019,
month = {11},
booktitle = {Vaccine},
url = {https://www.semanticscholar.org/paper/09d933efcaaaeedec22d08bceb09dc2b3e7b7efd},
}
@inproceedings{208277443,
title = {Identifying Model Weakness with Adversarial Examiner},
author = {{Michelle Shu} and {Chenxi Liu} and {Weichao Qiu} and {A. Yuille}},
year = 2019,
month = {11},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/1a0d3c0fc3c87c801ce3667eec4987fd0901e19d},
}
We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples ($\sim$1.7K–5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11–27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.
@inproceedings{stengel-eskin-etal-2019-discriminative,
title = "A Discriminative Neural Model for Cross-Lingual Word Alignment",
author = "Stengel-Eskin, Elias and
Su, Tzu-ray and
Post, Matt and
Van Durme, Benjamin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1084/",
doi = "10.18653/v1/D19-1084",
pages = "910--920",
abstract = "We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples ($\sim$1.7K--5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11--27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation."
}
@inproceedings{219573546,
title = {Deeply Shape-guided Instance Segmentation},
author = {{Hao Ding} and {Siyuan Qiao} and {A. Yuille} and {Wei Shen}},
year = 2019,
month = {11},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/cd79f35857cdd9660994b249a49679f7cf5bd8a4},
}
@inproceedings{214802613,
title = {Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition},
author = {{Nanxin Chen} and {Shinji Watanabe} and {J. Villalba} and {N. Dehak}},
year = 2019,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/2de8019fd7d04e3d1305d5efaeeb591f0d966550},
}
@InProceedings{li-eisner-2019,
aclid = "D19-1276",
doi = "10.18653/v1/D19-1276",
author = "Xiang Lisa Li and Jason Eisner",
title = "Specializing Word Embeddings (for Parsing) by
Information Bottleneck",
booktitle = "Proceedings of the 2019 Conference on Empirical
Methods in Natural Language Processing and 9th
International Joint Conference on Natural Language
Processing",
pages = "2744--2754",
year = "2019",
month = nov,
address = "Hong Kong",
note = "Best Paper Award.",
URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-2019",
}
@InProceedings{renduchintala-et-al-2019-emnlp,
aclid = "D19-1679",
doi = "10.18653/v1/D19-1679",
author = "Adithya Renduchintala and Philipp Koehn and Jason
Eisner",
title = "Spelling-Aware Construction of Macaronic Texts for
Teaching Foreign-Language Vocabulary",
booktitle = "Proceedings of the 2019 Conference on Empirical
Methods in Natural Language Processing and 9th
International Joint Conference on Natural Language
Processing",
pages = "6439--6444",
year = "2019",
month = nov,
address = "Hong Kong",
URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2019-emnlp",
}
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title = {Hierarchical Transformers for Long Document Classification},
author = {{R. Pappagari} and {Piotr Żelasko} and {J. Villalba} and {Yishay Carmiel} and {N. Dehak}},
year = 2019,
month = {10},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/46b3ba0f3cb8340bc94f26e0fdf6dc4e38f68948},
}
@inproceedings{204539408,
title = {Single-Shot 3D Mesh Estimation via Adversarial Domain Adaptation},
author = {{Arthita Ghosh} and {R. Chellappa}},
year = 2019,
month = {10},
booktitle = {SN Computer Science},
url = {https://www.semanticscholar.org/paper/b80646f9b8d51090dfe383575680b00a268410a4},
}
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title = {Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method},
author = {{Vishwanath A. Sindagi} and {R. Yasarla} and {Vishal M. Patel}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/eba021cb47869f0b7d5b94b3ab3c3b1ec702f071},
}
@inproceedings{208116583,
title = {Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.},
author = {{Reham Badawy} and {Farhan Hameed} and {Lauren Bataille} and {Max A. Little} and {Kasper Claes} and {S. Saria} and {J. Cedarbaum} and {D. Stephenson} and {J. Neville} and {W. Maetzler} and {A. Espay} and {B. Bloem} and {T. Simuni} and {D. Karlin}},
year = 2019,
month = {10},
booktitle = {Digital Biomarkers},
url = {https://www.semanticscholar.org/paper/74f408cb7953dadadc1461162a79f9c38506b79b},
}
@inproceedings{204539175,
title = {Lesion Detection by Efficiently Bridging 3D Context},
author = {{Zhishuai Zhang} and {Yuyin Zhou} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2019,
month = {10},
booktitle = {MLMI@MICCAI},
url = {https://www.semanticscholar.org/paper/2f659eeb4b0d97c8341042f46f6e6166160de811},
}
@inproceedings{204961310,
title = {Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models},
author = {{Andriy Mulyar} and {Elliot Schumacher} and {Masoud Rouhizadeh} and {Mark Dredze}},
year = 2019,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c975d19e3861621b287a05bba31f6e1e3f0c4285},
}
@inproceedings{208462059,
title = {Comparison of Automated Sepsis Identification Methods and Electronic Health Record–based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions},
author = {{K. Henry} and {D. Hager} and {T. Osborn} and {A. Wu} and {S. Saria}},
year = 2019,
month = {10},
booktitle = {Critical Care Explorations},
url = {https://www.semanticscholar.org/paper/16d96c9ae82aff4ee357907311a9b4dd1cbf068d},
}
@inproceedings{204976547,
title = {Low-Resource Domain Adaptation for Speaker Recognition Using Cycle-Gans},
author = {{P. S. Nidadavolu} and {Saurabh Kataria} and {J. Villalba} and {N. Dehak}},
year = 2019,
month = {10},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/373acc04096d80a03dba238f73ce96930a3abb7b},
}
@inproceedings{209460517,
title = {Analysis of Robustness of Deep Single-Channel Speech Separation Using Corpora Constructed From Multiple Domains},
author = {{Matthew Maciejewski} and {Gregory Sell} and {Yusuke Fujita} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2019,
month = {10},
booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
url = {https://www.semanticscholar.org/paper/e929c9b53c66d52ae5ea56f0dc2764aef4cc67f6},
}
@inproceedings{203837027,
title = {Exact and/or Fast Nearest Neighbors},
author = {{Matthew Francis-Landau} and {Benjamin Van Durme}},
year = 2019,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/198a2b07e71037e47b45b989a072418060ec4422},
}
@inproceedings{203902626,
title = {ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence},
author = {{Chun Pong Lau} and {Hossein Souri} and {R. Chellappa}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/57fea03ab1b4e3d06fae5770a01875e7143118fa},
}
@inproceedings{204509539,
title = {How are attributes expressed in face DCNNs?},
author = {{Prithviraj Dhar} and {Ankan Bansal} and {C. Castillo} and {Joshua Gleason} and {P. Phillips} and {R. Chellappa}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/73587f97500203b94a9f312b0b86891f62326679},
}
@inproceedings{207781755,
title = {Localizing Occluders with Compositional Convolutional Networks},
author = {{Adam Kortylewski} and {Qing Liu} and {Huiyu Wang} and {Zhishuai Zhang} and {A. Yuille}},
year = 2019,
month = {10},
booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
url = {https://www.semanticscholar.org/paper/213db9136dfe6532146bbbc1aac3f1383eb21d0c},
}
@inproceedings{204907138,
title = {Unsupervised Feature Enhancement for Speaker Verification},
author = {{P. S. Nidadavolu} and {Saurabh Kataria} and {J. Villalba} and {Leibny Paola García-Perera} and {N. Dehak}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/df49e860305c871f5078bf7aa0b8cef7dcda11e7},
}
@inproceedings{203610481,
title = {Multilingual End-to-End Speech Translation},
author = {{H. Inaguma} and {Kevin Duh} and {Tatsuya Kawahara} and {Shinji Watanabe}},
year = 2019,
month = {10},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/8b231737e0048a400527d89aa56c712e8b9bc690},
}
@inproceedings{207901958,
title = {Disguised Faces in the Wild 2019},
author = {{Maneet Singh} and {M. Chawla} and {Richa Singh} and {Mayank Vatsa} and {R. Chellappa}},
year = 2019,
month = {10},
booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
url = {https://www.semanticscholar.org/paper/65e62791fc8df7d578991937533e41d5c4dc5263},
}
@inproceedings{204900778,
title = {Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection},
author = {{Latané Bullock} and {H. Bredin} and {Leibny Paola García-Perera}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/e483ff5e1f098398e619887bdf4dc8d3a003be78},
}
@inproceedings{204848948,
title = {Trends in Internet Searches for Cannabidiol (CBD) in the United States},
author = {{E. Leas} and {A. Nobles} and {Theodore L. Caputi} and {Mark Dredze} and {Davey M. Smith} and {J. Ayers}},
year = 2019,
month = {10},
booktitle = {JAMA Network Open},
url = {https://www.semanticscholar.org/paper/30672fad20fa70024c7311140b7e702b8201974c},
}
@inproceedings{204837862,
title = {End-to-End Domain-Adversarial Voice Activity Detection},
author = {{Marvin Lavechin} and {Marie-Philippe Gill} and {Ruben Bousbib} and {H. Bredin} and {Leibny Paola García-Perera}},
year = 2019,
month = {10},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/0694da1e2f5e1f62917a5c0f2a51d78981cd6f13},
}
@inproceedings{204976531,
title = {Feature Enhancement with Deep Feature Losses for Speaker Verification},
author = {{Saurabh Kataria} and {P. S. Nidadavolu} and {J. Villalba} and {Nanxin Chen} and {Paola García} and {N. Dehak}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/343fa6aea1bf71751f632be85fde936c66d21356},
}
@inproceedings{204838317,
title = {A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition},
author = {{Ruizhi Li} and {Gregory Sell} and {Xiaofei Wang} and {Shinji Watanabe} and {H. Hermansky}},
year = 2019,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/de5057c1da9391269e926d4661d4558072db9f18},
}
@inproceedings{210957413,
title = {Spine Surface Segmentation from Ultrasound Using Multi-feature Guided CNN},
author = {{Ahmed Z. Alsinan} and {M. Vives} and {Vishal M. Patel} and {I. Hacihaliloglu}},
year = 2019,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/dc2f6a0fe64a6046f0f4c6d2808671de4aff83f9},
}
@inproceedings{204824093,
title = {Transductive Parsing for Universal Decompositional Semantics},
author = {{Elias Stengel-Eskin} and {A. White} and {Sheng Zhang} and {Benjamin Van Durme}},
year = 2019,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1eba3538d3b5be4b1a2e30da2e6eed08ff61008a},
}
@inproceedings{209320431,
title = {CohereNet: A deep learning approach to coherence-based beamforming},
author = {{Alycen Wiacek} and {Eduardo A. Gonzalez} and {N. Dehak} and {M. L. Lediju Bell}},
year = 2019,
month = {10},
booktitle = {IUS},
url = {https://www.semanticscholar.org/paper/02eac7f9a573c7b2852235733bf8d1920ce788ee},
}
@inproceedings{202550367,
title = {Guest Editors' Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision},
author = {{Li Liu} and {M. Pietikäinen} and {Jie Chen} and {Guoying Zhao} and {Xiaogang Wang} and {R. Chellappa}},
year = 2019,
month = {10},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/3c0f6d2b76c9d68da37e319cdae9802298ca7c44},
}
We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques – sampling, filtering, iterative backtranslation, and continued training – previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.
@inproceedings{marchisio-etal-2019-johns,
title = "{J}ohns {H}opkins {U}niversity Submission for {WMT} News Translation Task",
author = "Marchisio, Kelly and
Lal, Yash Kumar and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5329/",
doi = "10.18653/v1/W19-5329",
pages = "287--293",
abstract = "We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques -- sampling, filtering, iterative backtranslation, and continued training -- previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described."
}
Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.
@inproceedings{ding-etal-2019-saliency,
title = "Saliency-driven Word Alignment Interpretation for Neural Machine Translation",
author = "Ding, Shuoyang and
Xu, Hainan and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5201/",
doi = "10.18653/v1/W19-5201",
pages = "1--12",
abstract = "Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools."
}
@inproceedings{post-etal-2019-exploration,
title = "An Exploration of Placeholding in Neural Machine Translation",
author = "Post, Matt and
Ding, Shuoyang and
Martindale, Marianna and
Wu, Winston",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6618/",
pages = "182--192"
}
Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.
@inproceedings{lippincott-etal-2019-jhu,
title = "{JHU} System Description for the {MADAR} {A}rabic Dialect Identification Shared Task",
author = "Lippincott, Tom and
Shapiro, Pamela and
Duh, Kevin and
McNamee, Paul",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4634/",
doi = "10.18653/v1/W19-4634",
pages = "264--268",
abstract = "Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning."
}
We describe the JHU submissions to the French–English, Japanese–English, and English–Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR$\rightarrow$EN) and a handful of hyperparameters settings (JAâEN). As expected, our systems performed reasonably.
@inproceedings{post-duh-2019-jhu,
title = "{JHU} 2019 Robustness Task System Description",
author = "Post, Matt and
Duh, Kevin",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5366/",
doi = "10.18653/v1/W19-5366",
pages = "552--558",
abstract = "We describe the JHU submissions to the French--English, Japanese--English, and English--Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR$\rightarrow$EN) and a handful of hyperparameters settings (JAâEN). As expected, our systems performed reasonably."
}
@inproceedings{marchisio-etal-2019-controlling,
title = "Controlling the Reading Level of Machine Translation Output",
author = "Marchisio, Kelly and
Guo, Jialiang and
Lai, Cheng-I and
Koehn, Philipp",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6619/",
pages = "193--203"
}
@inproceedings{ding-etal-2019-call,
title = "A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation",
author = "Ding, Shuoyang and
Renduchintala, Adithya and
Duh, Kevin",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6620/",
pages = "204--213"
}
We present a machine foreign-language teacher that takes documents written in a student’s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk “students” were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.
@inproceedings{renduchintala-etal-2019-simple,
title = "Simple Construction of Mixed-Language Texts for Vocabulary Learning",
author = "Renduchintala, Adithya and
Koehn, Philipp and
Eisner, Jason",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil\'an, Ildik\'o and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4439/",
doi = "10.18653/v1/W19-4439",
pages = "369--379",
abstract = "We present a machine foreign-language teacher that takes documents written in a student's native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk ``students'' were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document."
}
Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed 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 2\% and 10\% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.
@inproceedings{koehn-etal-2019-findings,
title = "Findings of the {WMT} 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions",
author = "Koehn, Philipp and
Guzm\'an, Francisco and
Chaudhary, Vishrav and
Pino, Juan",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5404/",
doi = "10.18653/v1/W19-5404",
pages = "54--72",
abstract = "Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed 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 2\% and 10\% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task."
}
We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models’ robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson’s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.
@inproceedings{li-etal-2019-findings,
title = "Findings of the First Shared Task on Machine Translation Robustness",
author = "Li, Xian and
Michel, Paul and
Anastasopoulos, Antonios and
Belinkov, Yonatan and
Durrani, Nadir and
Firat, Orhan and
Koehn, Philipp and
Neubig, Graham and
Pino, Juan and
Sajjad, Hassan",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5303/",
doi = "10.18653/v1/W19-5303",
pages = "91--102",
abstract = "We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models' robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson's r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment."
}
@inproceedings{martindale-etal-2019-identifying,
title = "Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation",
author = "Martindale, Marianna and
Carpuat, Marine and
Duh, Kevin and
McNamee, Paul",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6623/",
pages = "233--243"
}
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.
@inproceedings{barrault-etal-2019-findings,
title = "Findings of the 2019 Conference on Machine Translation ({WMT}19)",
author = {Barrault, Lo\"\i c and
Bojar, Ond\v rej and
Costa-juss\`a, Marta R. and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Koehn, Philipp and
Malmasi, Shervin and
Monz, Christof and
M\"uller, Mathias and
Pal, Santanu and
Post, Matt and
Zampieri, Marcos},
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5301/",
doi = "10.18653/v1/W19-5301",
pages = "1--61",
abstract = "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation."
}
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.
@inproceedings{chaudhary-etal-2019-low,
title = "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
author = "Chaudhary, Vishrav and
Tang, Yuqing and
Guzm\'an, Francisco and
Schwenk, Holger and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr\'e and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5435/",
doi = "10.18653/v1/W19-5435",
pages = "261--266",
abstract = "In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios."
}
@inproceedings{yarmohammadi-etal-2019-robust,
title = "Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings",
author = "Yarmohammadi, Mahsa and
Ma, Xutai and
Hisamoto, Sorami and
Rahman, Muhammad and
Wang, Yiming and
Xu, Hainan and
Povey, Daniel and
Koehn, Philipp and
Duh, Kevin",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6602/",
pages = "12--20"
}
@inproceedings{renduchintala-etal-2019-character,
title = "Character-Aware Decoder for Translation into Morphologically Rich Languages",
author = "Renduchintala, Adithya and
Shapiro, Pamela and
Duh, Kevin and
Koehn, Philipp",
editor = "Forcada, Mikel and
Way, Andy and
Haddow, Barry and
Sennrich, Rico",
booktitle = "Proceedings of Machine Translation Summit XVII: Research Track",
month = aug,
year = "2019",
address = "Dublin, Ireland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/W19-6624/",
pages = "244--255"
}
We present Deep Generalized Canonical Correlation Analysis (DGCCA) – a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic & articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.
@inproceedings{benton-etal-2019-deep,
title = "Deep Generalized Canonical Correlation Analysis",
author = "Benton, Adrian and
Khayrallah, Huda and
Gujral, Biman and
Reisinger, Dee Ann and
Zhang, Sheng and
Arora, Raman",
editor = "Augenstein, Isabelle and
Gella, Spandana and
Ruder, Sebastian and
Kann, Katharina and
Can, Burcu and
Welbl, Johannes and
Conneau, Alexis and
Ren, Xiang and
Rei, Marek",
booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4301/",
doi = "10.18653/v1/W19-4301",
pages = "1--6",
abstract = "We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic \& articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users."
}
@InProceedings{renduchintala-et-al-2019-bea,
aclid = "W19-4439",
doi = "10.18653/v1/W19-4439",
author = "Adithya Renduchintala and Philipp Koehn and Jason
Eisner",
title = "Simple Construction of Mixed-Language Texts for
Vocabulary Learning",
booktitle = "Proceedings of the 14th Workshop on Innovative Use of
NLP for Building Educational Applications (BEA)",
pages = "369--379",
year = "2019",
month = aug,
address = "Florence",
URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2019-bea",
}
Natural Language Inference (NLI) datasets often contain hypothesis-only biases–-artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.
@inproceedings{belinkov-etal-2019-dont,
title = "Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference",
author = "Belinkov, Yonatan and
Poliak, Adam and
Shieber, Stuart and
Van Durme, Benjamin and
Rush, Alexander",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1084/",
doi = "10.18653/v1/P19-1084",
pages = "877--891",
abstract = "Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets."
}
We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.
@inproceedings{vashishtha-etal-2019-fine,
title = "Fine-Grained Temporal Relation Extraction",
author = "Vashishtha, Siddharth and
Van Durme, Benjamin and
White, Aaron Steven",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1280/",
doi = "10.18653/v1/P19-1280",
pages = "2906--2919",
abstract = "We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations."
}
Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.
@inproceedings{li-etal-2019-learning,
title = "Learning to Rank for Plausible Plausibility",
author = "Li, Zhongyang and
Chen, Tongfei and
Van Durme, Benjamin",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1475/",
doi = "10.18653/v1/P19-1475",
pages = "4818--4823",
abstract = "Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss."
}
A large percentage of computational tools are concentrated in a very small subset of the planet’s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.
@inproceedings{nicolai-yarowsky-2019-learning,
title = "Learning Morphosyntactic Analyzers from the {B}ible via Iterative Annotation Projection across 26 Languages",
author = "Nicolai, Garrett and
Yarowsky, David",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1172/",
doi = "10.18653/v1/P19-1172",
pages = "1765--1774",
abstract = "A large percentage of computational tools are concentrated in a very small subset of the planet's languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system."
}
We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3\% on LDC2017T10) and AMR 1.0 (70.2\% on LDC2014T12).
@inproceedings{zhang-etal-2019-amr,
title = "{AMR} Parsing as Sequence-to-Graph Transduction",
author = "Zhang, Sheng and
Ma, Xutai and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1009/",
doi = "10.18653/v1/P19-1009",
pages = "80--94",
abstract = "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3\% on LDC2017T10) and AMR 1.0 (70.2\% on LDC2014T12)."
}
Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today’s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs – the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results – 83.54\% accuracy and 0.827 F1 score – on a benchmark dataset.
@inproceedings{lal-etal-2019-de,
title = "De-Mixing Sentiment from Code-Mixed Text",
author = "Lal, Yash Kumar and
Kumar, Vaibhav and
Dhar, Mrinal and
Shrivastava, Manish and
Koehn, Philipp",
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2052/",
doi = "10.18653/v1/P19-2052",
pages = "371--377",
abstract = "Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today's multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54\% accuracy and 0.827 F1 score - on a benchmark dataset."
}
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo’s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.
@inproceedings{wang-etal-2019-tell,
title = "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
author = "Wang, Alex and
Hula, Jan and
Xia, Patrick and
Pappagari, Raghavendra and
McCoy, R. Thomas and
Patel, Roma and
Kim, Najoung and
Tenney, Ian and
Huang, Yinghui and
Yu, Katherin and
Jin, Shuning and
Chen, Berlin and
Van Durme, Benjamin and
Grave, Edouard and
Pavlick, Ellie and
Bowman, Samuel R.",
editor = "Korhonen, Anna and
Traum, David and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1439/",
doi = "10.18653/v1/P19-1439",
pages = "4465--4476",
abstract = "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo's pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
}
@InProceedings{mielke-et-al-2019,
aclid = "P19-1491",
doi = "10.18653/v1/P19-1491",
author = "Sabrina J. Mielke and Ryan Cotterell and Kyle Gorman
and Brian Roark and Jason Eisner",
title = "What Kind of Language Is Hard to Language-Model?",
booktitle = "Proceedings of the 57th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "4975--4989",
year = "2019",
month = jul,
address = "Florence",
URL = "http://cs.jhu.edu/~jason/papers/#mielke-et-al-2019",
}
We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
@inproceedings{zhang-etal-2019-curriculum,
title = "Curriculum Learning for Domain Adaptation in Neural Machine Translation",
author = "Zhang, Xuan and
Shapiro, Pamela and
Kumar, Gaurav and
McNamee, Paul and
Carpuat, Marine and
Duh, Kevin",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1189/",
doi = "10.18653/v1/N19-1189",
pages = "1903--1915",
abstract = "We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs."
}
When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.
@inproceedings{shapiro-duh-2019-comparing,
title = "Comparing Pipelined and Integrated Approaches to Dialectal {A}rabic Neural Machine Translation",
author = "Shapiro, Pamela and
Duh, Kevin",
editor = {Zampieri, Marcos and
Nakov, Preslav and
Malmasi, Shervin and
Ljube\v si\'c, Nikola and
Tiedemann, J\"org and
Ali, Ahmed},
booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
month = jun,
year = "2019",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1424/",
doi = "10.18653/v1/W19-1424",
pages = "214--222",
abstract = "When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects."
}
Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC)–-a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable.
@inproceedings{thompson-etal-2019-overcoming,
title = "Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation",
author = "Thompson, Brian and
Gwinnup, Jeremy and
Khayrallah, Huda and
Duh, Kevin and
Koehn, Philipp",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1209/",
doi = "10.18653/v1/N19-1209",
pages = "2062--2068",
abstract = "Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC)---a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable."
}
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.
@inproceedings{belinkov-etal-2019-adversarial,
title = "On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference",
author = "Belinkov, Yonatan and
Poliak, Adam and
Shieber, Stuart and
Van Durme, Benjamin and
Rush, Alexander",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1028/",
doi = "10.18653/v1/S19-1028",
pages = "256--262",
abstract = "Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy."
}
Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.
@inproceedings{ding-koehn-2019-parallelizable,
title = "Parallelizable Stack Long Short-Term Memory",
author = "Ding, Shuoyang and
Koehn, Philipp",
editor = "Martins, Andre and
Vlachos, Andreas and
Kozareva, Zornitsa and
Ravi, Sujith and
Lampouras, Gerasimos and
Niculae, Vlad and
Kreutzer, Julia",
booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1501/",
doi = "10.18653/v1/W19-1501",
pages = "1--6",
abstract = "Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation."
}
Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.
@inproceedings{hu-etal-2019-improved,
title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting",
author = "Hu, J. Edward and
Khayrallah, Huda and
Culkin, Ryan and
Xia, Patrick and
Chen, Tongfei and
Post, Matt and
Van Durme, Benjamin",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1090/",
doi = "10.18653/v1/N19-1090",
pages = "839--850",
abstract = "Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three."
}
We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.
@inproceedings{adams-etal-2019-massively,
title = "Massively Multilingual Adversarial Speech Recognition",
author = "Adams, Oliver and
Wiesner, Matthew and
Watanabe, Shinji and
Yarowsky, David",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1009/",
doi = "10.18653/v1/N19-1009",
pages = "96--108",
abstract = "We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective."
}
We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG–-our most syntactic objective–-performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.
@inproceedings{kim-etal-2019-probing,
title = "Probing What Different {NLP} Tasks Teach Machines about Function Word Comprehension",
author = "Kim, Najoung and
Patel, Roma and
Poliak, Adam and
Xia, Patrick and
Wang, Alex and
McCoy, Tom and
Tenney, Ian and
Ross, Alexis and
Linzen, Tal and
Van Durme, Benjamin and
Bowman, Samuel R. and
Pavlick, Ellie",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1026/",
doi = "10.18653/v1/S19-1026",
pages = "235--249",
abstract = "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG---our most syntactic objective---performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
}
The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.
@inproceedings{amir-etal-2019-mental,
title = "Mental Health Surveillance over Social Media with Digital Cohorts",
author = "Amir, Silvio and
Dredze, Mark and
Ayers, John W.",
editor = "Niederhoffer, Kate and
Hollingshead, Kristy and
Resnik, Philip and
Resnik, Rebecca and
Loveys, Kate",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3013/",
doi = "10.18653/v1/W19-3013",
pages = "114--120",
abstract = "The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data."
}
This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.
@inproceedings{lippincott-2019-graph,
title = "Graph convolutional networks for exploring authorship hypotheses",
author = "Lippincott, Tom",
editor = "Alex, Beatrice and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2510/",
doi = "10.18653/v1/W19-2510",
pages = "76--81",
abstract = "This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work."
}
@InProceedings{mei-et-al-2019,
author = "Hongyuan Mei and Guanghui Qin and Jason Eisner",
title = "Imputing Missing Events in Continuous-Time Event
Streams",
booktitle = "Proceedings of the 36th International Conference on
Machine Learning",
year = "2019",
month = jun,
address = "Long Beach, California",
URL = "http://cs.jhu.edu/~jason/papers/#mei-et-al-2019",
}
@InProceedings{lin-et-al-2019,
aclid = "N19-1024",
doi = "10.18653/v1/N19-1024",
author = "Chu-Cheng Lin and Hao Zhu and Matthew Gormley and
Jason Eisner",
title = "Neural Finite-State Transducers: Beyond Rational
Relations",
booktitle = "Proceedings of the 2019 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "272--283",
year = "2019",
month = jun,
address = "Minneapolis",
URL = "http://cs.jhu.edu/~jason/papers/#lin-et-al-2019",
}
@InProceedings{vylomova-et-al-2019,
aclid = "N19-1203",
doi = "10.18653/v1/N19-1203",
author = "Ekaterina Vylomova and Ryan Cotterell and Tim Baldwin
and Trevor Cohn and Jason Eisner",
title = "Contextualization of Morphological Inflection",
booktitle = "Proceedings of the 2019 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "2018--2024",
year = "2019",
month = jun,
address = "Minneapolis",
URL = "http://cs.jhu.edu/~jason/papers/#vylomova-et-al-2019",
}
This paper describes the ESPnet submissions to the How2 Speech Translation task at IWSLT2019. In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST). We first compare RNN-based models and Transformer, and then confirm Transformer models significantly and consistently outperform RNN models in all tasks and corpora. Next, we investigate pre-training of E2E-ST models with the ASR and MT tasks. On top of the pre-training, we further explore knowledge distillation from the NMT model and the deeper speech encoder, and confirm drastic improvements over the baseline model. All of our codes are publicly available in ESPnet.
@inproceedings{inaguma-etal-2019-espnet,
title = "{ESP}net How2 Speech Translation System for {IWSLT} 2019: Pre-training, Knowledge Distillation, and Going Deeper",
author = "Inaguma, Hirofumi and
Kiyono, Shun and
Soplin, Nelson Enrique Yalta and
Suzuki, Jun and
Duh, Kevin and
Watanabe, Shinji",
editor = {Niehues, Jan and
Cattoni, Rolando and
St\"uker, Sebastian and
Negri, Matteo and
Turchi, Marco and
Ha, Thanh-Le and
Salesky, Elizabeth and
Sanabria, Ramon and
Barrault, Loic and
Specia, Lucia and
Federico, Marcello},
booktitle = "Proceedings of the 16th International Conference on Spoken Language Translation",
month = nov # " 2-3",
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2019.iwslt-1.4/",
abstract = "This paper describes the ESPnet submissions to the How2 Speech Translation task at IWSLT2019. In this year, we mainly build our systems based on Transformer architectures in all tasks and focus on the end-to-end speech translation (E2E-ST). We first compare RNN-based models and Transformer, and then confirm Transformer models significantly and consistently outperform RNN models in all tasks and corpora. Next, we investigate pre-training of E2E-ST models with the ASR and MT tasks. On top of the pre-training, we further explore knowledge distillation from the NMT model and the deeper speech encoder, and confirm drastic improvements over the baseline model. All of our codes are publicly available in ESPnet."
}
@inproceedings{167217854,
title = {The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance},
author = {{Adarsh Subbaswamy} and {Bryant Chen} and {S. Saria}},
year = 2019,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/65048225d39e13c74fd9fb1cd10c5ffa6060071b},
}
@inproceedings{202743401,
title = {A Study of a Cross-Language Perception Based on Cortical Analysis Using Biomimetic STRFs},
author = {{Sangwook Park} and {D. Han} and {Mounya Elhilali}},
year = 2019,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/90e41f373a7b203267fa3f34896c07ce7a58af0d},
}
@inproceedings{201060023,
title = {Do no harm: a roadmap for responsible machine learning for health care},
author = {{J. Wiens} and {S. Saria} and {M. Sendak} and {M. Ghassemi} and {V. Liu} and {F. Doshi-Velez} and {Kenneth Jung} and {K. Heller} and {David C. Kale} and {Mohammed Saeed} and {P. Ossorio} and {Sonoo Thadaney-Israni} and {A. Goldenberg}},
year = 2019,
month = {8},
booktitle = {Nature Network Boston},
url = {https://www.semanticscholar.org/paper/b0f8a829450e782fe879d9d48a188d611b6dd74d},
}
@inproceedings{59599823,
title = {Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author = {{Francisco Guzmán} and {Peng-Jen Chen} and {Myle Ott} and {J. Pino} and {Guillaume Lample} and {Philipp Koehn} and {Vishrav Chaudhary} and {Marc'Aurelio Ranzato}},
year = 2019,
month = {2},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8fd47bff451220ce612463dbfb5bff2423fb06ab},
}
@inproceedings{201124751,
title = {FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans},
author = {{Fengze Liu} and {Yuyin Zhou} and {E. Fishman} and {A. Yuille}},
year = 2019,
month = {8},
booktitle = {MLMI@MICCAI},
url = {https://www.semanticscholar.org/paper/a176e6bf676eb7a598b697185c91ff9fbebdbec8},
}
@inproceedings{198903372,
title = {Supplementary Materials-Unsupervised Domain-Specific Deblurring via Disentangled Representations},
author = {{Boyu Lu} and {Jun-Cheng Chen} and {R. Chellappa}},
year = 2019,
booktitle = {},
url = {https://www.semanticscholar.org/paper/81c164685fc68af5f166a03d4add19eadf58d367},
}
@inproceedings{91184086,
title = {Thickened 2D Networks for 3D Medical Image Segmentation},
author = {{Qihang Yu} and {Yingda Xia} and {Lingxi Xie} and {E. Fishman} and {A. Yuille}},
year = 2019,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/5e386522ce309d625078400be54ecfe71fd888b3},
}
@inproceedings{133152590,
title = {GOOGLE SEARCHES ACCURATELY FORECAST RESPIRATORY SYNCYTIAL VIRUS HOSPITALIZATIONS},
author = {{B. Althouse} and {D. Weinberger} and {S. Scarpino} and {V. Pitzer} and {J. Ayers} and {E. Wenger} and {I. C. Fung} and {Mark Dredze} and {Hao Hu}},
year = 2019,
month = {4},
booktitle = {Chest},
url = {https://www.semanticscholar.org/paper/53025df53d3c1fb3afe10b72784cd0b9d724a876},
}
@inproceedings{202573084,
title = {Real Image Domain Real Image Domain Training Testing Virtual Image Domain 3 D Model with Aggregated Annotations},
author = {{Yutong Bai} and {Qing Liu} and {Lingxi Xie} and {Yan Zheng} and {Weichao Qiu} and {A. Yuille}},
year = 2019,
booktitle = {},
url = {https://www.semanticscholar.org/paper/ae2ce685a2c43343de55690363ae3c81c4e89ccf},
}
@inproceedings{202715570,
title = {x-Vector DNN Refinement with Full-Length Recordings for Speaker Recognition},
author = {{D. Garcia-Romero} and {David Snyder} and {Gregory Sell} and {A. McCree} and {Daniel Povey} and {S. Khudanpur}},
year = 2019,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/ec7ff1cefcd86523f98652150686de7ae1531287},
}
@inproceedings{198182536,
title = {Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding},
author = {{Pirazh Khorramshahi} and {Neehar Peri} and {Amit Kumar} and {Anshul B. Shah} and {R. Chellappa}},
year = 2019,
booktitle = {CVPR Workshops},
url = {https://www.semanticscholar.org/paper/7c9f730a1b786b1d744bb1d6fdb20ee277428259},
}
@inproceedings{204817446,
title = {Graphical Model Transformation Analysis for Cognitive Computing and Machine Learning on the SpiNNaker Chip Multiprocessor},
author = {{A. Andreou} and {Daniel R. Mendat}},
year = 2019,
month = {8},
booktitle = {Euromicro Symposium on Digital Systems Design},
url = {https://www.semanticscholar.org/paper/109e16dcd24bbb1a8c676f7a4f15c4d8ab3fe0a7},
}
@inproceedings{202762315,
title = {Ignoring Data Delays Our Reaction to Emerging Public Health Tragedies Like 13 Reasons Why.},
author = {{E. Leas} and {Mark Dredze} and {J. Ayers}},
year = 2019,
booktitle = {JAMA psychiatry},
url = {https://www.semanticscholar.org/paper/ff75b93ee14abdeeac342b8e7153a8b618482833},
}
@inproceedings{202542557,
title = {UPC: Learning Universal Physical Camouflage Attacks on Object Detectors},
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booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/21e49a3c37f3dbc55273b94127abd58eb074f5ed},
}
@inproceedings{202687195,
title = {Author Correction: Do no harm: a roadmap for responsible machine learning for health care},
author = {{J. Wiens} and {S. Saria} and {M. Sendak} and {M. Ghassemi} and {V. Liu} and {F. Doshi-Velez} and {Kenneth Jung} and {K. Heller} and {David C. Kale} and {Mohammed Saeed} and {P. Ossorio} and {Sonoo Thadaney-Israni} and {A. Goldenberg}},
year = 2019,
month = {9},
booktitle = {Nature Network Boston},
url = {https://www.semanticscholar.org/paper/00d818c9597788e5bcb926db9eafd34275c0d561},
}
@inproceedings{85562057,
title = {Using ASR Methods for OCR},
author = {{Ashish Arora} and {Chun-Chieh Chang} and {Babak Rekabdar} and {Daniel Povey} and {David Etter} and {Desh Raj} and {Hossein Hadian} and {J. Trmal} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {Vimal Manohar} and {Yiwen Shao} and {S. Khudanpur}},
year = 2019,
month = {9},
booktitle = {IEEE International Conference on Document Analysis and Recognition},
url = {https://www.semanticscholar.org/paper/affb8d759af00540458c19696532220dd1c1373a},
}
@inproceedings{121333255,
title = {Characterization of a pseudo-DRAM Crossbar Computational Memory Array in 55nm CMOS : (Invited Paper)},
author = {{Gaspar Tognetti} and {Jonah P. Sengupta} and {P. Pouliquen} and {A. Andreou}},
year = 2019,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/5b8b185653130318cc2b8e2e60334cd1c35a6ea0},
}
@inproceedings{189895660,
title = {Seeing the Meaning: Vision Meets Semantics in Solving Pictorial Analogy Problems},
author = {{Hongjing Lu} and {Qing Liu} and {Nicholas Ichien} and {A. Yuille} and {K. Holyoak}},
year = 2019,
booktitle = {Annual Meeting of the Cognitive Science Society},
url = {https://www.semanticscholar.org/paper/0f186350c101af1eecdd1cbe62ca02501ba8140d},
}
@inproceedings{196684617,
title = {Neural correlates of perceptual switching while listening to bistable auditory streaming stimuli},
author = {{N. Higgins} and {DF Little} and {BD Yerkes} and {KM Nave} and {A. Kuruvilla-Mathew} and {Mounya Elhilali} and {JS Snyder}},
year = 2019,
month = {6},
booktitle = {NeuroImage},
url = {https://www.semanticscholar.org/paper/f27c8b82cfa120e85adab46ee1fc01fdffdf971c},
}
@inproceedings{57598771,
title = {Coding and decoding of messages in human speech communication: Implications for machine recognition of speech},
author = {{H. Hermansky}},
year = 2019,
month = {1},
booktitle = {Speech Communication},
url = {https://www.semanticscholar.org/paper/213522c3955a8a760247fea45ef68ffdf13a946a},
}
@inproceedings{118685884,
title = {Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis},
author = {{Xing Di} and {B. Riggan} and {Shuowen Hu} and {Nathan J. Short} and {Vishal M. Patel}},
year = 2019,
month = {4},
booktitle = {International Conference on Biometrics},
url = {https://www.semanticscholar.org/paper/d017644fbbff3953095d0da64cdb9e9bc40770c6},
}
@InProceedings{mielke-eisner-2019,
doi = "10.1609/aaai.v33i01.33016843",
author = "Sabrina J. Mielke and Jason Eisner",
title = "Spell Once, Summon Anywhere: {A} Two-Level
Open-Vocabulary Language Model",
booktitle = "Proceedings of the 33rd AAAI Conference on Artificial
Intelligence",
pages = "6843--6850",
year = "2019",
month = jan,
address = "Honolulu",
URL = "http://cs.jhu.edu/~jason/papers/#mielke-eisner-2019",
}
@inproceedings{53979606,
title = {MetaReg: Towards Domain Generalization using Meta-Regularization},
author = {{Y. Balaji} and {S. Sankaranarayanan} and {R. Chellappa}},
year = 2018,
month = {12},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/3dd8bf5cca76b1690a2642b73b509fb3a27e4f36},
}
@inproceedings{58669977,
title = {Evaluation of Neurological Diseases by Means of Speech Processing and Multimodal Analysis},
author = {{N. Dehak}},
year = 2018,
month = {12},
booktitle = {IEEE Signal Processing in Medicine and Biology Symposium},
url = {https://www.semanticscholar.org/paper/c59513c624e1f54bd78809efb8ccd5bea7dce50f},
}
@inproceedings{54796531,
title = {Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks},
author = {{He Zhang} and {B. Riggan} and {Shuowen Hu} and {Nathan J. Short} and {Vishal M. Patel}},
year = 2018,
month = {12},
booktitle = {International Journal of Computer Vision},
url = {https://www.semanticscholar.org/paper/2550b5e0dba7e8f57f36ac5dc0a4e2f968493ac4},
}
@inproceedings{54549797,
title = {An Automatic System for Unconstrained Video-Based Face Recognition},
author = {{Jingxiao Zheng} and {Rajeev Ranjan} and {Ching-Hui Chen} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {12},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/da3f1fd1362426540d66c9f993469f50dacddc99},
}
@inproceedings{174797158,
title = {CRAVES: Controlling Robotic Arm With a Vision-Based Economic System},
author = {{Yiming Zuo} and {Weichao Qiu} and {Lingxi Xie} and {Fangwei Zhong} and {Yizhou Wang} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/fd7c12e1eac960a4b4e7d72499c94b8eb747eefe},
}
@inproceedings{104292135,
title = {ELASTIC: Improving CNNs With Dynamic Scaling Policies},
author = {{Huiyu Wang} and {Aniruddha Kembhavi} and {Ali Farhadi} and {A. Yuille} and {Mohammad Rastegari}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/182df380eb4cba1e1b324cbef4ad177433d00ac1},
}
@inproceedings{61808960,
title = {Improving LF-MMI Using Unconstrained Supervisions for ASR},
author = {{Hossein Hadian} and {Daniel Povey} and {H. Sameti} and {J. Trmal} and {S. Khudanpur}},
year = 2018,
month = {12},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/3f1431686216c96e0e812d830bf3328a6814fa73},
}
@inproceedings{61811270,
title = {A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models},
author = {{Vimal Manohar} and {Pegah Ghahremani} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {12},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/70f79646bf2ff4ca86a444cbc90fb05999ea914c},
}
@inproceedings{199442340,
title = {Pretraining by Backtranslation for End-to-End ASR in Low-Resource Settings},
author = {{Matthew Wiesner} and {Adithya Renduchintala} and {Shinji Watanabe} and {Chunxi Liu} and {N. Dehak} and {S. Khudanpur}},
year = 2018,
month = {12},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/bd8922f8cc8284553dc9e6db529af309298451fe},
}
@inproceedings{167210438,
title = {Nonlinear Subspace Feature Enhancement for Image Set Classification},
author = {{Mohammed E. Fathy} and {A. Alavi} and {R. Chellappa}},
year = 2018,
month = {12},
booktitle = {Asian Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/12a15dfa452c7bbf7ee8d149d5141f6ed7c8e485},
}
@inproceedings{56212524,
title = {Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training},
author = {{Mahdi Abavisani} and {Hamid Reza Vaezi Joze} and {Vishal M. Patel}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/7c61a2f4349b55ef6e5d62e5606970c8ca3d09ae},
}
@inproceedings{67856414,
title = {Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport},
author = {{Adarsh Subbaswamy} and {Peter F. Schulam} and {S. Saria}},
year = 2018,
month = {12},
booktitle = {International Conference on Artificial Intelligence and Statistics},
url = {https://www.semanticscholar.org/paper/53bd10108f32ff2c98f333c97cf35570703239a3},
}
@inproceedings{59232359,
title = {ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge},
author = {{J. D. Arias-Londoño} and {Jorge Andrés Gómez García} and {L. Moro-Velázquez} and {Juan Ignacio Godino-Llorente}},
year = 2018,
month = {12},
booktitle = {2018 IEEE International Conference on Big Data (Big Data)},
url = {https://www.semanticscholar.org/paper/0321221cda17274122b4d81d5fe9ca717c81de9e},
}
@inproceedings{54443973,
title = {Elastic Boundary Projection for 3D Medical Image Segmentation},
author = {{Tianwei Ni} and {Lingxi Xie} and {Huangjie Zheng} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/afa1b2e96cea3bedf6777fc698e372e79022a116},
}
@inproceedings{54437811,
title = {Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning},
author = {{Chen Wei} and {Lingxi Xie} and {Xutong Ren} and {Yingda Xia} and {Chi Su} and {Jiaying Liu} and {Qi Tian} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/37ef97bd03a03d34a40ba5d6bbe4d3889c2b30f0},
}
@inproceedings{54225238,
title = {Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization},
author = {{Siyuan Qiao} and {Zhe L. Lin} and {Jianming Zhang} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/3ba9fdcd0d10f2a130fcbd678cebcbb5e8c6bd5f},
}
@inproceedings{55687967,
title = {ELASTIC: Improving CNNs with Instance Specific Scaling Policies},
author = {{Huiyu Wang} and {Aniruddha Kembhavi} and {Ali Farhadi} and {A. Yuille} and {Mohammad Rastegari}},
year = 2018,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/5fe7add7bb041eb52c9983fbdd792bfad1af9992},
}
@inproceedings{54462665,
title = {Feature Denoising for Improving Adversarial Robustness},
author = {{Cihang Xie} and {Yuxin Wu} and {L. Maaten} and {A. Yuille} and {Kaiming He}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/41071dbbbcbb27af3fec70de045f19c28535f5b7},
}
@inproceedings{54436113,
title = {Snapshot Distillation: Teacher-Student Optimization in One Generation},
author = {{Chenglin Yang} and {Lingxi Xie} and {Chi Su} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/a167d8a4ee261540c2b709dde2d94572c6ea3fc8},
}
@inproceedings{96429002,
title = {A Model for Statistical Regularity Extraction from Dynamic Sounds},
author = {{Benjamin Skerritt-Davis} and {Mounya Elhilali}},
year = 2018,
month = {12},
booktitle = {Acta Acustica united with Acustica},
url = {https://www.semanticscholar.org/paper/1d33a3c30514133e0a76c8ca58bd543052d411f2},
}
@inproceedings{126126611,
title = {Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images},
author = {{Yansheng Li} and {Yongjun Zhang} and {Xin Huang} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {Isprs Journal of Photogrammetry and Remote Sensing},
url = {https://www.semanticscholar.org/paper/c4ee8e650646f7c56a78352b8b68549756ccfd70},
}
@inproceedings{54441584,
title = {Towards Accurate Task Accomplishment with Low-Cost Robotic Arms},
author = {{Yiming Zuo} and {Weichao Qiu} and {Lingxi Xie} and {Fangwei Zhong} and {Yizhou Wang} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/8aa41170a9591ff2e5e56ed218d955a4222101b8},
}
@inproceedings{54462231,
title = {Learning Transferable Adversarial Examples via Ghost Networks},
author = {{Yingwei Li} and {S. Bai} and {Yuyin Zhou} and {Cihang Xie} and {Zhishuai Zhang} and {A. Yuille}},
year = 2018,
month = {12},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/67e856f76905f2269089cede51c9a6a7c4fd2f8c},
}
@inproceedings{186358203,
title = {Learning Predictive Models That Transport},
author = {{Adarsh Subbaswamy} and {Peter F. Schulam} and {S. Saria}},
year = 2018,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0381c7f7cc10ba97e003420ca5ab8fc7cfce3731},
}
@inproceedings{57757987,
title = {Better medicine through machine learning: What’s real, and what’s artificial?},
author = {{S. Saria} and {A. Butte} and {A. Sheikh}},
year = 2018,
month = {12},
booktitle = {PLoS Medicine},
url = {https://www.semanticscholar.org/paper/5c2776cef4186e5a436c63c7b016a0c9a0b9879b},
}
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.
@inproceedings{wood-doughty-etal-2018-convolutions,
title = "Convolutions Are All You Need (For Classifying Character Sequences)",
author = "Wood-Doughty, Zach and
Andrews, Nicholas and
Dredze, Mark",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6127/",
doi = "10.18653/v1/W18-6127",
pages = "208--213",
abstract = "While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data."
}
@inproceedings{54067407,
title = {Phase Collaborative Network for Multi-Phase Medical Imaging Segmentation},
author = {{Huangjie Zheng} and {Lingxi Xie} and {Tianwei Ni} and {Ya Zhang} and {Yanfeng Wang} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b40770062d22188a2dc21b6e2f96f8078ff3d8ed},
}
@inproceedings{53288567,
title = {Stream Attention-based Multi-array End-to-end Speech Recognition},
author = {{Xiaofei Wang} and {Ruizhi Li} and {Sri Harish Reddy Mallidi} and {Takaaki Hori} and {Shinji Watanabe} and {H. Hermansky}},
year = 2018,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/bf50833a46839d3932663b472d6145418f9d0bd6},
}
@inproceedings{53245942,
title = {Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition},
author = {{Jaejin Cho} and {Shinji Watanabe} and {Takaaki Hori} and {M. Baskar} and {H. Inaguma} and {J. Villalba} and {N. Dehak}},
year = 2018,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/8f963beca679cb1129df0a944c6de4b126e20fd5},
}
@inproceedings{53865412,
title = {Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection},
author = {{Hongyu Xu} and {Xutao Lv} and {Xiaoyu Wang} and {Zhou Ren} and {R. Chellappa}},
year = 2018,
month = {11},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/5232d6cba44a7bb67e8627ce4c2f4f93dce31e47},
}
@inproceedings{53288160,
title = {Multi-encoder multi-resolution framework for end-to-end speech recognition},
author = {{Ruizhi Li} and {Xiaofei Wang} and {Sri Harish Reddy Mallidi} and {Takaaki Hori} and {Shinji Watanabe} and {H. Hermansky}},
year = 2018,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/f17e182fcb7fbbff2257824174ed6f7df512a42b},
}
@inproceedings{54177614,
title = {JHU Diarization System Description},
author = {{Zili Huang} and {Leibny Paola García-Perera} and {J. Villalba} and {Daniel Povey} and {N. Dehak}},
year = 2018,
month = {11},
booktitle = {IberSPEECH Conference},
url = {https://www.semanticscholar.org/paper/23a109da0c4ce0314f6f016da679a4e1fd6960ef},
}
@inproceedings{53717388,
title = {Learning from Multiview Correlations in Open-domain Videos},
author = {{Nils Holzenberger} and {Shruti Palaskar} and {P. Madhyastha} and {Florian Metze} and {R. Arora}},
year = 2018,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/a01ae256dfe7bd10734fec8a66549fb7ea876a05},
}
@inproceedings{202566009,
title = {Phase Collaborative Network for Two-Phase Medical Image Segmentation},
author = {{Huangjie Zheng} and {Lingxi Xie} and {Tianwei Ni} and {Ya Zhang} and {Yanfeng Wang} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/c7794dd53f20e7b15d156bd92cda6d740314003e},
}
@inproceedings{53863913,
title = {Robust Face Detection via Learning Small Faces on Hard Images},
author = {{Zhishuai Zhang} and {Wei Shen} and {Siyuan Qiao} and {Yan Wang} and {Bo Wang} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/25a784f7f8c94c42821ee078587fc38dffcd00a4},
}
@inproceedings{53228380,
title = {Building Corpora for Single-Channel Speech Separation Across Multiple Domains},
author = {{Matthew Maciejewski} and {Gregory Sell} and {Leibny Paola García-Perera} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2018,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c5141ed9ed785a6a1df61b36883e6dfa19a59ff7},
}
@inproceedings{53728928,
title = {Recognizing Disguised Faces in the Wild},
author = {{Maneet Singh} and {Richa Singh} and {Mayank Vatsa} and {N. Ratha} and {R. Chellappa}},
year = 2018,
month = {11},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/47b14a600e6728fb964b3cc964433480560142fa},
}
@inproceedings{51977097,
title = {Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR},
author = {{Hossein Hadian} and {H. Sameti} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {11},
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/6aa83f912110c63f0da5dc8a8464c9dc2c589076},
}
@inproceedings{53776855,
title = {Learning Without Memorizing},
author = {{Prithviraj Dhar} and {Rajat Vikram Singh} and {Kuan-Chuan Peng} and {Ziyan Wu} and {R. Chellappa}},
year = 2018,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/162a4c6f964880ec90b40fefa6d4d99d3ad321ec},
}
Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification – where a tweet is categorized according to a viewpoint it espouses – the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pretraining method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited.
@inproceedings{benton-dredze-2018-using,
title = "Using Author Embeddings to Improve Tweet Stance Classification",
author = "Benton, Adrian and
Dredze, Mark",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6124/",
doi = "10.18653/v1/W18-6124",
pages = "184--194",
abstract = "Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification -- where a tweet is categorized according to a viewpoint it espouses -- the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pretraining method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited."
}
@inproceedings{53717843,
title = {A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos},
author = {{Joshua Gleason} and {Rajeev Ranjan} and {S. Schwarcz} and {C. Castillo} and {Jun-Cheng Chen} and {R. Chellappa}},
year = 2018,
month = {11},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/1a2e40b8ef509ed099bb7e77862ed5ddca52c3a2},
}
@inproceedings{53961017,
title = {Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data},
author = {{Yutong Bai} and {Qing Liu} and {Lingxi Xie} and {Yan Zheng} and {Weichao Qiu} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/87bed8d35625a75d8ba1953bb6bb71a3625020c0},
}
@inproceedings{53115077,
title = {KEPLER: Simultaneous estimation of keypoints and 3D pose of unconstrained faces in a unified framework by learning efficient H-CNN regressors},
author = {{Amit Kumar} and {A. Alavi} and {R. Chellappa}},
year = 2018,
month = {11},
booktitle = {Image and Vision Computing},
url = {https://www.semanticscholar.org/paper/e8d98b76d82065abfcf20194918a737b7e5e4c4b},
}
@inproceedings{53250287,
title = {Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection},
author = {{Sandeep Reddy Kothinti} and {Keisuke Imoto} and {D. Chakrabarty} and {Gregory Sell} and {Shinji Watanabe} and {Mounya Elhilali}},
year = 2018,
month = {11},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/e5efd7e2087e58c5a8860398dfcf143aa9dc865e},
}
@inproceedings{53268904,
title = {Next generation media monitoring: Global coverage of electronic nicotine delivery systems (electronic cigarettes) on Bing, Google and Twitter, 2013-2018},
author = {{J. Ayers} and {Mark Dredze} and {E. Leas} and {Theodore L. Caputi} and {Jon-Patrick Allem} and {Joanna E. Cohen}},
year = 2018,
month = {11},
booktitle = {PLoS ONE},
url = {https://www.semanticscholar.org/paper/637ed794b41f2a50f8b2efce4ef02b3b12c8c057},
}
@inproceedings{53226992,
title = {Individualized sepsis treatment using reinforcement learning},
author = {{S. Saria}},
year = 2018,
month = {11},
booktitle = {Nature Network Boston},
url = {https://www.semanticscholar.org/paper/556929f1e8097e090f121a52b9ef42461d2273f3},
}
@inproceedings{53285373,
title = {Policy Regret in Repeated Games},
author = {{R. Arora} and {M. Dinitz} and {T. V. Marinov} and {M. Mohri}},
year = 2018,
month = {11},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/c1482f2409af234da7d9771ddac4e88b45ec8e86},
}
@inproceedings{53295888,
title = {An Empirical Exploration of Curriculum Learning for Neural Machine Translation},
author = {{Xuan Zhang} and {Manish Kumar} and {Huda Khayrallah} and {Kenton Murray} and {Jeremy Gwinnup} and {Marianna J. Martindale} and {Paul McNamee} and {Kevin Duh} and {Marine Carpuat}},
year = 2018,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b43ffb0d4f8d1c66632b78ad74d92ab1218a6976},
}
@inproceedings{54223408,
title = {3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training},
author = {{Yingda Xia} and {Fengze Liu} and {D. Yang} and {Jinzheng Cai} and {Lequan Yu} and {Zhuotun Zhu} and {Daguang Xu} and {A. Yuille} and {H. Roth}},
year = 2018,
month = {11},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/d7000a609c9aa59c1fd893cdfa6f51cd9cd22354},
}
@inproceedings{119310436,
title = {Generalized Coarse-to-Fine Visual Recognition with Progressive Training},
author = {{Xutong Ren} and {Lingxi Xie} and {Chen Wei} and {Siyuan Qiao} and {Chi Su} and {Jiaying Liu} and {Qi Tian} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/4edd6898ba70c9a8a2099b0244c78de69ed9e52a},
}
There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns.
@inproceedings{lippincott-2018-portable,
title = "Portable, layer-wise task performance monitoring for {NLP} models",
author = "Lippincott, Tom",
editor = "Linzen, Tal and
Chrupa\l a, Grzegorz and
Alishahi, Afra",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5445/",
doi = "10.18653/v1/W18-5445",
pages = "350--352",
abstract = "There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns."
}
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages–-UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project’s annotations could be used to validate the other’s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13\% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.
@inproceedings{mccarthy-etal-2018-marrying,
title = "Marrying {U}niversal {D}ependencies and {U}niversal {M}orphology",
author = "McCarthy, Arya D. and
Silfverberg, Miikka and
Cotterell, Ryan and
Hulden, Mans and
Yarowsky, David",
editor = "de Marneffe, Marie-Catherine and
Lynn, Teresa and
Schuster, Sebastian",
booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6011/",
doi = "10.18653/v1/W18-6011",
pages = "91--101",
abstract = "The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages---UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project's annotations could be used to validate the other's. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13\% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects."
}
@inproceedings{54032226,
title = {Progressive Recurrent Learning for Visual Recognition},
author = {{Xutong Ren} and {Lingxi Xie} and {Chen Wei} and {Siyuan Qiao} and {Chi Su} and {Jiaying Liu} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/39223c8e64221062cd992e7e89e6db858014eac1},
}
@inproceedings{52292103,
title = {OriNet: A Fully Convolutional Network for 3D Human Pose Estimation},
author = {{Chenxu Luo} and {Xiao Chu} and {A. Yuille}},
year = 2018,
month = {11},
booktitle = {British Machine Vision Conference},
url = {https://www.semanticscholar.org/paper/ad7889a2525c345d701b8b57e441afe8ac3370ad},
}
@InProceedings{wang-eisner-2018-emnlp,
aclid = "D18-1163",
doi = "10.18653/v1/D18-1163",
author = "Dingquan Wang and Jason Eisner",
title = "Synthetic Data Made to Order: The Case of Parsing",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "1325--1337",
year = "2018",
month = nov,
address = "Brussels",
URL = "http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-emnlp",
}
@inproceedings{49652556,
title = {Neural variational entity set expansion for automatically populated knowledge graphs},
author = {{Pushpendre Rastogi} and {Adam Poliak} and {V. Lyzinski} and {Benjamin Van Durme}},
year = 2018,
month = {10},
booktitle = {Information Retrieval Journal},
url = {https://www.semanticscholar.org/paper/ba1bd9b465c26441555f73b9a2a4026dcfb11683},
}
@inproceedings{56717891,
title = {Word2vec Word Similarities on IBM's TrueNorth Neurosynaptic System},
author = {{Daniel R. Mendat} and {A. Cassidy} and {Guido Zarrella} and {A. Andreou}},
year = 2018,
month = {10},
booktitle = {Biomedical Circuits and Systems Conference},
url = {https://www.semanticscholar.org/paper/f185dbbf55b2229aecf82f486550a2f9d71be1d4},
}
@inproceedings{53109997,
title = {Attentive Filtering Networks for Audio Replay Attack Detection},
author = {{Cheng-I Lai} and {A. Abad} and {Korin Richmond} and {J. Yamagishi} and {N. Dehak} and {Simon King}},
year = 2018,
month = {10},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/f9a8ffe3778f4962de63d1153d5041722a7eba81},
}
@inproceedings{52191412,
title = {A Real-Time Multi-Task Single Shot Face Detector},
author = {{Jun-Cheng Chen} and {Wei-An Lin} and {Jingxiao Zheng} and {R. Chellappa}},
year = 2018,
month = {10},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/6043070c2f2f592601e90d2c71dc6fafca48056b},
}
@inproceedings{69742053,
title = {Machine Learning Driven Targeted Real-Time Early Warning System Improves Outcomes in Sepsis},
author = {{S. Saria}},
year = 2018,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/e62fbf12f0fff02cf78b0c5cba5e2de2e50972a9},
}
The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to “the” BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6319/",
doi = "10.18653/v1/W18-6319",
pages = "186--191",
abstract = "The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to ``the'' BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this."
}
@inproceedings{133604577,
title = {Dual-Minimax Probability Machines for One-class Mobile Active Authentication},
author = {{Pramuditha Perera} and {Vishal M. Patel}},
year = 2018,
month = {10},
booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
url = {https://www.semanticscholar.org/paper/abef2966d391e9d1b93611f48201004d3581b9ee},
}
@inproceedings{69464873,
title = {Sensemaking Research Roadmap},
author = {{K.P. Subbalakshmi} and {A. Galstyan} and {R. Chellappa} and {Charles Clancy}},
year = 2018,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/d93c6b12d5b2131dd196c790abc1135c9b6ffcab},
}
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component’s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.
@inproceedings{thompson-etal-2018-freezing,
title = "Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation",
author = "Thompson, Brian and
Khayrallah, Huda and
Anastasopoulos, Antonios and
McCarthy, Arya D. and
Duh, Kevin and
Marvin, Rebecca and
McNamee, Paul and
Gwinnup, Jeremy and
Anderson, Tim and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6313/",
doi = "10.18653/v1/W18-6313",
pages = "124--132",
abstract = "To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component's contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain."
}
@inproceedings{57572931,
title = {Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis},
author = {{Xing Di} and {He Zhang} and {Vishal M. Patel}},
year = 2018,
month = {10},
booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
url = {https://www.semanticscholar.org/paper/00c14b348635489fb7622a3982d388d8a2dbf9b9},
}
We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German–English, English– German, and Russian–English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.
@inproceedings{koehn-etal-2018-jhu,
title = "The {JHU} Machine Translation Systems for {WMT} 2018",
author = "Koehn, Philipp and
Duh, Kevin and
Thompson, Brian",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6417/",
doi = "10.18653/v1/W18-6417",
pages = "438--444",
abstract = "We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German--English, English-- German, and Russian--English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years."
}
@inproceedings{56717992,
title = {Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System},
author = {{Kate D. Fischl} and {A. Andreou} and {T. Stewart} and {Kaitlin L. Fair}},
year = 2018,
month = {10},
booktitle = {Biomedical Circuits and Systems Conference},
url = {https://www.semanticscholar.org/paper/55ce934130d3bd91f2144fb6efbb239c44822216},
}
@inproceedings{52939610,
title = {Discretizing Logged Interaction Data Biases Learning for Decision-Making},
author = {{Peter F. Schulam} and {S. Saria}},
year = 2018,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/bd4aa7f9b4941c71978c29dae7345e67ca4318da},
}
@inproceedings{cotterell-etal-2018-conll,
title = "The {C}o{NLL}--{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
author = "Cotterell, Ryan and
Kirov, Christo and
Sylak-Glassman, John and
Walther, G\'eraldine and
Vylomova, Ekaterina and
McCarthy, Arya D. and
Kann, Katharina and
Mielke, Sabrina J. and
Nicolai, Garrett and
Silfverberg, Miikka and
Yarowsky, David and
Eisner, Jason and
Hulden, Mans",
editor = "Hulden, Mans and
Cotterell, Ryan",
booktitle = "Proceedings of the {C}o{NLL}--{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
month = oct,
year = "2018",
address = "Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-3001/",
doi = "10.18653/v1/K18-3001",
pages = "1--27"
}
This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.
@inproceedings{khayrallah-etal-2018-jhu,
title = "The {JHU} Parallel Corpus Filtering Systems for {WMT} 2018",
author = "Khayrallah, Huda and
Xu, Hainan and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6479/",
doi = "10.18653/v1/W18-6479",
pages = "896--899",
abstract = "This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah."
}
@inproceedings{52987932,
title = {Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding},
author = {{Chenxu Luo} and {Zhenheng Yang} and {Peng Wang} and {Y. Wang} and {W. Xu} and {R. Nevatia} and {A. Yuille}},
year = 2018,
month = {10},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/ba5c19a02696029566b9e01f5f55e908d25763bd},
}
@inproceedings{56717729,
title = {StethoVest: A simultaneous multichannel wearable system for cardiac acoustic mapping},
author = {{Christos Sapsanis} and {Nathaniel Welsh} and {Michael Pozin} and {Guillaume Garreau} and {Gaspar Tognetti} and {Hani Bakhshaee} and {P. Pouliquen} and {R. Mittal} and {W. R. Thompson} and {A. Andreou}},
year = 2018,
month = {10},
booktitle = {Biomedical Circuits and Systems Conference},
url = {https://www.semanticscholar.org/paper/f0f1d49a014881e74de58f328db3a54aae6863b9},
}
@inproceedings{53116244,
title = {ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension},
author = {{Sheng Zhang} and {Xiaodong Liu} and {Jingjing Liu} and {Jianfeng Gao} and {Kevin Duh} and {Benjamin Van Durme}},
year = 2018,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a5b66ee341cb990f7f70a124b5fab3316d3b7e27},
}
@InProceedings{cotterell-et-al-2018-shared,
aclid = "K18-3001",
doi = "10.18653/v1/K18-3001",
author = "Ryan Cotterell and Christo Kirov and John
Sylak-Glassman and G{\'e}raldine Walther and Ekaterina
Vylomova and Arya D. McCarthy and Katharina Kann and
Sabrina Mielke and Garrett Nicolai and Miikka
Silfverberg and David Yarowsky and Jason Eisner and
Mans Hulden",
title = "The {CoNLL}--{SIGMORPHON} 2018 Shared Task: Universal
Morphological Reinflection",
booktitle = "Proceedings of the CoNLL SIGMORPHON 2018 Shared Task:
Universal Morphological Reinflection",
pages = "1--27",
year = "2018",
month = oct,
address = "Brussels",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-shared",
}
It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator’s corrections within the document itself. We focus on adaptation within a single document – appropriate for an interactive translation scenario where a model adapts to a human translator’s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3\% novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2\% novel word translation accuracy.
@inproceedings{kothur-etal-2018-document,
title = "Document-Level Adaptation for Neural Machine Translation",
author = "Kothur, Sachith Sri Ram and
Knowles, Rebecca and
Koehn, Philipp",
editor = "Birch, Alexandra and
Finch, Andrew and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2708/",
doi = "10.18653/v1/W18-2708",
pages = "64--73",
abstract = "It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator's corrections within the document itself. We focus on adaptation within a single document -- appropriate for an interactive translation scenario where a model adapts to a human translator's input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3\% novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2\% novel word translation accuracy."
}
We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 GermanâEnglish tasks.
@inproceedings{hoang-etal-2018-iterative,
title = "Iterative Back-Translation for Neural Machine Translation",
author = "Hoang, Vu Cong Duy and
Koehn, Philipp and
Haffari, Gholamreza and
Cohn, Trevor",
editor = "Birch, Alexandra and
Finch, Andrew and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2703/",
doi = "10.18653/v1/W18-2703",
pages = "18--24",
abstract = "We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 GermanâEnglish tasks."
}
Supervised domain adaptation–-where a large generic corpus and a smaller in-domain corpus are both available for training–-is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model’s output word distribution and that of the out-of-domain model to prevent the model’s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.
@inproceedings{khayrallah-etal-2018-regularized,
title = "Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation",
author = "Khayrallah, Huda and
Thompson, Brian and
Duh, Kevin and
Koehn, Philipp",
editor = "Birch, Alexandra and
Finch, Andrew and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2705/",
doi = "10.18653/v1/W18-2705",
pages = "36--44",
abstract = "Supervised domain adaptation---where a large generic corpus and a smaller in-domain corpus are both available for training---is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model's output word distribution and that of the out-of-domain model to prevent the model's output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU."
}
We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.
@inproceedings{sakaguchi-van-durme-2018-efficient,
title = "Efficient Online Scalar Annotation with Bounded Support",
author = "Sakaguchi, Keisuke and
Van Durme, Benjamin",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1020/",
doi = "10.18653/v1/P18-1020",
pages = "208--218",
abstract = "We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation."
}
We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.
@inproceedings{khayrallah-koehn-2018-impact,
title = "On the Impact of Various Types of Noise on Neural Machine Translation",
author = "Khayrallah, Huda and
Koehn, Philipp",
editor = "Birch, Alexandra and
Finch, Andrew and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2709/",
doi = "10.18653/v1/W18-2709",
pages = "74--83",
abstract = "We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence."
}
We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).
@inproceedings{liu-etal-2018-stochastic,
title = "Stochastic Answer Networks for Machine Reading Comprehension",
author = "Liu, Xiaodong and
Shen, Yelong and
Duh, Kevin and
Gao, Jianfeng",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1157/",
doi = "10.18653/v1/P18-1157",
pages = "1694--1704",
abstract = "We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO)."
}
Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.
@inproceedings{loveys-etal-2018-cross,
title = "Cross-cultural differences in language markers of depression online",
author = "Loveys, Kate and
Torrez, Jonathan and
Fine, Alex and
Moriarty, Glen and
Coppersmith, Glen",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud'hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0608/",
doi = "10.18653/v1/W18-0608",
pages = "78--87",
abstract = "Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly."
}
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.
@inproceedings{mei-etal-2018-halo,
title = "{H}alo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction",
author = "Mei, Hongyuan and
Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2017/",
doi = "10.18653/v1/S18-2017",
pages = "142--147",
abstract = "Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings."
}
We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage
@inproceedings{poliak-etal-2018-evaluation,
title = "On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference",
author = "Poliak, Adam and
Belinkov, Yonatan and
Glass, James and
Van Durme, Benjamin",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2082/",
doi = "10.18653/v1/N18-2082",
pages = "513--523",
abstract = "We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage"
}
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user’s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection
@inproceedings{wood-doughty-etal-2018-predicting,
title = "Predicting {T}witter User Demographics from Names Alone",
author = "Wood-Doughty, Zach and
Andrews, Nicholas and
Marvin, Rebecca and
Dredze, Mark",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara and
Wagner, Claudia",
booktitle = "Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1114/",
doi = "10.18653/v1/W18-1114",
pages = "105--111",
abstract = "Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user's name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection"
}
We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.
@inproceedings{rudinger-etal-2018-neural-models,
title = "Neural Models of Factuality",
author = "Rudinger, Rachel and
White, Aaron Steven and
Van Durme, Benjamin",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1067/",
doi = "10.18653/v1/N18-1067",
pages = "731--744",
abstract = "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well."
}
In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67\% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5\%. We further demonstrate the effectiveness of our learned parser on image retrieval applications.
@inproceedings{wang-etal-2018-scene,
title = "Scene Graph Parsing as Dependency Parsing",
author = "Wang, Yu-Siang and
Liu, Chenxi and
Zeng, Xiaohui and
Yuille, Alan",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1037/",
doi = "10.18653/v1/N18-1037",
pages = "397--407",
abstract = "In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67\% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5\%. We further demonstrate the effectiveness of our learned parser on image retrieval applications."
}
Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features – a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR’s model fit is less sensitive to training parameters than DMR.
@inproceedings{benton-dredze-2018-deep,
title = "Deep {D}irichlet Multinomial Regression",
author = "Benton, Adrian and
Dredze, Mark",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1034/",
doi = "10.18653/v1/N18-1034",
pages = "365--374",
abstract = "Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features -- a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR's model fit is less sensitive to training parameters than DMR."
}
Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information. A particularly attractive property of some tweets is *geo-tagging*, where a user account has opted-in to attaching their current location to each message. Unfortunately (from a researcher’s perspective) only a fraction of Twitter accounts agree to this, and these accounts are likely to have systematic diffences with the general population. This work is an exploratory study of these differences across the full range of Twitter content, and complements previous studies that focus on the English-language subset. Additionally, we compare methods for querying users by self-identified properties, finding that the constrained semantics of the “description” field provides cleaner, higher-volume results than more complex regular expressions.
@inproceedings{lippincott-carrell-2018-observational,
title = "Observational Comparison of Geo-tagged and Randomly-drawn Tweets",
author = "Lippincott, Tom and
Carrell, Annabelle",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara and
Wagner, Claudia",
booktitle = "Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1107/",
doi = "10.18653/v1/W18-1107",
pages = "50--55",
abstract = "Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information. A particularly attractive property of some tweets is *geo-tagging*, where a user account has opted-in to attaching their current location to each message. Unfortunately (from a researcher's perspective) only a fraction of Twitter accounts agree to this, and these accounts are likely to have systematic diffences with the general population. This work is an exploratory study of these differences across the full range of Twitter content, and complements previous studies that focus on the English-language subset. Additionally, we compare methods for querying users by self-identified properties, finding that the constrained semantics of the ``description'' field provides cleaner, higher-volume results than more complex regular expressions."
}
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.
@inproceedings{poliak-etal-2018-hypothesis,
title = "Hypothesis Only Baselines in Natural Language Inference",
author = "Poliak, Adam and
Naradowsky, Jason and
Haldar, Aparajita and
Rudinger, Rachel and
Van Durme, Benjamin",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2023/",
doi = "10.18653/v1/S18-2023",
pages = "180--191",
abstract = "We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context."
}
Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.
@inproceedings{wood-doughty-etal-2018-johns,
title = "{J}ohns {H}opkins or johnny-hopkins: Classifying Individuals versus Organizations on {T}witter",
author = "Wood-Doughty, Zach and
Mahajan, Praateek and
Dredze, Mark",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara and
Wagner, Claudia",
booktitle = "Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1108/",
doi = "10.18653/v1/W18-1108",
pages = "56--61",
abstract = "Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available."
}
Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.
@inproceedings{shapiro-duh-2018-morphological,
title = "Morphological Word Embeddings for {A}rabic Neural Machine Translation in Low-Resource Settings",
author = "Shapiro, Pamela and
Duh, Kevin",
editor = {Faruqui, Manaal and
Sch\"utze, Hinrich and
Trancoso, Isabel and
Tsvetkov, Yulia and
Yaghoobzadeh, Yadollah},
booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
month = jun,
year = "2018",
address = "New Orleans",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1201/",
doi = "10.18653/v1/W18-1201",
pages = "1--11",
abstract = "Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system."
}
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context – both document and sentence level information – than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.
@inproceedings{zhang-etal-2018-fine,
title = "Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds",
author = "Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2022/",
doi = "10.18653/v1/S18-2022",
pages = "173--179",
abstract = "Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets."
}
The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm’s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.
@inproceedings{post-vilar-2018-fast,
title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation",
author = "Post, Matt and
Vilar, David",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1119/",
doi = "10.18653/v1/N18-1119",
pages = "1314--1324",
abstract = "The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm's remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye."
}
We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these “Winogender schemas,” we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.
@inproceedings{rudinger-etal-2018-gender,
title = "Gender Bias in Coreference Resolution",
author = "Rudinger, Rachel and
Naradowsky, Jason and
Leonard, Brian and
Van Durme, Benjamin",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2002/",
doi = "10.18653/v1/N18-2002",
pages = "8--14",
abstract = "We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these ``Winogender schemas,'' we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics."
}
Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user’s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073/",
doi = "10.18653/v1/N18-2073",
pages = "458--463",
abstract = "Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user's query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR."
}
@InProceedings{cotterell-et-al-2018-syncretism,
aclid = "N18-2087",
doi = "10.18653/v1/N18-2087",
author = "Ryan Cotterell and Christo Kirov and Sabrina J. Mielke
and Jason Eisner",
title = "Unsupervised Disambiguation of Syncretism in Inflected
Lexicons",
booktitle = "Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "548--553",
year = "2018",
month = jun,
address = "New Orleans",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-syncretism",
}
@InProceedings{cotterell-et-al-2018-lm,
aclid = "N18-2085",
doi = "10.18653/v1/N18-2085",
author = "Ryan Cotterell and Sabrina J. Mielke and Jason Eisner
and Brian Roark",
title = "Are All Languages Equally Hard to Language-Model?",
booktitle = "Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "536--541",
year = "2018",
month = jun,
address = "New Orleans",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-lm",
}
@InProceedings{lin-eisner-2018-naacl,
aclid = "N18-1085",
doi = "10.18653/v1/N18-1085",
author = "Chu-Cheng Lin and Jason Eisner",
title = "Neural Particle Smoothing for Sampling from
Conditional Sequence Models",
booktitle = "Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "929--941",
year = "2018",
month = jun,
address = "New Orleans",
URL = "http://cs.jhu.edu/~jason/papers/#lin-eisner-2018-naacl",
}
@InProceedings{cotterell-eisner-2018-naacl,
aclid = "N18-1004",
doi = "10.18653/v1/N18-1004",
author = "Ryan Cotterell and Jason Eisner",
title = "A Deep Generative Model of Vowel Formant Typology",
booktitle = "Proceedings of the 2018 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "37--46",
year = "2018",
month = jun,
address = "New Orleans",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2018-naacl",
}
@inproceedings{wu-yarowsky-2018-massively,
title = "Massively Translingual Compound Analysis and Translation Discovery",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Hasida, Koiti and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios and
Tokunaga, Takenobu",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1612/"
}
@inproceedings{wu-yarowsky-2018-creating,
title = "Creating Large-Scale Multilingual Cognate Tables",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Hasida, Koiti and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios and
Tokunaga, Takenobu",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1538/"
}
@inproceedings{wu-yarowsky-2018-comparative,
title = "A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Hasida, Koiti and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios and
Tokunaga, Takenobu",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1150/"
}
The encoder-decoder with attention model has become the state of the art for machine translation. However, more investigations are still needed to understand the internal mechanism of this end-to-end model. In this paper, we focus on how neural machine translation (NMT) models consider source information while decoding. We propose a numerical measurement of source context dependency in the NMT models and analyze the behaviors of the NMT decoder with this measurement under several circumstances. Experimental results show that this measurement is an appropriate estimate for source context dependency and consistent over different domains.
@inproceedings{ma-etal-2018-analysis,
title = "An Analysis of Source Context Dependency in Neural Machine Translation",
author = "Ma, Xutai and
Li, Ke and
Koehn, Philipp",
editor = "P\'erez-Ortiz, Juan Antonio and
S\'anchez-Mart\'\i nez, Felipe and
Espl\`a-Gomis, Miquel and
Popovi\'c, Maja and
Rico, Celia and
Martins, Andr\'e and
Van den Bogaert, Joachim and
Forcada, Mikel L.",
booktitle = "Proceedings of the 21st Annual Conference of the European Association for Machine Translation",
month = may,
year = "2018",
address = "Alicante, Spain",
url = "https://aclanthology.org/2018.eamt-main.19/",
pages = "209--218",
abstract = "The encoder-decoder with attention model has become the state of the art for machine translation. However, more investigations are still needed to understand the internal mechanism of this end-to-end model. In this paper, we focus on how neural machine translation (NMT) models consider source information while decoding. We propose a numerical measurement of source context dependency in the NMT models and analyze the behaviors of the NMT decoder with this measurement under several circumstances. Experimental results show that this measurement is an appropriate estimate for source context dependency and consistent over different domains."
}
@inproceedings{kirov-etal-2018-unimorph,
title = "{U}ni{M}orph 2.0: {U}niversal {M}orphology",
author = {Kirov, Christo and
Cotterell, Ryan and
Sylak-Glassman, John and
Walther, G\'eraldine and
Vylomova, Ekaterina and
Xia, Patrick and
Faruqui, Manaal and
Mielke, Sabrina J. and
McCarthy, Arya and
K\"ubler, Sandra and
Yarowsky, David and
Eisner, Jason and
Hulden, Mans},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Hasida, Koiti and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios and
Tokunaga, Takenobu",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1293/"
}
@inproceedings{wu-etal-2018-creating,
title = "Creating a Translation Matrix of the {B}ible's Names Across 591 Languages",
author = "Wu, Winston and
Vyas, Nidhi and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Hasida, Koiti and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H\'el\`ene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios and
Tokunaga, Takenobu",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1263/"
}
@InProceedings{UNIMORPH-2018,
author = "Christo Kirov and Ryan Cotterell and John
Sylak{-}Glassman and G{\'{e}}raldine Walther and
Ekaterina Vylomova and Patrick Xia and Manaal Faruqui
and Sabrina J. Mielke and Arya D. McCarthy and Sandra
K{\"{u}}bler and David Yarowsky and Jason Eisner and
Mans Hulden",
title = "{UniMorph} 2.0: Universal Morphology",
booktitle = "Proceedings of the Eleventh International Conference
on Language Resources and Evaluation (LREC 2018)",
year = "2018",
month = may,
address = "Miyazaki, Japan",
URL = "http://cs.jhu.edu/~jason/papers/#UNIMORPH-2018",
}
@inproceedings{knowles-koehn-2018-lightweight,
title = "Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction",
author = "Knowles, Rebecca and
Koehn, Philipp",
editor = "Astudillo, Ram\'on and
Gra\c ca, Jo\~ao and
Martins, Andr\'e",
booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
month = mar,
year = "2018",
address = "Boston, MA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/W18-2102/",
pages = "35--40"
}
@inproceedings{shearing-etal-2018-improving,
title = "Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)",
author = "Shearing, Steven and
Kirov, Christo and
Khayrallah, Huda and
Yarowsky, David",
editor = "Cherry, Colin and
Neubig, Graham",
booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
month = mar,
year = "2018",
address = "Boston, MA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/W18-1813/",
pages = "132--139"
}
@inproceedings{hieber-etal-2018-sockeye,
title = "The Sockeye Neural Machine Translation Toolkit at {AMTA} 2018",
author = "Hieber, Felix and
Domhan, Tobias and
Denkowski, Michael and
Vilar, David and
Sokolov, Artem and
Clifton, Ann and
Post, Matt",
editor = "Cherry, Colin and
Neubig, Graham",
booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
month = mar,
year = "2018",
address = "Boston, MA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/W18-1820/",
pages = "200--207"
}
@inproceedings{knowles-etal-2018-comparison,
title = "A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair",
author = "Knowles, Rebecca and
Ortega, John and
Koehn, Philipp",
editor = "Astudillo, Ram\'on and
Gra\c ca, Jo\~ao and
Martins, Andr\'e",
booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
month = mar,
year = "2018",
address = "Boston, MA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/W18-2108/",
pages = "249--255"
}
@inproceedings{marvin-koehn-2018-exploring,
title = "Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)",
author = "Marvin, Rebecca and
Koehn, Philipp",
editor = "Cherry, Colin and
Neubig, Graham",
booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
month = mar,
year = "2018",
address = "Boston, MA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/W18-1812/",
pages = "125--131"
}
@InProceedings{filardo-eisner-2018-sysml,
author = "Jason Eisner and Nathaniel Wesley Filardo",
title = "Treating Machine Learning Algorithms as Declaratively
Specified Circuits",
booktitle = "Proceedings of the Conference on Systems and Machine
Learning (SysML)",
year = "2018",
month = feb,
address = "Palo Alto",
URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2018-sysml",
}
@InProceedings{wang-eisner-2018-scil,
doi = "10.7275/R5F769RV",
author = "Dingquan Wang and Jason Eisner",
title = "Predicting Fine-Grained Syntactic Typology from
Surface Features",
booktitle = "Proceedings of the Society for Computation in
Linguistics (SCiL)",
year = "2018",
month = jan,
volume = "1",
address = "Salt Lake City",
URL = "http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-scil",
}
@InProceedings{cotterell-et-al-2018-scil,
doi = "10.7275/R57P8WK1",
author = "Ryan Cotterell and Christo Kirov and Mans Hulden and
Jason Eisner",
title = "Quantifying the Trade-off Between Two Types of
Morphological Complexity",
booktitle = "Proceedings of the Society for Computation in
Linguistics (SCiL)",
year = "2018",
month = jan,
volume = "1",
pages = "209--210",
address = "Salt Lake City",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-scil",
}
@inproceedings{4552998,
title = {SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data},
author = {{Qi Chen} and {Weichao Qiu} and {Yi Zhang} and {Lingxi Xie} and {A. Yuille}},
year = 2018,
month = {4},
booktitle = {British Machine Vision Conference},
url = {https://www.semanticscholar.org/paper/aae1bf434983545c8a99a5dbfc2ce37435c76e03},
}
@inproceedings{47016865,
title = {Bridging the Gap Between 2D and 3D Organ Segmentation},
author = {{Yingda Xia} and {Lingxi Xie} and {Fengze Liu} and {Zhuotun Zhu} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {4},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/8388d1ef48d17b77d2c6784ab4836f97d1144a67},
}
@inproceedings{46848810,
title = {Enhancement and Analysis of Conversational Speech: JSALT 2017},
author = {{Neville Ryant} and {Elika Bergelson} and {Kenneth Ward Church} and {Alejandrina Cristia} and {Jun Du} and {Sriram Ganapathy} and {S. Khudanpur} and {Diana Kowalski} and {Mahesh Krishnamoorthy} and {Rajat Kulshreshta} and {M. Liberman} and {Yu-Ding Lu} and {Matthew Maciejewski} and {Florian Metze} and {Jan Profant} and {Lei Sun} and {Yu Tsao} and {Zhou Yu}},
year = 2018,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/eb469747ced0cd5d383430d3d723c50d85cfc72e},
}
@inproceedings{3295267,
title = {Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker},
author = {{X. Lan} and {Shengping Zhang} and {P. Yuen} and {R. Chellappa}},
year = 2018,
month = {4},
booktitle = {IEEE Transactions on Image Processing},
url = {https://www.semanticscholar.org/paper/3c37c72458d01fc3b949aa4177631beaf3bf6696},
}
@inproceedings{3966049,
title = {Deep Co-Training for Semi-Supervised Image Recognition},
author = {{Siyuan Qiao} and {Wei Shen} and {Zhishuai Zhang} and {Bo Wang} and {A. Yuille}},
year = 2018,
month = {3},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/40c6a2b1cb312f11f8225a733545fdabd436e347},
}
@inproceedings{3795577,
title = {Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults},
author = {{N. Mathioudakis} and {Estelle M. Everett} and {Shuvodra Routh} and {P. Pronovost} and {H. Yeh} and {S. Golden} and {S. Saria}},
year = 2018,
month = {3},
booktitle = {BMJ Open Diabetes Research & Care},
url = {https://www.semanticscholar.org/paper/23e72bc60d37fbd37125c1431f9ae2cfe57918ca},
}
@inproceedings{4799963,
title = {Zero-Shot Object Detection},
author = {{Ankan Bansal} and {Karan Sikka} and {Gaurav Sharma} and {R. Chellappa} and {Ajay Divakaran}},
year = 2018,
month = {4},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/32bc9334ad0edaec29540320b9f00c9a7aab81f8},
}
@inproceedings{13666307,
title = {An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes},
author = {{S. Barnes} and {S. Saria} and {S. Levin}},
year = 2018,
month = {3},
booktitle = {Journal of Healthcare Engineering},
url = {https://www.semanticscholar.org/paper/65005f6136132c570060a69c55d94fffe68881ba},
}
@inproceedings{49193444,
title = {Bio-Inspired Human Action Recognition With a Micro-Doppler Sonar System},
author = {{Thomas S. Murray} and {Daniel R. Mendat} and {Kayode A. Sanni} and {P. Pouliquen} and {A. Andreou}},
year = 2018,
booktitle = {IEEE Access},
url = {https://www.semanticscholar.org/paper/f39b7b7af204e9c769653c574284508fc027ce31},
}
@inproceedings{52338594,
title = {Predicting Argumenthood of English Preposition Phrases},
author = {{Najoung Kim} and {Kyle Rawlins} and {Benjamin Van Durme} and {P. Smolensky}},
year = 2018,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/611f838579b0fa05ef6ac4557bd3b7e8f5956ad6},
}
@inproceedings{49429041,
title = {Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN},
author = {{Puyang Wang} and {Vishal M. Patel} and {I. Hacihaliloglu}},
year = 2018,
month = {6},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/f0ccdc9b66e26ea98a079a8fa6375d444add3794},
}
@inproceedings{46741434,
title = {Efficient and Low Latency Detection of Intruders in Mobile Active Authentication},
author = {{Pramuditha Perera} and {Vishal M. Patel}},
year = 2018,
month = {6},
booktitle = {IEEE Transactions on Information Forensics and Security},
url = {https://www.semanticscholar.org/paper/88420bbdf2929710a44678bc41b8170c2d40e62b},
}
@inproceedings{51967626,
title = {Continuous Authentication of Smartphones Based on Application Usage},
author = {{U. Mahbub} and {Jukka Komulainen} and {Denzil Ferreira} and {R. Chellappa}},
year = 2018,
month = {7},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/cc3e70186745b7a2476c8773cf614c294f02f53c},
}
@inproceedings{195346736,
title = {The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection},
author = {{Matthew Wiesner} and {Chunxi Liu} and {Lucas Ondel} and {Craig Harman} and {Vimal Manohar} and {J. Trmal} and {Zhongqiang Huang} and {S. Khudanpur} and {N. Dehak}},
year = 2018,
month = {2},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/cf6ac917a3fa0610242b465bc24e32e9297ec706},
}
@inproceedings{44178108,
title = {Detecting change in stochastic sound sequences},
author = {{Benjamin Skerritt-Davis} and {Mounya Elhilali}},
year = 2018,
month = {5},
booktitle = {PLoS Comput. Biol.},
url = {https://www.semanticscholar.org/paper/3205ed8e22ef75e474c5782d4574f960db354413},
}
@inproceedings{52188103,
title = {Deep Neural Networks for Emotion Recognition Combining Audio and Transcripts},
author = {{Jaejin Cho} and {R. Pappagari} and {Purva Kulkarni} and {J. Villalba} and {Yishay Carmiel} and {N. Dehak}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/75b2843539dc8567b1502a19b3788adf6a015eb6},
}
@inproceedings{85554700,
title = {FOR MULTI-SPEAKER CONVERSATIONS USING X-VECTORS},
author = {{David Snyder} and {D. Garcia-Romero} and {Gregory Sell} and {A. McCree} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b808cfac9c44f27d3716f9280dad4dc2a9bbc8df},
}
We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at \url{https://preschool-lab.github.io/PreCo/}.
@inproceedings{chen-etal-2018-preco,
title = "{P}re{C}o: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution",
author = "Chen, Hong and
Fan, Zhenhua and
Lu, Hao and
Yuille, Alan and
Rong, Shu",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1016/",
doi = "10.18653/v1/D18-1016",
pages = "172--181",
abstract = "We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at \url{https://preschool-lab.github.io/PreCo/}."
}
@inproceedings{86742791,
title = {Unconstrained Face Identification and Verification Using Deep Convolutional Features},
author = {{Jun-Cheng Chen} and {Rajeev Ranjan} and {Vishal M. Patel} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/b3da5ca0428dfa0adbaab9f6c37f8ee4e13c5837},
}
@inproceedings{7849608,
title = {Semi-supervised FusedGAN for Conditional Image Generation},
author = {{Navaneeth Bodla} and {G. Hua} and {R. Chellappa}},
year = 2018,
month = {1},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/2727927c7493cef9785b3a06a38f5c1ce126fc23},
}
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title = {Deep Learning in Radiology: Now the Real Work Begins.},
author = {{Carolina Lugo-Fagundo} and {B. Vogelstein} and {A. Yuille} and {E. Fishman}},
year = 2018,
booktitle = {Journal of the American College of Radiology},
url = {https://www.semanticscholar.org/paper/1b16160d4e58dac5b7ead14afa8046fc53df01ea},
}
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title = {Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans},
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year = 2018,
month = {1},
booktitle = {IEEE Signal Processing Magazine},
url = {https://www.semanticscholar.org/paper/5a564d108b43c6ff006a86d2fc981cd36c6c54dd},
}
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title = {Large-Scale Paraphrasing for Natural Language Understanding},
author = {{Chris Callison-Burch} and {Benjamin Van Durme}},
year = 2018,
month = {4},
booktitle = {},
url = {https://www.semanticscholar.org/paper/9480a81158aadf2776b62d11f3bc674218153a11},
}
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title = {The NVIDIA AI City Challenge 2018},
author = {{M. Naphade} and {Ming-Ching Chang} and {Anuj Sharma} and {D. Anastasiu} and {Vamsi Jagarlamudi} and {Pranamesh Chakraborty} and {Tingting Huang} and {Shuo Wang} and {Ming-Yu Liu} and {R. Chellappa} and {Jenq-Neng Hwang} and {Siwei Lyu}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/e23c0ab73b8a098d6e3e01200cade2d7603c70e3},
}
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title = {The MIT Lincoln Laboratory / JHU / EPITA-LSE LRE17 System},
author = {{Fred Richardson} and {P. Torres-Carrasquillo} and {Jonas Borgstrom} and {D. Sturim} and {Youngjune Gwon} and {J. Villalba} and {J. Trmal} and {Nanxin Chen} and {Réda Dehak} and {N. Dehak}},
year = 2018,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/555f7af2e2f3e8274184e6b02bcd148890644371},
}
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title = {Improving Transferability of Adversarial Examples With Input Diversity},
author = {{Cihang Xie} and {Zhishuai Zhang} and {Jianyu Wang} and {Yuyin Zhou} and {Zhou Ren} and {A. Yuille}},
year = 2018,
month = {3},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/f78a911f516625d6b7b76a9a33c1eb14613341c4},
}
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title = {Acoustic Modeling from Frequency Domain Representations of Speech},
author = {{Pegah Ghahremani} and {Hossein Hadian} and {Hang Lv} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/476a781840a3a906cc8fdb045108c4702e089601},
}
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title = {Proximity-Aware Hierarchical Clustering of unconstrained faces},
author = {{Wei-An Lin} and {Jun-Cheng Chen} and {Rajeev Ranjan} and {Ankan Bansal} and {S. Sankaranarayanan} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {9},
booktitle = {Image and Vision Computing},
url = {https://www.semanticscholar.org/paper/bfe5e4d55af4b9aa7f7fe3dcc08cdd2a7bbfae6c},
}
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title = {Rethinking Monocular Depth Estimation with Adversarial Training},
author = {{Richard J. Chen} and {Faisal Mahmood} and {A. Yuille} and {N. Durr}},
year = 2018,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/762b20ed0b5ac4f4193415a51099432beff99eb5},
}
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title = {An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification},
author = {{Boyu Lu} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {8},
booktitle = {IEEE Transactions on Biometrics Behavior and Identity Science},
url = {https://www.semanticscholar.org/paper/8f1abae983acc7123257bece2afd334549dfe94d},
}
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title = {FIPIP: A novel fine-grained parallel partition based intra-frame prediction on heterogeneous many-core systems},
author = {{Wenbin Jiang} and {Min Long} and {L. Yang} and {Xiaobai Liu} and {Hai Jin} and {A. Yuille} and {Ye Chi}},
year = 2018,
booktitle = {Future generations computer systems},
url = {https://www.semanticscholar.org/paper/37946deeedea0c444c9fa58bbf7604c7983d92bb},
}
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title = {C L ] 1 1 A ug 2 01 6 Automatic Dialect Detection in Arabic Broadcast Speech},
author = {{Ahmed M. Ali} and {N. Dehak} and {P. Cardinal} and {Sameer Khurana} and {S. Yella} and {James R. Glass} and {P. Bell} and {S. Renals}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/dc29a72b5f8d06e42d07a46ef582d59c84bbaef3},
}
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title = {Stochastic Answer Networks for SQuAD 2.0},
author = {{Xiaodong Liu} and {Wei Li} and {Yuwei Fang} and {Aerin Kim} and {Kevin Duh} and {Jianfeng Gao}},
year = 2018,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/d0095adcaa33bc549e273a824c3b66d92897fad8},
}
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title = {Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features},
author = {{Enayat Ullah} and {Poorya Mianjy} and {T. V. Marinov} and {R. Arora}},
year = 2018,
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/3fe99499b945ad77c3d76875609c7cffbf3e0299},
}
@inproceedings{64729063,
title = {Why policymakers should care about “big data” in healthcare},
author = {{D. Bates} and {Axel Heitmueller} and {Meetali Kakad} and {S. Saria}},
year = 2018,
month = {6},
booktitle = {Health Policy and Technology},
url = {https://www.semanticscholar.org/paper/405f502abf7a8228305791cea3d6b0bd2dcc8bd9},
}
@inproceedings{49865314,
title = {Person Recognition beyond the Visible Spectrum: Combining Body Shape and Texture from mmW Images},
author = {{E. González-Sosa} and {R. Vera-Rodríguez} and {Julian Fierrez} and {Vishal M. Patel}},
year = 2018,
month = {2},
booktitle = {International Conference on Biometrics},
url = {https://www.semanticscholar.org/paper/91dec705d119cb3cc40da18f51aafac3c5c191ce},
}
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title = {Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs},
author = {{Y. Balaji} and {Hamed Hassani} and {R. Chellappa} and {S. Feizi}},
year = 2018,
month = {9},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/e8d2ad861e4d107ae2c0d1b7bb053d06022dfe1c},
}
@inproceedings{13740930,
title = {Joint Shape Representation and Classification for Detecting PDAC},
author = {{Fengze Liu} and {Lingxi Xie} and {Yingda Xia} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {4},
booktitle = {MLMI@MICCAI},
url = {https://www.semanticscholar.org/paper/dcc190c51eb3c911d0cfd1f907e31cf499adc436},
}
@inproceedings{19166570,
title = {Person Authentication Using Head Images},
author = {{Aakarsh Malhotra} and {Richa Singh} and {Mayank Vatsa} and {Vishal M. Patel}},
year = 2018,
month = {3},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/b9b0b8807a206eee79e69e9abe6c346f9e2eae9b},
}
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title = {Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training},
author = {{Yuyin Zhou} and {Yan Wang} and {Peng Tang} and {S. Bai} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {4},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/ef769353501330596f56858042168708b8e257de},
}
@inproceedings{49868415,
title = {Low-Resource Contextual Topic Identification on Speech},
author = {{Chunxi Liu} and {Matthew Wiesner} and {Shinji Watanabe} and {Craig Harman} and {J. Trmal} and {N. Dehak} and {S. Khudanpur}},
year = 2018,
month = {7},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/96ed7a7da69d654668b35b50344debd44e87c1a1},
}
@inproceedings{38114028,
title = {Enhancing Scientific Collaboration Through Knowledge Base Population and Linking for Meetings},
author = {{Ning Gao} and {Mark Dredze} and {Douglas W. Oard}},
year = 2018,
month = {1},
booktitle = {Hawaii International Conference on System Sciences},
url = {https://www.semanticscholar.org/paper/7240c4e11f30827cca6e35cd12396b572bf24685},
}
@inproceedings{52957540,
title = {Weakly Supervised Region Proposal Network and Object Detection},
author = {{Peng Tang} and {Xinggang Wang} and {Angtian Wang} and {Yongluan Yan} and {Wenyu Liu} and {Junzhou Huang} and {A. Yuille}},
year = 2018,
month = {9},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/d612a1dd7aa359f1e315a22a825936b4dcb641e2},
}
@inproceedings{102353879,
title = {Towards Resisting Large Data Variations via Introspective Learning},
author = {{Yunhan Zhao} and {Ye Tian} and {Wei Shen} and {A. Yuille}},
year = 2018,
month = {5},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/4f5a47b856cbed8352d83a0d454773f4c54f9a6d},
}
@inproceedings{3843870,
title = {Face-MagNet: Magnifying Feature Maps to Detect Small Faces},
author = {{Pouya Samangouei} and {Mahyar Najibi} and {L. Davis} and {R. Chellappa}},
year = 2018,
month = {3},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/219c660625b6899120462ab08af1b037d39f5523},
}
@inproceedings{19100258,
title = {Task-Aware Compressed Sensing with Generative Adversarial Networks},
author = {{Maya Kabkab} and {Pouya Samangouei} and {R. Chellappa}},
year = 2018,
month = {2},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/8f30b061b4aa39fa1f203dbcab7472021f3c0411},
}
@inproceedings{53224644,
title = {Proceedings of the Third Conference on Machine Translation: Shared Task Papers},
author = {{Ondrej Bojar} and {Rajen Chatterjee} and {C. Federmann} and {Mark Fishel} and {Yvette Graham} and {B. Haddow} and {Matthias Huck} and {Antonio Jimeno Yepes} and {Philipp Koehn} and {Christof Monz} and {Matteo Negri} and {Aurélie Névéol} and {M. Neves} and {Matt Post} and {Lucia Specia} and {Marco Turchi} and {Karin M. Verspoor}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/32f6756542eafed5906783dcc6567057f95550f4},
}
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title = {Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound},
author = {{Yan Wang} and {Yuyin Zhou} and {Peng Tang} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {4},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/28450559de0946a23fd532a086d8a52a653db000},
}
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title = {Online Sales of Marijuana: An Unrecognized Public Health Dilemma.},
author = {{Theodore L. Caputi} and {E. Leas} and {Mark Dredze} and {J. Ayers}},
year = 2018,
month = {3},
booktitle = {American Journal of Preventive Medicine},
url = {https://www.semanticscholar.org/paper/67f7bdd965ed18165acc66bef3b04a6bd4cff28d},
}
@inproceedings{13675505,
title = {Special issue on Video Surveillance-oriented Biometrics},
author = {{Changxing Ding} and {Kaiqi Huang} and {Vishal M. Patel} and {B. Lovell}},
year = 2018,
month = {5},
booktitle = {Pattern Recognition Letters},
url = {https://www.semanticscholar.org/paper/197f59775a3d57b96db3c97cd89dbb399b2dcd0d},
}
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title = {Semi-supervised multi-organ segmentation via multi-planar co-training},
author = {{Yuyin Zhou} and {Yan Wang} and {Peng Tang} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2018,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/69be7707763cbc7f4a5e5519b7663c55dcde4c18},
}
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title = {Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI},
author = {{Vimal Manohar} and {Hossein Hadian} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/33ff2582bff06988d2684eb4de02b3f13ec6a8f6},
}
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title = {Constraints and Development in Children's Block Construction},
author = {{Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {B. Landau} and {A. Shelton}},
year = 2018,
booktitle = {Annual Meeting of the Cognitive Science Society},
url = {https://www.semanticscholar.org/paper/0c1afbd9626b55e21ec44de1de55cb6bd44b744b},
}
@inproceedings{52187418,
title = {Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge},
author = {{Gregory Sell} and {David Snyder} and {A. McCree} and {D. Garcia-Romero} and {J. Villalba} and {Matthew Maciejewski} and {Vimal Manohar} and {N. Dehak} and {Daniel Povey} and {Shinji Watanabe} and {S. Khudanpur}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/cf6352c789ab51320fa7ca9b1440c685b57fd769},
}
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title = {Stream Attention for Distributed Multi-Microphone Speech Recognition},
author = {{Xiaofei Wang} and {Ruizhi Li} and {H. Hermansky}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/53fd176111f2ffc5d9ef86394647cb7f65a6e21e},
}
We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.
@inproceedings{zhang-etal-2018-cross,
title = "Cross-lingual Decompositional Semantic Parsing",
author = "Zhang, Sheng and
Ma, Xutai and
Rudinger, Rachel and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1194/",
doi = "10.18653/v1/D18-1194",
pages = "1664--1675",
abstract = "We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score."
}
@inproceedings{49310502,
title = {Soft Sampling for Robust Object Detection},
author = {{Zhe Wu} and {Navaneeth Bodla} and {Bharat Singh} and {Mahyar Najibi} and {R. Chellappa} and {L. Davis}},
year = 2018,
month = {6},
booktitle = {British Machine Vision Conference},
url = {https://www.semanticscholar.org/paper/af12144e6f113c5de20c74eff5c179a97065eabe},
}
Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.
@inproceedings{knowles-koehn-2018-context,
title = "Context and Copying in Neural Machine Translation",
author = "Knowles, Rebecca and
Koehn, Philipp",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1339/",
doi = "10.18653/v1/D18-1339",
pages = "3034--3041",
abstract = "Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior."
}
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month = {5},
booktitle = {Discover medicine},
url = {https://www.semanticscholar.org/paper/02eea1717c357baa1eab58fc79d59860c0f5b002},
}
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year = 2018,
month = {6},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/714e3e81ce270518e20d56c56967475eaffedee3},
}
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month = {7},
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url = {https://www.semanticscholar.org/paper/740f94e0325b67e6ff5efba0f5112c977e21a75a},
}
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month = {5},
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title = {Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals},
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month = {8},
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title = {PCL: Proposal Cluster Learning for Weakly Supervised Object Detection},
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title = {Don’t quote me: reverse identification of research participants in social media studies},
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month = {8},
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title = {Character-Aware Decoder for Neural Machine Translation},
author = {{Adithya Renduchintala} and {Pamela Shapiro} and {Kevin Duh} and {Philipp Koehn}},
year = 2018,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/635473bd46ff5a3d3123d7731bb1dd2c2259bf8b},
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title = {Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms},
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year = 2018,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/605e16c4cb3cfbfec1f14196f0e263dceeea6ca3},
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title = {Characterizing Performance of Speaker Diarization Systems on Far-Field Speech Using Standard Methods},
author = {{Matthew Maciejewski} and {David Snyder} and {Vimal Manohar} and {N. Dehak} and {S. Khudanpur}},
year = 2018,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/70217e8f8655923cfe1298c7b10be4fe2c1bab88},
}
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title = {Natural Language Processing of Social Media as Screening for Suicide Risk},
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year = 2018,
month = {8},
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url = {https://www.semanticscholar.org/paper/c8ff66d1b15e2349c53a7c63ec740dc424787d74},
}
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title = {From BoW to CNN: Two Decades of Texture Representation for Texture Classification},
author = {{Li Liu} and {Jie Chen} and {P. Fieguth} and {Guoying Zhao} and {R. Chellappa} and {M. Pietikäinen}},
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month = {1},
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}
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month = {5},
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}
This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system’s parameters is important for training on small datasets.
@inproceedings{inaguma-etal-2018-jhu,
title = "The {JHU}/{K}yoto{U} Speech Translation System for {IWSLT} 2018",
author = "Inaguma, Hirofumi and
Zhang, Xuan and
Wang, Zhiqi and
Renduchintala, Adithya and
Watanabe, Shinji and
Duh, Kevin",
editor = "Turchi, Marco and
Niehues, Jan and
Frederico, Marcello",
booktitle = "Proceedings of the 15th International Conference on Spoken Language Translation",
month = oct # " 29-30",
year = "2018",
address = "Brussels",
publisher = "International Conference on Spoken Language Translation",
url = "https://aclanthology.org/2018.iwslt-1.23/",
pages = "153--159",
abstract = "This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system's parameters is important for training on small datasets."
}
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month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/b35ff9985aaee9371588330bcef0dfc88d1401d7},
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year = 2018,
month = {4},
booktitle = {2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
url = {https://www.semanticscholar.org/paper/686170608fdda879c0e8f613ba7271db5c7458b0},
}
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year = 2018,
month = {7},
booktitle = {IEEE Transactions on Biomedical Engineering},
url = {https://www.semanticscholar.org/paper/85dfb5e6653b293b2bfcfe07d398dad4c3d5f55d},
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url = {https://www.semanticscholar.org/paper/e45f68147a64fdadb64cf8103a486d5d0986f9e5},
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year = 2018,
month = {9},
booktitle = {5th International Workshop on Speech Processing in Everyday Environments (CHiME 2018)},
url = {https://www.semanticscholar.org/paper/f86c036b18fc576c3d40d8f55f203e7684787fba},
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year = 2018,
month = {6},
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}
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title = {Output-Gate Projected Gated Recurrent Unit for Speech Recognition},
author = {{Gaofeng Cheng} and {Daniel Povey} and {Lu Huang} and {Ji Xu} and {S. Khudanpur} and {Yonghong Yan}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/b199f4815db1110a4c27bec1303f930b8b8da618},
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@inproceedings{57189285,
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year = 2018,
month = {9},
booktitle = {arXiv.org},
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}
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title = {Neural underpinnnings of auditory salience natural soundscapes},
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year = 2018,
month = {8},
booktitle = {bioRxiv},
url = {https://www.semanticscholar.org/paper/ff2f71a240d33a97e3aa916db9084283e2c130bf},
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year = 2018,
month = {4},
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title = {We start by defining the recurrent architecture as implemented in S OCKEYE , following},
author = {{F. Hieber} and {Tobias Domhan} and {Michael J. Denkowski} and {David Vilar} and {Artem Sokolov} and {Ann Clifton} and {Matt Post}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/2ecb9944ff5ab08924729a6e87d90a9ff3662851},
}
@inproceedings{46984433,
title = {End-to-end Speech Recognition Using Lattice-free MMI},
author = {{Hossein Hadian} and {H. Sameti} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/dcaeb29ad3307e2bdab2218416c81cb0c4e548b2},
}
@inproceedings{52110004,
title = {Streaming Kernel PCA with Õ(√n) Random Features},
author = {{Enayat Ullah} and {Poorya Mianjy} and {T. V. Marinov} and {R. Arora}},
year = 2018,
month = {8},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/53f4f6f14ea5f426704d880de6ba3a35a62ebbd1},
}
@inproceedings{52860200,
title = {Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network},
author = {{He Zhang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2018,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/fe7a1ba13abbf391a638d2da18dfbbb7202684cd},
}
@inproceedings{46942616,
title = {Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition},
author = {{Ke Li} and {Hainan Xu} and {Yiming Wang} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/6186e2b40cda1af7b6675e18489e403baf26dbd1},
}
@inproceedings{53236426,
title = {Proceedings of the Third Conference on Machine Translation: Research Papers},
author = {{Ondrej Bojar} and {Rajen Chatterjee} and {C. Federmann} and {Mark Fishel} and {Yvette Graham} and {B. Haddow} and {Matthias Huck} and {Antonio Jimeno Yepes} and {Philipp Koehn} and {Christof Monz} and {Matteo Negri} and {Aurélie Névéol} and {Mariana Neves} and {Matt Post} and {Lucia Specia} and {Marco Turchi} and {Karin M. Verspoor}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7266efe95819ec5c2d52dea9a70db1f2e5acc94d},
}
@inproceedings{58027607,
title = {TFGAN: Improving Conditioning for Text-to-Video Synthesis},
author = {{Y. Balaji} and {Martin Renqiang Min} and {Bing Bai} and {R. Chellappa} and {H. Graf}},
year = 2018,
month = {9},
booktitle = {},
url = {https://www.semanticscholar.org/paper/dfe3b8b3c88a998267792da07867fbe1fc655667},
}
@inproceedings{52287748,
title = {Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program},
author = {{Gregory Sell} and {Kevin Duh} and {David Snyder} and {David Etter} and {D. Garcia-Romero}},
year = 2018,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/4f623e3821d14553b3b286e20910db9225fb723f},
}
@inproceedings{51906494,
title = {Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study},
author = {{A. Hammond} and {Michael J. Paul} and {J. Hobelmann} and {Animesh Koratana} and {Mark Dredze} and {M. Chisolm}},
year = 2018,
month = {8},
booktitle = {JMIR Mental Health},
url = {https://www.semanticscholar.org/paper/47934ca0f914d869d8a756869949cf3ab95e1390},
}
@inproceedings{52192285,
title = {Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning},
author = {{Ran Zhao} and {Yuntian Deng} and {Mark Dredze} and {Arun Verma} and {David S. Rosenberg} and {Amanda Stent}},
year = 2018,
month = {9},
booktitle = {The Florida AI Research Society},
url = {https://www.semanticscholar.org/paper/4df4f7461b2ac1003c9d963b2d8f1021ad5c6008},
}
@inproceedings{49309939,
title = {Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages},
author = {{Matthew Wiesner} and {Chunxi Liu} and {Lucas Ondel} and {Craig Harman} and {Vimal Manohar} and {J. Trmal} and {Zhongqiang Huang} and {N. Dehak} and {S. Khudanpur}},
year = 2018,
month = {2},
booktitle = {arXiv: Computation and Language},
url = {https://www.semanticscholar.org/paper/e43de3888fcea68c30559cc3e186ad366ac9daa7},
}
@inproceedings{54180130,
title = {Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms},
author = {{Adarsh Subbaswamy} and {S. Saria}},
year = 2018,
booktitle = {Conference on Uncertainty in Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/660476883f72712eee13cca5a68804ad21d7e8dc},
}
We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.
@inproceedings{white-etal-2018-lexicosyntactic,
title = "Lexicosyntactic Inference in Neural Models",
author = "White, Aaron Steven and
Rudinger, Rachel and
Rawlins, Kyle and
Van Durme, Benjamin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1501/",
doi = "10.18653/v1/D18-1501",
pages = "4717--4724",
abstract = "We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction."
}
@inproceedings{52192343,
title = {End-to-end Deep Neural Network Age Estimation},
author = {{Pegah Ghahremani} and {P. S. Nidadavolu} and {Nanxin Chen} and {J. Villalba} and {Daniel Povey} and {S. Khudanpur} and {N. Dehak}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/c936edddcb803b9eb065b6128c6d0e28d5234db1},
}
@inproceedings{4881011,
title = {Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning},
author = {{Hongyu Xu} and {Jingjing Zheng} and {A. Alavi} and {R. Chellappa}},
year = 2018,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/92ec85037f5e195c8aa184534a59b356c6ef7599},
}
We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.
@inproceedings{rudinger-etal-2018-neural,
title = "Neural-{D}avidsonian Semantic Proto-role Labeling",
author = "Rudinger, Rachel and
Teichert, Adam and
Culkin, Ryan and
Zhang, Sheng and
Van Durme, Benjamin",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1114/",
doi = "10.18653/v1/D18-1114",
pages = "944--955",
abstract = "We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision."
}
@inproceedings{28945696,
title = {Segment-Based Methods for Facial Attribute Detection from Partial Faces},
author = {{U. Mahbub} and {Sayantan Sarkar} and {R. Chellappa}},
year = 2018,
month = {1},
booktitle = {IEEE Transactions on Affective Computing},
url = {https://www.semanticscholar.org/paper/1824ad94533c138d9f424f64a8f17117ba72d74b},
}
@inproceedings{5033176,
title = {Classifying Individuals versus Organizations on Twitter},
author = {{Zach Wood-Doughty} and {Praateek Mahajan} and {Mark Dredze}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/fca1ba9e9fdb3f950e35edba6b463bf83cb56f49},
}
@inproceedings{53560998,
title = {Deep Features for Recognizing Disguised Faces in the Wild},
author = {{Ankan Bansal} and {Rajeev Ranjan} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/a50fa5048c61209149de0711b5f1b1806b43da00},
}
@inproceedings{52846701,
title = {Deep Density Clustering of Unconstrained Faces ( Supplementary Material )},
author = {{Wei-An Lin} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/23dd8d17ce09c22d367e4d62c1ccf507bcbc64da},
}
@inproceedings{69833386,
title = {Predicting the Argumenthood of English Prepositional Phrases},
author = {{Najoung Kim} and {Kyle Rawlins} and {Benjamin Van Durme} and {P. Smolensky}},
year = 2018,
month = {9},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/4b9bdc6b9a53ea860a8664330f65c49fb40b70a1},
}
@inproceedings{4560151,
title = {Visual Robot Task Planning},
author = {{Chris Paxton} and {Yotam Barnoy} and {Kapil D. Katyal} and {R. Arora} and {Gregory Hager}},
year = 2018,
month = {3},
booktitle = {IEEE International Conference on Robotics and Automation},
url = {https://www.semanticscholar.org/paper/fba7f7b8d606d1ef276a4f6256cdb5acfe37a337},
}
@inproceedings{51913642,
title = {Visualizing Phoneme Category Adaptation in Deep Neural Networks},
author = {{O. Scharenborg} and {Sebastian Tiesmeyer} and {M. Hasegawa-Johnson} and {N. Dehak}},
year = 2018,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/1d52a71fba0120568d03cc97717b25c1bb13f1e2},
}
@inproceedings{52275517,
title = {Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis},
author = {{Yuki Lama} and {Tao Chen} and {Mark Dredze} and {Amelia M. Jamison} and {S. Quinn} and {David A. Broniatowski}},
year = 2018,
month = {9},
booktitle = {Journal of Medical Internet Research},
url = {https://www.semanticscholar.org/paper/9089dbdeb2b9bb82195f7f893b3c028425c7f36c},
}
@inproceedings{195347078,
title = {A Survey of Recent Advances in Texture Representation},
author = {{Li Liu} and {Jie Chen} and {P. Fieguth} and {Guoying Zhao} and {R. Chellappa} and {M. Pietikäinen}},
year = 2018,
month = {1},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/89972c0aae3c1f047f870138a2838025ab1be215},
}
@inproceedings{51868677,
title = {Stochastic PCA with (cid:96) 2 and (cid:96) 1 Regularization},
author = {{Poorya Mianjy} and {R. Arora}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/23d95f2f7bdb16ceeb35b45348a2ca89ea8330fd},
}
@inproceedings{52284939,
title = {Sensory Mapping Adaptation Under Multiple Task Scenarios},
author = {{Ashwin Bellur} and {Mounya Elhilali}},
year = 2018,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/cd0f8fe0494eb1f57e7833d11ccd4d5033821d80},
}
@inproceedings{29165607,
title = {Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation},
author = {{Wei Shen} and {Yilu Guo} and {Yan Wang} and {Kai Zhao} and {Bo Wang} and {A. Yuille}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b},
}
@inproceedings{53561559,
title = {Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages},
author = {{Michelle Yuan} and {Benjamin Van Durme} and {Jordan L. Ying}},
year = 2018,
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/007ce8877754550ddececf3f1722a43fdcbd6861},
}
@inproceedings{51772236,
title = {End-to-End versus Embedding Neural Networks for Language Recognition in Mismatched Conditions},
author = {{Jesús Antonio Villalba López} and {N. Brümmer} and {N. Dehak}},
year = 2018,
month = {6},
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/c92137e033c263bd4adde173438ccd2c90e8f170},
}
@inproceedings{22235515,
title = {Multimodal sparse and low-rank subspace clustering},
author = {{Mahdi Abavisani} and {Vishal M. Patel}},
year = 2018,
booktitle = {Information Fusion},
url = {https://www.semanticscholar.org/paper/fcd62fbb4031ae078dc6471a7a8bd63966719157},
}
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.
@inproceedings{wood-doughty-etal-2018-challenges,
title = "Challenges of Using Text Classifiers for Causal Inference",
author = "Wood-Doughty, Zach and
Shpitser, Ilya and
Dredze, Mark",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1488/",
doi = "10.18653/v1/D18-1488",
pages = "4586--4598",
abstract = "Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference."
}
@inproceedings{4732699,
title = {A GPU-based WFST Decoder with Exact Lattice Generation},
author = {{Zhehuai Chen} and {Justin Luitjens} and {Hainan Xu} and {Yiming Wang} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {4},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/974eedec872cf965a530fc7edfe38f59a7af8a00},
}
@inproceedings{3886820,
title = {Adversarial domain adaptive subspace clustering},
author = {{Mahdi Abavisani} and {Vishal M. Patel}},
year = 2018,
month = {3},
booktitle = {International Conference on Identity, Security and Behavior Analysis},
url = {https://www.semanticscholar.org/paper/5262e3cf23e9fef86010bed22e69dd41284657a6},
}
@inproceedings{54986302,
title = {Training Deep Neural Networks in Generations: A More Tolerant Teacher Educates Better Students},
author = {{Chenglin Yang} and {Lingxi Xie} and {Siyuan Qiao} and {A. Yuille}},
year = 2018,
month = {5},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/e2c72b79c2f3ca6b980c540b821323467456ad4a},
}
@inproceedings{44117685,
title = {Automatic real-time CNN-based neonatal brain ventricles segmentation},
author = {{Puyang Wang} and {Nick. G. Cuccolo} and {R. Tyagi} and {I. Hacihaliloglu} and {Vishal M. Patel}},
year = 2018,
month = {4},
booktitle = {IEEE International Symposium on Biomedical Imaging},
url = {https://www.semanticscholar.org/paper/4ec2f32591b3918a14079853991c507a1afc77fc},
}
@inproceedings{199408152,
title = {: Estimation of 3D Category-Specific Object Structure: Symmetry, Manhattan and/or Multiple Images},
author = {{Yuan Gao} and {A. Yuille}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/2f60ee1c71f980cdbaa4644487bdc785141b6e0c},
}
@inproceedings{19216053,
title = {Doing the Best We Can With What We Have: Multi-Label Balancing With Selective Learning for Attribute Prediction},
author = {{Emily M. Hand} and {C. Castillo} and {R. Chellappa}},
year = 2018,
month = {4},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/f36f15e49ce81d13622348bc2e8bfa16ab54aa03},
}
@inproceedings{54045327,
title = {Differentially Private Robust Low-Rank Approximation},
author = {{R. Arora} and {V. Braverman} and {Jalaj Upadhyay}},
year = 2018,
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/83ef6de2e9fb2d59f18fe19dd7e6386a0513c2c3},
}
@inproceedings{53646702,
title = {Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery},
author = {{Arthita Ghosh} and {Max Ehrlich} and {Sohil Shah} and {L. Davis} and {R. Chellappa}},
year = 2018,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/020ffb0a682ab6ddfad36e2f448a1e6e086083d7},
}
@inproceedings{205710736,
title = {Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease},
author = {{L. Moro-Velázquez} and {Jorge Andrés Gómez García} and {Juan Ignacio Godino-Llorente} and {J. Villalba} and {J. Orozco-Arroyave} and {N. Dehak}},
year = 2018,
booktitle = {Applied Soft Computing},
url = {https://www.semanticscholar.org/paper/e1ed45247074aafc0fed0b1c7253a3e96785b583},
}
@inproceedings{3626281,
title = {Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment},
author = {{Amit Kumar} and {R. Chellappa}},
year = 2018,
month = {2},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/8a85f0865930ea65239adb5ec2b97407c1446fa4},
}
@inproceedings{4544136,
title = {A Novel Linelet-Based Representation for Line Segment Detection},
author = {{Nam-Gyu Cho} and {A. Yuille} and {Seong-Whan Lee}},
year = 2018,
month = {5},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/6882ba3491d735a499dc09186a2a2ff9998b75b3},
}
@inproceedings{53417428,
title = {Predicting Dynamical Evolution of Human Activities from a Single Image},
author = {{Suhas Lohit} and {Ankan Bansal} and {Nitesh Shroff} and {Jaishanker K. Pillai} and {P. Turaga} and {R. Chellappa}},
year = 2018,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/a53ccdab3bf4736fd6d6793436f028c52a8dc233},
}
@inproceedings{3386998,
title = {Low Latency Acoustic Modeling Using Temporal Convolution and LSTMs},
author = {{Vijayaditya Peddinti} and {Yiming Wang} and {Daniel Povey} and {S. Khudanpur}},
year = 2018,
month = {3},
booktitle = {IEEE Signal Processing Letters},
url = {https://www.semanticscholar.org/paper/4c0f4fa6f38f14c66c89528d9d62bc868bdc2d4a},
}
@inproceedings{53377470,
title = {The 2018 NVIDIA AI City Challenge},
author = {{M. Naphade} and {Ming-Ching Chang} and {Anuj Sharma} and {D. Anastasiu} and {Vamsi Jagarlamudi} and {Pranamesh Chakraborty} and {Tingting Huang} and {Shuo Wang} and {Ming-Yu Liu} and {R. Chellappa} and {Jenq-Neng Hwang} and {Siwei Lyu}},
year = 2018,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/fb488b445a348af720fff18c1912e7c21b3aeda0},
}
@inproceedings{3706231,
title = {Ground-Truth Data Set and Baseline Evaluations for Base-Detail Separation Algorithms at the Part Level},
author = {{Xuan Dong} and {B. Bonev} and {Weixin Li} and {Weichao Qiu} and {Xianjie Chen} and {A. Yuille}},
year = 2018,
month = {3},
booktitle = {IEEE transactions on circuits and systems for video technology (Print)},
url = {https://www.semanticscholar.org/paper/704dac63384ab70612b2a7fc6af7783125246ef5},
}
@inproceedings{49655124,
title = {Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma},
author = {{Zhuotun Zhu} and {Yingda Xia} and {Lingxi Xie} and {E. Fishman} and {A. Yuille}},
year = 2018,
month = {7},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/2d7b41da8fdf24890e8bbfa762cdffd67ee774e5},
}
@inproceedings{197640428,
title = {WEOTAWEO 2 LSTM WEO 1 Encoding WSS 1 WSS 2 WSSTB Decoding Attention Cell},
author = {{Chang Liu} and {F. Sun} and {Changhu Wang} and {Feng Wang} and {A. Yuille}},
year = 2018,
booktitle = {},
url = {https://www.semanticscholar.org/paper/2e5ae83f9f44b606898b1795906de5464ac3482e},
}
@inproceedings{263426695,
title = {Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score},
author = {{A. Zhan} and {Srihari Mohan} and {Christopher G. Tarolli} and {Ruth B. Schneider} and {Jamie L. Adams} and {Saloni Sharma} and {Molly Elson} and {Kelsey L. Spear} and {Alistair Glidden} and {Max A. Little} and {A. Terzis} and {E. R. Dorsey} and {S. Saria}},
year = 2018,
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URL = "http://cs.jhu.edu/~jason/papers/#mei-eisner-2017",
}
What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible’s unique role as alignable bitext for over 800 of the world’s languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself.
@inproceedings{xia-yarowsky-2017-deriving,
title = "Deriving Consensus for Multi-Parallel Corpora: an {E}nglish {B}ible Study",
author = "Xia, Patrick and
Yarowsky, David",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2076/",
pages = "448--453",
abstract = "What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible's unique role as alignable bitext for over 800 of the world's languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself."
}
@inproceedings{656239,
title = {In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks},
author = {{Pramuditha Perera} and {Mahdi Abavisani} and {Vishal M. Patel}},
year = 2017,
month = {11},
booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/3220ee78ec1499fcd395e5cb212ee62b55bd1856},
}
@inproceedings{34988075,
title = {Stream Attention for far-field multi-microphone ASR},
author = {{Xiaofei Wang} and {Yonghong Yan} and {H. Hermansky}},
year = 2017,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/e06a85159fb29932ba8a4e99d19ba32b6191b681},
}
@inproceedings{27515766,
title = {Editorial- Deep Learning for Computer Vision},
author = {{Ross B. Girshick} and {Iasonas Kokkinos} and {I. Laptev} and {Jitendra Malik} and {G. Papandreou} and {A. Vedaldi} and {Xiaogang Wang} and {Shuicheng Yan} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {Computer Vision and Image Understanding},
url = {https://www.semanticscholar.org/paper/6b514a6db99bd37ce205e76ddb11f56d76ef3166},
}
@inproceedings{13739864,
title = {Visual Concepts and Compositional Voting},
author = {{Jianyu Wang} and {Zhishuai Zhang} and {Cihang Xie} and {Yuyin Zhou} and {Vittal Premachandran} and {Jun Zhu} and {Lingxi Xie} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a4d0338839d72034169f8661abcb2194ca713574},
}
@inproceedings{44412399,
title = {Gradually Updated Neural Networks for Large-Scale Image Recognition},
author = {{Siyuan Qiao} and {Zhishuai Zhang} and {Wei Shen} and {Bo Wang} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/123f9307da3d718c71af0ffb6f0cce74396e5759},
}
@inproceedings{63682836,
title = {Plenoptic Imaging: Representation and Processing},
author = {{F. Pereira} and {E. Silva} and {G. Lafruit} and {R. Chellappa} and {S. Theodoridis}},
year = 2017,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/dacfba59e24cb44605a7acb7372a3c5f565ad9dc},
}
@inproceedings{46821146,
title = {Few-shot Learning by Exploiting Visual Concepts within CNNs},
author = {{Boyang Deng} and {Qing Liu} and {Siyuan Qiao} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {arXiv: Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/2743ecfe5552329202523ac988e052faed34382b},
}
We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.
@inproceedings{sakaguchi-etal-2017-grammatical,
title = "Grammatical Error Correction with Neural Reinforcement Learning",
author = "Sakaguchi, Keisuke and
Post, Matt and
Van Durme, Benjamin",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2062/",
pages = "366--372",
abstract = "We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus."
}
Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.
@inproceedings{zhang-etal-2017-selective,
title = "Selective Decoding for Cross-lingual Open Information Extraction",
author = "Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-1084/",
pages = "832--842",
abstract = "Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement."
}
We show how to adapt bilingual word embeddings (BWE’s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.
@inproceedings{wang-etal-2017-multi,
title = "A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition",
author = "Wang, Dingquan and
Peng, Nanyun and
Duh, Kevin",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2065/",
pages = "383--388",
abstract = "We show how to adapt bilingual word embeddings (BWE's) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task."
}
@inproceedings{4540721,
title = {Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation},
author = {{S. Sankaranarayanan} and {Y. Balaji} and {Arpit Jain} and {Ser-Nam Lim} and {R. Chellappa}},
year = 2017,
month = {11},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/dfd72b994765a1979c6872fc8948657885a31752},
}
@inproceedings{25044213,
title = {Learning to Imagine Manipulation Goals for Robot Task Planning},
author = {{Chris Paxton} and {Kapil D. Katyal} and {C. Rupprecht} and {R. Arora} and {Gregory Hager}},
year = 2017,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/778f8258bad0620b996666d883ce261216558ddd},
}
@inproceedings{3526769,
title = {Mitigating adversarial effects through randomization},
author = {{Cihang Xie} and {Jianyu Wang} and {Zhishuai Zhang} and {Zhou Ren} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/9a089c56eec68df722b2a5a52727143aacdc2532},
}
@inproceedings{24142231,
title = {Adversarial Attacks Beyond the Image Space},
author = {{Xiaohui Zeng} and {Chenxi Liu} and {Yu-Siang Wang} and {Weichao Qiu} and {Lingxi Xie} and {Yu-Wing Tai} and {Chi-Keung Tang} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/704cffb06e002faf5d8822e5d9f9a2046deafa3a},
}
We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model’s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.
@inproceedings{white-etal-2017-inference,
title = "Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework",
author = "White, Aaron Steven and
Rastogi, Pushpendre and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-1100/",
pages = "996--1005",
abstract = "We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model's performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model."
}
Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.
@inproceedings{khayrallah-etal-2017-neural,
title = "Neural Lattice Search for Domain Adaptation in Machine Translation",
author = "Khayrallah, Huda and
Kumar, Gaurav and
Duh, Kevin and
Post, Matt and
Koehn, Philipp",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2004/",
pages = "20--25",
abstract = "Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts."
}
@inproceedings{24730594,
title = {Submodular Attribute Selection for Visual Recognition},
author = {{Jingjing Zheng} and {Zhuolin Jiang} and {R. Chellappa}},
year = 2017,
month = {11},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/58eb9174211d58af76023ce33ee05769de57236c},
}
@inproceedings{11247316,
title = {Unsupervised Domain Adaptation for Semantic Segmentation with GANs},
author = {{S. Sankaranarayanan} and {Y. Balaji} and {Arpit Jain} and {Ser-Nam Lim} and {R. Chellappa}},
year = 2017,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ccd3dcbccae7d903608530bddf6381db8e723a7d},
}
Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.
@inproceedings{van-durme-etal-2017-cadet,
title = "{CADET}: Computer Assisted Discovery Extraction and Translation",
author = "Van Durme, Benjamin and
Lippincott, Tom and
Duh, Kevin and
Burchfield, Deana and
Poliak, Adam and
Costello, Cash and
Finin, Tim and
Miller, Scott and
Mayfield, James and
Koehn, Philipp and
Harman, Craig and
Lawrie, Dawn and
May, Chandler and
Thomas, Max and
Carrell, Annabelle and
Chaloux, Julianne and
Chen, Tongfei and
Comerford, Alex and
Dredze, Mark and
Glass, Benjamin and
Hao, Shudong and
Martin, Patrick and
Rastogi, Pushpendre and
Sankepally, Rashmi and
Wolfe, Travis and
Tran, Ying-Ying and
Zhang, Ted",
editor = "Park, Seong-Bae and
Supnithi, Thepchai",
booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
month = nov,
year = "2017",
address = "Tapei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/I17-3002/",
pages = "5--8",
abstract = "Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift."
}
Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world’s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points.
@inproceedings{cotterell-duh-2017-low,
title = "Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields",
author = "Cotterell, Ryan and
Duh, Kevin",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2016/",
pages = "91--96",
abstract = "Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points."
}
Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. \%across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.
@inproceedings{shen-etal-2017-empirical,
title = "An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks",
author = "Shen, Yelong and
Liu, Xiaodong and
Duh, Kevin and
Gao, Jianfeng",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-1096/",
pages = "957--966",
abstract = "Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. \%across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets."
}
@inproceedings{13954919,
title = {Can a selfie promote public engagement with skin cancer?},
author = {{S. Noar} and {E. Leas} and {B. Althouse} and {Mark Dredze} and {Dannielle E Kelley} and {J. Ayers}},
year = 2017,
month = {11},
booktitle = {Preventive Medicine},
url = {https://www.semanticscholar.org/paper/66630b0725f4a0924cef2f000a4ff3b017876769},
}
@inproceedings{21360767,
title = {Unleashing the Potential of CNNs for Interpretable Few-Shot Learning},
author = {{Boyang Deng} and {Qing Liu} and {Siyuan Qiao} and {A. Yuille}},
year = 2017,
month = {11},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a5377cfabdd4fcb6cb6225bd89c362e3d147e665},
}
@inproceedings{3547546,
title = {Internet Searches for Suicide Following the Release of 13 Reasons Why},
author = {{J. Ayers} and {B. Althouse} and {E. Leas} and {Mark Dredze} and {Jon-Patrick Allem}},
year = 2017,
month = {10},
booktitle = {JAMA Internal Medicine},
url = {https://www.semanticscholar.org/paper/4691e85460b30358590c0fa109c543512c27499f},
}
@inproceedings{206849289,
title = {GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks},
author = {{Xing Di} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2017,
month = {10},
booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/7ab14d4a08d1a2c5194870c66719d23bee93adbb},
}
@inproceedings{3678780,
title = {High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks},
author = {{Lidan Wang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2017,
month = {10},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/71c7191815fd15045a7bfb2ebc21a193d41ab551},
}
@inproceedings{91732240,
title = {Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data},
author = {{G. Stein-O’Brien} and {R. Arora} and {A. Culhane} and {Alexander V. Favorov} and {C. Greene} and {L. Goff} and {Yifeng Li} and {Aloune Ngom} and {M. Ochs} and {Yanun Xu} and {E. Fertig}},
year = 2017,
month = {10},
booktitle = {bioRxiv},
url = {https://www.semanticscholar.org/paper/1db74eb4555795457185fb75a5b70d17e2047257},
}
@inproceedings{29303853,
title = {Person entity linking in email with NIL detection},
author = {{Ning Gao} and {Mark Dredze} and {Douglas W. Oard}},
year = 2017,
month = {10},
booktitle = {J. Assoc. Inf. Sci. Technol.},
url = {https://www.semanticscholar.org/paper/c7660f51186490edba345ca6dc7c987435484a9e},
}
@inproceedings{25120614,
title = {They’re heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products},
author = {{Theodore L. Caputi} and {E. Leas} and {Mark Dredze} and {Joanna E. Cohen} and {J. Ayers}},
year = 2017,
month = {10},
booktitle = {PLoS ONE},
url = {https://www.semanticscholar.org/paper/58ee4d7d99d2c34585324cc5e01a9eaf6fd8b448},
}
@inproceedings{ding-etal-2017-jhu,
title = "The {JHU} Machine Translation Systems for {WMT} 2017",
author = "Ding, Shuoyang and
Khayrallah, Huda and
Koehn, Philipp and
Post, Matt and
Kumar, Gaurav and
Duh, Kevin",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kreutzer, Julia",
booktitle = "Proceedings of the Second Conference on Machine Translation",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4724/",
doi = "10.18653/v1/W17-4724",
pages = "276--282"
}
@inproceedings{nadejde-etal-2017-predicting,
title = "Predicting Target Language {CCG} Supertags Improves Neural Machine Translation",
author = "N\u adejde, Maria and
Reddy, Siva and
Sennrich, Rico and
Dwojak, Tomasz and
Junczys-Dowmunt, Marcin and
Koehn, Philipp and
Birch, Alexandra",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kreutzer, Julia",
booktitle = "Proceedings of the Second Conference on Machine Translation",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4707/",
doi = "10.18653/v1/W17-4707",
pages = "68--79"
}
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4\%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.
@inproceedings{cotterell-etal-2017-paradigm,
title = "Paradigm Completion for Derivational Morphology",
author = "Cotterell, Ryan and
Vylomova, Ekaterina and
Khayrallah, Huda and
Kirov, Christo and
Yarowsky, David",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1074/",
doi = "10.18653/v1/D17-1074",
pages = "714--720",
abstract = "The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4\%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems."
}
In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.
@inproceedings{andy-etal-2017-constructing,
title = "Constructing an Alias List for Named Entities during an Event",
author = "Andy, Anietie and
Dredze, Mark and
Rwebangira, Mugizi and
Callison-Burch, Chris",
editor = "Derczynski, Leon and
Xu, Wei and
Ritter, Alan and
Baldwin, Tim",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4405/",
doi = "10.18653/v1/W17-4405",
pages = "40--44",
abstract = "In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline."
}
We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.
@inproceedings{xu-koehn-2017-zipporah,
title = "{Z}ipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora",
author = "Xu, Hainan and
Koehn, Philipp",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1319/",
doi = "10.18653/v1/D17-1319",
pages = "2945--2950",
abstract = "We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus."
}
@inproceedings{bojar-etal-2017-findings,
title = "Findings of the 2017 Conference on Machine Translation ({WMT}17)",
author = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huang, Shujian and
Huck, Matthias and
Koehn, Philipp and
Liu, Qun and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Rubino, Raphael and
Specia, Lucia and
Turchi, Marco",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kreutzer, Julia",
booktitle = "Proceedings of the Second Conference on Machine Translation",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4717/",
doi = "10.18653/v1/W17-4717",
pages = "169--214"
}
We present a feature-rich knowledge tracing method that captures a student’s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student’s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.
@inproceedings{renduchintala-etal-2017-knowledge,
title = "Knowledge Tracing in Sequential Learning of Inflected Vocabulary",
author = "Renduchintala, Adithya and
Koehn, Philipp and
Eisner, Jason",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1025/",
doi = "10.18653/v1/K17-1025",
pages = "238--247",
abstract = "We present a feature-rich knowledge tracing method that captures a student's acquisition and retention of knowledge during a foreign language phrase learning task. We model the student's behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model."
}
Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups’ differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter.
@inproceedings{wood-doughty-etal-2017-twitter,
title = "How Does {T}witter User Behavior Vary Across Demographic Groups?",
author = "Wood-Doughty, Zach and
Smith, Michael and
Broniatowski, David and
Dredze, Mark",
editor = {Hovy, Dirk and
Volkova, Svitlana and
Bamman, David and
Jurgens, David and
O'Connor, Brendan and
Tsur, Oren and
Do\u gru\"oz, A. Seza},
booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2912/",
doi = "10.18653/v1/W17-2912",
pages = "83--89",
abstract = "Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups' differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter."
}
We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.
@inproceedings{koehn-knowles-2017-six,
title = "Six Challenges for Neural Machine Translation",
author = "Koehn, Philipp and
Knowles, Rebecca",
editor = "Luong, Thang and
Birch, Alexandra and
Neubig, Graham and
Finch, Andrew",
booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
month = aug,
year = "2017",
address = "Vancouver",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3204/",
doi = "10.18653/v1/W17-3204",
pages = "28--39",
abstract = "We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation."
}
We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10\% gains over baselines.
@inproceedings{ferraro-etal-2017-frame,
title = "Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles",
author = "Ferraro, Francis and
Poliak, Adam and
Cotterell, Ryan and
Van Durme, Benjamin",
editor = "Ide, Nancy and
Herbelot, Aur\'elie and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1011/",
doi = "10.18653/v1/S17-1011",
pages = "97--103",
abstract = "We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10\% gains over baselines."
}
In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.
@inproceedings{niederhoffer-etal-2017-wildest,
title = "In your wildest dreams: the language and psychological features of dreams",
author = "Niederhoffer, Kate and
Schler, Jonathan and
Crutchley, Patrick and
Loveys, Kate and
Coppersmith, Glen",
editor = "Hollingshead, Kristy and
Ireland, Molly E. and
Loveys, Kate",
booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology --- From Linguistic Signal to Clinical Reality",
month = aug,
year = "2017",
address = "Vancouver, BC",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3102/",
doi = "10.18653/v1/W17-3102",
pages = "13--25",
abstract = "In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task."
}
Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.
@inproceedings{sarioglu-kayi-etal-2017-predictive,
title = "Predictive Linguistic Features of Schizophrenia",
author = "Sarioglu Kayi, Efsun and
Diab, Mona and
Pauselli, Luca and
Compton, Michael and
Coppersmith, Glen",
editor = "Ide, Nancy and
Herbelot, Aur\'elie and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1028/",
doi = "10.18653/v1/S17-1028",
pages = "241--250",
abstract = "Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best."
}
Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.
@inproceedings{loveys-etal-2017-small,
title = "Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language",
author = "Loveys, Kate and
Crutchley, Patrick and
Wyatt, Emily and
Coppersmith, Glen",
editor = "Hollingshead, Kristy and
Ireland, Molly E. and
Loveys, Kate",
booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology --- From Linguistic Signal to Clinical Reality",
month = aug,
year = "2017",
address = "Vancouver, BC",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3110/",
doi = "10.18653/v1/W17-3110",
pages = "85--95",
abstract = "Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls."
}
@inproceedings{cotterell-etal-2017-conll,
title = "{C}o{NLL}-{SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection in 52 Languages",
author = {Cotterell, Ryan and
Kirov, Christo and
Sylak-Glassman, John and
Walther, G\'eraldine and
Vylomova, Ekaterina and
Xia, Patrick and
Faruqui, Manaal and
K\"ubler, Sandra and
Yarowsky, David and
Eisner, Jason and
Hulden, Mans},
editor = "Hulden, Mans",
booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
month = aug,
year = "2017",
address = "Vancouver",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-2001/",
doi = "10.18653/v1/K17-2001",
pages = "1--30"
}
Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.
@inproceedings{peng-dredze-2017-multi,
title = "Multi-task Domain Adaptation for Sequence Tagging",
author = "Peng, Nanyun and
Dredze, Mark",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2612/",
doi = "10.18653/v1/W17-2612",
pages = "91--100",
abstract = "Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain."
}
@InProceedings{cotterell-et-al-2017-shared,
aclid = "K17-2001",
doi = "10.18653/v1/K17-2001",
author = "Ryan Cotterell and Christo Kirov and John
Sylak-Glassman and G\'{e}raldine Walther and Ekaterina
Vylomova and Patrick Xia and Manaal Faruqui and Sandra
K{\"{u}}bler and David Yarowsky and Jason Eisner and
Mans Hulden",
title = "{CoNLL}-{SIGMORPHON} 2017 Shared Task: Universal
Morphological Reinflection in 52 Languages",
booktitle = "Proceedings of the Conference on Natural Language
Learning: CoNLL-SIGMORPHON Shared Task System
Descriptions",
pages = "1--30",
year = "2017",
month = aug,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2017-shared",
}
@InProceedings{renduchintala-et-al-2017-conll,
aclid = "K17-1025",
doi = "10.18653/v1/K17-1025",
author = "Adithya Renduchintala and Philipp Koehn and Jason
Eisner",
title = "Knowledge Tracing in Sequential Learning of Inflected
Vocabulary",
booktitle = "Proceedings of the Conference on Natural Language
Learning (CoNLL)",
pages = "238--247",
year = "2017",
month = aug,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2017-conll",
}
@InProceedings{cotterell-eisner-2017-acl,
aclid = "P17-1109",
doi = "10.18653/v1/P17-1109",
author = "Ryan Cotterell and Jason Eisner",
title = "Probabilistic Typology: Deep Generative Models of
Vowel Inventories",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
pages = "1182--1192",
year = "2017",
month = aug,
address = "Vancouver",
note = "Best Long Paper Award.",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2017-acl",
}
@InProceedings{andrews-et-al-2017,
aclid = "P17-1095",
doi = "10.18653/v1/P17-1095",
author = "Nicholas Andrews and Mark Dredze and Benjamin Van
Durme and Jason Eisner",
title = "Bayesian Modeling of Lexical Resources for
Low-Resource Settings",
booktitle = "Proceedings of the Association for Computational
Linguistics (ACL)",
pages = "1029--1039",
year = "2017",
month = aug,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#andrews-et-al-2017",
}
We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.
@inproceedings{sakaguchi-etal-2017-error,
title = "Error-repair Dependency Parsing for Ungrammatical Texts",
author = "Sakaguchi, Keisuke and
Post, Matt and
Van Durme, Benjamin",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2030/",
doi = "10.18653/v1/P17-2030",
pages = "189--195",
abstract = "We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme."
}
Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.
@inproceedings{andrews-etal-2017-bayesian,
title = "{B}ayesian Modeling of Lexical Resources for Low-Resource Settings",
author = "Andrews, Nicholas and
Dredze, Mark and
Van Durme, Benjamin and
Eisner, Jason",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1095/",
doi = "10.18653/v1/P17-1095",
pages = "1029--1039",
abstract = "Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition."
}
Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.
@inproceedings{wolfe-etal-2017-pocket,
title = "Pocket Knowledge Base Population",
author = "Wolfe, Travis and
Dredze, Mark and
Van Durme, Benjamin",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2048/",
doi = "10.18653/v1/P17-2048",
pages = "305--310",
abstract = "Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems."
}
@InProceedings{vieira-et-al-2017,
doi = "10.1145/3088525.3088562",
author = "Tim Vieira and Matthew Francis-Landau and Nathaniel
Wesley Filardo and Farzad Khorasani and Jason Eisner",
title = "Dyna: Toward a Self-Optimizing Declarative Language
for Machine Learning Applications",
booktitle = "Proceedings of the First ACM SIGPLAN Workshop on
Machine Learning and Programming Languages (MAPL)",
pages = "8--17",
year = "2017",
month = jun,
address = "Barcelona",
publisher = "ACM",
ISBN = "978-1-4503-5071-6",
URL = "http://cs.jhu.edu/~jason/papers/#vieira-et-al-2017",
}
We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.
@inproceedings{rudinger-etal-2017-social,
title = "Social Bias in Elicited Natural Language Inferences",
author = "Rudinger, Rachel and
May, Chandler and
Van Durme, Benjamin",
editor = "Hovy, Dirk and
Spruit, Shannon and
Mitchell, Margaret and
Bender, Emily M. and
Strube, Michael and
Wallach, Hanna",
booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1609/",
doi = "10.18653/v1/W17-1609",
pages = "74--79",
abstract = "We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples."
}
A traditional claim in linguistics is that all human languages are equally expressive–-able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.
@inproceedings{kirov-etal-2017-rich,
title = "A Rich Morphological Tagger for {E}nglish: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax",
author = "Kirov, Christo and
Sylak-Glassman, John and
Knowles, Rebecca and
Cotterell, Ryan and
Post, Matt",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2018/",
pages = "112--117",
abstract = "A traditional claim in linguistics is that all human languages are equally expressive---able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation."
}
Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research.
@inproceedings{benton-etal-2017-ethical,
title = "Ethical Research Protocols for Social Media Health Research",
author = "Benton, Adrian and
Coppersmith, Glen and
Dredze, Mark",
editor = "Hovy, Dirk and
Spruit, Shannon and
Mitchell, Margaret and
Bender, Emily M. and
Strube, Michael and
Wallach, Hanna",
booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1612/",
doi = "10.18653/v1/W17-1612",
pages = "94--102",
abstract = "Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research."
}
The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.
@inproceedings{cotterell-etal-2017-explaining,
title = "Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis",
author = "Cotterell, Ryan and
Poliak, Adam and
Van Durme, Benjamin and
Eisner, Jason",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2028/",
pages = "175--181",
abstract = "The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram."
}
We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty’s thematic proto-role linking theory.
@inproceedings{white-etal-2017-semantic,
title = "The Semantic Proto-Role Linking Model",
author = "White, Aaron Steven and
Rawlins, Kyle and
Van Durme, Benjamin",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2015/",
pages = "92--98",
abstract = "We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty's thematic proto-role linking theory."
}
We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.
@inproceedings{poliak-etal-2017-efficient,
title = "Efficient, Compositional, Order-sensitive n-gram Embeddings",
author = "Poliak, Adam and
Rastogi, Pushpendre and
Martin, M. Patrick and
Van Durme, Benjamin",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2081/",
pages = "503--508",
abstract = "We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks."
}
We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34\%-43\% improvement in recall for candidate triage for QA.
@inproceedings{chen-van-durme-2017-discriminative,
title = "Discriminative Information Retrieval for Question Answering Sentence Selection",
author = "Chen, Tongfei and
Van Durme, Benjamin",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2114/",
pages = "719--725",
abstract = "We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34\%-43\% improvement in recall for candidate triage for QA."
}
Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.
@inproceedings{zhang-etal-2017-mt,
title = "{MT}/{IE}: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models",
author = "Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2011/",
pages = "64--70",
abstract = "Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1."
}
@InProceedings{cotterell-et-al-2017-eacl,
aclid = "E17-2028",
doi = "10.18653/v1/E17-2028",
author = "Ryan Cotterell and Adam Poliak and Benjamin Van Durme
and Jason Eisner",
title = "Explaining and Generalizing Skip-Gram through
Exponential Family Principal Component Analysis",
booktitle = "Proceedings of the Conference of the European Chapter
of the Association for Computational Linguistics: Human
Language Technologies (EACL)",
pages = "175--181",
year = "2017",
month = apr,
address = "Valencia, Spain",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2017-eacl",
}
@inproceedings{27453299,
title = {Extreme Value Analysis for Mobile Active User Authentication},
author = {{Pramuditha Perera} and {Vishal M. Patel}},
year = 2017,
month = {5},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/58e73ff915eea6617401ea1ffba0b86c944f1b3d},
}
@inproceedings{4702621,
title = {Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation},
author = {{Qihang Yu} and {Lingxi Xie} and {Yan Wang} and {Yuyin Zhou} and {E. Fishman} and {A. Yuille}},
year = 2017,
month = {9},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/0bffe8c90fda09f7181a97e1b1b705aab483755b},
}
@inproceedings{22467389,
title = {Characterization of RTN noise in the analog front-end of digital pixel imagers},
author = {{Charbel G. Rizk} and {Francisco Tejada} and {J. Hughes} and {David Barbehenn} and {P. Pouliquen} and {A. Andreou}},
year = 2017,
month = {5},
booktitle = {International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/1552d2dab3995244ca13bc99e5ce0870759da33a},
}
@inproceedings{31697546,
title = {Predicting Asymmetric Transitive Relations in Knowledge Bases},
author = {{Pushpendre Rastogi} and {Benjamin Van Durme}},
year = 2017,
booktitle = {KG4IR@SIGIR},
url = {https://www.semanticscholar.org/paper/27a5a2da3e41b694870c91c36c430b031d6070ba},
}
@inproceedings{11858941,
title = {Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease},
author = {{J. C. Vásquez-Correa} and {J. Orozco-Arroyave} and {R. Arora} and {Elmar Nöth} and {N. Dehak} and {H. Christensen} and {Frank Rudzicz} and {T. Bocklet} and {M. Cernak} and {H. Chinaei} and {J. Hannink} and {P. S. Nidadavolu} and {Maria Yancheva} and {A. Vann} and {Nikolai Vogler}},
year = 2017,
month = {2},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/8ffd686ebfad702e253d390b51eeed9586a273c3},
}
@inproceedings{45140222,
title = {REDUNDANT CODING AND DECODING OF MESSAGES IN HUMAN SPEECH COMMUNICATION},
author = {{H. Hermansky}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/abe0b94e7d13ed5279b0a22c2abf298cb4f18a5f},
}
@inproceedings{196138447,
title = {Proceedings of the Conference on Machine Translation (WMT), Volume 1: Research Papers},
author = {{Maria Nadejde} and {Siva Reddy} and {Rico Sennrich} and {Tomasz Dwojak} and {Marcin Junczys-Dowmunt} and {Philipp Koehn} and {Alexandra Birch}},
year = 2017,
month = {9},
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/e77657a5bc40f47957707f5758960fa333786de7},
}
@inproceedings{35809056,
title = {Extraction with Neural Sequence-to-Sequence Models},
author = {{Sheng Zhang} and {Kevin Duh} and {Benjamin Van Durme}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/5bd7012eb3d843e0825995a36a127756fdb45b80},
}
@inproceedings{3527722,
title = {A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery},
author = {{N. Ahmidi} and {Lingling Tao} and {S. Sefati} and {Yixin Gao} and {Colin S. Lea} and {B. B. Haro} and {L. Zappella} and {S. Khudanpur} and {R. Vidal} and {Gregory Hager}},
year = 2017,
month = {1},
booktitle = {IEEE Transactions on Biomedical Engineering},
url = {https://www.semanticscholar.org/paper/a4044c27188bd7279e406e0508f4a9548ba909c3},
}
@inproceedings{1284385,
title = {SAR Image Despeckling Using a Convolutional Neural Network},
author = {{Puyang Wang} and {He Zhang} and {Vishal M. Patel}},
year = 2017,
month = {6},
booktitle = {IEEE Signal Processing Letters},
url = {https://www.semanticscholar.org/paper/6152d44e73e83c7c6f6b789f2b4b6c3fe9b91663},
}
@inproceedings{15364102,
title = {Multi-context Attention for Human Pose Estimation},
author = {{Xiao Chu} and {Wei Yang} and {Wanli Ouyang} and {Cheng Ma} and {A. Yuille} and {Xiaogang Wang}},
year = 2017,
month = {2},
booktitle = {Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/9e671c01163c9ce0ea5699ff81b5173dd03730cf},
}
@inproceedings{8501142,
title = {Label Distribution Learning Forests},
author = {{Wei Shen} and {Kai Zhao} and {Yilu Guo} and {A. Yuille}},
year = 2017,
month = {2},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/4febe3cb6a4d31c3ef83c8baf320e2270f5266e9},
}
@inproceedings{4631378,
title = {Correction to ‘Modelling auditory attention’},
author = {{Emine Merve Kaya} and {Mounya Elhilali}},
year = 2017,
month = {8},
booktitle = {Philosophical Transactions of the Royal Society B: Biological Sciences},
url = {https://www.semanticscholar.org/paper/8b2e56c17251ed32618d5ea9944b339e03c4094d},
}
@inproceedings{27377753,
title = {Low-Rank and Joint Sparse Representations for Multi-Modal Recognition},
author = {{He Zhang} and {Vishal M. Patel} and {R. Chellappa}},
year = 2017,
month = {6},
booktitle = {IEEE Transactions on Image Processing},
url = {https://www.semanticscholar.org/paper/47197819a17c11c88ee2f262600e3bd6c20e3d34},
}
@inproceedings{7597775,
title = {Deep Heterogeneous Feature Fusion for Template-Based Face Recognition},
author = {{Navaneeth Bodla} and {Jingxiao Zheng} and {Hongyu Xu} and {Jun-Cheng Chen} and {C. Castillo} and {R. Chellappa}},
year = 2017,
month = {2},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/f017e25b4269e88e077239f8d47777a779b624e8},
}
@inproceedings{10319901,
title = {Large Margin Multi-Modal Triplet Metric Learning},
author = {{Xing Di} and {Vishal M. Patel}},
year = 2017,
month = {5},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/ae47339c4a2bff1d29d6d4bb202b56678ffd11d5},
}
@inproceedings{20270268,
title = {UPSET and ANGRI : Breaking High Performance Image Classifiers},
author = {{Sayantan Sarkar} and {Ankan Bansal} and {U. Mahbub} and {R. Chellappa}},
year = 2017,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/defdab5b6a3ca1ac1d2bfa472c5a1cd69fc84d68},
}
@inproceedings{3456350,
title = {Regularizing face verification nets for pain intensity regression},
author = {{Feng Wang} and {Xiang Xiang} and {Chang Liu} and {T. Tran} and {A. Reiter} and {Gregory Hager} and {H. Quon} and {Jian Cheng} and {A. Yuille}},
year = 2017,
month = {9},
booktitle = {International Conference on Information Photonics},
url = {https://www.semanticscholar.org/paper/b0e42d179abfd2d30d4ac543358b805c7c9777ac},
}
@inproceedings{3180965,
title = {Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study},
author = {{E. McCollum} and {Daniel E Park} and {Nora L. Watson} and {W. Buck} and {C. Bunthi} and {A. Devendra} and {B. Ebruke} and {Mounya Elhilali} and {Dimitra Emmanouilidou} and {A. Garcia-Prats} and {L. Githinji} and {L. Hossain} and {S. Madhi} and {D. Moore} and {J. Mulindwa} and {D. Olson} and {J. Awori} and {W. Vandepitte} and {C. Verwey} and {James E. West} and {M. Knoll} and {K. O'Brien} and {D. Feikin} and {Laura L Hammit}},
year = 2017,
month = {6},
booktitle = {BMJ Open Respiratory Research},
url = {https://www.semanticscholar.org/paper/5432a065eb5d88b3e0c29bdb310020d06bb4f20d},
}
@inproceedings{2099022,
title = {Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs},
author = {{Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2017,
month = {8},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/0059525922079e1ff112139e01f44b8e2d69264b},
}
@inproceedings{4547917,
title = {Generate to Adapt: Aligning Domains Using Generative Adversarial Networks},
author = {{S. Sankaranarayanan} and {Y. Balaji} and {C. Castillo} and {R. Chellappa}},
year = 2017,
month = {4},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/15168665f4b8eb11466086e69780ed98e5280059},
}
@inproceedings{19231876,
title = {A Deep Cascade Network for Unaligned Face Attribute Classification},
author = {{Hui Ding} and {Hao Zhou} and {S. Zhou} and {R. Chellappa}},
year = 2017,
month = {9},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/7a16f37ecccca4f9703ce190dc596149b4ccc8d2},
}
@inproceedings{15702293,
title = {Scalable mental health analysis in the clinical whitespace via natural language processing},
author = {{Glen A. Coppersmith} and {Casey Hilland} and {O. Frieder} and {Ryan Leary}},
year = 2017,
booktitle = {2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)},
url = {https://www.semanticscholar.org/paper/5ed8dc3e7fe13421d36c057268692c249abd3bf8},
}
@inproceedings{195347070,
title = {Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation},
author = {{Qihang Yu} and {Lingxi Xie} and {Yan Wang} and {Yuyin Zhou} and {E. Fishman} and {A. Yuille}},
year = 2017,
month = {9},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/c8967b94baeddd82db710d6ac450b881dd1b2e9b},
}
@inproceedings{23138179,
title = {A study on data augmentation of reverberant speech for robust speech recognition},
author = {{Tom Ko} and {Vijayaditya Peddinti} and {Daniel Povey} and {M. Seltzer} and {S. Khudanpur}},
year = 2017,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/5005a3295dc2c931526438dd6d3f8fae8e34b641},
}
@inproceedings{29259462,
title = {Learning Treatment-Response Models from Multivariate Longitudinal Data.},
author = {{Hossein Soleimani} and {Adarsh Subbaswamy} and {S. Saria}},
year = 2017,
booktitle = {Conference on Uncertainty in Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/7c2075d4912479c458941ddbedc9bcf383e984b9},
}
@inproceedings{196152770,
title = {Active authentication using facial attributes},
author = {{Pouya Samangouei} and {Emily M. Hand} and {Vishal M. Patel} and {R. Chellappa}},
year = 2017,
month = {9},
booktitle = {},
url = {https://www.semanticscholar.org/paper/7a3764a4ea3026de50ec0a4c3e00f0cae0bffc0c},
}
@inproceedings{32850737,
title = {The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System},
author = {{P. Torres-Carrasquillo} and {Fred Richardson} and {S. Nercessian} and {D. Sturim} and {W. Campbell} and {Youngjune Gwon} and {Swaroop Vattam} and {N. Dehak} and {Sri Harish Reddy Mallidi} and {P. S. Nidadavolu} and {Ruizhi Li} and {Réda Dehak}},
year = 2017,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/d6c6f46725f538cf5960d3a4a21eea2e9605f3a8},
}
@inproceedings{9495965,
title = {Characterizing spatial construction processes: Toward computational tools to understand cognition},
author = {{Cathryn S. Cortesa} and {Jonathan D. Jones} and {Gregory Hager} and {S. Khudanpur} and {A. Shelton} and {B. Landau}},
year = 2017,
booktitle = {Annual Meeting of the Cognitive Science Society},
url = {https://www.semanticscholar.org/paper/2c7b2d214ead69910e98995286b8cafa8f35db62},
}
@inproceedings{27805933,
title = {Neuromorphic self-driving robot with retinomorphic vision and spike-based processing/closed-loop control},
author = {{Kate D. Fischl} and {Gaspar Tognetti} and {Daniel R. Mendat} and {G. Orchard} and {J. Rattray} and {Christos Sapsanis} and {Laura F. Campbell} and {Laxaviera Elphage} and {T. Niebur} and {Alejandro Pasciaroni} and {V. Rennoll} and {Heather Romney} and {Shamaria Walker} and {P. Pouliquen} and {A. Andreou}},
year = 2017,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/4cdaf34f9e01fbb577bdd66296b8afb1b2110f58},
}
@inproceedings{2170930,
title = {DyNet: The Dynamic Neural Network Toolkit},
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year = 2017,
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author = {{Mohamed Al-Badrashiny} and {Jason Bolton} and {Arun Tejasvi Chaganty} and {Kevin Clark} and {Craig Harman} and {Lifu Huang} and {Matthew Lamm} and {Jinhao Lei} and {Di Lu} and {Xiaoman Pan} and {Ashwin Paranjape} and {Ellie Pavlick} and {Haoruo Peng} and {Peng Qi} and {Pushpendre Rastogi} and {A. See} and {Kai Sun} and {Max Thomas} and {Chen-Tse Tsai} and {Hao Wu} and {Boliang Zhang} and {Chris Callison-Burch} and {Claire Cardie} and {Heng Ji} and {Christopher D. Manning} and {S. Muresan} and {Owen Rambow} and {D. Roth} and {Mark Sammons} and {Benjamin Van Durme}},
year = 2017,
booktitle = {Text Analysis Conference},
url = {https://www.semanticscholar.org/paper/800a4295003902e59b2d423dffecb02b6b4f99ce},
}
@inproceedings{8147999,
title = {Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework},
author = {{Xiaohui Zhang} and {Vimal Manohar} and {Daniel Povey} and {S. Khudanpur}},
year = 2017,
month = {6},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/0884c2ffd34af1c6d0b9cc5aa08f9efac604fd60},
}
@inproceedings{54714125,
title = {DETERMINISTIC CONSTRUCTION OF SYNCHRONIZATION STRING OVER SMALL ALPHABET},
author = {{Ke Wu} and {X. Li} and {Yanif Ahmad} and {V. Braverman} and {Randal Burns} and {Zachary Burwell} and {M. Dinitz} and {Mark Dredze} and {Abhishek Jain} and {Philipp Koehn}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/72b9c8872c7c5a595a8ee6e41dffd7f91940d1f5},
}
@inproceedings{27865940,
title = {Topic identification of spoken documents using unsupervised acoustic unit discovery},
author = {{Santosh Kesiraju} and {R. Pappagari} and {Lucas Ondel} and {L. Burget} and {N. Dehak} and {S. Khudanpur} and {J. Černocký} and {S. Gangashetty}},
year = 2017,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/2e8d47fcba60cff5cf5ba9aa535192cccfc37db1},
}
@inproceedings{67110677,
title = {Discriminative Sparse Representations},
author = {{He Zhang} and {Vishal M. Patel}},
year = 2017,
booktitle = {Handbook of Convex Optimization Methods in Imaging Science},
url = {https://www.semanticscholar.org/paper/52eed6f19b62221f0ed07b9e86dbce163dc178c6},
}
@inproceedings{25639151,
title = {Convolutional Sparse and Low-Rank Coding-Based Rain Streak Removal},
author = {{He Zhang} and {Vishal M. Patel}},
year = 2017,
month = {3},
booktitle = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
url = {https://www.semanticscholar.org/paper/42bb73d91eb8f0e68ffb34adc9d38b8833d5af20},
}
@inproceedings{1987224,
title = {A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection},
author = {{Amit Kumar} and {R. Chellappa}},
year = 2017,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/1389ba6c3ff34cdf452ede130c738f37dca7e8cb},
}
@inproceedings{2313547,
title = {Learning from Ambiguously Labeled Face Images},
author = {{Ching-Hui Chen} and {Vishal M. Patel} and {R. Chellappa}},
year = 2017,
month = {2},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/58fc54590eb587c7227545fe85ec7d479a224fb7},
}
@inproceedings{7535022,
title = {On the impact of non-modal phonation on phonological features},
author = {{M. Cernak} and {Elmar Nöth} and {Frank Rudzicz} and {H. Christensen} and {J. Orozco-Arroyave} and {R. Arora} and {T. Bocklet} and {H. Chinaei} and {J. Hannink} and {P. S. Nidadavolu} and {J. C. Vásquez-Correa} and {Maria Yancheva} and {A. Vann} and {Nikolai Vogler}},
year = 2017,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/9cb2608434ce49e10837555b94f1381b3bd6f5e9},
}
@inproceedings{196010301,
title = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
author = {{Lucia Specia} and {Matt Post} and {Michael J. Paul}},
year = 2017,
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/0e9b853048d0e1b75b5cfef01705be766601e984},
}
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title = {NeuroSpeech: An open-source software for Parkinson's speech analysis},
author = {{J. Orozco-Arroyave} and {J. C. Vásquez-Correa} and {J. Vargas-Bonilla} and {R. Arora} and {N. Dehak} and {P. S. Nidadavolu} and {H. Christensen} and {Frank Rudzicz} and {Maria Yancheva} and {H. Chinaei} and {A. Vann} and {Nikolai Vogler} and {T. Bocklet} and {M. Cernak} and {J. Hannink} and {Elmar Nöth}},
year = 2017,
month = {7},
booktitle = {Digit. Signal Process.},
url = {https://www.semanticscholar.org/paper/2b2f10ffb9b25b8fbc5a4d9c2ac4cd23b1cc0531},
}
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title = {Evaluation of the Neurological State of People with Parkinson's Disease Using i-Vectors},
author = {{N. García} and {J. Orozco-Arroyave} and {L. F. D’Haro} and {N. Dehak} and {Elmar Nöth}},
year = 2017,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/edad4b36bf583c4949dd2f1272b143309f300bcd},
}
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title = {Generalized Hierarchical Word Sequence Framework for Language Modeling},
author = {{Xiaoyi Wu} and {Kevin Duh} and {Yuji Matsumoto}},
year = 2017,
month = {6},
booktitle = {Information and Media Technologies},
url = {https://www.semanticscholar.org/paper/129b987bc135db38a44995da420ca4146c6c2674},
}
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title = {Transferring a Face Verification Network for Expression Intensity Regression},
author = {{A. Yuille} and {Chang Liu} and {Xiang Xiang} and {T. Tran} and {A. Reiter} and {Feng Wang} and {Gregory Hager} and {H. Quon} and {Jian Cheng}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b1382f03acb78a6cb1021f4082b221018f4c7162},
}
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title = {Deep Network Shrinkage Applied to Cross-Spectrum Face Recognition},
author = {{Christopher Reale} and {Hyungtae Lee} and {H. Kwon} and {R. Chellappa}},
year = 2017,
month = {5},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/01e14d8ffd6767336d50c2b817a7b7744903e567},
}
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title = {Editorial: Special issue on ubiquitous biometrics},
author = {{R. He} and {B. Lovell} and {R. Chellappa} and {Anil K. Jain} and {Zhenan Sun}},
year = 2017,
month = {6},
booktitle = {Pattern Recognition},
url = {https://www.semanticscholar.org/paper/5aac6f1f916286cc6c4749bf9f4a60fc3089da52},
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title = {A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation},
author = {{Vishwanath A. Sindagi} and {Vishal M. Patel}},
year = 2017,
month = {7},
booktitle = {Pattern Recognition Letters},
url = {https://www.semanticscholar.org/paper/469c66794a24a3687a3e5cfb18216f6a3acebc09},
}
Neural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In particular, we apply the covariance matrix adaptation-evolution strategy (CMA-ES), and investigate a Pareto-based multi-objective CMA-ES to optimize the translation performance and computational time jointly. Experimental results show that the proposed method automatically finds NMT systems that outperform the initial manual setting.
@inproceedings{qin-etal-2017-evolution,
title = "Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems",
author = "Qin, Hao and
Shinozaki, Takahiro and
Duh, Kevin",
editor = "Sakti, Sakriani and
Utiyama, Masao",
booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation",
month = dec # " 14-15",
year = "2017",
address = "Tokyo, Japan",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2017.iwslt-1.17/",
pages = "120--128",
abstract = "Neural machine translation (NMT) systems have demonstrated promising results in recent years. However, non-trivial amounts of manual effort are required for tuning network architectures, training configurations, and pre-processing settings such as byte pair encoding (BPE). In this study, we propose an evolution strategy based automatic tuning method for NMT. In particular, we apply the covariance matrix adaptation-evolution strategy (CMA-ES), and investigate a Pareto-based multi-objective CMA-ES to optimize the translation performance and computational time jointly. Experimental results show that the proposed method automatically finds NMT systems that outperform the initial manual setting."
}
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title = {Path planning on the TrueNorth neurosynaptic system},
author = {{Kate D. Fischl} and {Kaitlin L. Fair} and {Wei-Yu Tsai} and {Jack Sampson} and {A. Andreou}},
year = 2017,
month = {5},
booktitle = {International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/b3771178abbc7a6d5a244a58cb799a776cbe57d2},
}
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title = {DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion},
author = {{Zhishuai Zhang} and {Cihang Xie} and {Jianyu Wang} and {Lingxi Xie} and {A. Yuille}},
year = 2017,
month = {9},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/adecc9cb7c4e71a401099b26ed5420b8d4f4e90a},
}
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title = {Strategy 3 with CNN 15 Input Word Embedding Linguistic Feature Embedding Left LSTMs Right LSTMs Left LSTMs Character Embedding Word Embedding Right LSTMs},
author = {{Mohamed Al-Badrashiny} and {Jason Bolton} and {Arun Tejasvi Chaganty} and {Kevin Clark} and {Craig Harman} and {Lifu Huang} and {Matthew Lamm} and {Jinhao Lei} and {Di Lu} and {Xiaoman Pan} and {Ashwini Paranjape} and {Ellie Pavlick} and {Haoruo Peng} and {Peng Qi} and {Pushpendre Rastogi} and {A. See} and {Kai Sun} and {Max Thomas} and {Chen-Tse Tsai} and {Hao Wu} and {Boliang Zhang} and {Chris Callison-Burch} and {Claire Cardie} and {Heng Ji} and {Christopher Manning} and {S. Muresan} and {Owen Rambow} and {D. Roth} and {Mark Sammons} and {Benjamin Van Durme}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/cd30f9fbf97809261fe8d0fef0bbeb132d07087f},
}
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title = {Support for Interactive Identification of Mentioned Entities in Conversational Speech},
author = {{Ning Gao} and {Douglas W. Oard} and {Mark Dredze}},
year = 2017,
month = {8},
booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
url = {https://www.semanticscholar.org/paper/b41d3eece00ba6b849b15cf2edd2417a410b8698},
}
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title = {Adversarial Examples for Semantic Segmentation and Object Detection},
author = {{Cihang Xie} and {Jianyu Wang} and {Zhishuai Zhang} and {Yuyin Zhou} and {Lingxi Xie} and {A. Yuille}},
year = 2017,
month = {3},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/e7867244de690a1ae11a7a6d5a021e868fa75a3c},
}
@inproceedings{85449899,
title = {Entity recommendations on a Cold Start Knowledge Graph},
author = {{Pushpendre Rastogi} and {V. Lyzinski} and {Benjamin Van Durme}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/6cb7ab25d87e8b07f18077539ab9497ceb6f9d19},
}
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title = {An empirical evaluation of zero resource acoustic unit discovery},
author = {{Chunxi Liu} and {Jinyi Yang} and {Ming Sun} and {Santosh Kesiraju} and {Alena Rott} and {Lucas Ondel} and {Pegah Ghahremani} and {N. Dehak} and {L. Burget} and {S. Khudanpur}},
year = 2017,
month = {2},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/ada0452efd5b0bc345de1bc66c875d0126c56d2c},
}
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title = {Growing Regression Tree Forests by Classification for Continuous Object Pose Estimation},
author = {{Kota Hara} and {R. Chellappa}},
year = 2017,
month = {4},
booktitle = {International Journal of Computer Vision},
url = {https://www.semanticscholar.org/paper/63f2d43d7407931fa6f5ceb3c901b4d4ec11fbcd},
}
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title = {Deep Learning for Tattoo Recognition},
author = {{Xing Di} and {Vishal M. Patel}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/fcca63a6168a7bded3a8dd793c55786aa385d8cc},
}
@inproceedings{4416337,
title = {The Charlie Sheen Effect on Rapid In-home Human Immunodeficiency Virus Test Sales},
author = {{Jon-Patrick Allem} and {E. Leas} and {Theodore L. Caputi} and {Mark Dredze} and {B. Althouse} and {S. Noar} and {J. Ayers}},
year = 2017,
month = {5},
booktitle = {Prevention Science},
url = {https://www.semanticscholar.org/paper/867285873edc3186560e6783c5359b4d9b5e9982},
}
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title = {Recent Developments in Video-Based Face Recognition},
author = {{Jingxiao Zheng} and {Vishal M. Patel} and {R. Chellappa}},
year = 2017,
booktitle = {Handbook of Biometrics for Forensic Science},
url = {https://www.semanticscholar.org/paper/b241ea25eb7ca0a94a46c0b2800a661609fb82bf},
}
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title = {Input 2 Encoder Feature Extractor Decoder Output 2 Latent Space Feature Extractor Input 1 Input N Feature Extractor Decoder Output M Decoder Output 1},
author = {{Pramuditha Perera} and {Mahdi Abavisani} and {Vishal M. Patel}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3748d173a071de6351b7399635e61c6a038b03e6},
}
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title = {Deep Collaborative Learning for Visual Recognition},
author = {{Yan Wang} and {Lingxi Xie} and {Ya Zhang} and {Wenjun Zhang} and {A. Yuille}},
year = 2017,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a2e86c23cde8899ac39d0df43d6c5e4dcf0ae2e6},
}
@inproceedings{zhang-etal-2017-evaluation,
title = "An Evaluation of {P}red{P}att and Open {IE} via Stage 1 Semantic Role Labeling",
author = "Zhang, Sheng and
Rudinger, Rachel and
Van Durme, Benjamin",
editor = "Gardent, Claire and
Retor\'e, Christian",
booktitle = "Proceedings of the 12th International Conference on Computational Semantics ({IWCS}) --- Short papers",
year = "2017",
url = "https://aclanthology.org/W17-6944/"
}
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title = {Training Relation Embeddings under Logical Constraints},
author = {{Pushpendre Rastogi} and {Adam Poliak} and {Benjamin Van Durme}},
year = 2017,
booktitle = {KG4IR@SIGIR},
url = {https://www.semanticscholar.org/paper/234178756b8bf3b2694671583084b22c76c47560},
}
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title = {Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015},
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year = 2017,
month = {3},
booktitle = {PLoS ONE},
url = {https://www.semanticscholar.org/paper/5c60a417ae333a4503f0f4642bed9f66d3264ff6},
}
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title = {Image De-Raining Using a Conditional Generative Adversarial Network},
author = {{He Zhang} and {Vishwanath A. Sindagi} and {Vishal M. Patel}},
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month = {1},
booktitle = {IEEE transactions on circuits and systems for video technology (Print)},
url = {https://www.semanticscholar.org/paper/920f0c070701caabf023c600f3e310f1906ca818},
}
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title = {Proceedings of ACL 2017, Student Research Workshop},
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year = 2017,
booktitle = {Annual Meeting of the Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/d89918edf45d255da45661636752c8b8a3d0b3f2},
}
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title = {Transfer of View-manifold Learning to Similarity Perception of Novel Objects},
author = {{Xingyu Lin} and {Hao Wang} and {Zhihao Li} and {Yimeng Zhang} and {A. Yuille} and {T. Lee}},
year = 2017,
month = {3},
booktitle = {International Conference on Learning Representations},
url = {https://www.semanticscholar.org/paper/01fce96b99aedfb5d041ef4411ad8e9699b2c4df},
}
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title = {Regularizing deep networks using efficient layerwise adversarial training},
author = {{S. Sankaranarayanan} and {Arpit Jain} and {R. Chellappa} and {Ser-Nam Lim}},
year = 2017,
month = {5},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/68409946aa855b9a14de341bd321c38762817122},
}
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title = {Robust MIL-Based Feature Template Learning for Object Tracking},
author = {{X. Lan} and {P. Yuen} and {R. Chellappa}},
year = 2017,
month = {2},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/f03054e94c013780d17bd4a6bfbcbec8bdd44938},
}
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title = {Modeling the Cocktail Party Problem},
author = {{Mounya Elhilali}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c6a74c63372963f9be6a90a986c528e6e24ba816},
}
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title = {Synthesis-based Robust Low Resolution Face Recognition},
author = {{Sumit Shekhar} and {Vishal M. Patel} and {R. Chellappa}},
year = 2017,
month = {7},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a49df923dde393e4ee84102408100ed9350db53a},
}
@inproceedings{32242456,
title = {A Psycholinguistic Model for the Marking of Discourse Relations},
author = {{Frances Yung} and {Kevin Duh} and {T. Komura} and {Yuji Matsumoto}},
year = 2017,
month = {1},
booktitle = {Dialogue and Discourse},
url = {https://www.semanticscholar.org/paper/9c95830fe00c4119234c5c1861b4145a2685e72b},
}
@inproceedings{3197876,
title = {SSH: Single Stage Headless Face Detector},
author = {{Mahyar Najibi} and {Pouya Samangouei} and {R. Chellappa} and {L. Davis}},
year = 2017,
month = {8},
booktitle = {IEEE International Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/a896ddeb0d253739c9aaef7fc1f170a2ba8407d3},
}
@inproceedings{59332257,
title = {Towards a Consistent Segmentation Level across Multiple Chinese Word Segmentation Corpora},
author = {{Fei Cheng} and {Kevin Duh} and {Yuji Matsumoto}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b8cebc298ab89f46274ce42ef7e9d6acfd19e345},
}
@inproceedings{33252535,
title = {Few-Shot Image Recognition by Predicting Parameters from Activations},
author = {{Siyuan Qiao} and {Chenxi Liu} and {Wei Shen} and {A. Yuille}},
year = 2017,
month = {6},
booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
url = {https://www.semanticscholar.org/paper/3e08a3912ebe494242f6bcd772929cc65307129c},
}
@inproceedings{19464052,
title = {Streaming Word Embeddings with the Space-Saving Algorithm},
author = {{Chandler May} and {Kevin Duh} and {Benjamin Van Durme} and {Ashwin Lall}},
year = 2017,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ef6fea9d88aa763460b6cf48b2f60d23c6e60e9c},
}
@inproceedings{16963265,
title = {Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections},
author = {{Ehsan Jahangiri} and {A. Yuille}},
year = 2017,
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/559295770dc2e2e3a1348df31ac5c3f3e66f1764},
}
@inproceedings{197493175,
title = {REGULARIZING FACE NET FOR DISCRETE-VALUED PAIN REGRESSION},
author = {{Feng Wang} and {Xiang Xiang} and {Chang Liu} and {T. Tran} and {A. Reiter} and {Gregory Hager} and {H. Quon} and {Jian Cheng} and {A. Yuille}},
year = 2017,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9e297343da13cf9ba0ad8b5b75c07723136f4885},
}
@inproceedings{22859147,
title = {Extracting Fourier descriptors from compressive measurements},
author = {{Puyang Wang} and {Vishal M. Patel}},
year = 2017,
month = {6},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/50947af0b2c17e68aefc958271db773b35f3c865},
}
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title = {Tied Variational Autoencoder Backends for i-Vector Speaker Recognition},
author = {{J. Villalba} and {N. Brümmer} and {N. Dehak}},
year = 2017,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/4488cba0d06ae06b4b7b99cbb3639731c9eefe32},
}
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title = {The Kaldi OpenKWS System: Improving Low Resource Keyword Search},
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year = 2017,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/3119267d581fb65c3866ded0c194cfac76cc349a},
}
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title = {Monitoring Real-time Spatial Public Health Discussions in the Context of Vaccine Hesitancy},
author = {{Michael C. Smith} and {Mark Dredze} and {S. Quinn} and {David A. Broniatowski}},
year = 2017,
booktitle = {SMM4H@AMIA},
url = {https://www.semanticscholar.org/paper/07e5af29b9c2c6de4e4bd4334b68510e0e7826ef},
}
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}
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year = 2017,
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url = {https://www.semanticscholar.org/paper/6ec6c2c9a03ad960bd4b23f4281a6a19d9c900be},
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month = {5},
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year = 2016,
month = {12},
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title = {Bayesian Networks to Model the Variability of Speaker Verification Scores in Adverse Environments},
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Name Variation in Community Question Answering Systems Abstract Community question answering systems are forums where users can ask and answer questions in various categories. Examples are Yahoo! Answers, Quora, and Stack Overflow. A common challenge with such systems is that a significant percentage of asked questions are left unanswered. In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo! Answers by reusing the answer to the most similar past resolved question to the unanswered question, from the site. Semantically similar questions could be worded differently, thereby making it difficult to find questions that have shared needs. For example, “Who is the best player for the Reds?” and “Who is currently the biggest star at Manchester United?” have a shared need but are worded differently; also, “Reds” and “Manchester United” are used to refer to the soccer team Manchester United football club. In this research, we focus on question categories that contain a large number of named entities and entity name variations. We show that in these categories, entity linking can be used to identify relevant past resolved questions with shared needs as a given question by disambiguating named entities and matching these questions based on the disambiguated entities, identified entities, and knowledge base information related to these entities. We evaluated our algorithm on a new dataset constructed from Yahoo! Answers. The dataset contains annotated question pairs, (Qgiven, [Qpast, Answer]). We carried out experiments on several question categories and show that an entity-based approach gives good performance when searching for similar questions in entity rich categories.
@inproceedings{andy-etal-2016-name,
title = "Name Variation in Community Question Answering Systems",
author = "Andy, Anietie and
Sekine, Satoshi and
Rwebangira, Mugizi and
Dredze, Mark",
editor = "Han, Bo and
Ritter, Alan and
Derczynski, Leon and
Xu, Wei and
Baldwin, Tim",
booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-3909/",
pages = "51--60",
abstract = "Name Variation in Community Question Answering Systems Abstract Community question answering systems are forums where users can ask and answer questions in various categories. Examples are Yahoo! Answers, Quora, and Stack Overflow. A common challenge with such systems is that a significant percentage of asked questions are left unanswered. In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo! Answers by reusing the answer to the most similar past resolved question to the unanswered question, from the site. Semantically similar questions could be worded differently, thereby making it difficult to find questions that have shared needs. For example, ``Who is the best player for the Reds?'' and ``Who is currently the biggest star at Manchester United?'' have a shared need but are worded differently; also, ``Reds'' and ``Manchester United'' are used to refer to the soccer team Manchester United football club. In this research, we focus on question categories that contain a large number of named entities and entity name variations. We show that in these categories, entity linking can be used to identify relevant past resolved questions with shared needs as a given question by disambiguating named entities and matching these questions based on the disambiguated entities, identified entities, and knowledge base information related to these entities. We evaluated our algorithm on a new dataset constructed from Yahoo! Answers. The dataset contains annotated question pairs, (Qgiven, [Qpast, Answer]). We carried out experiments on several question categories and show that an entity-based approach gives good performance when searching for similar questions in entity rich categories."
}
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booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/a8c3907b09d62457c3b1ebce203e2d9e4af0121e},
}
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month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/7cda13bf5eff7e6c331ef66a6d93e984611eb328},
}
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}
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year = 2016,
month = {12},
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url = {https://www.semanticscholar.org/paper/d1a760d034200c0a34aa1dbdaa0620756c2aa5e8},
}
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title = {Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization},
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year = 2016,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/efc0cc6f85bad1bc270b7405a8457ddd0506aa9b},
}
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month = {12},
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url = {https://www.semanticscholar.org/paper/3b092733f428b12f1f920638f868ed1e8663fe57},
}
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title = {Localization of skin features on the hand and wrist from small image patches},
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year = 2016,
month = {12},
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url = {https://www.semanticscholar.org/paper/ac4bc8c956fb8e1ed65f98d5ddeaf42b9bd6d699},
}
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title = {Performance monitoring for automatic speech recognition in noisy multi-channel environments},
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year = 2016,
month = {12},
booktitle = {Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/333c792893ad041b20bf6794f83f3464a4a3c44e},
}
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title = {A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation},
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year = 2016,
month = {12},
booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/d2fa4e6d74cf19f0704d5abeb714fc9f9f6e703b},
}
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year = 2016,
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booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/8d3e95c31c93548b8c71dbeee2e9f7180067a888},
}
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year = 2016,
month = {12},
booktitle = {International Conference on 3D Vision},
url = {https://www.semanticscholar.org/paper/06cf0efaf36a3f731b0127f874047758944183d2},
}
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title = {Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach},
author = {{Yuyin Zhou} and {Lingxi Xie} and {Wei Shen} and {E. Fishman} and {A. Yuille}},
year = 2016,
month = {12},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/788f341d02130e1807edf88c8c64a77e4096437e},
}
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title = {Quickest intrusion detection in mobile active user authentication},
author = {{Pramuditha Perera} and {Vishal M. Patel}},
year = 2016,
month = {12},
booktitle = {2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
url = {https://www.semanticscholar.org/paper/1c598dbdf288be9327f4b539323d94a240e229d5},
}
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title = {Regularized metric adaptation for unconstrained face verification},
author = {{Boyu Lu} and {Jun-Cheng Chen} and {R. Chellappa}},
year = 2016,
month = {12},
booktitle = {International Conference on Pattern Recognition},
url = {https://www.semanticscholar.org/paper/5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b},
}
@inproceedings{52065722,
title = {Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization},
author = {{Xingguo Li} and {Jarvis D. Haupt} and {Junwei Lu} and {Zhaoran Wang} and {R. Arora} and {Han Liu} and {T. Zhao}},
year = 2016,
month = {12},
booktitle = {IEEE Transactions on Information Theory},
url = {https://www.semanticscholar.org/paper/cf853068cefee2d78d4dbccc8bca1ea450fc3377},
}
@inproceedings{dredze-etal-2016-twitter,
title = "{T}witter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation",
author = "Dredze, Mark and
Andrews, Nicholas and
DeYoung, Jay",
editor = "Ku, Lun-Wei and
Hsu, Jane Yung-jen and
Li, Cheng-Te",
booktitle = "Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media",
month = nov,
year = "2016",
address = "Austin, TX, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-6204/",
doi = "10.18653/v1/W16-6204",
pages = "20--25"
}
@inproceedings{2926504,
title = {Bottleneck Based Front-End for Diarization Systems},
author = {{I. Viñals} and {Jesús Antonio Villalba López} and {Alfonso Ortega} and {A. Miguel} and {EDUARDO LLEIDA SOLANO}},
year = 2016,
month = {11},
booktitle = {IberSPEECH Conference},
url = {https://www.semanticscholar.org/paper/75f27636050d9a2e0a4cf3ea1856a3d7c31e83f5},
}
@inproceedings{9461243,
title = {Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images},
author = {{Junhua Mao} and {Jiajing Xu} and {Yushi Jing} and {A. Yuille}},
year = 2016,
month = {11},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/cc18cb42289fd570a06896b5543b085ebabee57b},
}
@inproceedings{2818046,
title = {Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning},
author = {{R. Passonneau} and {T. Yano} and {Thomas Lippincott} and {Judith L. Klavans}},
year = 2016,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/7118d3db196389f7ad52ceab022b856f9258ab88},
}
@inproceedings{15846993,
title = {Object Recognition with and without Objects},
author = {{Zhuotun Zhu} and {Lingxi Xie} and {A. Yuille}},
year = 2016,
month = {11},
booktitle = {International Joint Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/2f49a17ae3a8767a885ae398064948e28d53d670},
}
@inproceedings{66176,
title = {UMDFaces: An annotated face dataset for training deep networks},
author = {{Ankan Bansal} and {Anirudh Nanduri} and {C. Castillo} and {Rajeev Ranjan} and {R. Chellappa}},
year = 2016,
month = {11},
booktitle = {2017 IEEE International Joint Conference on Biometrics (IJCB)},
url = {https://www.semanticscholar.org/paper/ca45746d158e9d58bdb8a62b6d10163a23cf5b6f},
}
@inproceedings{67737867,
title = {Unsupervised Learning Using Generative Adversarial Training And Clustering},
author = {{Vittal Premachandran} and {A. Yuille}},
year = 2016,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/c8a5f9670b6d22f718c6815bf47cdaee57f82212},
}
@inproceedings{3482308,
title = {Understanding Deep Neural Networks with Rectified Linear Units},
author = {{R. Arora} and {A. Basu} and {Poorya Mianjy} and {Anirbit Mukherjee}},
year = 2016,
month = {11},
booktitle = {Electron. Colloquium Comput. Complex.},
url = {https://www.semanticscholar.org/paper/9375729d21a344a5ccccd5f53556ddf90b957cd9},
}
@inproceedings{wolfe-etal-2016-study,
title = "A Study of Imitation Learning Methods for Semantic Role Labeling",
author = "Wolfe, Travis and
Dredze, Mark and
Van Durme, Benjamin",
editor = "Chang, Kai-Wei and
Chang, Ming-Wei and
Rush, Alexander and
Srikumar, Vivek",
booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
month = nov,
year = "2016",
address = "Austin, TX",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-5905/",
doi = "10.18653/v1/W16-5905",
pages = "44--53"
}
@inproceedings{knowles-etal-2016-demographer,
title = "{D}emographer: Extremely Simple Name Demographics",
author = "Knowles, Rebecca and
Carroll, Josh and
Dredze, Mark",
editor = {Bamman, David and
Do\u gru\"oz, A. Seza and
Eisenstein, Jacob and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Oh, Alice and
Tsur, Oren and
Volkova, Svitlana},
booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-5614/",
doi = "10.18653/v1/W16-5614",
pages = "108--113"
}
@inproceedings{coppersmith-etal-2016-clinical,
title = "The Clinical Panel: Leveraging Psychological Expertise During {NLP} Research",
author = "Coppersmith, Glen and
Hollingshead, Kristy and
Schwartz, H. Andrew and
Ireland, Molly and
Resnik, Rebecca and
Loveys, Kate and
Foreman, April and
Ingraham, Loring",
editor = {Bamman, David and
Do\u gru\"oz, A. Seza and
Eisenstein, Jacob and
Hovy, Dirk and
Jurgens, David and
O'Connor, Brendan and
Oh, Alice and
Tsur, Oren and
Volkova, Svitlana},
booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-5617/",
doi = "10.18653/v1/W16-5617",
pages = "132--137"
}
@inproceedings{28972826,
title = {Contrasting Public Opinion Dynamics and Emotional Response During Crisis},
author = {{Svitlana Volkova} and {I. Chetviorkin} and {Dustin L. Arendt} and {Benjamin Van Durme}},
year = 2016,
month = {11},
booktitle = {Social Informatics},
url = {https://www.semanticscholar.org/paper/c15962d14b74780272bafea9bb00d65d6e7d3862},
}
@inproceedings{white-etal-2016-universal,
title = "Universal Decompositional Semantics on {U}niversal {D}ependencies",
author = "White, Aaron Steven and
Reisinger, Drew and
Sakaguchi, Keisuke and
Vieira, Tim and
Zhang, Sheng and
Rudinger, Rachel and
Rawlins, Kyle and
Van Durme, Benjamin",
editor = "Su, Jian and
Duh, Kevin and
Carreras, Xavier",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1177/",
doi = "10.18653/v1/D16-1177",
pages = "1713--1723"
}
@inproceedings{125693048,
title = {Evolutionary optimization of long short-term memory neural network language model},
author = {{Tomohiro Tanaka} and {Takafumi Moriya} and {T. Shinozaki} and {Shinji Watanabe} and {Takaaki Hori} and {Kevin Duh}},
year = 2016,
month = {11},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/faa4468f2ad1c7cedaf04bf56ebb20ae4b349952},
}
@inproceedings{11119843,
title = {Transition-Based Dependency Parsing Exploiting Supertags},
author = {{Hiroki Ouchi} and {Kevin Duh} and {Hiroyuki Shindo} and {Yuji Matsumoto}},
year = 2016,
month = {11},
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/72a927349c85f8786630fc28e2f8b9480cc08c51},
}
@inproceedings{lippincott-van-durme-2016-fluency,
title = "Fluency detection on communication networks",
author = "Lippincott, Tom and
Van Durme, Benjamin",
editor = "Su, Jian and
Duh, Kevin and
Carreras, Xavier",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1107/",
doi = "10.18653/v1/D16-1107",
pages = "1025--1029"
}
@inproceedings{12071891,
title = {The changing fortunes of pattern recognition and computer vision},
author = {{R. Chellappa}},
year = 2016,
month = {11},
booktitle = {Image and Vision Computing},
url = {https://www.semanticscholar.org/paper/c9434b58592e3e845262a3785012a042101ff547},
}
@inproceedings{5896299,
title = {An All-In-One Convolutional Neural Network for Face Analysis},
author = {{Rajeev Ranjan} and {S. Sankaranarayanan} and {C. Castillo} and {R. Chellappa}},
year = 2016,
month = {11},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
url = {https://www.semanticscholar.org/paper/93420d9212dd15b3ef37f566e4d57e76bb2fab2f},
}
@inproceedings{11823644,
title = {R3DG features: Relative 3D geometry-based skeletal representations for human action recognition},
author = {{Raviteja Vemulapalli} and {Felipe Arrate} and {R. Chellappa}},
year = 2016,
month = {11},
booktitle = {Computer Vision and Image Understanding},
url = {https://www.semanticscholar.org/paper/da1cc72354f70a187d46664c2318c58d8183c379},
}
@inproceedings{4314532,
title = {Handcrafted vs. learned representations for human action recognition},
author = {{Xiantong Zhen} and {Ling Shao} and {S. Maybank} and {R. Chellappa}},
year = 2016,
month = {11},
booktitle = {Image and Vision Computing},
url = {https://www.semanticscholar.org/paper/d5d55ad2848d908d3b237860327f3a2a19b53b75},
}
@InProceedings{francislandau-et-al-2016,
doi = "10.1109/IA3.2016.020",
author = "Matthew Francis-Landau and Bing Xue and Jason Eisner
and Vivek Sarkar",
title = "Fine-grained parallelism in probabilistic parsing with
{H}abanero {J}ava",
booktitle = "Proceedings of the Sixth Workshop on Irregular
Applications: Architectures and Algorithms (IA$^3$)",
pages = "78--81",
year = "2016",
month = nov,
address = "Salt Lake City",
publisher = "IEEE Press",
ISBN = "978-1-5090-3867-1",
URL = "http://cs.jhu.edu/~jason/papers/#francislandau-et-al-2016",
}
@InProceedings{eisner-2016,
aclid = "W16-5901",
doi = "10.18653/v1/W16-5901",
author = "Jason Eisner",
title = "Inside-Outside and Forward-Backward Algorithms are
Just Backprop",
booktitle = "Proceedings of the EMNLP Workshop on Structured
Prediction for NLP",
pages = "1--17",
year = "2016",
month = nov,
address = "Austin, TX",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2016",
}
@InProceedings{vieira-cotterell-eisner-2016,
aclid = "D16-1206",
doi = "10.18653/v1/D16-1206",
author = "Tim Vieira and Ryan Cotterell and Jason Eisner",
title = "Speed-Accuracy Tradeoffs in Tagging with
Variable-Order {CRF}s and Structured Sparsity",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "1973--1978",
year = "2016",
month = nov,
address = "Austin, TX",
URL = "http://cs.jhu.edu/~jason/papers/#vieira-cotterell-eisner-2016",
}
@inproceedings{15820106,
title = {PATH: Person authentication using trace histories},
author = {{U. Mahbub} and {R. Chellappa}},
year = 2016,
month = {10},
booktitle = {Ubiquitous Computing, Electronics & Mobile Communication Conference},
url = {https://www.semanticscholar.org/paper/25c1026057647027b4b633995d54b753e62e40bf},
}
@inproceedings{13428965,
title = {Rich Representation Spaces: Benefits in Digital Auscultation Signal Analysis},
author = {{Dimitra Emmanouilidou} and {Mounya Elhilali}},
year = 2016,
month = {10},
booktitle = {IEEE Workshop on Signal Processing Systems},
url = {https://www.semanticscholar.org/paper/4aab22de9017918f2d1f3afd17e5c7349c59d6f4},
}
@inproceedings{16247870,
title = {Computational linking theory},
author = {{A. White} and {D. Reisinger} and {Rachel Rudinger} and {Kyle Rawlins} and {Benjamin Van Durme}},
year = 2016,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/cc69df198651b0d28055a28615585dc73eff649b},
}
@inproceedings{16893864,
title = {After Sandy Hook Elementary: A Year in the Gun Control Debate on Twitter},
author = {{Adrian Benton} and {Braden Hancock} and {Glen A. Coppersmith} and {J. Ayers} and {Mark Dredze}},
year = 2016,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/0f19e11fb7190e4bc87a6e88529e3ee01831a2e3},
}
@inproceedings{6631177,
title = {Joint Image-Text Representation by Gaussian Visual-Semantic Embedding},
author = {{Zhou Ren} and {Hailin Jin} and {Zhe L. Lin} and {Chen Fang} and {A. Yuille}},
year = 2016,
month = {10},
booktitle = {ACM Multimedia},
url = {https://www.semanticscholar.org/paper/0eca0146c16a758c6b198966605561b4dec5c59a},
}
@inproceedings{wu-etal-2016-generalized,
title = "A Generalized Framework for Hierarchical Word Sequence Language Model",
author = "Wu, Xiaoyi and
Duh, Kevin and
Matsumoto, Yuji",
booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
month = oct,
year = "2016",
address = "Seoul, South Korea",
url = "https://aclanthology.org/Y16-2004/",
pages = "69--75"
}
@inproceedings{17135272,
title = {Discriminative Information Retrieval for Knowledge Discovery},
author = {{Tongfei Chen} and {Benjamin Van Durme}},
year = 2016,
month = {10},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/3dc8f5a7dcdfbdfad4a14bd9a4587de1fb1c2c22},
}
@inproceedings{20703069,
title = {Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor},
author = {{A. Reiter} and {Andy Ma} and {Nishi Rawat} and {Christine Shrock} and {S. Saria}},
year = 2016,
month = {10},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/055f98cbb279c0819469aeea897e31e3e6bd2af5},
}
@inproceedings{63756012,
title = {Incorporating end-user preferences in predictive models},
author = {{S. Saria} and {Daniel P. Robinson}},
year = 2016,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/cde2aa6b106db3a087c3e711c6c6fd3acaf55c77},
}
@inproceedings{bojar-etal-2016-findings,
title = "Findings of the 2016 Conference on Machine Translation",
author = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Popel, Martin and
Post, Matt and
Rubino, Raphael and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin and
Zampieri, Marcos",
editor = {Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Guillou, Liane and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Pecina, Pavel and
Popel, Martin and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Specia, Lucia and
Verspoor, Karin and
Tiedemann, J\"org and
Turchi, Marco},
booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-2301/",
doi = "10.18653/v1/W16-2301",
pages = "131--198"
}
@inproceedings{knowles-etal-2016-analyzing,
title = "Analyzing Learner Understanding of Novel {L}2 Vocabulary",
author = "Knowles, Rebecca and
Renduchintala, Adithya and
Koehn, Philipp and
Eisner, Jason",
editor = "Riezler, Stefan and
Goldberg, Yoav",
booktitle = "Proceedings of the 20th {SIGNLL} Conference on Computational Natural Language Learning",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K16-1013/",
doi = "10.18653/v1/K16-1013",
pages = "126--135"
}
@inproceedings{ding-etal-2016-jhu,
title = "The {JHU} Machine Translation Systems for {WMT} 2016",
author = "Ding, Shuoyang and
Duh, Kevin and
Khayrallah, Huda and
Koehn, Philipp and
Post, Matt",
editor = {Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Guillou, Liane and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Pecina, Pavel and
Popel, Martin and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Specia, Lucia and
Verspoor, Karin and
Tiedemann, J\"org and
Turchi, Marco},
booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-2310/",
doi = "10.18653/v1/W16-2310",
pages = "272--280"
}
@inproceedings{yung-etal-2016-modelling-interpretation,
title = "Modelling the Interpretation of Discourse Connectives by {B}ayesian Pragmatics",
author = "Yung, Frances and
Duh, Kevin and
Komura, Taku and
Matsumoto, Yuji",
editor = "Erk, Katrin and
Smith, Noah A.",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-2086/",
doi = "10.18653/v1/P16-2086",
pages = "531--536"
}
Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a “Home Edition” to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.
@inproceedings{koehn-2016-computer,
title = "Computer Aided Translation",
author = "Koehn, Philipp",
editor = "Birch, Alexandra and
Zuidema, Willem",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-5003/",
abstract = "Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a ``Home Edition'' to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology."
}
@inproceedings{nadejde-etal-2016-modeling,
title = "Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation",
author = "N\u adejde, Maria and
Birch, Alexandra and
Koehn, Philipp",
editor = {Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Guillou, Liane and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Pecina, Pavel and
Popel, Martin and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Specia, Lucia and
Verspoor, Karin and
Tiedemann, J\"org and
Turchi, Marco},
booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-2204/",
doi = "10.18653/v1/W16-2204",
pages = "32--42"
}
@inproceedings{buck-koehn-2016-quick,
title = "Quick and Reliable Document Alignment via {TF}/{IDF}-weighted Cosine Distance",
author = "Buck, Christian and
Koehn, Philipp",
editor = {Bojar, Ond\v rej and
Buck, Christian and
Chatterjee, Rajen and
Federmann, Christian and
Guillou, Liane and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
N\'ev\'eol, Aur\'elie and
Neves, Mariana and
Pecina, Pavel and
Popel, Martin and
Koehn, Philipp and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Specia, Lucia and
Verspoor, Karin and
Tiedemann, J\"org and
Turchi, Marco},
booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-2365/",
doi = "10.18653/v1/W16-2365",
pages = "672--678"
}
@inproceedings{cotterell-etal-2016-sigmorphon,
title = "The {SIGMORPHON} 2016 Shared {T}ask---{M}orphological Reinflection",
author = "Cotterell, Ryan and
Kirov, Christo and
Sylak-Glassman, John and
Yarowsky, David and
Eisner, Jason and
Hulden, Mans",
editor = "Elsner, Micha and
Kuebler, Sandra",
booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-2002/",
doi = "10.18653/v1/W16-2002",
pages = "10--22"
}
@inproceedings{renduchintala-etal-2016-user,
title = "User Modeling in Language Learning with Macaronic Texts",
author = "Renduchintala, Adithya and
Knowles, Rebecca and
Koehn, Philipp and
Eisner, Jason",
editor = "Erk, Katrin and
Smith, Noah A.",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-1175/",
doi = "10.18653/v1/P16-1175",
pages = "1859--1869"
}
@inproceedings{yung-etal-2016-modelling,
title = "Modelling the Usage of Discourse Connectives as Rational Speech Acts",
author = "Yung, Frances and
Duh, Kevin and
Komura, Taku and
Matsumoto, Yuji",
editor = "Riezler, Stefan and
Goldberg, Yoav",
booktitle = "Proceedings of the 20th {SIGNLL} Conference on Computational Natural Language Learning",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K16-1030/",
doi = "10.18653/v1/K16-1030",
pages = "302--313"
}
@inproceedings{renduchintala-etal-2016-creating,
title = "Creating Interactive Macaronic Interfaces for Language Learning",
author = "Renduchintala, Adithya and
Knowles, Rebecca and
Koehn, Philipp and
Eisner, Jason",
editor = "Pradhan, Sameer and
Apidianaki, Marianna",
booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-4023/",
doi = "10.18653/v1/P16-4023",
pages = "133--138"
}
@inproceedings{peng-dredze-2016-improving,
title = "Improving Named Entity Recognition for {C}hinese Social Media with Word Segmentation Representation Learning",
author = "Peng, Nanyun and
Dredze, Mark",
editor = "Erk, Katrin and
Smith, Noah A.",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-2025/",
doi = "10.18653/v1/P16-2025",
pages = "149--155"
}
@inproceedings{benton-etal-2016-learning,
title = "Learning Multiview Embeddings of {T}witter Users",
author = "Benton, Adrian and
Arora, Raman and
Dredze, Mark",
editor = "Erk, Katrin and
Smith, Noah A.",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-2003/",
doi = "10.18653/v1/P16-2003",
pages = "14--19"
}
@InProceedings{cotterell-et-al-2016-shared,
aclid = "W16-2002",
doi = "10.18653/v1/W16-2002",
author = "Ryan Cotterell and Christo Kirov and John
Sylak-Glassman and David Yarowsky and Jason Eisner and
Mans Hulden",
title = "The {SIGMORPHON 2016} Shared Task---Morphological
Reinflection",
booktitle = "Proceedings of the 14th SIGMORPHON Workshop on
Computational Research in Phonetics, Phonology, and
Morphology",
pages = "10--22",
year = "2016",
month = aug,
address = "Berlin",
note = "Supplementary material (4 pages) also available.",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2016-shared",
}
@InProceedings{knowles-et-al-2016,
aclid = "K16-1013",
doi = "10.18653/v1/K16-1013",
author = "Rebecca Knowles and Adithya Renduchintala and Philipp
Koehn and Jason Eisner",
title = "Analyzing Learner Understanding of Novel {L2}
Vocabulary",
booktitle = "Proceedings of the 20th SIGNLL Conference on
Computational Natural Language Learning (CoNLL)",
pages = "126--135",
year = "2016",
month = aug,
address = "Berlin",
URL = "http://cs.jhu.edu/~jason/papers/#knowles-et-al-2016",
}
@InProceedings{renduchintala-et-al-2016-acl-macui,
aclid = "P16-4023",
doi = "10.18653/v1/P16-4023",
author = "Adithya Renduchintala and Rebecca Knowles and Philipp
Koehn and Jason Eisner",
title = "Creating Interactive Macaronic Interfaces for Language
Learning",
booktitle = "Proceedings of ACL-2016 System Demonstrations",
pages = "133--138",
year = "2016",
month = aug,
address = "Berlin",
URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2016-acl-macui",
}
@InProceedings{renduchintala-et-al-2016-acl-macmodel,
aclid = "P16-1175",
doi = "10.18653/v1/P16-1175",
author = "Adithya Renduchintala and Rebecca Knowles and Philipp
Koehn and Jason Eisner",
title = "User Modeling in Language Learning with Macaronic
Texts",
booktitle = "Proceedings of the 54th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "1859--1869",
year = "2016",
month = aug,
address = "Berlin",
URL = "http://cs.jhu.edu/~jason/papers/#renduchintala-et-al-2016-acl-macmodel",
}
@InProceedings{cotterell-et-al-2016-acl,
aclid = "P16-1156",
doi = "10.18653/v1/P16-1156",
author = "Ryan Cotterell and Hinrich Sch{\"{u}}tze and Jason
Eisner",
title = "Morphological Smoothing and Extrapolation of Word
Embeddings",
booktitle = "Proceedings of the 54th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "1651--1660",
year = "2016",
month = aug,
address = "Berlin",
note = "Supplementary material (4 pages) also available.",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2016-acl",
}
@InProceedings{filardo-eisner-2016-ttatt,
author = "Nathaniel Wesley Filardo and Jason Eisner",
title = "Rigid Tree Automata With Isolation",
booktitle = "Proceedings of the Fourth International Workshop on
Trends in Tree Automata and Tree Transducers (TTATT)",
year = "2016",
month = aug,
address = "Seoul",
note = "7 pages",
URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2016-ttatt",
}
@inproceedings{napoles-etal-2016-sentential,
title = "Sentential Paraphrasing as Black-Box Machine Translation",
author = "Napoles, Courtney and
Callison-Burch, Chris and
Post, Matt",
editor = "DeNero, John and
Finlayson, Mark and
Reddy, Sravana",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-3013/",
doi = "10.18653/v1/N16-3013",
pages = "62--66"
}
@inproceedings{gao-etal-2016-knowledge,
title = "Knowledge Base Population for Organization Mentions in Email",
author = "Gao, Ning and
Dredze, Mark and
Oard, Douglas",
editor = "Pujara, Jay and
Rocktaschel, Tim and
Chen, Danqi and
Singh, Sameer",
booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
month = jun,
year = "2016",
address = "San Diego, CA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-1305/",
doi = "10.18653/v1/W16-1305",
pages = "24--28"
}
@inproceedings{coppersmith-etal-2016-exploratory,
title = "Exploratory Analysis of Social Media Prior to a Suicide Attempt",
author = "Coppersmith, Glen and
Ngo, Kim and
Leary, Ryan and
Wood, Anthony",
editor = "Hollingshead, Kristy and
Ungar, Lyle",
booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2016",
address = "San Diego, CA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W16-0311/",
doi = "10.18653/v1/W16-0311",
pages = "106--117"
}
@inproceedings{dredze-etal-2016-geolocation,
title = "Geolocation for {T}witter: Timing Matters",
author = "Dredze, Mark and
Osborne, Miles and
Kambadur, Prabhanjan",
editor = "Knight, Kevin and
Nenkova, Ani and
Rambow, Owen",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-1122/",
doi = "10.18653/v1/N16-1122",
pages = "1064--1069"
}
@inproceedings{yu-etal-2016-embedding,
title = "Embedding Lexical Features via Low-Rank Tensors",
author = "Yu, Mo and
Dredze, Mark and
Arora, Raman and
Gormley, Matthew R.",
editor = "Knight, Kevin and
Nenkova, Ani and
Rambow, Owen",
booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2016",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N16-1117/",
doi = "10.18653/v1/N16-1117",
pages = "1019--1029"
}
@InProceedings{rastogi-cotterell-eisner-2016,
aclid = "N16-1076",
doi = "10.18653/v1/N16-1076",
author = "Pushpendre Rastogi and Ryan Cotterell and Jason
Eisner",
title = "Weighting Finite-State Transductions With Neural
Context",
booktitle = "Proceedings of the 2016 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "623--633",
note = "11 pages. Supplementary material (1 page) also
available",
year = "2016",
month = jun,
address = "San Diego",
URL = "http://cs.jhu.edu/~jason/papers/#rastogi-cotterell-eisner-2016",
}
The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.
@inproceedings{chodroff-etal-2016-new,
title = "New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification",
author = "Chodroff, Eleanor and
Maciejewski, Matthew and
Trmal, Jan and
Khudanpur, Sanjeev and
Godfrey, John",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro\v z, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1210/",
pages = "1323--1327",
abstract = "The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements."
}
Structured, complete inflectional paradigm data exists for very few of the world’s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.
@inproceedings{sylak-glassman-etal-2016-remote,
title = "Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages",
author = "Sylak-Glassman, John and
Kirov, Christo and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro\v z, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1497/",
pages = "3116--3120",
abstract = "Structured, complete inflectional paradigm data exists for very few of the world's languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages."
}
Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.
@inproceedings{kirov-etal-2016-large,
title = "Very-large Scale Parsing and Normalization of {W}iktionary Morphological Paradigms",
author = "Kirov, Christo and
Sylak-Glassman, John and
Que, Roger and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro\v z, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1498/",
pages = "3121--3126",
abstract = "Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches."
}
@inproceedings{180260851,
title = {Fast Image Stabilization and Mosaicking},
author = {{C. Morimoto} and {R. Chellappa}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/68c287c03623610469673d2b3b27b2ff6468001e},
}
@inproceedings{2691283,
title = {Deep Tattoo Recognition},
author = {{Xing Di} and {Vishal M. Patel}},
year = 2016,
month = {6},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
url = {https://www.semanticscholar.org/paper/743a3c77e0dc572cdba3ced62315e153b53a3ca3},
}
@inproceedings{8558854,
title = {Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification},
author = {{Emily M. Hand} and {R. Chellappa}},
year = 2016,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/87147418f863e3d8ff8c97db0b42695a1c28195b},
}
@inproceedings{40408108,
title = {News and Internet Searches About Human Immunodeficiency Virus After Charlie Sheen's Disclosure.},
author = {{J. Ayers} and {B. Althouse} and {Mark Dredze} and {E. Leas} and {S. Noar}},
year = 2016,
month = {4},
booktitle = {JAMA Internal Medicine},
url = {https://www.semanticscholar.org/paper/cfb27c8b4a5856ad2150076afca7ea16671b16e3},
}
@inproceedings{31336923,
title = {Using Causal Inference to Estimate What-if Outcomes for Targeting Treatments},
author = {{Qing Liu} and {K. Henry} and {Yanbo Xu} and {S. Saria}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/cffd849c35e050288c0e68227280ebb1fd758802},
}
@inproceedings{32252301,
title = {A true Random Number Generator using RTN noise and a sigma delta converter},
author = {{Tomas Figliolia} and {P. Julián} and {Gaspar Tognetti} and {A. Andreou}},
year = 2016,
month = {5},
booktitle = {International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/ca8124f8343a219de726660dc78647bfab84cd47},
}
@inproceedings{67046386,
title = {Proceedings of the LREC 2016 Workshop “Translation Evaluation – From Fragmented Tools and Data Sets to an Integrated Ecosystem”},
author = {{Ondrej Bojar} and {C. Federmann} and {B. Haddow} and {Philipp Koehn} and {Matt Post} and {Lucia Specia}},
year = 2016,
month = {5},
booktitle = {},
url = {https://www.semanticscholar.org/paper/143397a2a8ad984731efd18c40839ff532bc496f},
}
@inproceedings{8347466,
title = {Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding},
author = {{Roozbeh Mottaghi} and {S. Fidler} and {A. Yuille} and {R. Urtasun} and {Devi Parikh}},
year = 2016,
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/be01be917a9ec0d907fab39b87ddd68a1db8d84a},
}
@inproceedings{5523629,
title = {Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples},
author = {{Yuan Gao} and {Jiayi Ma} and {A. Yuille}},
year = 2016,
month = {9},
booktitle = {IEEE Transactions on Image Processing},
url = {https://www.semanticscholar.org/paper/4906cb9954509ea18c557afae04cef6c131a87b3},
}
@inproceedings{70204104,
title = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
author = {{Adithya Renduchintala} and {Rebecca Knowles} and {Philipp Koehn} and {Jason Eisner}},
year = 2016,
month = {8},
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/3a29aa4eff48624752c07059a44d3288a678c8ab},
}
@inproceedings{10036335,
title = {A Unified Bayesian Model of Scripts, Frames and Language},
author = {{Francis Ferraro} and {Benjamin Van Durme}},
year = 2016,
month = {2},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/ae7221f4731570b8ebe763000f981c7e5b1664a8},
}
@inproceedings{17781514,
title = {Collective Supervision of Topic Models for Predicting Surveys with Social Media},
author = {{Adrian Benton} and {Michael J. Paul} and {Braden Hancock} and {Mark Dredze}},
year = 2016,
month = {2},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/1818bdee5bb946207b51a434e734fe60eacb9688},
}
@inproceedings{63310475,
title = {A Novel Parallelized Feature Extraction in Grouped Scale Space Based on Graphic Processing Units},
author = {{Wenbin Jiang} and {Bin Luo} and {Hai Jin} and {A. Yuille} and {Jinsheng Xiao}},
year = 2016,
month = {9},
booktitle = {Journal of Internet Technology},
url = {https://www.semanticscholar.org/paper/be3157f1ac01ead36ace362c57bd968d084d1646},
}
@inproceedings{39151366,
title = {Submodular Attribute Selection for Visual Recognition.},
author = {{Jingjing Zheng} and {Zhuolin Jiang} and {R. Chellappa}},
year = 2016,
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
url = {https://www.semanticscholar.org/paper/6c399614f2073a321d70887082b2f41f131cb686},
}
@inproceedings{39758868,
title = {Electronic Theses and Dissertations Title Mining Spatial and Spatio-Temporal ROIs for Action Recognition Permalink},
author = {{Xiaochen Lian} and {Qing Zhou} and {Nicolas Christou} and {A. Yuille}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/4cc681239c8fda3fb04ba7ac6a1b9d85b68af31d},
}
@inproceedings{5007321,
title = {An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization},
author = {{Xingguo Li} and {T. Zhao} and {R. Arora} and {Han Liu} and {Mingyi Hong}},
year = 2016,
month = {7},
booktitle = {International Conference on Artificial Intelligence and Statistics},
url = {https://www.semanticscholar.org/paper/5d4b71408a79c6780c5e238c871d431504425195},
}
@inproceedings{2375893,
title = {Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses},
author = {{Peter F. Schulam} and {S. Saria}},
year = 2016,
booktitle = {Journal of machine learning research},
url = {https://www.semanticscholar.org/paper/7855f065e668124cf65ab4c871c0c125bde7d794},
}
@inproceedings{124876232,
title = {Summarization and Search Over Geometric Spaces},
author = {{Nitesh Shroff} and {Rushil Anirudh} and {R. Chellappa}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7e55629c938420abf3975e9467fae71d56ca40a4},
}
@inproceedings{15393598,
title = {The Potential of Twitter as a Data Source for Patient Safety},
author = {{Atul Nakhasi} and {Sarah G. Bell} and {R. Passarella} and {Michael J. Paul} and {Mark Dredze} and {P. Pronovost}},
year = 2016,
month = {1},
booktitle = {Journal of patient safety},
url = {https://www.semanticscholar.org/paper/ab80430dd27c64bdb91382b7a0d1aa054364e56b},
}
@inproceedings{34772205,
title = {Non-Linear Similarity Learning for Compositionality},
author = {{Masashi Tsubaki} and {Kevin Duh} and {M. Shimbo} and {Yuji Matsumoto}},
year = 2016,
month = {2},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/3d6f95fa4e1771d13f4a523063b244e2328f1aa0},
}
@inproceedings{2879199,
title = {Exploiting Hidden-Layer Responses of Deep Neural Networks for Language Recognition},
author = {{Ruizhi Li} and {Sri Harish Reddy Mallidi} and {L. Burget} and {Oldrich Plchot} and {N. Dehak}},
year = 2016,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/8b96f71e6895b448841d79eaa3836150abea48f9},
}
@inproceedings{2450630,
title = {Triplet Similarity Embedding for Face Verification},
author = {{S. Sankaranarayanan} and {A. Alavi} and {R. Chellappa}},
year = 2016,
month = {2},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/729f9ef5dad29eadae16f33682b48c4806af2029},
}
@inproceedings{735949,
title = {Dependency Parsing with LSTMs: An Empirical Evaluation},
author = {{A. Kuncoro} and {Yu Sawai} and {Kevin Duh} and {Yuji Matsumoto}},
year = 2016,
month = {4},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/8ce0023611d4d5f0e9654a289e165f600216c76d},
}
@inproceedings{15870680,
title = {Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media},
author = {{M. Choudhury} and {Emre Kıcıman} and {Mark Dredze} and {Glen A. Coppersmith} and {Mrinal Kumar}},
year = 2016,
month = {5},
booktitle = {International Conference on Human Factors in Computing Systems},
url = {https://www.semanticscholar.org/paper/548f8d0f906f54427e1d6e86802080f500b35a8d},
}
@inproceedings{7342173,
title = {Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons},
author = {{Lingxi Xie} and {Qi Tian} and {John Flynn} and {Jingdong Wang} and {A. Yuille}},
year = 2016,
month = {7},
booktitle = {European Conference on Computer Vision},
url = {https://www.semanticscholar.org/paper/79eadd85c614b2aa5209e9f53692ca8383c71d12},
}
@inproceedings{14783169,
title = {Novel neural network based fusion for multistream ASR},
author = {{Sri Harish Reddy Mallidi} and {H. Hermansky}},
year = 2016,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/8610e26bf56cc256510519fc7da9ff68c02578d0},
}
@inproceedings{16665228,
title = {On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization},
author = {{Xingguo Li} and {T. Zhao} and {R. Arora} and {Han Liu} and {Mingyi Hong}},
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month = {7},
booktitle = {Journal of machine learning research},
url = {https://www.semanticscholar.org/paper/486956c45fa19b2d0d794501ec296f607ed1eeb5},
}
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title = {How Twitter is Changing the Nature of Financial News Discovery},
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year = 2016,
month = {6},
booktitle = {DSMM@SIGMOD},
url = {https://www.semanticscholar.org/paper/2b5d7d3baef51c66cce0f8dc4807d7b88bcb9239},
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title = {Proceedings of the First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE 2016)},
author = {{Zheng-Hua Tan} and {N. Dehak} and {J. Larsen} and {Zhanyu Ma}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a087c1d1dbedbfb9458e89883fd3059bbf0b1f8f},
}
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title = {Face Alignment by Local Deep Descriptor Regression},
author = {{Amit Kumar} and {Rajeev Ranjan} and {Vishal M. Patel} and {R. Chellappa}},
year = 2016,
month = {1},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/ceeb67bf53ffab1395c36f1141b516f893bada27},
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@inproceedings{148519249,
title = {Modeling of Facial Wrinkles for Applications in Computer Vision},
author = {{Nazre Batool} and {R. Chellappa}},
year = 2016,
booktitle = {},
url = {https://www.semanticscholar.org/paper/d75c22c18a45ab00eefd6b61522715954de3c015},
}
String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5\%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5\% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7\%.
@inproceedings{nadejde-etal-2016-neural,
title = "A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation",
author = "N\u adejde, Maria and
Birch, Alexandra and
Koehn, Philipp",
editor = {Cettolo, Mauro and
Niehues, Jan and
St\"uker, Sebastian and
Bentivogli, Luisa and
Cattoni, Rolando and
Federico, Marcello},
booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
month = dec # " 8-9",
year = "2016",
address = "Seattle, Washington D.C",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2016.iwslt-1.11/",
abstract = "String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5\%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model's accuracy by 1.5\% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7\%."
}
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title = {Multi-task Multi-domain Representation Learning for Sequence Tagging},
author = {{Nanyun Peng} and {Mark Dredze}},
year = 2016,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/58a644686a9c44708aff98f019602fa9553e88ff},
}
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title = {Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning},
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year = 2016,
month = {5},
booktitle = {International Conference on Machine Learning},
url = {https://www.semanticscholar.org/paper/e61ca3bf0331722c05478b61eb4b7ab3a86854b1},
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}
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title = {Cross-View Action Recognition via Transferable Dictionary Learning},
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month = {6},
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url = {https://www.semanticscholar.org/paper/d5727de9817e2018f0b72677fdd6405d6194bd1f},
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month = {3},
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}
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url = {https://www.semanticscholar.org/paper/9fa123f2de115cba39f57e30a20efdd03cb45f2e},
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title = {Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation (SedMT 2016)},
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year = 2016,
booktitle = {},
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}
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title = {Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction},
author = {{Xingguo Li} and {R. Arora} and {Han Liu} and {Jarvis D. Haupt} and {Tuo Zhao}},
year = 2016,
month = {5},
booktitle = {arXiv: Learning},
url = {https://www.semanticscholar.org/paper/a4edc4aa0bf3eb9d1d0e9aec8d248d088a96af73},
}
@inproceedings{8919622,
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month = {9},
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We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6\% vs. 43.3\%) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment.
@inproceedings{knowles-koehn-2016-neural,
title = "Neural Interactive Translation Prediction",
author = "Knowles, Rebecca and
Koehn, Philipp",
editor = "Green, Spence and
Schwartz, Lane",
booktitle = "Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track",
month = oct # " 28 - " # nov # " 1",
year = "2016",
address = "Austin, TX, USA",
publisher = "The Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2016.amta-researchers.9/",
pages = "107--120",
abstract = "We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6\% vs. 43.3\%) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment."
}
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booktitle = {Conference on Uncertainty in Artificial Intelligence},
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}
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booktitle = {},
url = {https://www.semanticscholar.org/paper/c5c225999b6d2ad316fe7693b398e290407a43c3},
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}
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year = 2015,
month = {12},
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url = {https://www.semanticscholar.org/paper/dcea9e96ba4940e7927a12ab6633962d64a67bb5},
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month = {12},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/d22701010f26fd11f6a03e8eae4d920f2da8f07b},
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month = {12},
booktitle = {Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/913585c08fd0e5e5a0e2fa4229911d369db103f7},
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month = {12},
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month = {11},
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author = {{Philipp Koehn}},
year = 2015,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/4d413832d6a658977743ee4ebab59e577158e1b0},
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month = {10},
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url = {https://www.semanticscholar.org/paper/9cee45ef1212ebbc7d468f9b1d7df24f5005e64d},
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month = {10},
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@inproceedings{gormley-etal-2015-improved,
title = "Improved Relation Extraction with Feature-Rich Compositional Embedding Models",
author = "Gormley, Matthew R. and
Yu, Mo and
Dredze, Mark",
editor = "M\`arquez, Llu\'\i s and
Callison-Burch, Chris and
Su, Jian",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1205/",
doi = "10.18653/v1/D15-1205",
pages = "1774--1784"
}
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title = "The {E}dinburgh/{JHU} Phrase-based Machine Translation Systems for {WMT} 2015",
author = "Haddow, Barry and
Huck, Matthias and
Birch, Alexandra and
Bogoychev, Nikolay and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajan and
Federmann, Christian and
Haddow, Barry and
Hokamp, Chris and
Huck, Matthias and
Logacheva, Varvara and
Pecina, Pavel",
booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-3013/",
doi = "10.18653/v1/W15-3013",
pages = "126--133"
}
@inproceedings{rudinger-etal-2015-script,
title = "Script Induction as Language Modeling",
author = "Rudinger, Rachel and
Rastogi, Pushpendre and
Ferraro, Francis and
Van Durme, Benjamin",
editor = "M\`arquez, Llu\'\i s and
Callison-Burch, Chris and
Su, Jian",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1195/",
doi = "10.18653/v1/D15-1195",
pages = "1681--1686"
}
@inproceedings{kumar-etal-2015-coarse,
title = "A Coarse-Grained Model for Optimal Coupling of {ASR} and {SMT} Systems for Speech Translation",
author = "Kumar, Gaurav and
Blackwood, Graeme and
Trmal, Jan and
Povey, Daniel and
Khudanpur, Sanjeev",
editor = "M\`arquez, Llu\'\i s and
Callison-Burch, Chris and
Su, Jian",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1218/",
doi = "10.18653/v1/D15-1218",
pages = "1902--1907"
}
@inproceedings{bojar-etal-2015-findings,
title = "Findings of the 2015 Workshop on Statistical Machine Translation",
author = "Bojar, Ond\v rej and
Chatterjee, Rajen and
Federmann, Christian and
Haddow, Barry and
Huck, Matthias and
Hokamp, Chris and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Post, Matt and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajan and
Federmann, Christian and
Haddow, Barry and
Hokamp, Chris and
Huck, Matthias and
Logacheva, Varvara and
Pecina, Pavel",
booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-3001/",
doi = "10.18653/v1/W15-3001",
pages = "1--46"
}
@inproceedings{yung-etal-2015-crosslingual,
title = "Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation",
author = "Yung, Frances and
Duh, Kevin and
Matsumoto, Yuji",
editor = "Webber, Bonnie and
Carpuat, Marine and
Popescu-Belis, Andrei and
Hardmeier, Christian",
booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-2519/",
doi = "10.18653/v1/W15-2519",
pages = "142--152"
}
@inproceedings{peng-dredze-2015-named,
title = "Named Entity Recognition for {C}hinese Social Media with Jointly Trained Embeddings",
author = "Peng, Nanyun and
Dredze, Mark",
editor = "M\`arquez, Llu\'\i s and
Callison-Burch, Chris and
Su, Jian",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1064/",
doi = "10.18653/v1/D15-1064",
pages = "548--554"
}
@inproceedings{williams-etal-2015-edinburghs,
title = "{E}dinburgh's Syntax-Based Systems at {WMT} 2015",
author = "Williams, Philip and
Sennrich, Rico and
Nadejde, Maria and
Huck, Matthias and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Chatterjee, Rajan and
Federmann, Christian and
Haddow, Barry and
Hokamp, Chris and
Huck, Matthias and
Logacheva, Varvara and
Pecina, Pavel",
booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-3024/",
doi = "10.18653/v1/W15-3024",
pages = "199--209"
}
@InProceedings{peng-cotterell-eisner-2015,
aclid = "D15-1108",
doi = "10.18653/v1/D15-1108",
author = "Nanyun Peng and Ryan Cotterell and Jason Eisner",
title = "Dual Decomposition Inference for Graphical Models over
Strings",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "917--927",
year = "2015",
month = sep,
address = "Lisbon",
URL = "http://cs.jhu.edu/~jason/papers/#peng-cotterell-eisner-2015",
}
@inproceedings{peng-etal-2015-empirical,
title = "An Empirical Study of {C}hinese Name Matching and Applications",
author = "Peng, Nanyun and
Yu, Mo and
Dredze, Mark",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2062/",
doi = "10.3115/v1/P15-2062",
pages = "377--383"
}
@inproceedings{pavlick-etal-2015-framenet,
title = "{F}rame{N}et+: Fast Paraphrastic Tripling of {F}rame{N}et",
author = "Pavlick, Ellie and
Wolfe, Travis and
Rastogi, Pushpendre and
Callison-Burch, Chris and
Dredze, Mark and
Van Durme, Benjamin",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2067/",
doi = "10.3115/v1/P15-2067",
pages = "408--413"
}
@inproceedings{cheng-etal-2015-synthetic,
title = "Synthetic Word Parsing Improves {C}hinese Word Segmentation",
author = "Cheng, Fei and
Duh, Kevin and
Matsumoto, Yuji",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2043/",
doi = "10.3115/v1/P15-2043",
pages = "262--267"
}
@inproceedings{napoles-etal-2015-ground,
title = "Ground Truth for Grammatical Error Correction Metrics",
author = "Napoles, Courtney and
Sakaguchi, Keisuke and
Post, Matt and
Tetreault, Joel",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2097/",
doi = "10.3115/v1/P15-2097",
pages = "588--593"
}
@inproceedings{ouchi-etal-2015-joint,
title = "Joint Case Argument Identification for {J}apanese Predicate Argument Structure Analysis",
author = "Ouchi, Hiroki and
Shindo, Hiroyuki and
Duh, Kevin and
Matsumoto, Yuji",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1093/",
doi = "10.3115/v1/P15-1093",
pages = "961--970"
}
@inproceedings{guo-etal-2015-cross,
title = "Cross-lingual Dependency Parsing Based on Distributed Representations",
author = "Guo, Jiang and
Che, Wanxiang and
Yarowsky, David and
Wang, Haifeng and
Liu, Ting",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1119/",
doi = "10.3115/v1/P15-1119",
pages = "1234--1244"
}
@inproceedings{pavlick-etal-2015-domain,
title = "Domain-Specific Paraphrase Extraction",
author = "Pavlick, Ellie and
Ganitkevitch, Juri and
Chan, Tsz Ping and
Yao, Xuchen and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2010/",
doi = "10.3115/v1/P15-2010",
pages = "57--62"
}
@inproceedings{pavlick-etal-2015-ppdb,
title = "{PPDB} 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification",
author = "Pavlick, Ellie and
Rastogi, Pushpendre and
Ganitkevitch, Juri and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2070/",
doi = "10.3115/v1/P15-2070",
pages = "425--430"
}
@inproceedings{sylak-glassman-etal-2015-language,
title = "A Language-Independent Feature Schema for Inflectional Morphology",
author = "Sylak-Glassman, John and
Kirov, Christo and
Yarowsky, David and
Que, Roger",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-2111/",
doi = "10.3115/v1/P15-2111",
pages = "674--680"
}
@inproceedings{yung-etal-2015-sequential,
title = "Sequential Annotation and Chunking of {C}hinese Discourse Structure",
author = "Yung, Frances and
Duh, Kevin and
Matsumoto, Yuji",
editor = "Yu, Liang-Chih and
Sui, Zhifang and
Zhang, Yue and
Ng, Vincent",
booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-3101/",
doi = "10.18653/v1/W15-3101",
pages = "1--6"
}
@inproceedings{pavlick-etal-2015-adding,
title = "Adding Semantics to Data-Driven Paraphrasing",
author = "Pavlick, Ellie and
Bos, Johan and
Nissim, Malvina and
Beller, Charley and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1146/",
doi = "10.3115/v1/P15-1146",
pages = "1512--1522"
}
@inproceedings{peng-etal-2015-concrete,
title = "A Concrete {C}hinese {NLP} Pipeline",
author = "Peng, Nanyun and
Ferraro, Francis and
Yu, Mo and
Andrews, Nicholas and
DeYoung, Jay and
Thomas, Max and
Gormley, Matthew R. and
Wolfe, Travis and
Harman, Craig and
Van Durme, Benjamin and
Dredze, Mark",
editor = "Gerber, Matt and
Havasi, Catherine and
Lacatusu, Finley",
booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
month = jun,
year = "2015",
address = "Denver, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N15-3018/",
doi = "10.3115/v1/N15-3018",
pages = "86--90"
}
@inproceedings{rudinger-etal-2015-learning,
title = "Learning to predict script events from domain-specific text",
author = "Rudinger, Rachel and
Demberg, Vera and
Modi, Ashutosh and
Van Durme, Benjamin and
Pinkal, Manfred",
editor = "Palmer, Martha and
Boleda, Gemma and
Rosso, Paolo",
booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2015",
address = "Denver, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S15-1024/",
doi = "10.18653/v1/S15-1024",
pages = "205--210"
}
@InProceedings{cotterell-eisner-2015,
aclid = "N15-1094",
doi = "10.3115/v1/N15-1094",
author = "Ryan Cotterell and Jason Eisner",
title = "Penalized Expectation Propagation for Graphical Models
Over Strings",
booktitle = "Proceedings of the 2015 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "932--942",
note = "Supplementary material (11 pages) also available",
year = "2015",
month = jun,
address = "Denver",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-eisner-2015",
}
@inproceedings{10979491,
title = {Depth-Gated LSTM},
author = {{K. Yao} and {Trevor Cohn} and {Ekaterina Vylomova} and {Kevin Duh} and {Chris Dyer}},
year = 2015,
month = {8},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/a739ae988ba0e3ff232f4507627dfc282ba7b3f4},
}
@inproceedings{mitchell-etal-2015-quantifying,
title = "Quantifying the Language of Schizophrenia in Social Media",
author = "Mitchell, Margaret and
Hollingshead, Kristy and
Coppersmith, Glen",
booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
month = jun # " 5",
year = "2015",
address = "Denver, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-1202/",
doi = "10.3115/v1/W15-1202",
pages = "11--20"
}
@inproceedings{6095465,
title = {Multilingual Topic Models for Bilingual Dictionary Extraction},
author = {{Xiaodong Liu} and {Kevin Duh} and {Yuji Matsumoto}},
year = 2015,
month = {6},
booktitle = {ACM Trans. Asian Low Resour. Lang. Inf. Process.},
url = {https://www.semanticscholar.org/paper/acdd7809fe86f300d659792b27733601646fc860},
}
@inproceedings{261968231,
title = {Explorer Edinburgh SLT and MT System Description for the IWSLT 2014 Evaluation},
author = {{Alexandra Birch} and {Matthias Huck} and {Nadir Durrani} and {Nikolay Bogoychev} and {Philipp Koehn}},
year = 2015,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7a95f6ff588c0b7af6499952a24092ab6829d2b1},
}
@inproceedings{11155786,
title = {Pronunciation and silence probability modeling for ASR},
author = {{Guoguo Chen} and {Hainan Xu} and {Minhua Wu} and {Daniel Povey} and {S. Khudanpur}},
year = 2015,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/b903e2a39cfc2d33f1a722af71adc438732cc6bc},
}
@inproceedings{8565845,
title = {Detection of speech tokens in noise using adaptive spectrotemporal receptive fields},
author = {{Ashwin Bellur} and {Mounya Elhilali}},
year = 2015,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/19de5e254179d52da95985bab4b5c698dd8a2136},
}
@inproceedings{20529470,
title = {The Hurricane Sandy Twitter Corpus},
author = {{Haoyu Wang} and {E. Hovy} and {Mark Dredze}},
year = 2015,
booktitle = {AAAI Workshop: WWW and Public Health Intelligence},
url = {https://www.semanticscholar.org/paper/bf7f220a2908268a7a1c432a2c2efd81afed6808},
}
@inproceedings{63987411,
title = {Machine learning:Trends, perspectives, and prospects},
author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
year = 2015,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a89762ae8574f4b55a1814e72750bfc3be57a70d},
}
@inproceedings{11812867,
title = {Design of a vanishing point algorithm for custom ASIC},
author = {{M. Villemur} and {M. D. Federico} and {P. Julián} and {A. Andreou} and {F. Masson} and {E. Nebot}},
year = 2015,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/8defdb81dbd9974a83c40ba16729c1fed79817e0},
}
@inproceedings{118259583,
title = {The Johns Hopkins University multimodal dataset for human action recognition},
author = {{Thomas S. Murray} and {Daniel R. Mendat} and {P. Pouliquen} and {A. Andreou}},
year = 2015,
month = {5},
booktitle = {Defense + Security Symposium},
url = {https://www.semanticscholar.org/paper/fa944a7ffa9e081e7cbd2ccfeea5421adcb6fbe2},
}
@inproceedings{7252198,
title = {Mapping the cardiac acousteome: An overview of technologies, tools and methods},
author = {{A. Andreou} and {T. Abraham} and {W. R. Thompson} and {J. Seo} and {R. Mittal}},
year = 2015,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/cd4f57bde7af3a2c33f3f676ae607ee82d61651b},
}
@inproceedings{188206416,
title = {EU-BRIDGE Final Report},
author = {{Sebastian Stüker} and {H. Ney} and {M. Simpson} and {Margit Rödder} and {Volker Steinbiss} and {A. Tescari} and {Marcello Federico} and {Philipp Koehn}},
year = 2015,
booktitle = {},
url = {https://www.semanticscholar.org/paper/6497ea71b3c738c54563bed1666598b64c6ff78c},
}
@inproceedings{9077931,
title = {ANALOG IMAGE PROCESSING WITH SILICON RETINAS},
author = {{K. Strohbehn} and {R. C. Meitzler} and {A. Andreou} and {R. E. Jenkins}},
year = 2015,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c1e0ce486ae3e7a6ef02af3a341d16176ff8ebd6},
}
@inproceedings{1151330,
title = {A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of-Speech Tagging},
author = {{John Sylak-Glassman} and {Christo Kirov} and {Matt Post} and {R. Que} and {David Yarowsky}},
year = 2015,
month = {9},
booktitle = {International Workshop on Systems and Frameworks for Computational Morphology},
url = {https://www.semanticscholar.org/paper/af68cc66f85fd634ae075fd0a2ae5db4f61cf793},
}
@inproceedings{201287,
title = {A diversity-penalizing ensemble training method for deep learning},
author = {{Xiaohui Zhang} and {Daniel Povey} and {S. Khudanpur}},
year = 2015,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/aad6fa33ad5da8808d527969414f7928a41ae6b1},
}
@inproceedings{5777625,
title = {Perceptual susceptibility to acoustic manipulations in speaker discrimination.},
author = {{Gregory Sell} and {C. Suied} and {Mounya Elhilali} and {S. Shamma}},
year = 2015,
month = {2},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/1a3a3184e201e603a4a43fd76f856096ef532967},
}
@inproceedings{9443248,
title = {Semi-supervised maximum mutual information training of deep neural network acoustic models},
author = {{Vimal Manohar} and {Daniel Povey} and {S. Khudanpur}},
year = 2015,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/83ac318274d1894dc93c3a47c66179d74c591bdf},
}
@inproceedings{17878129,
title = {Worldwide Influenza Surveillance through Twitter},
author = {{Michael J. Paul} and {Mark Dredze} and {David A. Broniatowski} and {N. Generous}},
year = 2015,
month = {4},
booktitle = {AAAI Workshop: WWW and Public Health Intelligence},
url = {https://www.semanticscholar.org/paper/37716db6dd12a67c74fc10d97011a1f59d7369be},
}
@inproceedings{volkova-etal-2015-social,
title = "Social Media Predictive Analytics",
author = "Volkova, Svitlana and
Van Durme, Benjamin and
Yarowsky, David and
Bachrach, Yoram",
editor = "Liu, Yang and
Solorio, Thamar",
booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = may # "â" # jun,
year = "2015",
address = "Denver, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N15-4005/",
doi = "10.3115/v1/N15-4005",
pages = "9"
}
@inproceedings{16886349,
title = {Mechanical design, instrumentation and measurements from a hemoacoustic cardiac phantom},
author = {{Hani Bakhshaee} and {Guillaume Garreau} and {Gaspar Tognetti} and {K. Shoele} and {R. Carrero} and {T. Kilmar} and {Chiang-Jiang Zhu} and {W. R. Thompson} and {J. Seo} and {R. Mittal} and {A. Andreou}},
year = 2015,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/b8f18feef8ea2225063ec977b1271dcb2736122d},
}
@inproceedings{18072917,
title = {Social Media as a Sensor of Air Quality and Public Response in China},
author = {{Shiliang Wang} and {Michael J. Paul} and {Mark Dredze}},
year = 2015,
month = {3},
booktitle = {Journal of Medical Internet Research},
url = {https://www.semanticscholar.org/paper/cc59eba7e3eab4eb95bb264a0db2496e4d19fcf7},
}
@inproceedings{6681632,
title = {Interactive Knowledge Base Population},
author = {{Travis Wolfe} and {Mark Dredze} and {J. Mayfield} and {Paul McNamee} and {Craig Harman} and {Timothy W. Finin} and {Benjamin Van Durme}},
year = 2015,
month = {5},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/b9d1d9d618c504b669e1d78e9fc6f5efa51e8ca5},
}
@inproceedings{17083060,
title = {Adding Semantics to Data-Driven Paraphrasing : Supplementary Material},
author = {{Ellie Pavlick} and {Charley Beller} and {Benjamin Van Durme} and {Chris Callison-Burch}},
year = 2015,
booktitle = {},
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author = {{Joy L. Lee} and {M. Decamp} and {Mark Dredze} and {M. Chisolm} and {Z. Berger}},
year = 2014,
month = {10},
booktitle = {Journal of Medical Internet Research},
url = {https://www.semanticscholar.org/paper/78de8021f889aa36ad38f128cbb90cc91c84db0c},
}
@inproceedings{federico-etal-2014-matecat,
title = "The {M}ate{C}at Tool",
author = {Federico, Marcello and
Bertoldi, Nicola and
Cettolo, Mauro and
Negri, Matteo and
Turchi, Marco and
Trombetti, Marco and
Cattelan, Alessandro and
Farina, Antonio and
Lupinetti, Domenico and
Martines, Andrea and
Massidda, Alberto and
Schwenk, Holger and
Barrault, Lo\"\i c and
Blain, Frederic and
Koehn, Philipp and
Buck, Christian and
Germann, Ulrich},
editor = "Tounsi, Lamia and
Rak, Rafal",
booktitle = "Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Dublin City University and Association for Computational Linguistics",
url = "https://aclanthology.org/C14-2028/",
pages = "129--132"
}
@inproceedings{durrani-etal-2014-investigating,
title = "Investigating the Usefulness of Generalized Word Representations in {SMT}",
author = "Durrani, Nadir and
Koehn, Philipp and
Schmid, Helmut and
Fraser, Alexander",
editor = "Tsujii, Junichi and
Hajic, Jan",
booktitle = "Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Dublin City University and Association for Computational Linguistics",
url = "https://aclanthology.org/C14-1041/",
pages = "421--432"
}
@inproceedings{freitag-etal-2014-eu,
title = "{EU-BRIDGE} {MT}: Combined Machine Translation",
author = "Freitag, Markus and
Peitz, Stephan and
Wuebker, Joern and
Ney, Hermann and
Huck, Matthias and
Sennrich, Rico and
Durrani, Nadir and
Nadejde, Maria and
Williams, Philip and
Koehn, Philipp and
Herrmann, Teresa and
Cho, Eunah and
Waibel, Alex",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3310/",
doi = "10.3115/v1/W14-3310",
pages = "105--113"
}
@inproceedings{aguilar-etal-2014-comparison,
title = "A Comparison of the Events and Relations Across {ACE}, {ERE}, {TAC}-{KBP}, and {F}rame{N}et Annotation Standards",
author = "Aguilar, Jacqueline and
Beller, Charley and
McNamee, Paul and
Van Durme, Benjamin and
Strassel, Stephanie and
Song, Zhiyi and
Ellis, Joe",
editor = "Mitamura, Teruko and
Hovy, Eduard and
Palmer, Martha",
booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-2907/",
doi = "10.3115/v1/W14-2907",
pages = "45--53"
}
@inproceedings{rudinger-van-durme-2014-stanford,
title = "Is the {S}tanford Dependency Representation Semantic?",
author = "Rudinger, Rachel and
Van Durme, Benjamin",
editor = "Mitamura, Teruko and
Hovy, Eduard and
Palmer, Martha",
booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-2908/",
doi = "10.3115/v1/W14-2908",
pages = "54--58"
}
@inproceedings{bojar-etal-2014-findings,
title = "Findings of the 2014 Workshop on Statistical Machine Translation",
author = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Leveling, Johannes and
Monz, Christof and
Pecina, Pavel and
Post, Matt and
Saint-Amand, Herve and
Soricut, Radu and
Specia, Lucia and
Tamchyna, Ale\v s",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3302/",
doi = "10.3115/v1/W14-3302",
pages = "12--58"
}
@inproceedings{williams-etal-2014-edinburghs,
title = "{E}dinburgh's Syntax-Based Systems at {WMT} 2014",
author = "Williams, Philip and
Sennrich, Rico and
Nadejde, Maria and
Huck, Matthias and
Hasler, Eva and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3324/",
doi = "10.3115/v1/W14-3324",
pages = "207--214"
}
@inproceedings{beller-etal-2014-predicting,
title = "Predicting Fine-grained Social Roles with Selectional Preferences",
author = "Beller, Charley and
Harman, Craig and
Van Durme, Benjamin",
editor = "Danescu-Niculescu-Mizil, Cristian and
Eisenstein, Jacob and
McKeown, Kathleen and
Smith, Noah A.",
booktitle = "Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science",
month = jun,
year = "2014",
address = "Baltimore, MD, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-2515/",
doi = "10.3115/v1/W14-2515",
pages = "50--55"
}
@inproceedings{gormley-etal-2014-low,
title = "Low-Resource Semantic Role Labeling",
author = "Gormley, Matthew R. and
Mitchell, Margaret and
Van Durme, Benjamin and
Dredze, Mark",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1111/",
doi = "10.3115/v1/P14-1111",
pages = "1177--1187"
}
@inproceedings{yu-dredze-2014-improving,
title = "Improving Lexical Embeddings with Semantic Knowledge",
author = "Yu, Mo and
Dredze, Mark",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2089/",
doi = "10.3115/v1/P14-2089",
pages = "545--550"
}
@inproceedings{peng-etal-2014-learning,
title = "Learning Polylingual Topic Models from Code-Switched Social Media Documents",
author = "Peng, Nanyun and
Wang, Yiming and
Dredze, Mark",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2110/",
doi = "10.3115/v1/P14-2110",
pages = "674--679"
}
@inproceedings{huck-etal-2014-augmenting,
title = "Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases",
author = "Huck, Matthias and
Hoang, Hieu and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3362/",
doi = "10.3115/v1/W14-3362",
pages = "486--498"
}
@inproceedings{yao-etal-2014-freebase,
title = "{F}reebase {QA}: Information Extraction or Semantic Parsing?",
author = "Yao, Xuchen and
Berant, Jonathan and
Van Durme, Benjamin",
editor = "Artzi, Yoav and
Kwiatkowski, Tom and
Berant, Jonathan",
booktitle = "Proceedings of the {ACL} 2014 Workshop on Semantic Parsing",
month = jun,
year = "2014",
address = "Baltimore, MD",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-2416/",
doi = "10.3115/v1/W14-2416",
pages = "82--86"
}
@inproceedings{beller-etal-2014-im,
title = "{I}'m a Belieber: Social Roles via Self-identification and Conceptual Attributes",
author = "Beller, Charley and
Knowles, Rebecca and
Harman, Craig and
Bergsma, Shane and
Mitchell, Margaret and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2030/",
doi = "10.3115/v1/P14-2030",
pages = "181--186"
}
@inproceedings{rastogi-van-durme-2014-augmenting,
title = "Augmenting {F}rame{N}et Via {PPDB}",
author = "Rastogi, Pushpendre and
Van Durme, Benjamin",
editor = "Mitamura, Teruko and
Hovy, Eduard and
Palmer, Martha",
booktitle = "Proceedings of the Second Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-2901/",
doi = "10.3115/v1/W14-2901",
pages = "1--5"
}
@inproceedings{coppersmith-kelly-2014-dynamic,
title = "Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis",
author = "Coppersmith, Glen and
Kelly, Erin",
editor = "Chuang, Jason and
Green, Spence and
Hearst, Marti and
Heer, Jeffrey and
Koehn, Philipp",
booktitle = "Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3103/",
doi = "10.3115/v1/W14-3103",
pages = "22--29"
}
@inproceedings{volkova-etal-2014-inferring,
title = "Inferring User Political Preferences from Streaming Communications",
author = "Volkova, Svitlana and
Coppersmith, Glen and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1018/",
doi = "10.3115/v1/P14-1018",
pages = "186--196"
}
@inproceedings{yao-van-durme-2014-information,
title = "Information Extraction over Structured Data: Question Answering with {F}reebase",
author = "Yao, Xuchen and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1090/",
doi = "10.3115/v1/P14-1090",
pages = "956--966"
}
@inproceedings{wintrode-khudanpur-2014-repeat,
title = "Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection",
author = "Wintrode, Jonathan and
Khudanpur, Sanjeev",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1124/",
doi = "10.3115/v1/P14-1124",
pages = "1316--1325"
}
@inproceedings{osborne-etal-2014-exponential,
title = "Exponential Reservoir Sampling for Streaming Language Models",
author = "Osborne, Miles and
Lall, Ashwin and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2112/",
doi = "10.3115/v1/P14-2112",
pages = "687--692"
}
@inproceedings{fine-etal-2014-biases,
title = "Biases in Predicting the Human Language Model",
author = "Fine, Alex B. and
Frank, Austin F. and
Jaeger, T. Florian and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2002/",
doi = "10.3115/v1/P14-2002",
pages = "7--12"
}
@inproceedings{andrews-etal-2014-robust,
title = "Robust Entity Clustering via Phylogenetic Inference",
author = "Andrews, Nicholas and
Eisner, Jason and
Dredze, Mark",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1073/",
doi = "10.3115/v1/P14-1073",
pages = "775--785"
}
@inproceedings{cao-khudanpur-2014-online,
title = "Online Learning in Tensor Space",
author = "Cao, Yuan and
Khudanpur, Sanjeev",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1063/",
doi = "10.3115/v1/P14-1063",
pages = "666--675"
}
@inproceedings{hasler-etal-2014-dynamic-topic,
title = "Dynamic Topic Adaptation for {SMT} using Distributional Profiles",
author = "Hasler, Eva and
Haddow, Barry and
Koehn, Philipp",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3358/",
doi = "10.3115/v1/W14-3358",
pages = "445--456"
}
@inproceedings{may-etal-2014-particle,
title = "Particle Filter Rejuvenation and {L}atent {D}irichlet {A}llocation",
author = "May, Chandler and
Clemmer, Alex and
Van Durme, Benjamin",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2073/",
doi = "10.3115/v1/P14-2073",
pages = "446--451"
}
@inproceedings{sakaguchi-etal-2014-efficient,
title = "Efficient Elicitation of Annotations for Human Evaluation of Machine Translation",
author = "Sakaguchi, Keisuke and
Post, Matt and
Van Durme, Benjamin",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3301/",
doi = "10.3115/v1/W14-3301",
pages = "1--11"
}
@inproceedings{coppersmith-etal-2014-quantifying,
title = "Quantifying Mental Health Signals in {T}witter",
author = "Coppersmith, Glen and
Dredze, Mark and
Harman, Craig",
editor = "Resnik, Philip and
Resnik, Rebecca and
Mitchell, Margaret",
booktitle = "Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3207/",
doi = "10.3115/v1/W14-3207",
pages = "51--60"
}
@inproceedings{durrani-etal-2014-edinburghs,
title = "{E}dinburgh's Phrase-based Machine Translation Systems for {WMT}-14",
author = "Durrani, Nadir and
Haddow, Barry and
Koehn, Philipp and
Heafield, Kenneth",
editor = "Bojar, Ond\v rej and
Buck, Christian and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Specia, Lucia",
booktitle = "Proceedings of the Ninth Workshop on Statistical Machine Translation",
month = jun,
year = "2014",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-3309/",
doi = "10.3115/v1/W14-3309",
pages = "97--104"
}
@inproceedings{koehn-etal-2014-refinements,
title = "Refinements to Interactive Translation Prediction Based on Search Graphs",
author = "Koehn, Philipp and
Tsoukala, Chara and
Saint-Amand, Herve",
editor = "Toutanova, Kristina and
Wu, Hua",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2094/",
doi = "10.3115/v1/P14-2094",
pages = "574--578"
}
@InProceedings{andrews-eisner-dredze-2014,
aclid = "P14-1073",
doi = "10.3115/v1/P14-1073",
author = "Nicholas Andrews and Jason Eisner and Mark Dredze",
title = "Robust Entity Clustering via Phylogenetic Inference",
booktitle = "Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "775--785",
year = "2014",
month = jun,
address = "Baltimore",
URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-dredze-2014",
}
@InProceedings{cotterell-peng-eisner-2014,
aclid = "P14-2102",
doi = "10.3115/v1/P14-2102",
author = "Ryan Cotterell and Nanyun Peng and Jason Eisner",
title = "Stochastic Contextual Edit Distance and Probabilistic
{FST}s",
booktitle = "Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics (Volume 2:
Short Papers)",
pages = "625--630",
year = "2014",
month = jun,
address = "Baltimore",
URL = "http://cs.jhu.edu/~jason/papers/#cotterell-peng-eisner-2014",
}
We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.
@inproceedings{drexler-etal-2014-wikipedia,
title = "A {W}ikipedia-based Corpus for Contextualized Machine Translation",
author = "Drexler, Jennifer and
Rastogi, Pushpendre and
Aguilar, Jacqueline and
Van Durme, Benjamin and
Post, Matt",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L14-1150/",
pages = "3593--3596",
abstract = "We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score."
}
@inproceedings{alabau-etal-2014-casmacat,
title = "{CASMACAT}: A Computer-assisted Translation Workbench",
author = "Alabau, Vicent and
Buck, Christian and
Carl, Michael and
Casacuberta, Francisco and
Garc\'\i a-Mart\'\i nez, Mercedes and
Germann, Ulrich and
Gonz\'alez-Rubio, Jes\'us and
Hill, Robin and
Koehn, Philipp and
Leiva, Luis and
Mesa-Lao, Bartolom\'e and
Ortiz-Mart\'\i nez, Daniel and
Saint-Amand, Herve and
Sanchis Trilles, Germ\'an and
Tsoukala, Chara",
editor = "Wintner, Shuly and
Tadi\'c, Marko and
Babych, Bogdan",
booktitle = "Proceedings of the Demonstrations at the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2014",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E14-2007/",
doi = "10.3115/v1/E14-2007",
pages = "25--28"
}
@inproceedings{williams-koehn-2014-using,
title = "Using Feature Structures to Improve Verb Translation in {E}nglish-to-{G}erman Statistical {MT}",
author = "Williams, Philip and
Koehn, Philipp",
editor = "Banchs, Rafael E. and
Costa-juss\`a, Marta R. and
Rapp, Reinhard and
Lambert, Patrik and
Eberle, Kurt and
Babych, Bogdan",
booktitle = "Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation ({H}y{T}ra)",
month = apr,
year = "2014",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W14-1005/",
doi = "10.3115/v1/W14-1005",
pages = "21--29"
}
@inproceedings{durrani-etal-2014-integrating,
title = "Integrating an Unsupervised Transliteration Model into Statistical Machine Translation",
author = "Durrani, Nadir and
Sajjad, Hassan and
Hoang, Hieu and
Koehn, Philipp",
editor = "Wintner, Shuly and
Riezler, Stefan and
Goldwater, Sharon",
booktitle = "Proceedings of the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics, volume 2: Short Papers",
month = apr,
year = "2014",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E14-4029/",
doi = "10.3115/v1/E14-4029",
pages = "148--153"
}
@inproceedings{hasler-etal-2014-dynamic,
title = "Dynamic Topic Adaptation for Phrase-based {MT}",
author = "Hasler, Eva and
Blunsom, Phil and
Koehn, Philipp and
Haddow, Barry",
editor = "Wintner, Shuly and
Goldwater, Sharon and
Riezler, Stefan",
booktitle = "Proceedings of the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2014",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E14-1035/",
doi = "10.3115/v1/E14-1035",
pages = "328--337"
}
@inproceedings{31566441,
title = {Facebook, Twitter and Google Plus for Breaking News: Is There a Winner?},
author = {{M. Osborne} and {Mark Dredze}},
year = 2014,
month = {5},
booktitle = {International Conference on Web and Social Media},
url = {https://www.semanticscholar.org/paper/6c78a1358f38995462c7358d1679b817edf88b6c},
}
@inproceedings{3217166,
title = {A long, deep and wide artificial neural net for robust speech recognition in unknown noise},
author = {{Feipeng Li} and {P. S. Nidadavolu} and {H. Hermansky}},
year = 2014,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/08d2dfb9b685501d2ecb01f1bffc85207308f723},
}
@inproceedings{44359048,
title = {The Machine Translation Leaderboard},
author = {{Matt Post} and {Adam Lopez}},
year = 2014,
month = {9},
booktitle = {Prague Bulletin of Mathematical Linguistics},
url = {https://www.semanticscholar.org/paper/66fd35e51221e46c2696ccffc06bfcc0e99fee2d},
}
@inproceedings{7103997,
title = {Improving Gender Prediction of Social Media Users via Weighted Annotator Rationales},
author = {{S. Volkova} and {David Yarowsky}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b373fcdda89f71eb100499e4ddbbd18468fb2fac},
}
@inproceedings{16185857,
title = {JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling},
author = {{Yixin Gao} and {S. Vedula} and {C. Reiley} and {N. Ahmidi} and {Balakrishnan Varadarajan} and {Henry C. Lin} and {Lingling Tao} and {L. Zappella} and {B. Béjar} and {D. Yuh} and {C. C. Chen} and {R. Vidal} and {S. Khudanpur} and {Gregory Hager}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/efe03a2940e09547bb15035d35e7e07ed59848bf},
}
@inproceedings{20700802,
title = {Adaptation for SMT using Distributional Profiles},
author = {{E. Hasler} and {B. Haddow} and {Philipp Koehn}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/0ba14263108d0c3e88cb82de211cb4c566167a6d},
}
@inproceedings{126421038,
title = {Demonstration #5: 2 balls standing (8Hz)},
author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/e27ef3733d7881876f91646e14cb4f3d9aadf8fc},
}
@inproceedings{113225783,
title = {Early Fuel Cell Market Deployments: ARRA and Combined (IAA, DLA, ARRA); Quarter 3 2012 Composite Data Products},
author = {{J. Kurtz} and {K. Wipke} and {S. Sprik} and {T. Ramsden} and {C. Ainscough} and {G. Saur} and {Matt Post}},
year = 2014,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/6cb8e38239f7501492dc03a65e56760b9002e390},
}
@inproceedings{18000624,
title = {The 11th Conference of the Association for Machine Translation in the Americas Workshop on Interactive and Adaptive Machine Translation},
author = {{F. Casacuberta} and {Marcello Federico} and {Philipp Koehn}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/5b27c09022333453e0901aae6666d39306250eb7},
}
@inproceedings{165014213,
title = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26-30, 2014, Gothenburg, Sweden},
author = {{E. Hasler} and {Phil Blunsom} and {Philipp Koehn} and {B. Haddow}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c041ae038e4b2dd70af332346bb1b4057c736e9f},
}
@inproceedings{61657545,
title = {HealthTweets.org: A Platform for Public Health Surveillance Using Twitter},
author = {{Mark Dredze} and {Renyuan Cheng} and {Michael J. Paul} and {David A. Broniatowski}},
year = 2014,
month = {6},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/3f83f06576ebfbee3978e6ad4a0a4cdc505f1753},
}
@inproceedings{20264828,
title = {Population health concerns during the United States' Great Recession.},
author = {{B. Althouse} and {Jon-Patrick Allem} and {Matthew A. Childers} and {Mark Dredze} and {J. Ayers}},
year = 2014,
month = {2},
booktitle = {American Journal of Preventive Medicine},
url = {https://www.semanticscholar.org/paper/1c89fa07ada14df5d5388642d173c8e805f7388f},
}
@inproceedings{205055790,
title = {Could behavioral medicine lead the web data revolution?},
author = {{J. Ayers} and {B. Althouse} and {Mark Dredze}},
year = 2014,
month = {4},
booktitle = {Journal of the American Medical Association (JAMA)},
url = {https://www.semanticscholar.org/paper/1f54cf05afa62ce9f959746b86a1dfffa45cf32b},
}
@inproceedings{196099345,
title = {Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)},
author = {{Philip Williams} and {Philipp Koehn}},
year = 2014,
month = {4},
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/d41134d22705effdfc8b98ca0efaacaefec7b852},
}
@inproceedings{195952133,
title = {Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation},
author = {{Philipp Koehn} and {Ulrich Germann}},
year = 2014,
month = {4},
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/bec1c30940d59fe21f3a69082e12af95ed9b4796},
}
@inproceedings{61355288,
title = {Using Word Repetition to Improve Spoken Term Detection},
author = {{Jonathan Wintrode} and {S. Khudanpur}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/ee1adde1aba63ee6e7180580858357e55056a0d2},
}
Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.
@inproceedings{hasler-etal-2014-combining,
title = "Combining domain and topic adaptation for {SMT}",
author = "Hasler, Eva and
Haddow, Barry and
Koehn, Philipp",
editor = "Al-Onaizan, Yaser and
Simard, Michel",
booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
month = oct # " 22-26",
year = "2014",
address = "Vancouver, Canada",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2014.amta-researchers.11/",
pages = "139--151",
abstract = "Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions."
}
@inproceedings{10121150,
title = {Evaluating speech features with the minimal-pair ABX task (II): resistance to noise},
author = {{Thomas Schatz} and {Vijayaditya Peddinti} and {Xuan-Nga Cao} and {F. Bach} and {H. Hermansky} and {Emmanuel Dupoux}},
year = 2014,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/697ce1db9496067a4e7253c058bd83393d366949},
}
@inproceedings{65075068,
title = {Proceedings of the 17th Annual conference of the European Association for Machine Translation, EAMT 2014, Dubrovnik, Croatia, June 16-18, 2014},
author = {{Marko Tadić} and {Philipp Koehn} and {Johann Roturier} and {Andy Way}},
year = 2014,
booktitle = {European Association for Machine Translation Conferences/Workshops},
url = {https://www.semanticscholar.org/paper/92e40989f356f26aea9a4c2c7a86be113493080c},
}
@inproceedings{16472807,
title = {Robust Feature Extraction Using Modulation Filtering of Autoregressive Models},
author = {{Sriram Ganapathy} and {Sri Harish Reddy Mallidi} and {H. Hermansky}},
year = 2014,
month = {8},
booktitle = {IEEE/ACM Transactions on Audio Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/852f0eff84ea211a40522f6dd3bf8ec73bc3f0d8},
}
@inproceedings{4805499,
title = {Interactive Translation Prediction vs. Conventional Post-editing in Practice: A Study with the CasMaCat Workbench},
author = {{Germán Sanchis-Trilles} and {Vicente Alabau} and {C. Buck} and {M. Carl} and {F. Casacuberta} and {Mercedes García-Martínez} and {Ulrich Germann} and {J. González-Rubio} and {Robin L. Hill} and {Philipp Koehn} and {Luis A. Leiva} and {B. Mesa-Lao} and {Daniel Ortiz-Martínez} and {Herve Saint-Amand} and {Chara Tsoukala} and {E. Vidal}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c00aed9fc95b763665c40e92e2c0d12bfcdaf553},
}
@inproceedings{61805046,
title = {Vertex nomination},
author = {{Glen A. Coppersmith}},
year = 2014,
month = {3},
booktitle = {Wiley Interdisciplinary Reviews: Computational Statistics},
url = {https://www.semanticscholar.org/paper/b56d8113b1f5c8a248aac2fc47ef966a9c1bf582},
}
Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations.
@inproceedings{kumar-etal-2014-translations,
title = "Translations of the Callhome {E}gyptian {A}rabic corpus for conversational speech translation",
author = "Kumar, Gaurav and
Cao, Yuan and
Cotterell, Ryan and
Callison-Burch, Chris and
Povey, Daniel and
Khudanpur, Sanjeev",
editor = {Federico, Marcello and
St\"uker, Sebastian and
Yvon, Fran\c cois},
booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Papers",
month = dec # " 4-5",
year = "2014",
address = "Lake Tahoe, California",
url = "https://aclanthology.org/2014.iwslt-papers.13/",
pages = "244--248",
abstract = "Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations."
}
@inproceedings{1129207,
title = {Improving deep neural network acoustic models using generalized maxout networks},
author = {{Xiaohui Zhang} and {J. Trmal} and {Daniel Povey} and {S. Khudanpur}},
year = 2014,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/6182e4b5151aa27ceb75c94543e3f584c991e00f},
}
@inproceedings{hoang-etal-2014-statistical,
title = "Statistical machine translation with the {M}oses toolkit",
author = "Hoang, Hieu and
Huck, Matthias and
Koehn, Philipp",
booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials",
month = oct # " 22-26",
year = "2014",
address = "Vancouver, Canada",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2014.amta-tutorials.5/"
}
@inproceedings{3159287,
title = {Some insights from translating conversational telephone speech},
author = {{Manish Kumar} and {Matt Post} and {Daniel Povey} and {S. Khudanpur}},
year = 2014,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/526992a93d76af65d4208bc91f67deed40ccdff2},
}
@inproceedings{125906262,
title = {Demonstration #6: 2 balls moving (2.67Hz)},
author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/55cd72ac42a28fea8f5e58ce66dc72439d0d88a4},
}
@inproceedings{11688792,
title = {A pitch extraction algorithm tuned for automatic speech recognition},
author = {{Pegah Ghahremani} and {B. BabaAli} and {Daniel Povey} and {K. Riedhammer} and {J. Trmal} and {S. Khudanpur}},
year = 2014,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/080644c599b813b920053a760d744d8abd615250},
}
@inproceedings{27461148,
title = {Statistical Techniques for Translating to Morphologically Rich Languages (Dagstuhl Seminar 14061)},
author = {{Alexander M. Fraser} and {Kevin Knight} and {Philipp Koehn} and {Helmut Schmid} and {H. Uszkoreit}},
year = 2014,
booktitle = {Dagstuhl Reports},
url = {https://www.semanticscholar.org/paper/d3ddd5ece028fb83745773de871900efcffe833c},
}
@inproceedings{640769,
title = {Do audio-visual motion cues promote segregation of auditory streams?},
author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {J. Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
year = 2014,
month = {4},
booktitle = {Frontiers in Neuroscience},
url = {https://www.semanticscholar.org/paper/378b134a47091a44dccb8e886b63be78b33f644e},
}
@inproceedings{16958018,
title = {Low-resource open vocabulary keyword search using point process models},
author = {{Chunxi Liu} and {A. Jansen} and {Guoguo Chen} and {Keith Kintzley} and {J. Trmal} and {S. Khudanpur}},
year = 2014,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/11796a16f69e15c277dd30f5324117c16053c045},
}
@inproceedings{67803383,
title = {Explorer Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation},
author = {{Philipp Koehn}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/21922bfe55ea4bd5abcc4e274f8dcdeed5b4694b},
}
@inproceedings{146311108,
title = {Stimulus: Incongruent/Moving/Joint},
author = {{L. Shestopalova} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {S. Denham} and {I. Winkler}},
year = 2014,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/83cf2445bf234b520b03dd7e47ea9282bc6512aa},
}
@inproceedings{45778614,
title = {Twitter: big data opportunities.},
author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
year = 2014,
month = {7},
booktitle = {Science},
url = {https://www.semanticscholar.org/paper/484589552d3941f25f9e722c4268784aa1b5d465},
}
@inproceedings{188651514,
title = {Demonstration #1: 1 ball standing (1Hz)},
author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/95ee2c21760910387899f99c8db5bc64439685b7},
}
@inproceedings{22399217,
title = {What's the healthiest day?: Circaseptan (weekly) rhythms in healthy considerations.},
author = {{J. Ayers} and {B. Althouse} and {Morgan Johnson} and {Mark Dredze} and {Joanna E. Cohen}},
year = 2014,
month = {7},
booktitle = {American Journal of Preventive Medicine},
url = {https://www.semanticscholar.org/paper/13ae9734f3924e419832b6e474001e62a1efbcd2},
}
@inproceedings{164269994,
title = {COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin, Ireland},
author = {{Nadir Durrani} and {Philipp Koehn} and {Helmut Schmid} and {Alexander M. Fraser}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/4f5209941e62dac3f6107621dc17edce1be9a2bf},
}
@inproceedings{7943211,
title = {Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children},
author = {{L. E. Ellington} and {Dimitra Emmanouilidou} and {Mounya Elhilali} and {R. Gilman} and {J. Tielsch} and {M. A. Chavez} and {J. Marin-Concha} and {D. Figueroa} and {James E. West} and {W. Checkley}},
year = 2014,
month = {6},
booktitle = {Lung},
url = {https://www.semanticscholar.org/paper/dcd5e0c02149105bced3c1e44962efcc23d98e6d},
}
@inproceedings{5868280,
title = {Discovering Health Topics in Social Media Using Topic Models},
author = {{Michael J. Paul} and {Mark Dredze}},
year = 2014,
month = {8},
booktitle = {PLoS ONE},
url = {https://www.semanticscholar.org/paper/07f07fb7c5993029222ffa21619f226a4feb2e76},
}
@inproceedings{2765949,
title = {Task-dependent neural representations of salient events in dynamic auditory scenes},
author = {{L. Shuai} and {Mounya Elhilali}},
year = 2014,
month = {7},
booktitle = {Frontiers in Neuroscience},
url = {https://www.semanticscholar.org/paper/8439c42440eebdea24eddf04b1e6b7abcae7d5bc},
}
@inproceedings{6016891,
title = {What is a Better Translation? Reflections on Six Years of Running Evaluation Campaigns.},
author = {{Philipp Koehn}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/444f184a18a69479bcd22814795a34e1536de638},
}
@inproceedings{27671912,
title = {Visualizing and quantifying charge distributions correlated to threshold voltage shifts in lateral organic transistors.},
author = {{Thomas J. Dawidczyk} and {Josué F Martínez Hardigree} and {G. Johns} and {R. Ozgun} and {Olivia Alley} and {A. Andreou} and {N. Marković} and {H. Katz}},
year = 2014,
month = {2},
booktitle = {ACS Nano},
url = {https://www.semanticscholar.org/paper/38222721fd352cfa963ad362ab9cf8b0b2af03a6},
}
@inproceedings{8322208,
title = {Investigating bottom-up auditory attention},
author = {{Emine Merve Kaya} and {Mounya Elhilali}},
year = 2014,
month = {5},
booktitle = {Frontiers in Human Neuroscience},
url = {https://www.semanticscholar.org/paper/f18f40d243a1570971dd14cdb8b62ecc343e4ffd},
}
@inproceedings{206468861,
title = {Social Media Analytics for Smart Health},
author = {{A. Abbasi} and {D. Adjeroh} and {Mark Dredze} and {Michael J. Paul} and {F. Zahedi} and {Huimin Zhao} and {N. Walia} and {H. Jain} and {Patrick Sanvanson} and {R. Shaker} and {Marco D. Huesch} and {Richard Beal} and {W. Zheng} and {M. Abate} and {Arun Ross}},
year = 2014,
month = {3},
booktitle = {IEEE Intelligent Systems},
url = {https://www.semanticscholar.org/paper/49caf06456ca595330040bfba38c22421c50c0d5},
}
@inproceedings{19894829,
title = {Principal components of auditory spectro-temporal receptive fields},
author = {{Nagaraj R. Mahajan} and {N. Mesgarani} and {H. Hermansky}},
year = 2014,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/022cbdf787bfbc0b59c03912833dc5b5be9eeb02},
}
@inproceedings{18170070,
title = {Concretely Annotated Corpora},
author = {{Francis Ferraro} and {Max Thomas} and {Matthew R. Gormley} and {Travis Wolfe} and {Craig Harman} and {Benjamin Van Durme}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/928ad9d973a85dfe39bb6c5ecd6633dbf6e2617f},
}
@inproceedings{186737415,
title = {Demonstration #9: Synchrony test (out of phase)},
author = {{Denham} and {L. I. Winkler} and {Tamás Bohm} and {A. Bendixen} and {A. Andreou} and {Julio Georgiou} and {Guillaume Garreau} and {Botond Hajdu} and {L. Susan}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3901b8342371370b47ae994c9729ab3ae104a784},
}
@inproceedings{17398392,
title = {Natural Language Processing for Health and Social Media},
author = {{Mark Dredze} and {Michael J. Paul}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a6dd47b4c1945b8e10c4d1e72f1dbb29ed35ab03},
}
EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German$\rightarrow$English spoken language translation (SLT) track as well as to the German$\rightarrow$English, English$\rightarrow$German and English$\rightarrow$French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.
@inproceedings{freitag-etal-2014-combined,
title = "Combined spoken language translation",
author = "Freitag, Markus and
Wuebker, Joern and
Peitz, Stephan and
Ney, Hermann and
Huck, Matthias and
Birch, Alexandra and
Durrani, Nadir and
Koehn, Philipp and
Mediani, Mohammed and
Slawik, Isabel and
Niehues, Jan and
Cho, Eunach and
Waibel, Alex and
Bertoldi, Nicola and
Cettolo, Mauro and
Federico, Marcello",
editor = {Federico, Marcello and
St\"uker, Sebastian and
Yvon, Fran\c cois},
booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign",
month = dec # " 4-5",
year = "2014",
address = "Lake Tahoe, California",
url = "https://aclanthology.org/2014.iwslt-evaluation.7/",
pages = "57--64",
abstract = "EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German$\rightarrow$English spoken language translation (SLT) track as well as to the German$\rightarrow$English, English$\rightarrow$German and English$\rightarrow$French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system."
}
@inproceedings{2361768,
title = {Featherweight phonetic keyword search for conversational speech},
author = {{Keith Kintzley} and {A. Jansen} and {H. Hermansky}},
year = 2014,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/3b50c10c61614ae378703d17dbd9cb283fa38f64},
}
@inproceedings{15885284,
title = {Faster ( and Better ) Entity Linking with Cascades},
author = {{Adrian Benton} and {Jay DeYoung} and {Adam R. Teichert} and {Stephen Mayhew} and {Mark Dredze} and {Benjamin Van Durme} and {Max Thomas}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/04cc3e2947f6183d4ae6959be13544ebd799a8f0},
}
@inproceedings{14612598,
title = {Measuring Post Traumatic Stress Disorder in Twitter},
author = {{Glen A. Coppersmith} and {Craig Harman} and {Mark Dredze}},
year = 2014,
month = {5},
booktitle = {International Conference on Web and Social Media},
url = {https://www.semanticscholar.org/paper/ea24d85e059d7d1dc201bd0380c76caf1f78f1e4},
}
@inproceedings{186004143,
title = {Proceedings of AMTA 2014},
author = {{E. Hasler} and {B. Haddow} and {Philipp Koehn}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3505ea44ff35f2933ede991edb7d05da4b27237f},
}
@inproceedings{6872247,
title = {Limited resource term detection for effective topic identification of speech},
author = {{Jonathan Wintrode} and {S. Khudanpur}},
year = 2014,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d39a86f6a64a143735e6f6e9a6f618cfe6fb06f2},
}
@inproceedings{39497762,
title = {Proceedings of the Ninth Workshop on Statistical Machine Translation, WMT@ACL 2014, June 26-27, 2014, Baltimore, Maryland, USA},
author = {{P. Williams} and {Rico Sennrich} and {Maria Nadejde} and {Matthias Huck} and {E. Hasler} and {Philipp Koehn}},
year = 2014,
month = {6},
booktitle = {WMT@ACL},
url = {https://www.semanticscholar.org/paper/2773f8d158d66d598cfaebe742d322bdc810998e},
}
@inproceedings{8870697,
title = {Exploring Health Topics in Chinese Social Media: An Analysis of Sina Weibo},
author = {{Shiliang Wang} and {Michael J. Paul} and {Mark Dredze}},
year = 2014,
month = {6},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/46a0f4c55951a2be5a8f220414ba660e6aba49a3},
}
@inproceedings{131772405,
title = {BEFORE THE NATIONAL GREEN TRIBUNAL SOUTHERN ZONE, CHENNAI Appeal No.66 of 2014 (SZ)},
author = {{Matt Post} and {S. Ravi} and {Anand Padmanabhan}},
year = 2014,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a7baa753d96f7d9b15f236fc53a3ed50422bc87e},
}
@inproceedings{17237888,
title = {Using proxies for OOV keywords in the keyword search task},
author = {{Guoguo Chen} and {Oguz Yilmaz} and {J. Trmal} and {Daniel Povey} and {S. Khudanpur}},
year = 2013,
month = {12},
booktitle = {2013 IEEE Workshop on Automatic Speech Recognition and Understanding},
url = {https://www.semanticscholar.org/paper/69952a2b100af2917abd64197d6d0a3d4b6d9c95},
}
@inproceedings{2346247,
title = {National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic},
author = {{David A. Broniatowski} and {Michael J. Paul} and {Mark Dredze}},
year = 2013,
month = {12},
booktitle = {PLoS ONE},
url = {https://www.semanticscholar.org/paper/687a6a77fcfe143198c311f734a0d68e00943ceb},
}
@inproceedings{yao-etal-2013-semi,
title = "Semi-{M}arkov Phrase-Based Monolingual Alignment",
author = "Yao, Xuchen and
Van Durme, Benjamin and
Callison-Burch, Chris and
Clark, Peter",
editor = "Yarowsky, David and
Baldwin, Timothy and
Korhonen, Anna and
Livescu, Karen and
Bethard, Steven",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D13-1056/",
pages = "590--600"
}
@inproceedings{mitchell-etal-2013-open,
title = "Open Domain Targeted Sentiment",
author = "Mitchell, Margaret and
Aguilar, Jacqui and
Wilson, Theresa and
Van Durme, Benjamin",
editor = "Yarowsky, David and
Baldwin, Timothy and
Korhonen, Anna and
Livescu, Karen and
Bethard, Steven",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D13-1171/",
pages = "1643--1654"
}
@inproceedings{52867967,
title = {Correction: Music in Our Ears: The Biological Bases of Musical Timbre Perception},
author = {{Kailash Patil} and {D. Pressnitzer} and {S. Shamma} and {Mounya Elhilali}},
year = 2013,
month = {10},
booktitle = {PLoS Computational Biology},
url = {https://www.semanticscholar.org/paper/5c4cc9ab4262a01bc892cbe359da7ab8d3915470},
}
@inproceedings{16567195,
title = {Reporting bias and knowledge acquisition},
author = {{Jonathan Gordon} and {Benjamin Van Durme}},
year = 2013,
month = {10},
booktitle = {Conference on Automated Knowledge Base Construction},
url = {https://www.semanticscholar.org/paper/cceb698cbbb828537f2f195fb70b6fdc586d3327},
}
@inproceedings{volkova-etal-2013-exploring,
title = "Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media",
author = "Volkova, Svitlana and
Wilson, Theresa and
Yarowsky, David",
editor = "Yarowsky, David and
Baldwin, Timothy and
Korhonen, Anna and
Livescu, Karen and
Bethard, Steven",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D13-1187/",
pages = "1815--1827"
}
@inproceedings{lopez-post-2013-beyond,
title = "Beyond Bitext: Five Open Problems in Machine Translation",
author = "Lopez, Adam and
Post, Matt",
editor = "Dyer, Chris and
Smith, Noah A. and
Blunsom, Phil",
booktitle = "Proceedings of the Workshop on Twenty Years of Bitext",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2013.bitext-1.1/"
}
@InProceedings{he-daume-eisner-2013,
aclid = "D13-1152",
author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
title = "Dynamic Feature Selection for Dependency Parsing",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "1455--1464",
year = "2013",
month = oct,
address = "Seattle",
URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2013",
}
@inproceedings{smith-etal-2013-dirt,
title = "Dirt Cheap Web-Scale Parallel Text from the {C}ommon {C}rawl",
author = "Smith, Jason R. and
Saint-Amand, Herve and
Plamada, Magdalena and
Koehn, Philipp and
Callison-Burch, Chris and
Lopez, Adam",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-1135/",
pages = "1374--1383"
}
@inproceedings{bergsma-van-durme-2013-using,
title = "Using Conceptual Class Attributes to Characterize Social Media Users",
author = "Bergsma, Shane and
Van Durme, Benjamin",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-1070/",
pages = "710--720"
}
@inproceedings{yao-etal-2013-lightweight,
title = "A Lightweight and High Performance Monolingual Word Aligner",
author = "Yao, Xuchen and
Van Durme, Benjamin and
Callison-Burch, Chris and
Clark, Peter",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2123/",
pages = "702--707"
}
@inproceedings{bojar-etal-2013-findings,
title = "Findings of the 2013 {W}orkshop on {S}tatistical {M}achine {T}ranslation",
author = "Bojar, Ond\v rej and
Buck, Christian and
Callison-Burch, Chris and
Federmann, Christian and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
editor = "Bojar, Ondrej and
Buck, Christian and
Callison-Burch, Chris and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Saint-Amand, Herve and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2201/",
pages = "1--44"
}
@inproceedings{irvine-callison-burch-2013-combining,
title = "Combining Bilingual and Comparable Corpora for Low Resource Machine Translation",
author = "Irvine, Ann and
Callison-Burch, Chris",
editor = "Bojar, Ondrej and
Buck, Christian and
Callison-Burch, Chris and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Saint-Amand, Herve and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2233/",
pages = "262--270"
}
@inproceedings{yao-etal-2013-automatic,
title = "Automatic Coupling of Answer Extraction and Information Retrieval",
author = "Yao, Xuchen and
Van Durme, Benjamin and
Clark, Peter",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2029/",
pages = "159--165"
}
@inproceedings{post-etal-2013-joshua,
title = "{J}oshua 5.0: Sparser, Better, Faster, Server",
author = "Post, Matt and
Ganitkevitch, Juri and
Orland, Luke and
Weese, Jonathan and
Cao, Yuan and
Callison-Burch, Chris",
editor = "Bojar, Ondrej and
Buck, Christian and
Callison-Burch, Chris and
Haddow, Barry and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Saint-Amand, Herve and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Eighth Workshop on Statistical Machine Translation",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2226/",
pages = "206--212"
}
@inproceedings{post-bergsma-2013-explicit,
title = "Explicit and Implicit Syntactic Features for Text Classification",
author = "Post, Matt and
Bergsma, Shane",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2150/",
pages = "866--872"
}
@inproceedings{wolfe-etal-2013-parma,
title = "{PARMA}: A Predicate Argument Aligner",
author = "Wolfe, Travis and
Van Durme, Benjamin and
Dredze, Mark and
Andrews, Nicholas and
Beller, Charley and
Callison-Burch, Chris and
DeYoung, Jay and
Snyder, Justin and
Weese, Jonathan and
Xu, Tan and
Yao, Xuchen",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2012/",
pages = "63--68"
}
@inproceedings{volkova-etal-2013-exploring-sentiment,
title = "Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual {T}witter Streams",
author = "Volkova, Svitlana and
Wilson, Theresa and
Yarowsky, David",
editor = "Schuetze, Hinrich and
Fung, Pascale and
Poesio, Massimo",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2090/",
pages = "505--510"
}
@InProceedings{ferraro-eisner-2013,
aclid = "W13-3411",
author = "Francis Ferraro and Jason Eisner",
title = "A Virtual Manipulative for Learning Log-Linear
Models",
booktitle = "Proceedings of the Fourth Workshop on Teaching NLP and
CL",
pages = "66--76",
year = "2013",
month = aug,
address = "Sofia, Bulgaria",
URL = "http://cs.jhu.edu/~jason/papers/#ferraro-eisner-2013",
}
@InProceedings{littell-et-al-2013,
aclid = "W13-3403",
author = "Patrick Littell and Lori Levin and Jason Eisner and
Dragomir Radev",
title = "Introducing Computational Concepts in a Linguistics
Olympiad",
booktitle = "Proceedings of the Fourth Workshop on Teaching NLP and
CL",
pages = "18--26",
year = "2013",
month = aug,
address = "Sofia, Bulgaria",
URL = "http://cs.jhu.edu/~jason/papers/#littell-et-al-2013",
}
@InProceedings{gormley-eisner-2013,
aclid = "P13-1044",
author = "Matthew Gormley and Jason Eisner",
title = "Nonconvex Global Optimization for Latent-Variable
Models",
booktitle = "Proceedings of the 51st Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "444--454",
year = "2013",
month = aug,
address = "Sofia, Bulgaria",
URL = "http://cs.jhu.edu/~jason/papers/#gormley-eisner-2013",
}
@inproceedings{ganitkevitch-etal-2013-ppdb,
title = "{PPDB}: The Paraphrase Database",
author = "Ganitkevitch, Juri and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1092/",
pages = "758--764"
}
@inproceedings{bergsma-etal-2013-broadly,
title = "Broadly Improving User Classification via Communication-Based Name and Location Clustering on {T}witter",
author = "Bergsma, Shane and
Dredze, Mark and
Van Durme, Benjamin and
Wilson, Theresa and
Yarowsky, David",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1121/",
pages = "1010--1019"
}
@inproceedings{lamb-etal-2013-separating,
title = "Separating Fact from Fear: Tracking Flu Infections on {T}witter",
author = "Lamb, Alex and
Paul, Michael J. and
Dredze, Mark",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1097/",
pages = "789--795"
}
@inproceedings{yao-etal-2013-answer,
title = "Answer Extraction as Sequence Tagging with Tree Edit Distance",
author = "Yao, Xuchen and
Van Durme, Benjamin and
Callison-Burch, Chris and
Clark, Peter",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1106/",
pages = "858--867"
}
@inproceedings{joshi-etal-2013-whats,
title = "What's in a Domain? Multi-Domain Learning for Multi-Attribute Data",
author = "Joshi, Mahesh and
Dredze, Mark and
Cohen, William W. and
Ros\'e, Carolyn P.",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1080/",
pages = "685--690"
}
@inproceedings{paul-dredze-2013-drug,
title = "Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models",
author = "Paul, Michael J. and
Dredze, Mark",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1017/",
pages = "168--178"
}
@inproceedings{snyder-etal-2013-topic,
title = "Topic Models and Metadata for Visualizing Text Corpora",
author = "Snyder, Justin and
Knowles, Rebecca and
Dredze, Mark and
Gormley, Matthew and
Wolfe, Travis",
editor = "Dyer, Chris and
Higgins, Derrick",
booktitle = "Proceedings of the 2013 {NAACL} {HLT} Demonstration Session",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-3002/",
pages = "5--9"
}
@inproceedings{irvine-callison-burch-2013-supervised,
title = "Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals",
author = "Irvine, Ann and
Callison-Burch, Chris",
editor = "Vanderwende, Lucy and
Daum\'e III, Hal and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2013 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-1056/",
pages = "518--523"
}
@inproceedings{palmer-etal-2013-semantic,
title = "Semantic Role Labeling",
author = "Palmer, Martha and
Titov, Ivan and
Wu, Shumin",
editor = "Lin, Jimmy and
Erk, Katrin",
booktitle = "NAACL HLT 2013 Tutorial Abstracts",
month = jun,
year = "2013",
address = "Atlanta, Georgia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N13-4004/",
pages = "10--12"
}
@InProceedings{jiang-et-al-2013,
author = "Jiarong Jiang and Taesun Moon and Hal {Daum\'{e} III}
and Jason Eisner",
title = "Prioritized Asynchronous Belief Propagation",
booktitle = "ICML Workshop on Inferning: Interactions between
Inference and Learning",
note = "5 pages",
year = "2013",
month = jun,
address = "Atlanta",
URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2013",
}
@inproceedings{54210602,
title = {The role of perceived source location in auditory stream segregation: Separation affects sound organization, common fate does not},
author = {{Tamás Bohm} and {L. Shestopalova} and {A. Bendixen} and {A. Andreou} and {J. Georgiou} and {Guillaume Garreau} and {P. Pouliquen} and {A. Cassidy} and {S. Denham} and {I. Winkler}},
year = 2013,
month = {6},
booktitle = {Learning & Perception},
url = {https://www.semanticscholar.org/paper/d085354dfe17f80bdd52435896c63faf019641ff},
}
@inproceedings{15860900,
title = {Discriminative feature extraction for language identification},
author = {{Shuai Huang} and {Glen A. Coppersmith}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/fdbaa0d7a4a20fa54591d271cf85e645c9be3dde},
}
@inproceedings{1222906,
title = {A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition},
author = {{A. Jansen} and {Emmanuel Dupoux} and {S. Goldwater} and {Mark Johnson} and {S. Khudanpur} and {Kenneth Ward Church} and {Naomi H Feldman} and {H. Hermansky} and {Florian Metze} and {R. Rose} and {M. Seltzer} and {P. Clark} and {Ian McGraw} and {Balakrishnan Varadarajan} and {Erin D. Bennett} and {Benjamin Börschinger} and {J. Chiu} and {Ewan Dunbar} and {Abdellah Fourtassi} and {David Harwath} and {Chia-ying Lee} and {Keith D. Levin} and {A. Norouzian} and {Vijayaditya Peddinti} and {Rachael Richardson} and {Thomas Schatz} and {Samuel Thomas}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/a2a0f0adb2b61ba21c8146b554b4416fb96d7aae},
}
@inproceedings{7739650,
title = {A model of auditory deviance detection},
author = {{Emine Merve Kaya} and {Mounya Elhilali}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/7da35847719e70b946079bbd761a734fc444a2e5},
}
@inproceedings{7701315,
title = {Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization},
author = {{A. Cassidy} and {J. Georgiou} and {A. Andreou}},
year = 2013,
month = {9},
booktitle = {Neural Networks},
url = {https://www.semanticscholar.org/paper/4ad2573355f5f957faaf792153aa782254cbb31d},
}
@inproceedings{16301165,
title = {Predictive analysis of two tone stream segregation via extended Kalman filter},
author = {{D. Chakrabarty} and {Mounya Elhilali}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/dd77dc5f35168fd91dd3fa0ccf0a46333d7cf291},
}
@inproceedings{512926,
title = {A Multistream Feature Framework Based on Bandpass Modulation Filtering for Robust Speech Recognition},
author = {{Sridhar Krishna Nemala} and {Kailash Patil} and {Mounya Elhilali}},
year = 2013,
month = {2},
booktitle = {IEEE Transactions on Audio, Speech, and Language Processing},
url = {https://www.semanticscholar.org/paper/99858bad1dcdadcb75c3a6a3f34bc4cc178bfccf},
}
@inproceedings{91844,
title = {Multimodal Integration of Micro-Doppler Sonar and auditory signals for Behavior Classification with convolutional Networks},
author = {{S. Dura-Bernal} and {Guillaume Garreau} and {J. Georgiou} and {A. Andreou} and {S. Denham} and {T. Wennekers}},
year = 2013,
month = {8},
booktitle = {International Journal of Neural Systems},
url = {https://www.semanticscholar.org/paper/98259ddcfb6e054d516de404f8b0de5d442f1420},
}
@inproceedings{824237,
title = {Weak top-down constraints for unsupervised acoustic model training},
author = {{A. Jansen} and {Samuel Thomas} and {H. Hermansky}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/2888b31b5b79a39c8b224d5eb577dc21af6d7443},
}
Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.
@inproceedings{post-etal-2013-improved,
title = "Improved speech-to-text translation with the Fisher and Callhome {S}panish-{E}nglish speech translation corpus",
author = "Post, Matt and
Kumar, Gaurav and
Lopez, Adam and
Karakos, Damianos and
Callison-Burch, Chris and
Khudanpur, Sanjeev",
editor = "Zhang, Joy Ying",
booktitle = "Proceedings of the 10th International Workshop on Spoken Language Translation: Papers",
month = dec # " 5-6",
year = "2013",
address = "Heidelberg, Germany",
url = "https://aclanthology.org/2013.iwslt-papers.14/",
abstract = "Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon's Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score."
}
@inproceedings{9011125,
title = {Sub-Lexical and Contextual Modeling of Out-of-Vocabulary Words in Speech Recognition},
author = {{Carolina Parada} and {Mark Dredze} and {A. Sethy} and {A. Rastrow}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/06988c9227cd8328ce54fc243c77797d40976020},
}
@inproceedings{14694094,
title = {All digital programmable Gaussian pulse generator for ultra-wideband transmitter},
author = {{Joseph H. Lin} and {P. Pouliquen} and {A. Andreou}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/afd123b749f1a22016501bb9efbd7b8f86316be7},
}
@inproceedings{5541073,
title = {Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds},
author = {{Michael A. Carlin} and {Mounya Elhilali}},
year = 2013,
month = {3},
booktitle = {PLoS Comput. Biol.},
url = {https://www.semanticscholar.org/paper/8f76f5349b56e978757844d2a012cd263bc85699},
}
@inproceedings{443817,
title = {Multistream Recognition of Speech: Dealing With Unknown Unknowns},
author = {{H. Hermansky}},
year = 2013,
month = {2},
booktitle = {Proceedings of the IEEE},
url = {https://www.semanticscholar.org/paper/3771538560a23d6233ce17015ccd97fa3d88ba2f},
}
@inproceedings{17988849,
title = {Carmen: A Twitter Geolocation System with Applications to Public Health},
author = {{Mark Dredze} and {Michael J. Paul} and {S. Bergsma} and {Hieu V. Tran}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9bc46fb12f2c7fae0e9e56e734e6efb9ca07fd98},
}
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title = {Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems},
author = {{F. Morabito} and {A. Andreou} and {E. Chicca}},
year = 2013,
month = {9},
booktitle = {Neural Networks},
url = {https://www.semanticscholar.org/paper/3264cbe12b2305543a7c1c14c61d509d3c945fa6},
}
@inproceedings{64853495,
title = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
author = {{David Yarowsky} and {Timothy Baldwin} and {A. Korhonen} and {Karen Livescu} and {Steven Bethard}},
year = 2013,
booktitle = {Conference on Empirical Methods in Natural Language Processing},
url = {https://www.semanticscholar.org/paper/edefb0d659077de8254c713cc201fe3cf5e78568},
}
@inproceedings{14800378,
title = {Mean temporal distance: Predicting ASR error from temporal properties of speech signal},
author = {{H. Hermansky} and {Ehsan Variani} and {Vijayaditya Peddinti}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/cd6486d6a52ee1225ddc49676c71875f65289397},
}
@inproceedings{16372867,
title = {Signal to symbol converters: Overview, opportunities and challenges},
author = {{A. Andreou} and {Thomas S. Murray} and {P. Pouliquen}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/7a142dddd7c15a3915f8079822c11cdabb3c1dcd},
}
@inproceedings{64631563,
title = {International Workshop on Spoken Language Translation (IWSLT 2013)},
author = {{Matt Post} and {G. Kumar} and {Adam Lopez} and {Damianos G. Karakos} and {Chris Callison-Burch} and {S. Khudanpur}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/05e928a860baa780df70e4fbd75c0dc460ebd11d},
}
@inproceedings{34957630,
title = {Long, Deep and Wide Artificial Neural Nets for Dealing with Unexpected Noise in Machine Recognition of Speech},
author = {{H. Hermansky}},
year = 2013,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/dfeb0fdb6735d3b23a04d5c315c2f92b4004f7f2},
}
@inproceedings{9360664,
title = {Characterization of noise contaminations in lung sound recordings},
author = {{Dimitra Emmanouilidou} and {Mounya Elhilali}},
year = 2013,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/3c937c10101d1cd2dcc13c28057a6e95658433e3},
}
@inproceedings{56578217,
title = {8-channel 20 kHz to 200 MHz Nyquist and compressive sampler in 0.5 μm CMOS},
author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
year = 2013,
month = {1},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/261ac06da691014ee1c0206956fda2a749f2248f},
}
@inproceedings{16548630,
title = {Deep neural network features and semi-supervised training for low resource speech recognition},
author = {{Samuel Thomas} and {M. Seltzer} and {Kenneth Ward Church} and {H. Hermansky}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/23bd8ab0ec8c6a79d3c501daa7bb1284cd2c7849},
}
@inproceedings{12931494,
title = {Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and},
author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
year = 2013,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/03c0a941a1364d59e94c550375222e61fcb1cd8d},
}
@inproceedings{14717117,
title = {Temporal coherence and the streaming of complex sounds.},
author = {{S. Shamma} and {Mounya Elhilali} and {Ling Ma} and {C. Micheyl} and {A. Oxenham} and {D. Pressnitzer} and {Pingbo Yin} and {Yanbo Xu}},
year = 2013,
booktitle = {Advances in Experimental Medicine and Biology},
url = {https://www.semanticscholar.org/paper/68ee363dd0ab8f4909d12e8a9bd7fdf7198ee2a5},
}
@inproceedings{25342858,
title = {Bayesian Tree Substitution Grammars as a Usage-based Approach},
author = {{Matt Post} and {D. Gildea}},
year = 2013,
month = {9},
booktitle = {Language and Speech},
url = {https://www.semanticscholar.org/paper/6e1c3999af289c2aeb2dbe0f156fde7dae9cd361},
}
@inproceedings{20162731,
title = {Auditory modulation of visual proto-object formation in a hierarchical auditory-visual saliency map},
author = {{Tomas Figliolia} and {Daniel R. Mendat} and {A. Russell} and {Thomas S. Murray} and {Ernst Nieburyk} and {R. Etienne-Cummings} and {A. Andreou}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/72d30db866851bf56321690cafa2a98d12b3cf40},
}
@inproceedings{86766538,
title = {Reporting Bias and Knowledge Extraction},
author = {{Jonathan Gordon} and {Benjamin Van Durme}},
year = 2013,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/46e123a8ba5d4277b935c97529d48f2e678a9b45},
}
@inproceedings{7761542,
title = {Effect of filter bandwidth and spectral sampling rate of analysis filterbank on automatic phoneme recognition},
author = {{Feipeng Li} and {H. Hermansky}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d85e2f43ad4df178ba92ab23e1d491e34410bf8a},
}
@inproceedings{18693156,
title = {Task-driven attentional mechanisms for auditory scene recognition},
author = {{Kailash Patil} and {Mounya Elhilali}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/a634074eabf17f29414fac0497fd38522777d277},
}
@inproceedings{1932370,
title = {Multi-stream recognition of noisy speech with performance monitoring},
author = {{Ehsan Variani} and {Feipeng Li} and {H. Hermansky}},
year = 2013,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/0042da1086b720eaa81f715ee0de93054e3bf811},
}
@inproceedings{8630565,
title = {Factor Analysis of Auto-Associative Neural Networks With Application in Speaker Verification},
author = {{S. Garimella} and {H. Hermansky}},
year = 2013,
month = {1},
booktitle = {IEEE Transactions on Neural Networks and Learning Systems},
url = {https://www.semanticscholar.org/paper/168d011e5d94dca30ba6094f0dac12a48deccb9a},
}
@inproceedings{17242638,
title = {Audio-visual saliency map: Overview, basic models and hardware implementation},
author = {{Sudarshan Ramenahalli} and {Daniel R. Mendat} and {S. Dura-Bernal} and {E. Culurciello} and {E. Niebur} and {A. Andreou}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/ef483498459b9fbb64b24b807ce3743535d919d1},
}
@inproceedings{24095543,
title = {Representation of temporal coherence: CHAINS algorithm and FPGA implementation},
author = {{Tomas Figliolia} and {A. Andreou}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/083ad5dd283be19f45b1988c7e95f178016d7903},
}
@inproceedings{10786550,
title = {What Affects Patient (Dis)satisfaction? Analyzing Online Doctor Ratings with a Joint Topic-Sentiment Model},
author = {{Michael J. Paul} and {Byron C. Wallace} and {Mark Dredze}},
year = 2013,
month = {6},
booktitle = {AAAI Conference on Artificial Intelligence},
url = {https://www.semanticscholar.org/paper/a23628e9d88cacafc35454ba77047f3de2e69f86},
}
@inproceedings{16069287,
title = {MULTIRESOLUTION AUDITORY REPRESENTATIONS FOR SCENE CLASS IFICATION},
author = {{Kailash Patil} and {Mounya Elhilali}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/032f134770a3ef46c1b1f757cc86877a83f5d040},
}
@inproceedings{30083992,
title = {Organic diode implementations in configurable architectures and temperature sensors},
author = {{R. Ozgun} and {H. Katz} and {A. Andreou}},
year = 2013,
month = {5},
booktitle = {2013 Microsystems for Measurement and Instrumentation: Fulfilling the Promise (MAMNA)},
url = {https://www.semanticscholar.org/paper/8c99056b72e9c431450ac04b3629b12495d81da0},
}
@inproceedings{6420241,
title = {Entity Linking: Finding Extracted Entities in a Knowledge Base},
author = {{D. Rao} and {Paul McNamee} and {Mark Dredze}},
year = 2013,
booktitle = {Multi-source, Multilingual Information Extraction and Summarization},
url = {https://www.semanticscholar.org/paper/35d4af572e687228a8dd2241f85d7a833fcf5e5d},
}
@inproceedings{15808834,
title = {Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation},
author = {{Damianos G. Karakos} and {Mark Dredze} and {S. Khudanpur}},
year = 2013,
month = {3},
booktitle = {arXiv.org},
url = {https://www.semanticscholar.org/paper/860a3390dd415290981591a5158ac6dc602d8a5f},
}
@inproceedings{20167467,
title = {String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery},
author = {{N. Ahmidi} and {Yixin Gao} and {B. B. Haro} and {S. Vedula} and {S. Khudanpur} and {R. Vidal} and {Gregory Hager}},
year = 2013,
month = {9},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/555530521a391df62edca0c602c1df05af1e2761},
}
@inproceedings{15678666,
title = {Evaluating Progress in Probabilistic Programming through Topic Models},
author = {{Francis Ferraro} and {Benjamin Van Durme} and {Yanif Ahmad}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/584779cfaf2e32b462d604ecd2bba01bd4e7a149},
}
@inproceedings{37367537,
title = {Improvements in language identification on the RATS noisy speech corpus},
author = {{Jeff Z. Ma} and {Bing Zhang} and {S. Matsoukas} and {Sri Harish Reddy Mallidi} and {Feipeng Li} and {H. Hermansky}},
year = 2013,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/7afff4a3104f7e93fb92649defe1c358862d87d0},
}
@inproceedings{6134861,
title = {Frequency offset correction in speech without detecting pitch},
author = {{P. Clark} and {Sri Harish Reddy Mallidi} and {A. Jansen} and {H. Hermansky}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/9f7928802113eaf8157e8150e5dbf6f055540985},
}
@inproceedings{62711320,
title = {Design of configurable chipping sequence generator for high-speed parallel samplers},
author = {{Thomas S. Murray} and {P. Pouliquen} and {A. Andreou}},
year = 2013,
month = {7},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/eeda41683e5fc2b3d051e734272ab7577b6d3488},
}
@inproceedings{14519020,
title = {Robust speaker recognition using spectro-temporal autoregressive models},
author = {{Sri Harish Reddy Mallidi} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2013,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/5bdc663d3e65356c5b2b143858d7b0f495647489},
}
@inproceedings{15718743,
title = {Bayesian inference in auditory scenes},
author = {{Mounya Elhilali}},
year = 2013,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/2b71d33dffbc5eef24fe5f1464b4a5db77c7f235},
}
@inproceedings{18178418,
title = {Filter-bank optimization for Frequency Domain Linear Prediction},
author = {{Vijayaditya Peddinti} and {H. Hermansky}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/1d94cb19b4ca1860ae59a5c27951b7470d2ed069},
}
@inproceedings{1758169,
title = {Abnormality detection in noisy biosignals},
author = {{Emine Merve Kaya} and {Mounya Elhilali}},
year = 2013,
month = {7},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/28c2e91e1d8f303cc97ee05e4ab438fbebab5a05},
}
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title = {Nerit: Named Entity Recognition for Informal Text},
author = {{David Etter} and {Francis Ferraro} and {Ryan Cotterell} and {Olivia Buzek} and {Benjamin Van Durme}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/5920903e1c2c7ea165ad84a8fe6dbafdd586cbe7},
}
@inproceedings{2808498,
title = {Quantifying the value of pronunciation lexicons for keyword search in lowresource languages},
author = {{Guoguo Chen} and {S. Khudanpur} and {Daniel Povey} and {J. Trmal} and {David Yarowsky} and {Oguz Yilmaz}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/95c439ccffd9ea069cd19a26933c03b2b4aa7197},
}
@inproceedings{216147164,
title = {Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline},
author = {{Thomas Schatz} and {Vijayaditya Peddinti} and {F. Bach} and {A. Jansen} and {H. Hermansky} and {Emmanuel Dupoux}},
year = 2013,
month = {8},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/6d683f57a1498676090965141b8962b1e7b0fb4a},
}
@inproceedings{210169840,
title = {A linear systems view to the concept of STRFs},
author = {{Mounya Elhilali} and {S. Shamma} and {J. Simon} and {J. Fritz}},
year = 2013,
booktitle = {},
url = {https://www.semanticscholar.org/paper/51224996f4ccf98b7d371af6add04965fb1bfa66},
}
@inproceedings{17049673,
title = {Perceptual Properties of Current Speech Recognition Technology},
author = {{H. Hermansky} and {Jordan Cohen} and {R. Stern}},
year = 2013,
month = {7},
booktitle = {Proceedings of the IEEE},
url = {https://www.semanticscholar.org/paper/7d6c8044d155c0b9d6a74cab21f68c04c3b82616},
}
@inproceedings{40634023,
title = {Stream selection and integration in multistream ASR using GMM-based performance monitoring},
author = {{Tetsuji Ogawa} and {Feipeng Li} and {H. Hermansky}},
year = 2013,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/fdfbb1adb0a72c0abb48f367901829eb10df0096},
}
@inproceedings{1791325,
title = {Developing a speaker identification system for the DARPA RATS project},
author = {{Oldrich Plchot} and {S. Matsoukas} and {P. Matejka} and {N. Dehak} and {Jeff Z. Ma} and {Sandro Cumani} and {O. Glembek} and {H. Hermansky} and {Sri Harish Reddy Mallidi} and {N. Mesgarani} and {R. Schwartz} and {Mehdi Soufifar} and {Zheng-Hua Tan} and {Samuel Thomas} and {Bing Zhang} and {Xinhui Zhou}},
year = 2013,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/f00bea11eed756c3ae90dae416945d8708155caf},
}
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title = {Text-to-speech inspired duration modeling for improved whole-word acoustic models},
author = {{Keith Kintzley} and {A. Jansen} and {H. Hermansky}},
year = 2013,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/e70051d7a9ce795e22f78e5b807108f6627c31db},
}
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title = {Person localization through ground vibrations using a sand-scorpion inspired spiking neural network},
author = {{Guillaume Garreau} and {Eleni Proxenou} and {A. Andreou} and {J. Georgiou}},
year = 2013,
month = {3},
booktitle = {Annual Conference on Information Sciences and Systems},
url = {https://www.semanticscholar.org/paper/99669fcc500dfaf71212ca746692094fc44c174e},
}
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title = {Factorial LDA: Sparse Multi-Dimensional Text Models},
author = {{Michael J. Paul} and {Mark Dredze}},
year = 2012,
month = {12},
booktitle = {Neural Information Processing Systems},
url = {https://www.semanticscholar.org/paper/8fefa7f27808f578ee6b01443dce8a658201f0c8},
}
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title = {Overview of the special session on semantics and sociolinguistics in social media},
author = {{Mona T. Diab} and {Mark Dredze} and {S. Harabagiu} and {Dragomir R. Radev}},
year = 2012,
month = {12},
booktitle = {},
url = {https://www.semanticscholar.org/paper/4a9d1cb69246bd3530d1eef08eaf666aa6ee1fdb},
}
@inproceedings{6682247,
title = {Recognizing the message and the messenger: biomimetic spectral analysis for robust speech and speaker recognition},
author = {{Sridhar Krishna Nemala} and {Kailash Patil} and {Mounya Elhilali}},
year = 2012,
month = {12},
booktitle = {International Journal of Speech Technology},
url = {https://www.semanticscholar.org/paper/e407e38bc238cc08842c621b69213268f579b85e},
}
@InProceedings{jiang-et-al-2012-nips,
author = "Jiarong Jiang and Adam Teichert and Hal {Daum\'{e}
III} and Jason Eisner",
title = "Learned Prioritization for Trading Off Accuracy and
Speed",
booktitle = "Advances in Neural Information Processing Systems 25
(NeurIPS)",
pages = "1331--1339",
year = "2012",
month = dec,
address = "Lake Tahoe, NV",
URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2012-nips",
}
@InProceedings{he-daume-eisner-2012-nips,
author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
title = "Imitation Learning by Coaching",
booktitle = "Advances in Neural Information Processing Systems 25
(NeurIPS)",
pages = "3149--3157",
year = "2012",
month = dec,
address = "Lake Tahoe, NV",
URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2012-nips",
}
@InProceedings{stoyanov-eisner-2012-coling,
aclid = "C12-1154",
author = "Veselin Stoyanov and Jason Eisner",
title = "Easy-first Coreference Resolution",
booktitle = "Proceedings of the 24th International Conference on
Computational Linguistics (COLING)",
pages = "2519--2534",
year = "2012",
month = dec,
address = "Mumbai",
URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-coling",
}
@inproceedings{157114152,
title = {Models for Mining Public Health Information from Social Media},
author = {{Mark Dredze}},
year = 2012,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/9a0f64733f6f68217932c5e4c9742fe3a02f1728},
}
@inproceedings{16290687,
title = {Music in Our Ears: The Biological Bases of Musical Timbre Perception},
author = {{Kailash Patil} and {D. Pressnitzer} and {S. Shamma} and {Mounya Elhilali}},
year = 2012,
month = {11},
booktitle = {PLoS Comput. Biol.},
url = {https://www.semanticscholar.org/paper/a9008fe9176219b88c323366c8306b856db3a4aa},
}
@inproceedings{7104064,
title = {A multiresolution analysis for detection of abnormal lung sounds},
author = {{Dimitra Emmanouilidou} and {Kailash Patil} and {James E. West} and {Mounya Elhilali}},
year = 2012,
month = {11},
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/18c997f4ca5c35181770acdcae8ff1d16ba6c5e3},
}
@inproceedings{31827846,
title = {Malpractice and Malcontent: Analyzing Medical Complaints in Twitter},
author = {{Atul Nakhasi} and {R. Passarella} and {Sarah G. Bell} and {Michael J. Paul} and {Mark Dredze} and {P. Pronovost}},
year = 2012,
month = {10},
booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
url = {https://www.semanticscholar.org/paper/fd7064b1f28ada678659656311879ab7c377d04c},
}
@inproceedings{1573045,
title = {Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics},
author = {{Alex Lamb} and {Michael J. Paul} and {Mark Dredze}},
year = 2012,
month = {10},
booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
url = {https://www.semanticscholar.org/paper/0aa787fb15a9a5aef417eed43b07418410f2cfaa},
}
@inproceedings{222602753,
title = {Miniature Absolute Scalar Magnetometer Based on the Rubidium Isotope 87Rb},
author = {{H. Korth} and {K. Strohbehn} and {Francisco Tejada} and {A. Andreou} and {J. Kitching} and {S. Knappe}},
year = 2012,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/31417310e9bd7abd7ee5275ca73cc2bf4b7b7495},
}
@inproceedings{18646289,
title = {Temporal resolution analysis in frequency domain linear prediction.},
author = {{Sriram Ganapathy} and {H. Hermansky}},
year = 2012,
month = {10},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/e09f801058e6ec59fb4ca18b0b8a969a6c39a92a},
}
@inproceedings{17766644,
title = {Regularized Auto-Associative Neural Networks for Speaker Verification},
author = {{S. Garimella} and {Sri Harish Reddy Mallidi} and {H. Hermansky}},
year = 2012,
month = {10},
booktitle = {IEEE Signal Processing Letters},
url = {https://www.semanticscholar.org/paper/e43a3e614f0d48fbc0bdd22801ab606f0e8fa9eb},
}
@inproceedings{3048394,
title = {Experimenting with Drugs (and Topic Models): Multi-Dimensional Exploration of Recreational Drug Discussions},
author = {{Michael J. Paul} and {Mark Dredze}},
year = 2012,
month = {10},
booktitle = {AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text},
url = {https://www.semanticscholar.org/paper/8d7a70d094901d2bd700ab23fa6d2e9b066bde46},
}
@InProceedings{filardo-eisner-2012-iclp,
author = "Nathaniel Wesley Filardo and Jason Eisner",
title = "A Flexible Solver for Finite Arithmetic Circuits",
booktitle = "Technical Communications of the 28th International
Conference on Logic Programming (ICLP)",
editor = "Agostino Dovier and V\'{\i}tor Santos Costa",
series = "Leibniz International Proceedings in Informatics
(LIPIcs)",
volume = "17",
pages = "425--438",
ISBN = "978-3-939897-43-9",
year = "2012",
month = sep,
address = "Budapest",
URL = "http://cs.jhu.edu/~jason/papers/#filardo-eisner-2012-iclp",
}
@inproceedings{van-durme-2012-streaming,
title = "Streaming Analysis of Discourse Participants",
author = "Van Durme, Benjamin",
editor = "Tsujii, Jun'ichi and
Henderson, James and
Pa\c sca, Marius",
booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
month = jul,
year = "2012",
address = "Jeju Island, Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D12-1005/",
pages = "48--58"
}
@inproceedings{prabhakaran-etal-2012-statistical,
title = "Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing",
author = "Prabhakaran, Vinodkumar and
Bloodgood, Michael and
Diab, Mona and
Dorr, Bonnie and
Levin, Lori and
Piatko, Christine D. and
Rambow, Owen and
Van Durme, Benjamin",
editor = "Morante, Roser and
Sporleder, Caroline",
booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics",
month = jul,
year = "2012",
address = "Jeju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3807/",
pages = "57--64"
}
@inproceedings{andrews-etal-2012-name,
title = "Name Phylogeny: A Generative Model of String Variation",
author = "Andrews, Nicholas and
Eisner, Jason and
Dredze, Mark",
editor = "Tsujii, Jun'ichi and
Henderson, James and
Pa\c sca, Marius",
booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
month = jul,
year = "2012",
address = "Jeju Island, Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D12-1032/",
pages = "344--355"
}
@inproceedings{rastrow-etal-2012-fast,
title = "Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining",
author = "Rastrow, Ariya and
Dredze, Mark and
Khudanpur, Sanjeev",
editor = "Li, Haizhou and
Lin, Chin-Yew and
Osborne, Miles and
Lee, Gary Geunbae and
Park, Jong C.",
booktitle = "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2012",
address = "Jeju Island, Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P12-1019/",
pages = "175--183"
}
@inproceedings{joshi-etal-2012-multi,
title = "Multi-Domain Learning: When Do Domains Matter?",
author = "Joshi, Mahesh and
Dredze, Mark and
Cohen, William W. and
Ros\'e, Carolyn",
editor = "Tsujii, Jun'ichi and
Henderson, James and
Pa\c sca, Marius",
booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
month = jul,
year = "2012",
address = "Jeju Island, Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D12-1119/",
pages = "1302--1312"
}
@InProceedings{andrews-eisner-dredze-2012,
aclid = "D12-1032",
author = "Nicholas Andrews and Jason Eisner and Mark Dredze",
title = "Name Phylogeny: {A} Generative Model of String
Variation",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing and Computational Natural
Language Learning (EMNLP-CoNLL)",
pages = "344--355",
year = "2012",
month = jul,
address = "Jeju, Korea",
URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-dredze-2012",
}
@inproceedings{weese-etal-2012-using,
title = "Using Categorial Grammar to Label Translation Rules",
author = "Weese, Jonathan and
Callison-Burch, Chris and
Lopez, Adam",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3127/",
pages = "222--231"
}
@inproceedings{gormley-etal-2012-shared,
title = "Shared Components Topic Models",
author = "Gormley, Matthew R. and
Dredze, Mark and
Van Durme, Benjamin and
Eisner, Jason",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1096/",
pages = "783--792"
}
@inproceedings{green-etal-2012-entity,
title = "Entity Clustering Across Languages",
author = "Green, Spence and
Andrews, Nicholas and
Gormley, Matthew R. and
Dredze, Mark and
Manning, Christopher D.",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1007/",
pages = "60--69"
}
@inproceedings{callison-burch-etal-2012-findings,
title = "Findings of the 2012 Workshop on Statistical Machine Translation",
author = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3102/",
pages = "10--51"
}
@inproceedings{irvine-etal-2012-processing,
title = "Processing Informal, {R}omanized {P}akistani Text Messages",
author = "Irvine, Ann and
Weese, Jonathan and
Callison-Burch, Chris",
editor = "Sood, Sara Owsley and
Nagarajan, Meenakshi and
Gamon, Michael",
booktitle = "Proceedings of the Second Workshop on Language in Social Media",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-2109/",
pages = "75--78"
}
@inproceedings{ferraro-etal-2012-toward,
title = "Toward Tree Substitution Grammars with Latent Annotations",
author = "Ferraro, Francis and
Van Durme, Benjamin and
Post, Matt",
editor = "Cohn, Trevor and
Blunsom, Phil and
Graca, Joao",
booktitle = "Proceedings of the {NAACL}-{HLT} Workshop on the Induction of Linguistic Structure",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-1904/",
pages = "23--30"
}
@inproceedings{napoles-etal-2012-annotated,
title = "Annotated {G}igaword",
author = "Napoles, Courtney and
Gormley, Matthew and
Van Durme, Benjamin",
editor = "Fan, James and
Hoffman, Raphael and
Kalyanpur, Aditya and
Riedel, Sebastian and
Suchanek, Fabian and
Talukdar, Partha Pratim",
booktitle = "Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction ({AKBC}-{WEKEX})",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3018/",
pages = "95--100"
}
@inproceedings{bergsma-etal-2012-stylometric,
title = "Stylometric Analysis of Scientific Articles",
author = "Bergsma, Shane and
Post, Matt and
Yarowsky, David",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1033/",
pages = "327--337"
}
@inproceedings{kjersten-van-durme-2012-space,
title = "Space Efficiencies in Discourse Modeling via Conditional Random Sampling",
author = "Kjersten, Brian and
Van Durme, Benjamin",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1056/",
pages = "513--517"
}
@inproceedings{yao-etal-2012-expectations,
title = "Expectations of Word Sense in Parallel Corpora",
author = "Yao, Xuchen and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1078/",
pages = "621--625"
}
@inproceedings{rastrow-etal-2012-revisiting,
title = "Revisiting the Case for Explicit Syntactic Information in Language Models",
author = "Rastrow, Ariya and
Khudanpur, Sanjeev and
Dredze, Mark",
editor = "Ramabhadran, Bhuvana and
Khudanpur, Sanjeev and
Arisoy, Ebru",
booktitle = "Proceedings of the {NAACL}-{HLT} 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for {HLT}",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-2707/",
pages = "50--58"
}
@inproceedings{zbib-etal-2012-machine,
title = "Machine Translation of {A}rabic Dialects",
author = "Zbib, Rabih and
Malchiodi, Erika and
Devlin, Jacob and
Stallard, David and
Matsoukas, Spyros and
Schwartz, Richard and
Makhoul, John and
Zaidan, Omar F. and
Callison-Burch, Chris",
editor = "Fosler-Lussier, Eric and
Riloff, Ellen and
Bangalore, Srinivas",
booktitle = "Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N12-1006/",
pages = "49--59"
}
@inproceedings{post-etal-2012-constructing,
title = "Constructing Parallel Corpora for Six {I}ndian Languages via Crowdsourcing",
author = "Post, Matt and
Callison-Burch, Chris and
Osborne, Miles",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3152/",
pages = "401--409"
}
@inproceedings{ferraro-etal-2012-judging,
title = "Judging Grammaticality with Count-Induced Tree Substitution Grammars",
author = "Ferraro, Francis and
Post, Matt and
Van Durme, Benjamin",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia",
booktitle = "Proceedings of the Seventh Workshop on Building Educational Applications Using {NLP}",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-2013/",
pages = "116--121"
}
@inproceedings{ganitkevitch-etal-2012-joshua,
title = "{J}oshua 4.0: Packing, {PRO}, and Paraphrases",
author = "Ganitkevitch, Juri and
Cao, Yuan and
Weese, Jonathan and
Post, Matt and
Callison-Burch, Chris",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Post, Matt and
Soricut, Radu and
Specia, Lucia",
booktitle = "Proceedings of the Seventh Workshop on Statistical Machine Translation",
month = jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W12-3134/",
pages = "283--291"
}
@InProceedings{jiang-et-al-2012-icmlw,
author = "Jiarong Jiang and Adam Teichert and Hal {Daum\'{e}
III} and Jason Eisner",
title = "Learned Prioritization for Trading Off Accuracy and
Speed",
booktitle = "ICML Workshop on Inferning: Interactions between
Inference and Learning",
note = "7 pages",
year = "2012",
month = jun,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#jiang-et-al-2012-icmlw",
}
@InProceedings{he-daume-eisner-2012-icmlw,
author = "He He and Hal {Daum\'{e} III} and Jason Eisner",
title = "Cost-Sensitive Dynamic Feature Selection",
booktitle = "ICML Workshop on Inferning: Interactions between
Inference and Learning",
note = "6 pages",
year = "2012",
month = jun,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#he-daume-eisner-2012-icmlw",
}
@InProceedings{stoyanov-eisner-2012-icmlw,
author = "Veselin Stoyanov and Jason Eisner",
title = "Fast and Accurate Prediction via Evidence-Specific
{MRF} Structure",
booktitle = "ICML Workshop on Inferning: Interactions between
Inference and Learning",
note = "6 pages",
year = "2012",
month = jun,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-icmlw",
}
@InProceedings{gormley-et-al-2012,
aclid = "N12-1096",
author = "Matthew R. Gormley and Mark Dredze and Benjamin {Van
Durme} and Jason Eisner",
title = "Shared Components Topic Models",
booktitle = "Proceedings of the 2012 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "783--792",
year = "2012",
month = jun,
address = "Montreal",
URL = "http://cs.jhu.edu/~jason/papers/#gormley-et-al-2012",
}
@InProceedings{paul-eisner-2012,
aclid = "N12-1024",
author = "Michael Paul and Jason Eisner",
title = "Implicitly Intersecting Weighted Automata using Dual
Decomposition",
booktitle = "Proceedings of the 2012 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "232--242",
year = "2012",
month = jun,
address = "Montreal",
URL = "http://cs.jhu.edu/~jason/papers/#paul-eisner-2012",
}
@InProceedings{smith-eisner-2012,
aclid = "N12-1014",
author = "Jason Smith and Jason Eisner",
title = "Unsupervised Learning on an Approximate Corpus",
booktitle = "Proceedings of the 2012 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "131--141",
year = "2012",
month = jun,
address = "Montreal",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2012",
}
@InProceedings{stoyanov-eisner-2012-naacl,
aclid = "N12-1013",
author = "Veselin Stoyanov and Jason Eisner",
title = "Minimum-Risk Training of Approximate {CRF}-Based {NLP}
Systems",
booktitle = "Proceedings of the 2012 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies (NAACL-HLT)",
pages = "120--130",
year = "2012",
month = jun,
address = "Montreal",
URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2012-naacl",
}
@inproceedings{klementiev-etal-2012-toward,
title = "Toward Statistical Machine Translation without Parallel Corpora",
author = "Klementiev, Alexandre and
Irvine, Ann and
Callison-Burch, Chris and
Yarowsky, David",
editor = "Daelemans, Walter",
booktitle = "Proceedings of the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2012",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E12-1014/",
pages = "130--140"
}
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}
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title = {Intrinsic Spectral Analysis for Zero and High Resource Speech Recognition},
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booktitle = {Interspeech},
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}
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title = {Printed Organic Electronic Sensors},
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title = {DIRAC: Detection and Identification of Rare Audio-Visual Events},
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title = {Continuous space discriminative language modeling},
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title = {Flexible readout and integration sensor (FRIS): a bio-inspired, system-on-chip, event-based readout architecture},
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title = {Robust Phoneme Recognition Using High Resolution Temporal Envelopes},
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Discriminative training for MT usually involves numerous features and requires large-scale training set to reach reliable parameter estimation. Other than using the expensive human-labeled parallel corpora for training, semi-supervised methods have been proposed to generate huge amount of “hallucinated” data which relieves the data sparsity problem. However the large training set contains both good samples which are suitable for training and bad ones harmful to the training. How to select training samples from vast amount of data can greatly affect the training performance. In this paper we propose a method for selecting samples that are most suitable for discriminative training according to a criterion measuring the dataset quality. Our experimental results show that by adding samples to the training set selectively, we are able to exceed the performance of system trained with the same amount of samples selected randomly.
@inproceedings{cao-khudanpur-2012-sample,
title = "Sample Selection for Large-scale {MT} Discriminative Training",
author = "Cao, Yuan and
Khudanpur, Sanjeev",
booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 28-" # nov # " 1",
year = "2012",
address = "San Diego, California, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2012.amta-papers.3/",
abstract = "Discriminative training for MT usually involves numerous features and requires large-scale training set to reach reliable parameter estimation. Other than using the expensive human-labeled parallel corpora for training, semi-supervised methods have been proposed to generate huge amount of ``hallucinated'' data which relieves the data sparsity problem. However the large training set contains both good samples which are suitable for training and bad ones harmful to the training. How to select training samples from vast amount of data can greatly affect the training performance. In this paper we propose a method for selecting samples that are most suitable for discriminative training according to a criterion measuring the dataset quality. Our experimental results show that by adding samples to the training set selectively, we are able to exceed the performance of system trained with the same amount of samples selected randomly."
}
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url = {https://www.semanticscholar.org/paper/6f98545b1510b03b49e034c53525a8d4986dd27f},
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title = {An FPGA-based approach for parameter estimation in spiking neural networks},
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title = {A Generative Model of String Variation},
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booktitle = {},
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}
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title = {Deriving conversation-based features from unlabeled speech for discriminative language modeling},
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url = {https://www.semanticscholar.org/paper/b4bffa0abd34e3ff6ba0d3f4e305a81df65ec719},
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month = {3},
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url = {https://www.semanticscholar.org/paper/51e5e7093e0183feab61b00ca6c3df61cd8c46de},
}
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booktitle = {IEEE Transactions on Biomedical Circuits and Systems},
url = {https://www.semanticscholar.org/paper/5224c9da3353cdec21724c67ebdc22fb63c9cc9c},
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title = {The UMD-JHU 2011 speaker recognition system},
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title = {Jerboa: A Toolkit for Randomized and Streaming Algorithms},
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}
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title = {Feature extraction using 2-d autoregressive models for speaker recognition},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2012,
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/45f1394a7920da4b054cc744709a74c1663ded04},
}
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title = {Constrained Maximum Mutual Information Dimensionality Reduction for Language Identification},
author = {{Shuai Huang} and {Glen A. Coppersmith} and {Damianos G. Karakos}},
year = 2012,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/255f055d8c678b53a8a0c9c73f5f6480b125bdb5},
}
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title = {Multilevel speech intelligibility for robust speaker recognition},
author = {{Sridhar Krishna Nemala} and {Mounya Elhilali}},
year = 2012,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/bcfc719d680dd9dc4a2b879b193796a2897321b0},
}
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title = {Prior probabilities tune attentional bandwidth},
author = {{M. Wolmetz} and {Mounya Elhilali}},
year = 2012,
month = {9},
booktitle = {},
url = {https://www.semanticscholar.org/paper/cc48185b4d37c063924143c5936ed3afdc90a448},
}
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title = {Adaptation transforms of auto-associative neural networks as features for speaker verification},
author = {{Samuel Thomas} and {Sri Harish Reddy Mallidi} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2012,
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/ac0ffb98b45ce9c0aed3f33a823e6c4ef4443290},
}
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title = {Phase AutoCorrelation (PAC) features for noise robust speech recognition},
author = {{S. Ikbal} and {Hemant Misra} and {H. Hermansky} and {M. Magimai-Doss}},
year = 2012,
month = {9},
booktitle = {Speech Communication},
url = {https://www.semanticscholar.org/paper/f648f462d8c03ed24d5d8d6e6af8d01898d8d42c},
}
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title = {Practical and efficient incorporation of syntactic features into statistical language models},
author = {{S. Khudanpur} and {A. Rastrow}},
year = 2012,
booktitle = {},
url = {https://www.semanticscholar.org/paper/d425c2baa9a6c7cfcc9442f0c86e45f0cb2d9924},
}
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title = {Auditory scene analysis: It's all about expectations!},
author = {{Mounya Elhilali}},
year = 2012,
month = {4},
booktitle = {},
url = {https://www.semanticscholar.org/paper/99698acf3a7c5ddad44929ed3b1dc7629ceb35c7},
}
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title = {Robust phoneme recognition based on biomimetic speech contours},
author = {{Michael A. Carlin} and {Kailash Patil} and {Sridhar Krishna Nemala} and {Mounya Elhilali}},
year = 2012,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/637e7a7b79f4f928b9a2d8a75385bb03d11674cc},
}
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title = {Phone recognition in critical bands using sub-band temporal modulations},
author = {{Feipeng Li} and {Sri Harish Reddy Mallidi} and {H. Hermansky}},
year = 2012,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/bc164350718e2c3a768f745a5ef2c14203473324},
}
@inproceedings{208023170,
title = {Proceedings of the Seventh Workshop on Statistical Machine Translation},
author = {{Jonathan Weese} and {Chris Callison-Burch} and {Adam Lopez}},
year = 2012,
month = {6},
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/13bdb72e9aa1cb37db75478d0a2945db9c30d733},
}
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title = {Erratum to Incremental Syntactic Language Models for Phrase-based Translation},
author = {{Lane Schwartz} and {Chris Callison-Burch} and {William Schuler} and {Stephen T Wu}},
year = 2012,
booktitle = {},
url = {https://www.semanticscholar.org/paper/8144b43346d0d143d2ac7ffb251bbb050c698706},
}
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title = {Factor analysis of mixture of auto-associative neural networks for speaker verification},
author = {{Sri Garimella} and {H. Hermansky}},
year = 2012,
booktitle = {The Speaker and Language Recognition Workshop},
url = {https://www.semanticscholar.org/paper/87aafe3bab34e4400757339c8680f5974b059de4},
}
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title = {from Rule-based Annotations and Crowdsourcing},
author = {{Vinodkumar Prabhakaran} and {Michael Bloodgood} and {Mona T. Diab} and {B. Dorr} and {Lori S. Levin} and {C. Piatko} and {Owen Rambow} and {Benjamin Van Durme}},
year = 2012,
booktitle = {},
url = {https://www.semanticscholar.org/paper/4f44bbdec0ecc7d0181ba884024489018df5fffc},
}
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title = {Beyond Amdahl's Law: An Objective Function That Links Multiprocessor Performance Gains to Delay and Energy},
author = {{A. Cassidy} and {A. Andreou}},
year = 2012,
month = {8},
booktitle = {IEEE transactions on computers},
url = {https://www.semanticscholar.org/paper/c4466acbd9ba0a2d2e139ef5a793b613aaf02b1e},
}
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title = {Phrasal Cohort Based Unsupervised Discriminative Language Modeling},
author = {{Puyang Xu} and {Brian Roark} and {S. Khudanpur}},
year = 2012,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/101be1f118db1aba0e4f93ca6e9b3c0a8f645139},
}
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title = {Data-driven Posterior Features for Low Resource Speech Recognition Applications},
author = {{Samuel Thomas} and {Sriram Ganapathy} and {A. Jansen} and {H. Hermansky}},
year = 2012,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/b93109dbfd2c441f8adacbe80994b3d47b0e988c},
}
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title = {Robust speech processing by humans and machines: the role of spectro-temporal modulations},
author = {{Mounya Elhilali} and {Sridhar Krishna Nemala}},
year = 2012,
booktitle = {},
url = {https://www.semanticscholar.org/paper/323d426a62e4e30fbda05b1a02a64dcad22676b7},
}
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title = "Monolingual Distributional Similarity for Text-to-Text Generation",
author = "Ganitkevitch, Juri and
Van Durme, Benjamin and
Callison-Burch, Chris",
editor = "Agirre, Eneko and
Bos, Johan and
Diab, Mona and
Manandhar, Suresh and
Marton, Yuval and
Yuret, Deniz",
booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics -- Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
month = "7-8 " # jun,
year = "2012",
address = "Montr\'eal, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S12-1034/",
pages = "256--264"
}
@inproceedings{6914375,
title = {Estimating document frequencies in a speech corpus},
author = {{Damianos G. Karakos} and {Mark Dredze} and {K. Church} and {A. Jansen} and {S. Khudanpur}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/990c9260b6d2a33aeaba30e4640ec59d709864fc},
}
@inproceedings{14363701,
title = {Adapting n-gram maximum entropy language models with conditional entropy regularization},
author = {{A. Rastrow} and {Mark Dredze} and {S. Khudanpur}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/46e3aa6c828f372e6d43f0b1fb00613f02bb0a8e},
}
@inproceedings{17498735,
title = {Efficient spoken term discovery using randomized algorithms},
author = {{A. Jansen} and {Benjamin Van Durme}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/5561d01b9cc08bac589bccdfc2f68019c58f36e7},
}
@inproceedings{9645306,
title = {Efficient discriminative training of long-span language models},
author = {{A. Rastrow} and {Mark Dredze} and {S. Khudanpur}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/8924c38cdd8fed464a9808524e45930a6164ca37},
}
@inproceedings{16825268,
title = {Randomized maximum entropy language models},
author = {{Puyang Xu} and {S. Khudanpur} and {A. Gunawardana}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/b4fec4831d83708f81ecf6f7296b105e081d2d4c},
}
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title = {Bootstrapping a spoken language identification system using unsupervised integrated sensing and processing decision trees},
author = {{Shuai Huang} and {Damianos G. Karakos} and {Glen A. Coppersmith} and {Kenneth Ward Church} and {Sabato Marco Siniscalchi}},
year = 2011,
month = {12},
booktitle = {2011 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/5ffa9b8521e17c4f13f3fc2eff8fcbe4cf7892f2},
}
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title = {Interview with Andreas G. Andreou},
author = {{A. Andreou}},
year = 2011,
month = {12},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/f313ea547da8588136cd316f99601b0f1ee7f7bf},
}
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title = {Gait-based person and gender recognition using micro-doppler signatures},
author = {{Guillaume Garreau} and {C. M. Andreou} and {A. Andreou} and {J. Georgiou} and {S. Dura-Bernal} and {T. Wennekers} and {S. Denham}},
year = 2011,
month = {12},
booktitle = {Biomedical Circuits and Systems Conference},
url = {https://www.semanticscholar.org/paper/10f61c695ecd47086397481753793b3dd0d264d7},
}
@InProceedings{eisner-daume-2011,
author = "Jason Eisner and Hal {Daum\'{e} III}",
title = "Learning Speed-Accuracy Tradeoffs in Nondeterministic
Inference Algorithms",
booktitle = "COST: NeurIPS Workshop on Computational Trade-offs in
Statistical Learning",
note = "5 pages",
year = "2011",
month = dec,
address = "Sierra Nevada, Spain",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-daume-2011",
}
@InProceedings{stoyanov-eisner-2011,
author = "Veselin Stoyanov and Jason Eisner",
title = "Learning Cost-Aware, Loss-Aware Approximate Inference
Policies for Probabilistic Graphical Models",
booktitle = "COST: NeurIPS Workshop on Computational Trade-offs in
Statistical Learning",
note = "5 pages",
year = "2011",
month = dec,
address = "Sierra Nevada, Spain",
URL = "http://cs.jhu.edu/~jason/papers/#stoyanov-eisner-2011",
}
@InProceedings{andrews-eisner-2011,
author = "Nicholas Andrews and Jason Eisner",
title = "Transformation Process Priors",
booktitle = "NeurIPS Workshop on {B}ayesian Nonparametrics: Hope or
Hype?",
note = "Extended abstract (3 pages)",
year = "2011",
month = dec,
address = "Sierra Nevada, Spain",
URL = "http://cs.jhu.edu/~jason/papers/#andrews-eisner-2011",
}
@InProceedings{gormley-et-al-2011,
author = "Matthew R. Gormley and Mark Dredze and Benjamin {Van
Durme} and Jason Eisner",
title = "Shared Components Topic Models with Application to
Selectional Preference",
booktitle = "NeurIPS Workshop on Learning Semantics",
note = "Extended abstract (3 pages)",
year = "2011",
month = dec,
address = "Sierra Nevada, Spain",
URL = "http://cs.jhu.edu/~jason/papers/#gormley-et-al-2011",
}
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title = {Speech recognition from spectral dynamics},
author = {{H. Hermansky}},
year = 2011,
month = {11},
booktitle = {Sadhana},
url = {https://www.semanticscholar.org/paper/2c2e22c0e23062fc1caecda3b9a5b00abf7fec56},
}
@inproceedings{11550724,
title = {Multi-layer perceptron based speech activity detection for speaker verification},
author = {{Sriram Ganapathy} and {Padmanabhan Rajan} and {H. Hermansky}},
year = 2011,
month = {11},
booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
url = {https://www.semanticscholar.org/paper/65a27221bcef478ab1b7ef717de42c97d33807a7},
}
@inproceedings{65038808,
title = {A Pendulum Swung too Far},
author = {{Kenneth Ward Church}},
year = 2011,
month = {11},
booktitle = {Linguistic Issues in Language Technology},
url = {https://www.semanticscholar.org/paper/38f3a353652713ac478b9e5c80f1479816cc95b0},
}
@inproceedings{28206308,
title = {Human Action Categorization Using Ultrasound Micro-Doppler Signatures},
author = {{S. Dura-Bernal} and {Guillaume Garreau} and {C. M. Andreou} and {A. Andreou} and {J. Georgiou} and {T. Wennekers} and {S. Denham}},
year = 2011,
month = {11},
booktitle = {International Workshop on Human Behavior Unterstanding},
url = {https://www.semanticscholar.org/paper/c6afda2d2fa7306af39dd70c5395142daa8694a6},
}
@inproceedings{120916036,
title = {Dealing with unknown unknowns in speech},
author = {{H. Hermansky}},
year = 2011,
month = {10},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/61181890e46971de51977f3498fc9e6bf63cd937},
}
@inproceedings{7808482,
title = {NADA: A Robust System for Non-referential Pronoun Detection},
author = {{S. Bergsma} and {David Yarowsky}},
year = 2011,
month = {10},
booktitle = {Discourse Anaphora and Anaphor Resolution Colloquium},
url = {https://www.semanticscholar.org/paper/f3fa7a194b813e57282c1503a0b27a91764bb9ee},
}
@inproceedings{137297745,
title = {High Aspect Ratio Fine Gridline for Front Side Metallization of Industrial Silicon Solar Cells by Direct Printing},
author = {{A. Rohatgi} and {I. Cooper} and {H. Yang} and {Kenneth Ward Church} and {X. Chen}},
year = 2011,
month = {10},
booktitle = {},
url = {https://www.semanticscholar.org/paper/548a07ffc1b9f771b7fce1680b2a5755d9867362},
}
@inproceedings{28505411,
title = {Guest Editorial - Special Issue on Selected Papers From BioCAS 2010},
author = {{J. Georgiou} and {A. Andreou}},
year = 2011,
month = {10},
booktitle = {IEEE Trans. Biomed. Circuits Syst.},
url = {https://www.semanticscholar.org/paper/535c18dc7cf4b5ec4484183beca7618f230223c6},
}
@inproceedings{li-etal-2011-minimum,
title = "Minimum Imputed-Risk: Unsupervised Discriminative Training for Machine Translation",
author = "Li, Zhifei and
Wang, Ziyuan and
Eisner, Jason and
Khudanpur, Sanjeev and
Roark, Brian",
editor = "Barzilay, Regina and
Johnson, Mark",
booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
month = jul,
year = "2011",
address = "Edinburgh, Scotland, UK.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D11-1085/",
pages = "920--929"
}
@inproceedings{weese-etal-2011-joshua,
title = "{J}oshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor",
author = "Weese, Jonathan and
Ganitkevitch, Juri and
Callison-Burch, Chris and
Post, Matt and
Lopez, Adam",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Zaidan, Omar F.",
booktitle = "Proceedings of the Sixth Workshop on Statistical Machine Translation",
month = jul,
year = "2011",
address = "Edinburgh, Scotland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-2160/",
pages = "478--484"
}
@inproceedings{ganitkevitch-etal-2011-learning,
title = "Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation",
author = "Ganitkevitch, Juri and
Callison-Burch, Chris and
Napoles, Courtney and
Van Durme, Benjamin",
editor = "Barzilay, Regina and
Johnson, Mark",
booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
month = jul,
year = "2011",
address = "Edinburgh, Scotland, UK.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D11-1108/",
pages = "1168--1179"
}
@inproceedings{deoras-etal-2011-fast,
title = "A Fast Re-scoring Strategy to Capture Long-Distance Dependencies",
author = "Deoras, Anoop and
Mikolov, Tom\'a\v s and
Church, Kenneth",
editor = "Barzilay, Regina and
Johnson, Mark",
booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
month = jul,
year = "2011",
address = "Edinburgh, Scotland, UK.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D11-1103/",
pages = "1116--1127"
}
@inproceedings{chan-etal-2011-reranking,
title = "Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity",
author = "Chan, Tsz Ping and
Callison-Burch, Chris and
Van Durme, Benjamin",
editor = "Pado, Sebastian and
Peirsman, Yves",
booktitle = "Proceedings of the {GEMS} 2011 Workshop on {GE}ometrical Models of Natural Language Semantics",
month = jul,
year = "2011",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-2504/",
pages = "33--42"
}
@inproceedings{callison-burch-etal-2011-findings,
title = "Findings of the 2011 Workshop on Statistical Machine Translation",
author = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Zaidan, Omar",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Zaidan, Omar F.",
booktitle = "Proceedings of the Sixth Workshop on Statistical Machine Translation",
month = jul,
year = "2011",
address = "Edinburgh, Scotland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-2103/",
pages = "22--64"
}
@inproceedings{xu-etal-2011-efficient,
title = "Efficient Subsampling for Training Complex Language Models",
author = "Xu, Puyang and
Gunawardana, Asela and
Khudanpur, Sanjeev",
editor = "Barzilay, Regina and
Johnson, Mark",
booktitle = "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing",
month = jul,
year = "2011",
address = "Edinburgh, Scotland, UK.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D11-1104/",
pages = "1128--1136"
}
@InProceedings{dreyer-eisner-2011,
aclid = "D11-1057",
author = "Markus Dreyer and Jason Eisner",
title = "Discovering Morphological Paradigms from Plain Text
Using a {D}irichlet Process Mixture Model",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "616--627",
note = "Supplementary material (9 pages) also available",
year = "2011",
month = jul,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2011",
}
@InProceedings{li-et-al-2011,
aclid = "D11-1085",
author = "Zhifei Li and Jason Eisner and Ziyuan Wang and Sanjeev
Khudanpur and Brian Roark",
title = "Minimum Imputed Risk: Unsupervised Discriminative
Training for Machine Translation",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "920--929",
year = "2011",
month = jul,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2011",
}
@inproceedings{parada-etal-2011-learning,
title = "Learning Sub-Word Units for Open Vocabulary Speech Recognition",
author = "Parada, Carolina and
Dredze, Mark and
Sethy, Abhinav and
Rastrow, Ariya",
editor = "Lin, Dekang and
Matsumoto, Yuji and
Mihalcea, Rada",
booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P11-1072/",
pages = "712--721"
}
@inproceedings{yao-van-durme-2011-nonparametric,
title = "Nonparametric {B}ayesian Word Sense Induction",
author = "Yao, Xuchen and
Van Durme, Benjamin",
editor = "Matveeva, Irina and
Moschitti, Alessandro and
M\`arquez, Llu\'\i s and
Massimo Zanzotto, Fabio",
booktitle = "Proceedings of {T}ext{G}raphs-6: Graph-based Methods for Natural Language Processing",
month = jun,
year = "2011",
address = "Portland, Oregon",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-1102/",
pages = "10--14"
}
@inproceedings{wang-callison-burch-2011-paraphrase,
title = "Paraphrase Fragment Extraction from Monolingual Comparable Corpora",
author = "Wang, Rui and
Callison-Burch, Chris",
editor = "Zweigenbaum, Pierre and
Rapp, Reinhard and
Sharoff, Serge",
booktitle = "Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web",
month = jun,
year = "2011",
address = "Portland, Oregon",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-1208/",
pages = "52--60"
}
@inproceedings{bergsma-etal-2011-using,
title = "Using Large Monolingual and Bilingual Corpora to Improve Coordination Disambiguation",
author = "Bergsma, Shane and
Yarowsky, David and
Church, Kenneth",
editor = "Lin, Dekang and
Matsumoto, Yuji and
Mihalcea, Rada",
booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P11-1135/",
pages = "1346--1355"
}
@inproceedings{post-2011-judging,
title = "Judging Grammaticality with Tree Substitution Grammar Derivations",
author = "Post, Matt",
editor = "Lin, Dekang and
Matsumoto, Yuji and
Mihalcea, Rada",
booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P11-2038/",
pages = "217--222"
}
@inproceedings{van-durme-lall-2011-efficient,
title = "Efficient Online Locality Sensitive Hashing via Reservoir Counting",
author = "Van Durme, Benjamin and
Lall, Ashwin",
editor = "Lin, Dekang and
Matsumoto, Yuji and
Mihalcea, Rada",
booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P11-2004/",
pages = "18--23"
}
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title = "Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion",
author = "Napoles, Courtney and
Callison-Burch, Chris and
Ganitkevitch, Juri and
Van Durme, Benjamin",
editor = "Filippova, Katja and
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month = jun,
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url = {https://www.semanticscholar.org/paper/791b977f734aa819478749a5e7791bc94bb4c093},
}
@inproceedings{12600270,
title = {Variational approximation of long-span language models for lvcsr},
author = {{Anoop Deoras} and {Tomas Mikolov} and {Stefan Kombrink} and {M. Karafiát} and {S. Khudanpur}},
year = 2011,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d4e81f4fd59723dcb08b1ec6ef30cb115eafda1c},
}
@inproceedings{7610591,
title = {Performance monitoring for robustness in automatic recognition of speechi},
author = {{H. Hermansky} and {N. Mesgarani} and {Samuel Thomas}},
year = 2011,
booktitle = {Symposium on Machine Learning in Speech and Language Processing},
url = {https://www.semanticscholar.org/paper/46d4eed8f4ac08d00620f31fbed127c8249e13c9},
}
@inproceedings{17164940,
title = {Hierarchical Bayesian Models for Latent Attribute Detection in Social Media},
author = {{D. Rao} and {Michael J. Paul} and {Clayton Fink} and {David Yarowsky} and {Timothy Oates} and {Glen A. Coppersmith}},
year = 2011,
month = {7},
booktitle = {International Conference on Web and Social Media},
url = {https://www.semanticscholar.org/paper/0289f1123158d931ebe9539a2fdeb68614ee8972},
}
@inproceedings{11674778,
title = {Statistical Inference on Random Graphs: Comparative Power Analyses via Monte Carlo},
author = {{H. Pao} and {Glen A. Coppersmith} and {C. Priebe}},
year = 2011,
month = {1},
booktitle = {Journal of Computational and Graphical Statistics},
url = {https://www.semanticscholar.org/paper/cfe7962ae47d449d608561a670e23ec6c48d6c71},
}
@inproceedings{59653108,
title = {MLP Based Phoneme Detectors for Speech Recognition},
author = {{Samuel Thomas} and {Patrick Nguyen} and {G. Zweig} and {H. Hermansky}},
year = 2011,
booktitle = {},
url = {https://www.semanticscholar.org/paper/f9574986559ec37286de4a65e556307634eaf0c7},
}
@inproceedings{14606101,
title = {A high-level analytical model for application specific CMP design exploration},
author = {{A. Cassidy} and {Kai Yu} and {Haolang Zhou} and {A. Andreou}},
year = 2011,
month = {3},
booktitle = {Design, Automation and Test in Europe},
url = {https://www.semanticscholar.org/paper/0087d3f48a3014668dc6da36027c134d9850dc13},
}
@inproceedings{218360,
title = {Temporal envelope compensation for robust phoneme recognition using modulation spectrum.},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2010,
month = {12},
booktitle = {Journal of the Acoustical Society of America},
url = {https://www.semanticscholar.org/paper/b5d2363a56ec9fdb080892dbeb6c9c0e9af46cb7},
}
@inproceedings{9336480,
title = {Model combination for Speech Recognition using Empirical Bayes Risk minimization},
author = {{Anoop Deoras} and {Denis Filimonov} and {M. Harper} and {F. Jelinek}},
year = 2010,
month = {12},
booktitle = {2010 IEEE Spoken Language Technology Workshop},
url = {https://www.semanticscholar.org/paper/c9a903827447a39a2b34669f9ffad2505dadec52},
}
@inproceedings{2801713,
title = {Entailment Inference in a Natural Logic-like General Reasoner},
author = {{Lenhart K. Schubert} and {Benjamin Van Durme} and {Marzieh Bazrafshan}},
year = 2010,
month = {11},
booktitle = {AAAI Fall Symposium: Commonsense Knowledge},
url = {https://www.semanticscholar.org/paper/7f21805a4a34fd967d8578b52a102dce09aa5a07},
}
@inproceedings{35894994,
title = {A spike based 3D imager chip using a mixed mode encoding readout},
author = {{Andre Harrison} and {R. Ozgun} and {Joseph H. Lin} and {A. Andreou} and {R. Etienne-Cummings}},
year = 2010,
month = {11},
booktitle = {Biomedical Circuits and Systems Conference},
url = {https://www.semanticscholar.org/paper/0371f6816c8478fc69b80e2c766718d12959db5f},
}
@inproceedings{51622068,
title = {Reviewers for Volume 31},
author = {{Steven P. Abney} and {S. Kurohashi} and {S. Bangalore} and {Irene Langkilde-Geary} and {Christopher Brew} and {Mirella Lapata} and {Sharon A. Caraballo} and {C. Leacock} and {Bob Carpenter} and {B. Levin} and {Stanley F. Chen} and {D. Litman} and {Kenneth Ward Church} and {I. Mani} and {Michael Collins} and {Christopher Manning} and {Ann A. Copestake} and {D. Marcu} and {M. Crocker} and {E. Marsi} and {P. Deane} and {Diana McCarthy} and {Mona T. Diab} and {I. D. Melamed} and {M. Dras} and {J. W. Minett} and {Jason Eisner} and {Robert C. Moore} and {E. Fosler-Lussier} and {Thomas Morton} and {George Foster} and {H. Ney} and {R. Frank} and {G. Ngai} and {Jianfeng Gao} and {Kemal Oflazer} and {Claire Gardent} and {Massimo Poesio} and {Tanja Gaustad van Zaanen} and {Judita Preiss} and {D. Gildea} and {Ehud Reiter} and {Andrew R. Golding} and {P. Resnik} and {Joshua Goodman} and {Roni Rosenfeld} and {G. Grefenstette} and {Frank Schilder} and {Mohammad Haji-Abdolhosseini} and {Lenhart K. Schubert} and {P. Heeman} and {Advaith Siddharthan} and {Derrick Higgins} and {R. Sproat} and {J. Hockenmaier} and {M. Strube} and {H. Horacek} and {M. Swerts} and {Diana Inkpen} and {Simone Teufel} and {Martin Jansche} and {Kees van Deemter} and {Mark Johnson} and {Ye-Yi Wang} and {Frank Keller} and {B. Webber} and {A. Kilgarriff} and {Janyce Wiebe} and {Kevin Knight} and {Florian Wolf}},
year = 2010,
month = {11},
booktitle = {Computational Linguistics},
url = {https://www.semanticscholar.org/paper/d7c3435dfafa3f7fdc546de6dbd53dab74a604a4},
}
@inproceedings{11204110,
title = {Speech Recognition with Segmental Conditional Random Fields: Final Report from the 2010 JHU Summer Workshop},
author = {{G. Zweig} and {Patrick Nguyen} and {Dirk Van Compernolle} and {Kris Demuynck} and {L. Atlas} and {P. Clark} and {Gregory Sell} and {Fei Sha} and {Meihong Wang} and {A. Jansen} and {H. Hermansky} and {Damianos G. Karakos} and {Keith Kintzley} and {Samuel Thomas} and {Sivaram Gsvs} and {Samuel R. Bowman} and {Justine T. Kao}},
year = 2010,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/ed058d8aa1a902006e04a105c8de23cb989c620a},
}
@inproceedings{15532406,
title = {Classifying latent user attributes in twitter},
author = {{D. Rao} and {David Yarowsky} and {Abhishek Shreevats} and {Manaswi Gupta}},
year = 2010,
month = {10},
booktitle = {SMUC '10},
url = {https://www.semanticscholar.org/paper/740f183eb134f75cb943fa9ae0bac97366c9cdcf},
}
@inproceedings{dredze-etal-2010-nlp,
title = "{NLP} on Spoken Documents Without {ASR}",
author = "Dredze, Mark and
Jansen, Aren and
Coppersmith, Glen and
Church, Ken",
editor = "Li, Hang and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2010",
address = "Cambridge, MA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D10-1045/",
pages = "460--470"
}
@inproceedings{17257760,
title = {Frame and arithmetic pipelining for a radix-4 FFT streamed core},
author = {{J. A. Rodriguez} and {P. Julián} and {A. Andreou}},
year = 2010,
month = {10},
booktitle = {Argentine School of Micro-Nanoelectronics, Technology and Applications},
url = {https://www.semanticscholar.org/paper/ef0d9f52109e7382b7a276b2930566ee25bb0204},
}
@inproceedings{dredze-etal-2010-kansas,
title = "We're Not in {K}ansas Anymore: Detecting Domain Changes in Streams",
author = "Dredze, Mark and
Oates, Tim and
Piatko, Christine",
editor = "Li, Hang and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2010",
address = "Cambridge, MA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D10-1057/",
pages = "585--595"
}
@inproceedings{rao-etal-2010-streaming,
title = "Streaming Cross Document Entity Coreference Resolution",
author = "Rao, Delip and
McNamee, Paul and
Dredze, Mark",
editor = "Huang, Chu-Ren and
Jurafsky, Dan",
booktitle = "Coling 2010: Posters",
month = aug,
year = "2010",
address = "Beijing, China",
publisher = "Coling 2010 Organizing Committee",
url = "https://aclanthology.org/C10-2121/",
pages = "1050--1058"
}
@inproceedings{pitler-etal-2010-using,
title = "Using Web-scale N-grams to Improve Base {NP} Parsing Performance",
author = "Pitler, Emily and
Bergsma, Shane and
Lin, Dekang and
Church, Kenneth",
editor = "Huang, Chu-Ren and
Jurafsky, Dan",
booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)",
month = aug,
year = "2010",
address = "Beijing, China",
publisher = "Coling 2010 Organizing Committee",
url = "https://aclanthology.org/C10-1100/",
pages = "886--894"
}
@inproceedings{dredze-etal-2010-entity,
title = "Entity Disambiguation for Knowledge Base Population",
author = "Dredze, Mark and
McNamee, Paul and
Rao, Delip and
Gerber, Adam and
Finin, Tim",
editor = "Huang, Chu-Ren and
Jurafsky, Dan",
booktitle = "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)",
month = aug,
year = "2010",
address = "Beijing, China",
publisher = "Coling 2010 Organizing Committee",
url = "https://aclanthology.org/C10-1032/",
pages = "277--285"
}
@inproceedings{li-etal-2010-unsupervised,
title = "Unsupervised Discriminative Language Model Training for Machine Translation using Simulated Confusion Sets",
author = "Li, Zhifei and
Wang, Ziyuan and
Khudanpur, Sanjeev and
Eisner, Jason",
editor = "Huang, Chu-Ren and
Jurafsky, Dan",
booktitle = "Coling 2010: Posters",
month = aug,
year = "2010",
address = "Beijing, China",
publisher = "Coling 2010 Organizing Committee",
url = "https://aclanthology.org/C10-2075/",
pages = "656--664"
}
@InProceedings{li-et-al-2010,
aclid = "C10-2075",
author = "Zhifei Li and Ziyuan Wang and Sanjeev Khudanpur and
Jason Eisner",
title = "Unsupervised Discriminative Language Model Training
for Machine Translation using Simulated Confusion
Sets",
booktitle = "Proceedings of the 23rd International Conference on
Computational Linguistics (COLING)",
pages = "656--664",
year = "2010",
month = aug,
address = "Beijing",
URL = "http://cs.jhu.edu/~jason/papers/#li-et-al-2010",
}
@inproceedings{callison-burch-etal-2010-findings,
title = "Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation",
author = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Peterson, Kay and
Przybocki, Mark and
Zaidan, Omar",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Peterson, Kay and
Zaidan, Omar",
booktitle = "Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and {M}etrics{MATR}",
month = jul,
year = "2010",
address = "Uppsala, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-1703/",
pages = "17--53"
}
@inproceedings{van-durme-lall-2010-online,
title = "Online Generation of Locality Sensitive Hash Signatures",
author = "Van Durme, Benjamin and
Lall, Ashwin",
editor = "Haji\v c, Jan and
Carberry, Sandra and
Clark, Stephen and
Nivre, Joakim",
booktitle = "Proceedings of the {ACL} 2010 Conference Short Papers",
month = jul,
year = "2010",
address = "Uppsala, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P10-2043/",
pages = "231--235"
}
@inproceedings{li-etal-2010-joshua,
title = "{J}oshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies",
author = "Li, Zhifei and
Callison-Burch, Chris and
Dyer, Chris and
Ganitkevitch, Juri and
Irvine, Ann and
Khudanpur, Sanjeev and
Schwartz, Lane and
Thornton, Wren and
Wang, Ziyuan and
Weese, Jonathan and
Zaidan, Omar",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Peterson, Kay and
Zaidan, Omar",
booktitle = "Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and {M}etrics{MATR}",
month = jul,
year = "2010",
address = "Uppsala, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-1718/",
pages = "133--137"
}
@inproceedings{bloodgood-callison-burch-2010-bucking,
title = "Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation",
author = "Bloodgood, Michael and
Callison-Burch, Chris",
editor = "Haji\v c, Jan and
Carberry, Sandra and
Clark, Stephen and
Nivre, Joakim",
booktitle = "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2010",
address = "Uppsala, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P10-1088/",
pages = "854--864"
}
@inproceedings{momtazi-etal-2010-comparative,
title = "A Comparative Study of Word Co-occurrence for Term Clustering in Language Model-based Sentence Retrieval",
author = "Momtazi, Saeedeh and
Khudanpur, Sanjeev and
Klakow, Dietrich",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1046/",
pages = "325--328"
}
@inproceedings{napoles-dredze-2010-learning,
title = "Learning {S}imple {W}ikipedia: A Cogitation in Ascertaining Abecedarian Language",
author = "Napoles, Courtney and
Dredze, Mark",
editor = "Piotrowski, Michael and
Mahlow, Cerstin and
Dale, Robert",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids",
month = jun,
year = "2010",
address = "Los Angeles, CA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0406/",
pages = "42--50"
}
@inproceedings{parada-etal-2010-contextual,
title = "Contextual Information Improves {OOV} Detection in Speech",
author = "Parada, Carolina and
Dredze, Mark and
Filimonov, Denis and
Jelinek, Frederick",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1025/",
pages = "216--224"
}
@inproceedings{bloodgood-callison-burch-2010-using,
title = "Using {M}echanical {T}urk to Build Machine Translation Evaluation Sets",
author = "Bloodgood, Michael and
Callison-Burch, Chris",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0733/",
pages = "208--211"
}
@inproceedings{gormley-etal-2010-non,
title = "Non-Expert Correction of Automatically Generated Relation Annotations",
author = "Gormley, Matthew R. and
Gerber, Adam and
Harper, Mary and
Dredze, Mark",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0732/",
pages = "204--207"
}
@inproceedings{finin-etal-2010-annotating,
title = "Annotating Named Entities in {T}witter Data with Crowdsourcing",
author = "Finin, Tim and
Murnane, William and
Karandikar, Anand and
Keller, Nicholas and
Martineau, Justin and
Dredze, Mark",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0713/",
pages = "80--88"
}
@inproceedings{gordon-etal-2010-evaluation,
title = "Evaluation of Commonsense Knowledge with {M}echanical {T}urk",
author = "Gordon, Jonathan and
Van Durme, Benjamin and
Schubert, Lenhart",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0724/",
pages = "159--162"
}
@inproceedings{rastrow-etal-2010-unsupervised,
title = "Unsupervised Model Adaptation using Information-Theoretic Criterion",
author = "Rastrow, Ariya and
Jelinek, Frederick and
Sethy, Abhinav and
Ramabhadran, Bhuvana",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1022/",
pages = "190--197"
}
@inproceedings{levenberg-etal-2010-stream,
title = "Stream-based Translation Models for Statistical Machine Translation",
author = "Levenberg, Abby and
Callison-Burch, Chris and
Osborne, Miles",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1062/",
pages = "394--402"
}
@inproceedings{callison-burch-dredze-2010-creating,
title = "Creating Speech and Language Data With {A}mazon's {M}echanical {T}urk",
author = "Callison-Burch, Chris and
Dredze, Mark",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0701/",
pages = "1--12"
}
@inproceedings{novotney-callison-burch-2010-cheap,
title = "Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription",
author = "Novotney, Scott and
Callison-Burch, Chris",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1024/",
pages = "207--215"
}
@inproceedings{wang-callison-burch-2010-cheap,
title = "Cheap Facts and Counter-Facts",
author = "Wang, Rui and
Callison-Burch, Chris",
editor = "Callison-Burch, Chris and
Dredze, Mark",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Workshop on Creating Speech and Language Data with {A}mazon's Mechanical Turk",
month = jun,
year = "2010",
address = "Los Angeles",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-0725/",
pages = "163--167"
}
@inproceedings{zaidan-callison-burch-2010-predicting,
title = "Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators",
author = "Zaidan, Omar F. and
Callison-Burch, Chris",
editor = "Kaplan, Ron and
Burstein, Jill and
Harper, Mary and
Penn, Gerald",
booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N10-1057/",
pages = "369--372"
}
@inproceedings{chung-etal-2010-factors,
title = "Factors Affecting the Accuracy of {K}orean Parsing",
author = "Chung, Tagyoung and
Post, Matt and
Gildea, Daniel",
editor = "Seddah, Djame and
Koebler, Sandra and
Tsarfaty, Reut",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages",
month = jun,
year = "2010",
address = "Los Angeles, CA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-1406/",
pages = "49--57"
}
While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges.
@inproceedings{lin-etal-2010-new,
title = "New Tools for Web-Scale N-grams",
author = "Lin, Dekang and
Church, Kenneth and
Ji, Heng and
Sekine, Satoshi and
Yarowsky, David and
Bergsma, Shane and
Patil, Kailash and
Pitler, Emily and
Lathbury, Rachel and
Rao, Vikram and
Dalwani, Kapil and
Narsale, Sushant",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L10-1158/",
abstract = "While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges."
}
@inproceedings{7114327,
title = {Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, WMT@ACL 2010, Uppsala, Sweden, July 15-16, 2010},
author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {Kay Peterson} and {Omar Zaidan}},
year = 2010,
month = {7},
booktitle = {WMT@ACL},
url = {https://www.semanticscholar.org/paper/19c253bafd9fc88e00d3bb234f167cde5c732ed7},
}
@inproceedings{2045383,
title = {Robust spectro-temporal features based on autoregressive models of Hilbert envelopes},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2010,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/c25a5742b9ef703683da13d529b0189d5fbd46cc},
}
@inproceedings{616568,
title = {NUMBER 93 JANUARY 2010 157 – 166 Hierarchical Phrase-Based Grammar Extraction in Joshua Suffix Arrays and Prefix Trees},
author = {{Lane Schwartz} and {Chris Callison-Burch}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3a97ec98f1678f4c2932e5fe20ca224c06125922},
}
@inproceedings{4541668,
title = {PERFORMANCE OF ENHANCED-UMTS HSDPA USING TRANSMIT DIVERSITY AND POWER CONTROL SCHEMES},
author = {{L. Bahl} and {J. Cocke} and {F. Jelinek} and {J. Raviv}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b76a6509ead3f10a8d81e8c8ee306f2b374d6f77},
}
@inproceedings{209337018,
title = {A greedy algorithm for sparse recovery using precise metrics},
author = {{Balakrishnan Varadarajan} and {S. Khudanpur} and {T. Tran}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9111c2e64cfa194d27fe2aaf869b4e65fed0be51},
}
@inproceedings{62253545,
title = {Web N-gram Workshop Workshop of the 33 rd Annual International},
author = {{Acm Sigir} and {ChengXiang Zhai} and {David Yarowsky} and {E. Viegas} and {Kuansan Wang} and {S. Vogel}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b3709cce58b27b1f90fe4fab68e56cb8cb1c5291},
}
@inproceedings{120830830,
title = {Posterior‐based attributes in machine recognition of speech.},
author = {{H. Hermansky}},
year = 2010,
month = {3},
booktitle = {},
url = {https://www.semanticscholar.org/paper/104e01f12c6ef3b1dad8ab93d4250e5fdccfe6af},
}
@inproceedings{15828105,
title = {Unsupervised Acquisition of Lexical Knowledge From N-grams : Final Report of the 2009 JHU CLSP Workshop},
author = {{Dekang Lin} and {Kenneth Ward Church} and {Heng Ji} and {S. Sekine} and {David Yarowsky} and {S. Bergsma} and {Kailash Patil} and {Emily Pitler} and {Rachel Lathbury} and {Vikram Rao} and {Kapil Dalwani} and {Sushant Narsale}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b268eb411c846b04159a0bef2c30e30489517713},
}
@inproceedings{14362083,
title = {Cross-lingual and multi-stream posterior features for low resource LVCSR systems},
author = {{Samuel Thomas} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/7ceb4cb210ecd38a18d370dabdeb8f09f76f7446},
}
@inproceedings{4632000,
title = {Sparse coding for speech recognition},
author = {{Garimella S. V. S. Sivaram} and {Sridhar Krishna Nemala} and {Mounya Elhilali} and {T. Tran} and {H. Hermansky}},
year = 2010,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/365cabf42c8d5a57a5843ec52cc62d43f2e1bfba},
}
@inproceedings{15099587,
title = {Data-Driven and Feedback Based Spectro-Temporal Features for Speech Recognition},
author = {{Garimella S. V. S. Sivaram} and {Sridhar Krishna Nemala} and {N. Mesgarani} and {H. Hermansky}},
year = 2010,
month = {9},
booktitle = {IEEE Signal Processing Letters},
url = {https://www.semanticscholar.org/paper/a5716b84696755dc64fed8f3d89a393061989ce6},
}
@inproceedings{7427096,
title = {Towards spoken term discovery at scale with zero resources},
author = {{A. Jansen} and {Kenneth Ward Church} and {H. Hermansky}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/7a29bbb30bf72cfc7ac52a351a04a1178e29dd7f},
}
@inproceedings{94175074,
title = {Air‐Operable, High‐Mobility Organic Transistors with Semifluorinated Side Chains and Unsubstituted Naphthalenetetracarboxylic Diimide Cores: High Mobility and Environmental and Bias Stress Stability from the Perfluorooctylpropyl Side Chain},
author = {{Byung-Jun Jung} and {Kyu-Chul Lee} and {Jia Sun} and {A. Andreou} and {H. Katz}},
year = 2010,
month = {9},
booktitle = {Advanced Functional Materials},
url = {https://www.semanticscholar.org/paper/ab3af82be39b3bd0a9b7685ecb6121e1fb42596e},
}
@inproceedings{112934259,
title = {Direct Printing/Micro-dispensing Solution for 3D Coating Applications},
author = {{Xudong Chen} and {Kenneth Ward Church}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/84a2929615a231151bb7177d1fe495181538fdcb},
}
@inproceedings{417567,
title = {Autoregressive Models of Amplitude Modulations in Audio Compression},
author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky}},
year = 2010,
month = {8},
booktitle = {IEEE Transactions on Audio, Speech, and Language Processing},
url = {https://www.semanticscholar.org/paper/7d5e611f3c13b0fd1445b98563664e65f48a5c63},
}
@inproceedings{109435897,
title = {Optimum bias of CMOS organic field effect transistor inverter through threshold adjustment of both p- and n-type devices},
author = {{B. Dhar} and {R. Ozgun} and {Byung-Jun Jung} and {H. Katz} and {A. Andreou}},
year = 2010,
month = {9},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/8f9b6992e3a852b739d2612646f07940b7e0ed33},
}
@inproceedings{11301541,
title = {Learning from the Web: Extracting General World Knowledge from Noisy Text},
author = {{Jonathan Gordon} and {Benjamin Van Durme} and {Lenhart K. Schubert}},
year = 2010,
booktitle = {Collaboratively-Built Knowledge Sources and AI},
url = {https://www.semanticscholar.org/paper/2a42a1df4860e026a1b5eb9973c3b5c7b427c079},
}
@inproceedings{38146728,
title = {Detecting Latent User Properties in Social Media},
author = {{D. Rao} and {David Yarowsky}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/d815d10afdeb957aa688c47e2efdb107db0e921c},
}
@inproceedings{42420298,
title = {Recovery of Rare Words in Lecture Speech},
author = {{Stefan Kombrink} and {M. Hannemann} and {L. Burget} and {H. Hermansky}},
year = 2010,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/54ea8716151e1d2727c6cd63b5ebb4f51b8afff4},
}
@inproceedings{12295680,
title = {Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk},
author = {{Chris Callison-Burch} and {Mark Dredze}},
year = 2010,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/e79470389fe3f73d37e8fc099439cd17e1c7748d},
}
@inproceedings{218982630,
title = {More is More},
author = {{Kenneth Ward Church}},
year = 2010,
booktitle = {A Way with Words},
url = {https://www.semanticscholar.org/paper/33d70a6cbf5fd15a865ebc09bfa134477b65b616},
}
@inproceedings{26704443,
title = {History of modulation spectrum in ASR},
author = {{H. Hermansky}},
year = 2010,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/efe183fd3f3ae6cd5a7c0ca7fdc18918e3104888},
}
@inproceedings{19277242,
title = {PWL cores for nonlinear array processing},
author = {{M. D. Federico} and {P. Julián} and {P. Mandolesi} and {A. Andreou}},
year = 2010,
month = {8},
booktitle = {Proceedings of 2010 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/3d1a3da98efd49701de9f25898812b663250f8af},
}
@inproceedings{122928409,
title = {Discriminative training and variational decoding in machine translation via novel algorithms for weighted hypergraphs},
author = {{S. Khudanpur} and {Zhifei Li}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/ab04d2d1fdd3c6744ce77b005b81b0cc460bdd89},
}
@inproceedings{14073180,
title = {Two Self-supervised Learning Techniques for Speech Recognition},
author = {{Damianos G. Karakos} and {Haolang Zhou} and {Puyang Xu} and {S. Khudanpur} and {A. Andreou}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/59e9355329b0418f99608a9d5c615963bcd495f1},
}
@inproceedings{1023659,
title = {A spoken term detection framework for recovering out-of-vocabulary words using the web},
author = {{Carolina Parada} and {A. Sethy} and {Mark Dredze} and {F. Jelinek}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/c936ae825c7d6acd856d935564db78a455016e40},
}
@inproceedings{124482575,
title = {Chip-Scale Absolute Scalar Magnetometer for Space Applications},
author = {{H. Korth} and {K. Strohbehn} and {Francisco Tejada} and {A. Andreou} and {S. Mcveigh} and {J. Kitching} and {S. Knappe}},
year = 2010,
booktitle = {Johns Hopkins Apl Technical Digest},
url = {https://www.semanticscholar.org/paper/70af5c414fda1d60416d275e8ab73837306a652f},
}
@inproceedings{88492782,
title = {'HFRGLQJ LQ -RVKXD},
author = {{Lane Schwartz} and {Chris Callison-Burch}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/08ebc9cff52a98a4573995e3d91a2cdd519d177c},
}
@inproceedings{16064355,
title = {Flexible Readout and Integration Sensor (FRIS): New Class of Imaging Sensor Arrays Optimized for Air and Missile Defense},
author = {{Charbel G. Rizk} and {P. Pouliquen} and {A. Andreou}},
year = 2010,
booktitle = {Johns Hopkins Apl Technical Digest},
url = {https://www.semanticscholar.org/paper/8e20e16338c79dddcab19c1afd95c1fd30d7dfcf},
}
@inproceedings{17048224,
title = {Recurrent neural network based language model},
author = {{Tomas Mikolov} and {M. Karafiát} and {L. Burget} and {J. Černocký} and {S. Khudanpur}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/9819b600a828a57e1cde047bbe710d3446b30da5},
}
Much of the previous work on transliteration has depended on resources and attributes specific to particular language pairs. In this work, rather than focus on a single language pair, we create robust models for transliterating from all languages in a large, diverse set to English. We create training data for 150 languages by mining name pairs from Wikipedia. We train 13 systems and analyze the effects of the amount of training data on transliteration performance. We also present an analysis of the types of errors that the systems make. Our analyses are particularly valuable for building machine translation systems for low resource languages, where creating and integrating a transliteration module for a language with few NLP resources may provide substantial gains in translation performance.
@inproceedings{irvine-etal-2010-transliterating,
title = "Transliterating From All Languages",
author = "Irvine, Ann and
Callison-Burch, Chris and
Klementiev, Alexandre",
booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 31-" # nov # " 4",
year = "2010",
address = "Denver, Colorado, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2010.amta-papers.12/",
abstract = "Much of the previous work on transliteration has depended on resources and attributes specific to particular language pairs. In this work, rather than focus on a single language pair, we create robust models for transliterating from all languages in a large, diverse set to English. We create training data for 150 languages by mining name pairs from Wikipedia. We train 13 systems and analyze the effects of the amount of training data on transliteration performance. We also present an analysis of the types of errors that the systems make. Our analyses are particularly valuable for building machine translation systems for low resource languages, where creating and integrating a transliteration module for a language with few NLP resources may provide substantial gains in translation performance."
}
@inproceedings{597808,
title = {A phoneme recognition framework based on auditory spectro-temporal receptive fields},
author = {{Samuel Thomas} and {Kailash Patil} and {Sriram Ganapathy} and {N. Mesgarani} and {H. Hermansky}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/9a8ae9d3f65111724ca0ad5f516c7574fc73df7e},
}
@inproceedings{14461089,
title = {Models for Synchronous Grammar Induction},
author = {{Chris Callison-Burch} and {Trevor Cohn} and {Chris Dyer} and {Jonathan Graehl} and {Adam Lopez} and {Jan A. Botha} and {Vladimir Eidelman} and {ThuyLinh Nguyen} and {Ziyuan Wang} and {Jonathan Weese} and {Olivia Buzek} and {Desai Chen}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/0565d24885fb253c45ce46d2411b0267743de810},
}
@inproceedings{3131310,
title = {The use of spike-based representations for hardware audition systems},
author = {{Shih-Chii Liu} and {N. Mesgarani} and {J. Harris} and {H. Hermansky}},
year = 2010,
month = {8},
booktitle = {Proceedings of 2010 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/4a26d523c302fd5c9a1db152021f29e390df67db},
}
@inproceedings{10205241,
title = {Sparse auto-associative neural networks: theory and application to speech recognition},
author = {{Garimella S. V. S. Sivaram} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/348a4b11c2543072471747533553cbb579181d2b},
}
@inproceedings{17247831,
title = {Shared task: crowdsourced accessibility elicitation of Wikipedia articles},
author = {{Scott Novotney} and {Chris Callison-Burch}},
year = 2010,
month = {6},
booktitle = {HLT-NAACL 2010},
url = {https://www.semanticscholar.org/paper/2327f9117d9c8daa98ca80d3d05cbcd827d91d80},
}
@inproceedings{7822049,
title = {Multi-domain learning by confidence-weighted parameter combination},
author = {{Mark Dredze} and {Alex Kulesza} and {K. Crammer}},
year = 2010,
month = {5},
booktitle = {Machine-mediated learning},
url = {https://www.semanticscholar.org/paper/5959ca92fe68e5c06fa4feedc32d9a94d1b2c03a},
}
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality–-and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.
@inproceedings{baker-etal-2010-semantically,
title = "Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach",
author = "Baker, Kathryn and
Bloodgood, Michael and
Callison-Burch, Chris and
Dorr, Bonnie and
Filardo, Nathaniel and
Levin, Lori and
Miller, Scott and
Piatko, Christine",
booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 31-" # nov # " 4",
year = "2010",
address = "Denver, Colorado, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2010.amta-papers.7/",
abstract = "We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality---and further demonstrates that large gains can be achieved for low-resource languages with different word order than English."
}
@inproceedings{15119111,
title = {Hypothesis ranking and two-pass approaches for machine translation system combination},
author = {{Damianos G. Karakos} and {Jason R. Smith} and {S. Khudanpur}},
year = 2010,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/508ff046d420cb6594aabb884a254dc4163b190c},
}
@inproceedings{263890750,
title = {Preface},
author = {{Catherine Havasi} and {Douglas B. Lenat} and {Benjamin Van Durme}},
year = 2010,
booktitle = {AAAI Fall Symposium: Commonsense Knowledge},
url = {https://www.semanticscholar.org/paper/699748263b85a2dba4fa2a203e079e545695b711},
}
@inproceedings{8374317,
title = {Comparison of modulation features for phoneme recognition},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2010,
month = {3},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/e5686dccacc769ea2765e70ba0a17725c14143a9},
}
@inproceedings{38682978,
title = {Word Sense Disambiguation},
author = {{David Yarowsky}},
year = 2010,
booktitle = {Handbook of Natural Language Processing},
url = {https://www.semanticscholar.org/paper/aa9dc3e361f50b52d11cd266de4871046c12733d},
}
@inproceedings{12217795,
title = {Visualizing Data Structures in Parsing-Based Machine Translation},
author = {{Jonathan Weese} and {Chris Callison-Burch}},
year = 2010,
booktitle = {Prague Bulletin of Mathematical Linguistics},
url = {https://www.semanticscholar.org/paper/2271401a236b80e9ab352067a2f363515b42f130},
}
@inproceedings{57091916,
title = {Commonsense knowledge : papers from the AAAI Fall Symposium},
author = {{Catherine Havasi} and {D. Lenat} and {Benjamin Van Durme}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/0121c30e08749c884975e6e346a663d01ff81ef1},
}
@inproceedings{14129598,
title = {Exploiting Feature Covariance in High-Dimensional Online Learning},
author = {{Justin Ma} and {Alex Kulesza} and {Mark Dredze} and {K. Crammer} and {L. Saul} and {Fernando C Pereira}},
year = 2010,
month = {3},
booktitle = {International Conference on Artificial Intelligence and Statistics},
url = {https://www.semanticscholar.org/paper/6b5061fbbe1727c0dabbbed48012cbfac7e255c9},
}
@inproceedings{8384802,
title = {Hierarchical Phrase-Based Grammar Extraction in Joshua:},
author = {{Lane Schwartz} and {Chris Callison-Burch}},
year = 2010,
booktitle = {Prague Bulletin of Mathematical Linguistics},
url = {https://www.semanticscholar.org/paper/f58bf7e514da19263743ec736a80f571cc30eb91},
}
@inproceedings{65076078,
title = {Three topics in single-chip parallel computing: theoretical foundations, speech recognition, and the silicon cortex},
author = {{A. Andreou} and {A. Cassidy}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/1acb073706f67208f9188ebe68285303079ac073},
}
@inproceedings{5552588,
title = {Integrating Output from Specialized Modules in Machine Translation: Transliterations in Joshua},
author = {{Ann Irvine} and {Mike Kayser} and {Zhifei Li} and {Wren N. G. Thornton} and {Chris Callison-Burch}},
year = 2010,
booktitle = {Prague Bulletin of Mathematical Linguistics},
url = {https://www.semanticscholar.org/paper/bfbe11202871ad92c6a82db05c165a15bd843359},
}
@inproceedings{14790161,
title = {Wide-Band Audio Coding Based on Frequency-Domain Linear Prediction},
author = {{P. Motlícek} and {Sriram Ganapathy} and {H. Hermansky} and {H. Garudadri}},
year = 2010,
booktitle = {EURASIP Journal on Audio, Speech, and Music Processing},
url = {https://www.semanticscholar.org/paper/fdf0a0c45c3a4ebcd505815193770a398e2de5b9},
}
@inproceedings{12003886,
title = {A multistream multiresolution framework for phoneme recognition},
author = {{N. Mesgarani} and {Samuel Thomas} and {H. Hermansky}},
year = 2010,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/4b76d644d4946438d34b40db30a61643c6c39bb0},
}
@inproceedings{10179133,
title = {HLTCOE Technical Reports},
author = {{A. Klementiev} and {Chris Callison-Burch} and {Ann Irvine}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c2b70267e9bb6ae59b744b48741af0463900c9b7},
}
@inproceedings{61231407,
title = {Syntax-based language models for statistical machine translation},
author = {{D. Gildea} and {Matt Post}},
year = 2010,
booktitle = {},
url = {https://www.semanticscholar.org/paper/4611bb703286114d36a67a1853ac4b6f700d7439},
}
@inproceedings{11487120,
title = {Fully integrated 500uW speech detection wake-up circuit},
author = {{T. Delbrück} and {T. Koch} and {R. Berner} and {H. Hermansky}},
year = 2010,
month = {8},
booktitle = {Proceedings of 2010 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/20d6446f45669e1130376a826bbc0423ba786a22},
}
@inproceedings{14626552,
title = {Applications of signal analysis using autoregressive models for amplitude modulation},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {P. Motlícek} and {H. Hermansky}},
year = 2009,
month = {12},
booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
url = {https://www.semanticscholar.org/paper/0d1708547eb4ebf51718eead4696eca4ce69a92b},
}
@inproceedings{15223401,
title = {Iterative decoding: A novel re-scoring framework for confusion networks},
author = {{Anoop Deoras} and {F. Jelinek}},
year = 2009,
month = {12},
booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/1c4b03de4d1afea69833ff29d5a105de0ddab8d4},
}
@inproceedings{1338321,
title = {Temporal envelope subtraction for robust speech recognition using modulation spectrum},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2009,
month = {12},
booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
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}
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title = {Self-supervised discriminative training of statistical language models},
author = {{Puyang Xu} and {Damianos G. Karakos} and {S. Khudanpur}},
year = 2009,
month = {12},
booktitle = {2009 IEEE Workshop on Automatic Speech Recognition & Understanding},
url = {https://www.semanticscholar.org/paper/0963942fdcba17f7b94d1d636431d4a772476711},
}
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title = {HLTCOE Approaches to Knowledge Base Population at TAC 2009},
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year = 2009,
month = {11},
booktitle = {Text Analysis Conference},
url = {https://www.semanticscholar.org/paper/35cbf98266b94d7d31d67e09faf57f8ea6f2204f},
}
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title = {Likelihood-Based Semi-Supervised Model Selection With Applications to Speech Processing},
author = {{Christopher M. White} and {S. Khudanpur} and {P. Wolfe}},
year = 2009,
month = {11},
booktitle = {IEEE Journal on Selected Topics in Signal Processing},
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}
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title = {MDCT for Encoding Residual Signals in Frequency Domain Linear Prediction},
author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky}},
year = 2009,
month = {10},
booktitle = {Journal of The Audio Engineering Society},
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title = {Data-Derived Models for Segmentation with Application to Surgical Assessment and Training},
author = {{Balakrishnan Varadarajan} and {C. Reiley} and {Henry Lin} and {S. Khudanpur} and {Gregory Hager}},
year = 2009,
month = {10},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
url = {https://www.semanticscholar.org/paper/067ec3a26063e20984d61aecfd93f36743a8f706},
}
@inproceedings{rao-yarowsky-2009-ranking,
title = "Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-Reduce",
author = "Rao, Delip and
Yarowsky, David",
editor = "Choudhury, Monojit and
Hassan, Samer and
Mukherjee, Animesh and
Muresan, Smaranda",
booktitle = "Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing ({T}ext{G}raphs-4)",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W09-3209/",
pages = "58--65"
}
@inproceedings{crammer-etal-2009-multi,
title = "Multi-Class Confidence Weighted Algorithms",
author = "Crammer, Koby and
Dredze, Mark and
Kulesza, Alex",
editor = "Koehn, Philipp and
Mihalcea, Rada",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1052/",
pages = "496--504"
}
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title = "Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases",
author = "Marton, Yuval and
Callison-Burch, Chris and
Resnik, Philip",
editor = "Koehn, Philipp and
Mihalcea, Rada",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1040/",
pages = "381--390"
}
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title = "What lies beneath: Semantic and syntactic analysis of manually reconstructed spontaneous speech",
author = "Fitzgerald, Erin and
Jelinek, Frederick and
Frank, Robert",
editor = "Su, Keh-Yih and
Su, Jian and
Wiebe, Janyce and
Li, Haizhou",
booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P09-1084/",
pages = "746--754"
}
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title = "Integrating sentence- and word-level error identification for disfluency correction",
author = "Fitzgerald, Erin and
Jelinek, Frederick and
Hall, Keith",
editor = "Koehn, Philipp and
Mihalcea, Rada",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1080/",
pages = "765--774"
}
@inproceedings{zaidan-callison-burch-2009-feasibility,
title = "Feasibility of Human-in-the-loop Minimum Error Rate Training",
author = "Zaidan, Omar F. and
Callison-Burch, Chris",
editor = "Koehn, Philipp and
Mihalcea, Rada",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1006/",
pages = "52--61"
}
@inproceedings{garera-yarowsky-2009-modeling,
title = "Modeling Latent Biographic Attributes in Conversational Genres",
author = "Garera, Nikesh and
Yarowsky, David",
editor = "Su, Keh-Yih and
Su, Jian and
Wiebe, Janyce and
Li, Haizhou",
booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P09-1080/",
pages = "710--718"
}
@inproceedings{callison-burch-2009-fast,
title = "Fast, Cheap, and Creative: Evaluating Translation Quality Using {A}mazon's {M}echanical {T}urk",
author = "Callison-Burch, Chris",
editor = "Koehn, Philipp and
Mihalcea, Rada",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1030/",
pages = "286--295"
}
@inproceedings{sayeed-etal-2009-arabic,
title = "{A}rabic Cross-Document Coreference Resolution",
author = "Sayeed, Asad and
Elsayed, Tamer and
Garera, Nikesh and
Alexander, David and
Xu, Tan and
Oard, Doug and
Yarowsky, David and
Piatko, Christine",
editor = "Su, Keh-Yih and
Su, Jian and
Wiebe, Janyce and
Li, Haizhou",
booktitle = "Proceedings of the {ACL}-{IJCNLP} 2009 Conference Short Papers",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P09-2090/",
pages = "357--360"
}
@inproceedings{li-etal-2009-variational,
title = "Variational Decoding for Statistical Machine Translation",
author = "Li, Zhifei and
Eisner, Jason and
Khudanpur, Sanjeev",
editor = "Su, Keh-Yih and
Su, Jian and
Wiebe, Janyce and
Li, Haizhou",
booktitle = "Proceedings of the Joint Conference of the 47th Annual Meeting of the {ACL} and the 4th International Joint Conference on Natural Language Processing of the {AFNLP}",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P09-1067/",
pages = "593--601"
}
@inproceedings{li-etal-2009-demonstration,
title = "Demonstration of {J}oshua: An Open Source Toolkit for Parsing-based Machine Translation",
author = "Li, Zhifei and
Callison-Burch, Chris and
Dyer, Chris and
Ganitkevitch, Juri and
Khudanpur, Sanjeev and
Schwartz, Lane and
Thornton, Wren N. G. and
Weese, Jonathan and
Zaidan, Omar F.",
editor = "Lee, Gary Geunbae and
Schulte im Walde, Sabine",
booktitle = "Proceedings of the {ACL}-{IJCNLP} 2009 Software Demonstrations",
month = aug,
year = "2009",
address = "Suntec, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P09-4007/",
pages = "25--28"
}
@InProceedings{li-eisner-2009,
aclid = "D09-1005",
author = "Zhifei Li and Jason Eisner",
title = "First- and Second-Order Expectation Semirings with
Applications to Minimum-Risk Training on Translation
Forests",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "40--51",
year = "2009",
month = aug,
address = "Singapore",
URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-2009",
}
@InProceedings{dreyer-eisner-2009,
aclid = "D09-1011",
author = "Markus Dreyer and Jason Eisner",
title = "Graphical Models over Multiple Strings",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "101--110",
year = "2009",
month = aug,
address = "Singapore",
URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2009",
}
@InProceedings{smith-eisner-2009,
aclid = "D09-1086",
author = "David A. Smith and Jason Eisner",
title = "Parser Adaptation and Projection with
Quasi-Synchronous Grammar Features",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "822--831",
year = "2009",
month = aug,
address = "Singapore",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2009",
}
@InProceedings{tromble-eisner-2009,
aclid = "D09-1105",
author = "Roy Tromble and Jason Eisner",
title = "Learning Linear Ordering Problems for Better
Translation",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "1007--1016",
year = "2009",
month = aug,
address = "Singapore",
URL = "http://cs.jhu.edu/~jason/papers/#tromble-eisner-2009",
}
@InProceedings{li-eisner-khudanpur-2009,
aclid = "P09-1067",
author = "Zhifei Li and Jason Eisner and Sanjeev Khudanpur",
title = "Variational Decoding for Statistical Machine
Translation",
booktitle = "Proceedings of the 47th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "593--601",
year = "2009",
month = aug,
address = "Singapore",
note = "Nominated for Best Paper Award.",
URL = "http://cs.jhu.edu/~jason/papers/#li-eisner-khudanpur-2009",
}
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title = "Efficient Extraction of Oracle-best Translations from Hypergraphs",
author = "Li, Zhifei and
Khudanpur, Sanjeev",
editor = "Ostendorf, Mari and
Collins, Michael and
Narayanan, Shri and
Oard, Douglas W. and
Vanderwende, Lucy",
booktitle = "Proceedings of Human Language Technologies: The 2009 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers",
month = jun,
year = "2009",
address = "Boulder, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N09-2003/",
pages = "9--12"
}
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title = "Improving Translation Lexicon Induction from Monolingual Corpora via Dependency Contexts and Part-of-Speech Equivalences",
author = "Garera, Nikesh and
Callison-Burch, Chris and
Yarowsky, David",
editor = "Stevenson, Suzanne and
Carreras, Xavier",
booktitle = "Proceedings of the Thirteenth Conference on Computational Natural Language Learning ({C}o{NLL}-2009)",
month = jun,
year = "2009",
address = "Boulder, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W09-1117/",
pages = "129--137"
}
@inproceedings{li-etal-2009-joshua,
title = "{J}oshua: An Open Source Toolkit for Parsing-Based Machine Translation",
author = "Li, Zhifei and
Callison-Burch, Chris and
Dyer, Chris and
Khudanpur, Sanjeev and
Schwartz, Lane and
Thornton, Wren and
Weese, Jonathan and
Zaidan, Omar",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Schroeder, Josh",
booktitle = "Proceedings of the Fourth Workshop on Statistical Machine Translation",
month = mar,
year = "2009",
address = "Athens, Greece",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W09-0424/",
pages = "135--139"
}
@inproceedings{fitzgerald-etal-2009-reconstructing,
title = "Reconstructing False Start Errors in Spontaneous Speech Text",
author = "Fitzgerald, Erin and
Hall, Keith and
Jelinek, Frederick",
editor = "Lascarides, Alex and
Gardent, Claire and
Nivre, Joakim",
booktitle = "Proceedings of the 12th Conference of the {E}uropean Chapter of the {ACL} ({EACL} 2009)",
month = mar,
year = "2009",
address = "Athens, Greece",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E09-1030/",
pages = "255--263"
}
@inproceedings{garera-yarowsky-2009-structural,
title = "Structural, Transitive and Latent Models for Biographic Fact Extraction",
author = "Garera, Nikesh and
Yarowsky, David",
editor = "Lascarides, Alex and
Gardent, Claire and
Nivre, Joakim",
booktitle = "Proceedings of the 12th Conference of the {E}uropean Chapter of the {ACL} ({EACL} 2009)",
month = mar,
year = "2009",
address = "Athens, Greece",
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url = "https://aclanthology.org/E09-1035/",
pages = "300--308"
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title = "Findings of the 2009 {W}orkshop on {S}tatistical {M}achine {T}ranslation",
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editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Schroeder, Josh",
booktitle = "Proceedings of the Fourth Workshop on Statistical Machine Translation",
month = mar,
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pages = "1--28"
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Ralph Weischedel and Tan Xu and David Yarowsky",
title = "Cross-Document Coreference Resolution: {A} Key
Technology for Learning by Reading",
booktitle = "Proceedings of the AAAI 2009 Spring Symposium on
Learning by Reading and Learning to Read",
year = "2009",
month = mar,
address = "Stanford",
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URL = "http://cs.jhu.edu/~jason/papers/#dreyer-smith-eisner-2008",
}
@inproceedings{rao-etal-2008-affinity,
title = "Affinity Measures Based on the Graph {L}aplacian",
author = "Rao, Delip and
Yarowsky, David and
Callison-Burch, Chris",
editor = "Matveeva, Irina and
Biemann, Chris and
Choudhury, Monojit and
Diab, Mona",
booktitle = "Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing",
month = aug,
year = "2008",
address = "Manchester, UK",
publisher = "Coling 2008 Organizing Committee",
url = "https://aclanthology.org/W08-2006/",
pages = "41--48"
}
@inproceedings{callison-burch-etal-2008-parametric,
title = "{P}ara{M}etric: An Automatic Evaluation Metric for Paraphrasing",
author = "Callison-Burch, Chris and
Cohn, Trevor and
Lapata, Mirella",
editor = "Scott, Donia and
Uszkoreit, Hans",
booktitle = "Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)",
month = aug,
year = "2008",
address = "Manchester, UK",
publisher = "Coling 2008 Organizing Committee",
url = "https://aclanthology.org/C08-1013/",
pages = "97--104"
}
@inproceedings{varadarajan-etal-2008-unsupervised,
title = "Unsupervised Learning of Acoustic Sub-word Units",
author = "Varadarajan, Balakrishnan and
Khudanpur, Sanjeev and
Dupoux, Emmanuel",
editor = "Moore, Johanna D. and
Teufel, Simone and
Allan, James and
Furui, Sadaoki",
booktitle = "Proceedings of ACL-08: HLT, Short Papers",
month = jun,
year = "2008",
address = "Columbus, Ohio",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P08-2042/",
pages = "165--168"
}
@inproceedings{li-khudanpur-2008-scalable,
title = "A Scalable Decoder for Parsing-Based Machine Translation with Equivalent Language Model State Maintenance",
author = "Li, Zhifei and
Khudanpur, Sanjeev",
editor = "Chiang, David and
Wu, Dekai",
booktitle = "Proceedings of the {ACL}-08: {HLT} Second Workshop on Syntax and Structure in Statistical Translation ({SSST}-2)",
month = jun,
year = "2008",
address = "Columbus, Ohio",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W08-0402/",
pages = "10--18"
}
@inproceedings{karakos-etal-2008-machine,
title = "Machine Translation System Combination using {ITG}-based Alignments",
author = "Karakos, Damianos and
Eisner, Jason and
Khudanpur, Sanjeev and
Dreyer, Markus",
editor = "Moore, Johanna D. and
Teufel, Simone and
Allan, James and
Furui, Sadaoki",
booktitle = "Proceedings of ACL-08: HLT, Short Papers",
month = jun,
year = "2008",
address = "Columbus, Ohio",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P08-2021/",
pages = "81--84"
}
@inproceedings{callison-burch-etal-2008-meta,
title = "Further Meta-Evaluation of Machine Translation",
author = "Callison-Burch, Chris and
Fordyce, Cameron and
Koehn, Philipp and
Monz, Christof and
Schroeder, Josh",
editor = "Callison-Burch, Chris and
Koehn, Philipp and
Monz, Christof and
Schroeder, Josh and
Fordyce, Cameron Shaw",
booktitle = "Proceedings of the Third Workshop on Statistical Machine Translation",
month = jun,
year = "2008",
address = "Columbus, Ohio",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W08-0309/",
pages = "70--106"
}
@inproceedings{li-yarowsky-2008-unsupervised,
title = "Unsupervised Translation Induction for {C}hinese Abbreviations using Monolingual Corpora",
author = "Li, Zhifei and
Yarowsky, David",
editor = "Moore, Johanna D. and
Teufel, Simone and
Allan, James and
Furui, Sadaoki",
booktitle = "Proceedings of ACL-08: HLT",
month = jun,
year = "2008",
address = "Columbus, Ohio",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P08-1049/",
pages = "425--433"
}
@InProceedings{eisner-smith-2008-tnlp,
aclid = "W08-0212",
author = "Jason Eisner and Noah A. Smith",
title = "Competitive Grammar Writing",
booktitle = "Proceedings of the Third Workshop on Issues in
Teaching Computational Linguistics",
pages = "97--105",
year = "2008",
month = jun,
address = "Columbus, Ohio",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-smith-2008-tnlp",
}
@InProceedings{karakos-et-al-2008,
aclid = "P08-2021",
author = "Damianos Karakos and Jason Eisner and Sanjeev
Khudanpur and Markus Dreyer",
title = "Machine Translation System Combination using
{ITG}-based Alignments",
booktitle = "Proceedings of ACL-08: HLT, Short Papers",
pages = "81--84",
year = "2008",
month = jun,
address = "Columbus, Ohio",
URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2008",
}
The output of a speech recognition system is not always ideal for subsequent downstream processing, in part because speakers themselves often make mistakes. A system would accomplish speech reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content-preserving English, the message that the speaker intended to convey. These cleaner speech transcripts would allow for more accurate language processing as needed for NLP tasks such as machine translation and conversation summarization, which often rely on grammatical input. Recognizing that supervised statistical methods to identify and transform ill-formed areas of the transcript will require richly labeled resources, we have built the Spontaneous Speech Reconstruction corpus. This small corpus of reconstructed and aligned conversational telephone speech transcriptions for the Fisher conversational telephone speech corpus (Strassel and Walker, 2004) was annotated on several levels including string transformations and predicate-argument structure, and will be shared with the linguistic research community.
@inproceedings{fitzgerald-jelinek-2008-linguistic,
title = "Linguistic Resources for Reconstructing Spontaneous Speech Text",
author = "Fitzgerald, Erin and
Jelinek, Frederick",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L08-1530/",
abstract = "The output of a speech recognition system is not always ideal for subsequent downstream processing, in part because speakers themselves often make mistakes. A system would accomplish speech reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content-preserving English, the message that the speaker intended to convey. These cleaner speech transcripts would allow for more accurate language processing as needed for NLP tasks such as machine translation and conversation summarization, which often rely on grammatical input. Recognizing that supervised statistical methods to identify and transform ill-formed areas of the transcript will require richly labeled resources, we have built the Spontaneous Speech Reconstruction corpus. This small corpus of reconstructed and aligned conversational telephone speech transcriptions for the Fisher conversational telephone speech corpus (Strassel and Walker, 2004) was annotated on several levels including string transformations and predicate-argument structure, and will be shared with the linguistic research community."
}
@inproceedings{111078621,
title = {Single photon avalanche photodetector with integrated quenching fabricated in TSMC 0.18 μm 1.8 V CMOS process},
author = {{M. Marwick} and {A. Andreou}},
year = 2008,
month = {5},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/0f0d4f9bbafa7ed95df980493e7ef77739e9f7d9},
}
@inproceedings{137931537,
title = {Pentacene‐Zinc Oxide Vertical Diode with Compatible Grains and 15‐MHz Rectification},
author = {{B. Pal} and {Jia Sun} and {Byung-Jun Jung} and {E. Choi} and {A. Andreou} and {H. Katz}},
year = 2008,
month = {3},
booktitle = {Advanced Materials},
url = {https://www.semanticscholar.org/paper/57e2a96aefbc0876cadd8807e970a95915624b2f},
}
@inproceedings{15187306,
title = {Autoregressive Modelling of Hilbert Envelopes for Wide-band Audio Coding},
author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky} and {H. Garudadri}},
year = 2008,
month = {5},
booktitle = {Journal of The Audio Engineering Society},
url = {https://www.semanticscholar.org/paper/84017ac1edfda2f43d45da2f1ca070abb70f5f5b},
}
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title = {TECHNICAL CO-SPONSORING SOCIETIES Computational Intelligence},
author = {{M. Soma} and {Zhi-Pei Liang} and {G. Yen} and {G. Cauwenberghs} and {R. Etienne-Cummings} and {A. Andreou} and {A. Bermak} and {A. Burdett} and {Toumaz Uk Ltd} and {S. Carrara} and {K. Chakrabarty} and {S. Chakrabartty} and {P. Chiang} and {David Cumming} and {T. Delbruck} and {T. Denison} and {S. DeWeerth} and {E. Drakakis} and {D. Ham} and {E. Jovanov} and {Edmund Y. L Am} and {Yong Lian} and {Shih-Chii Liu} and {Wentai Liu} and {A. J. Mason} and {T. Roska} and {R. Sarpeshkar} and {M. Sawan} and {K. Shepard} and {Bertram E. Shi} and {M. Stanaćević} and {J. Spiegel}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/c266eb27639c51533c576d5ce82f6e70b6cda282},
}
@inproceedings{13562623,
title = {Exploiting temporal context for speech/non-speech detection},
author = {{S. Parthasarathi} and {P. Motlícek} and {H. Hermansky}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/bc7ad43abaae286d65ba59e8979db24b71603ccf},
}
@inproceedings{27337172,
title = {Proceedings of the Third Workshop on Statistical Machine Translation},
author = {{Chris Callison-Burch} and {Philipp Koehn} and {Christof Monz} and {Josh Schroeder} and {C. Fordyce}},
year = 2008,
month = {6},
booktitle = {WMT@ACL},
url = {https://www.semanticscholar.org/paper/9a70f460056088ac55f8919105c9fc643f87500c},
}
@inproceedings{7386241,
title = {Introducing temporal asymmetries in feature extraction for automatic speech recognition},
author = {{Garimella S. V. S. Sivaram} and {H. Hermansky}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/bf56690f6f6ebb7212bcb514da62e188e7fa0fac},
}
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title = "Translating Compounds by Learning Component Gloss Translation Models via Multiple Languages",
author = "Garera, Nikesh and
Yarowsky, David",
booktitle = "Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-{I}",
year = "2008",
url = "https://aclanthology.org/I08-1053/"
}
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title = {Multilingual Spoken Term Detection: Finding and Testing New Pronunciations},
author = {{R. Sproat} and {J. Baker} and {Martin Jansche} and {B. Ramabhadran} and {M. Riley} and {A. Sethy} and {P. Wolfe} and {S. Khudanpur} and {Arnab Ghoshal} and {Kristy Hollingshead} and {Christopher M. White} and {Ting Qian} and {Erica Cooper} and {Morgan Ulinski}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/7e4d1d69b983edf70d71d8ce4223073b033979b2},
}
@inproceedings{6153448,
title = {Hilbert envelope based spectro-temporal features for phoneme recognition in telephone speech},
author = {{Samuel Thomas} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/6326a07515397f896a37804a3bff7781c4c5d05b},
}
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title = {Sangita Tibrewala and Hynek Hermansky, "multi-stream Approach in Acoustic Modeling ," in Proc. Lvcsr-hub5 Workshop, Multi-stream Approach in Acoustic Modeling 1. the Multi-stream Concept},
author = {{H. Hermansky}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3c051d415c582d85c17e453ef8cf1b0b0c773374},
}
@inproceedings{932901,
title = {On the minimization of concave information functionals for unsupervised classification via decision trees},
author = {{Damianos G. Karakos} and {S. Khudanpur} and {D. Marchette} and {A. Papamarcou} and {C. Priebe}},
year = 2008,
month = {6},
booktitle = {Statistics & Probability Letters},
url = {https://www.semanticscholar.org/paper/19a0954b9ba9e4c46d06db38a2db1737d5ee4611},
}
@inproceedings{2578118,
title = {Fast Approximate Spoken Term Detection from Sequence of Phonemes},
author = {{Joel Pinto} and {Igor Szöke} and {S. Prasanna} and {H. Hermansky}},
year = 2008,
booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
url = {https://www.semanticscholar.org/paper/8c69cddbc38a57921e51b38ad562ba1aaf55587c},
}
@inproceedings{146548217,
title = {A computational investigation into maladaptive aggression},
author = {{R. Melloni} and {Glen A. Coppersmith}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/98cc163db9c649480cc1139ef52eca6dc3373a96},
}
@inproceedings{202653002,
title = {Contents Vol. 64, 2007},
author = {{K. Kohler} and {W. Barry} and {Didier Demolin} and {R. Diehl} and {O. Engstrand} and {Nina Grønnum} and {Sarah Hawkins} and {H. Hermansky} and {V. V. Heuven} and {J. Kingston} and {Francis Nolan} and {J. Ohala} and {D. Recasens} and {A. Simpson} and {J. Vaissière} and {Yi Xu}},
year = 2008,
month = {4},
booktitle = {Phonetica: International Journal of Phonetic Science},
url = {https://www.semanticscholar.org/paper/d83330c41f4dbd60e197b8088fca4f07c856f28a},
}
@inproceedings{110307007,
title = {An Electronically Tunable Linear or Nonlinear MOS Resistor},
author = {{A. Andreou}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/930f882af99aef208f1eff7d7e8fc5bd2bc0139b},
}
@inproceedings{15488070,
title = {Hierarchical and parallel processing of modulation spectrum for ASR applications},
author = {{F. Valente} and {H. Hermansky}},
year = 2008,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/8005bfed3aa0c847dd458ee9f74bffcfd0001736},
}
@inproceedings{18526025,
title = {Sample selection for automatic language identification},
author = {{David Farris} and {Christopher M. White} and {S. Khudanpur}},
year = 2008,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/75b265926f8896af46d30ccdd35503accfcf0c9b},
}
@inproceedings{10605182,
title = {Using comparison of parallel phoneme probability streams for OOV word detection},
author = {{Tamara Tosic} and {M. Magimai-Doss} and {H. Hermansky}},
year = 2008,
month = {8},
booktitle = {European Signal Processing Conference},
url = {https://www.semanticscholar.org/paper/25396b14e1fafaea1e9b5bfe29b468f1f3ec1cc8},
}
@inproceedings{2502917,
title = {Reverse Correlation for Analyzing MLP Posterior Features in ASR},
author = {{Joel Pinto} and {Garimella S. V. S. Sivaram} and {H. Hermansky}},
year = 2008,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/1c7aa21f8f785408385d711e47f844a70acd04f1},
}
We extend discriminative n-gram language modeling techniques originally proposed for automatic speech recognition to a statistical machine translation task. In this context, we propose a novel data selection method that leads to good models using a fraction of the training data. We carry out systematic experiments on several benchmark tests for Chinese to English translation using a hierarchical phrase-based machine translation system, and show that a discriminative language model significantly improves upon a state-of-the-art baseline. The experiments also highlight the benefits of our data selection method.
@inproceedings{li-khudanpur-2008-large,
title = "Large-scale Discriminative n-gram Language Models for Statistical Machine Translation",
author = "Li, Zhifei and
Khudanpur, Sanjeev",
booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 21-25",
year = "2008",
address = "Waikiki, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2008.amta-papers.12/",
pages = "133--142",
abstract = "We extend discriminative n-gram language modeling techniques originally proposed for automatic speech recognition to a statistical machine translation task. In this context, we propose a novel data selection method that leads to good models using a fraction of the training data. We carry out systematic experiments on several benchmark tests for Chinese to English translation using a hierarchical phrase-based machine translation system, and show that a discriminative language model significantly improves upon a state-of-the-art baseline. The experiments also highlight the benefits of our data selection method."
}
@inproceedings{4390364,
title = {MODIFIED DISCRETE COSINE TRANSFORM FOR ENCODING RESIDUAL SIGNALS IN FREQUENCY DOMAIN LINEAR PREDICTION},
author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/a1980c3a1ac0e53aaf122e4f92be4e03c2251795},
}
@inproceedings{16856359,
title = {Experimental results of simplicial cnn digital pixel processor},
author = {{M. D. Federico} and {P. Mandolesi} and {P. Julián} and {A. Andreou}},
year = 2008,
month = {1},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/b03ac210ef13fe095178dc5606a0c138147d888b},
}
@inproceedings{5905702,
title = {Exploiting contextual information for improved phoneme recognition},
author = {{Joel Pinto} and {B. Yegnanarayana} and {H. Hermansky} and {M. Magimai-Doss}},
year = 2008,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/4c93e00e9b54d1c30143b9f005460acb0f643fe4},
}
@inproceedings{garera-yarowsky-2008-minimally,
title = "Minimally Supervised Multilingual Taxonomy and Translation Lexicon Induction",
author = "Garera, Nikesh and
Yarowsky, David",
booktitle = "Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-{I}",
year = "2008",
url = "https://aclanthology.org/I08-1061/"
}
@inproceedings{14567343,
title = {Exploiting prosodic breaks in language modeling with random forests},
author = {{Yi Su} and {F. Jelinek}},
year = 2008,
month = {5},
booktitle = {Proceedings of the International Conference on Speech Prosody},
url = {https://www.semanticscholar.org/paper/0e8ac5d5e439a42cadd47bc0c37f1e3fac1465f9},
}
@inproceedings{110935662,
title = {Photo-battery fabricated in silicon on sapphire CMOS},
author = {{M. Marwick} and {A. Andreou}},
year = 2008,
month = {6},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/96233510e6db4c6f00d7c8b5cea6f9600412644f},
}
@inproceedings{13085783,
title = {Combination of strongly and weakly constrained recognizers for reliable detection of OOVS},
author = {{L. Burget} and {Petr Schwarz} and {P. Matejka} and {M. Hannemann} and {A. Rastrow} and {Christopher M. White} and {S. Khudanpur} and {H. Hermansky} and {J. Černocký}},
year = 2008,
month = {5},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d98c4a8178e7a56884bf687d62c0b77a2c976ae3},
}
@inproceedings{9241695,
title = {Spectral noise shaping: improvements in speech/audio codec based on linear prediction in spectral domain},
author = {{Sriram Ganapathy} and {P. Motlícek} and {H. Hermansky} and {H. Garudadri}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/098502f8c52c7318dddc470a120694b00f64ca2b},
}
@inproceedings{12994161,
title = {On the combination of auditory and modulation frequency channels for ASR applications},
author = {{F. Valente} and {H. Hermansky}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/5b115a575f558b5b76625d10477786b1bfadfd40},
}
@inproceedings{931260,
title = {Exploiting Contextual Information for Speech/Non-Speech Detection},
author = {{S. Parthasarathi} and {P. Motlícek} and {H. Hermansky}},
year = 2008,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/0e1f43fe736ee0b0af110cb5698a04525fadf914},
}
@inproceedings{18661445,
title = {An investigation of acoustic models for multilingual code-switching},
author = {{Christopher M. White} and {S. Khudanpur} and {J. Baker}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/5a71064aae459c32e64b7d880e36ff588963baae},
}
@inproceedings{61042354,
title = {Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator},
author = {{Joel Pinto} and {Garimella S. V. S. Sivaram} and {H. Hermansky} and {M. Magimai-Doss}},
year = 2008,
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/d4aaf6bd66ae98468bb82b18b7434391aa5eba5e},
}
@inproceedings{14555520,
title = {Pushing the Envelope – Aside : Beyond the Spectral Envelope as the Fundamental Representation for Speech Recognition},
author = {{N. Morgan} and {Q. Zhu} and {A. Stolcke} and {Kemal Sönmez} and {S. Sivadas} and {T. Shinozaki} and {Mari Ostendorf} and {P. Jain} and {H. Hermansky} and {D. Ellis} and {G. Doddington} and {Barry Y. Chen} and {Ö. Çetin} and {H. Bourlard} and {M. Athineos}},
year = 2008,
booktitle = {},
url = {https://www.semanticscholar.org/paper/4818ba90b1c5db6bbd8f857695f8117130babfce},
}
@inproceedings{45200299,
title = {Toward the Ultimate ASR Language Model},
author = {{F. Jelinek} and {Carolina Parada}},
year = 2008,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/340c911911511edbbe8a827c915169cc0b6d34e9},
}
@inproceedings{2790438,
title = {Spectro-temporal features for Automatic Speech Recognition using Linear Prediction in spectral domain},
author = {{Samuel Thomas} and {Sriram Ganapathy} and {H. Hermansky}},
year = 2008,
month = {8},
booktitle = {European Signal Processing Conference},
url = {https://www.semanticscholar.org/paper/bd0a38e6a8123e380b949a3ed8dbb826706f03f1},
}
@inproceedings{1162126,
title = {Front-end for far-field speech recognition based on frequency domain linear prediction},
author = {{Sriram Ganapathy} and {Samuel Thomas} and {H. Hermansky}},
year = 2008,
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/8327906e4d7896538f5c4cc93f707fbbd4ae01d3},
}
@inproceedings{28321471,
title = {A low-power silicon-on-sapphire tunable ultra-wideband transmitter},
author = {{Wei Tang} and {A. Andreou} and {E. Culurciello}},
year = 2008,
month = {5},
booktitle = {2008 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/6b9260e6b025777c032eb81f6a5a109293ba358c},
}
@inproceedings{195718969,
title = {Proceedings of the Third Workshop on Statistical Machine Translation (StatMT '08)},
author = {{Chris Callison-Burch} and {C. Fordyce} and {Philipp Koehn} and {Christof Monz} and {Josh Schroeder}},
year = 2008,
booktitle = {The Association for Computational Linguistics},
url = {https://www.semanticscholar.org/paper/282c69ba6e6b38ea2df099974586a22b760c2500},
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month = {12},
booktitle = {Nanomedicine: Nanotechnology, Biology and Medicine},
url = {https://www.semanticscholar.org/paper/e40043919c5a4ec0fb0c201f0eb1ade9e0183866},
}
@inproceedings{28828770,
title = {Capacitive Inter-Chip Data and Power Transfer for 3-D VLSI},
author = {{E. Culurciello} and {A. Andreou}},
year = 2006,
month = {12},
booktitle = {IEEE Transactions on Circuits and Systems - II - Express Briefs},
url = {https://www.semanticscholar.org/paper/bf171d71b22d5cf219ad410fd2a9c06c55c3bbb8},
}
@inproceedings{6736227,
title = {VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network},
author = {{David H. Goldberg} and {A. Andreou} and {P. Julián} and {P. Pouliquen} and {Laurence Riddle} and {Rich Rosasco}},
year = 2006,
month = {11},
booktitle = {TOSN},
url = {https://www.semanticscholar.org/paper/e3bb4400e6eedfedc9f37f687a86039bca8757cd},
}
@inproceedings{17549385,
title = {Integrated PDMS/CMOS Microsystem for Autonomous Incubation and Imaging in Cell Culture Studies},
author = {{J.M. Blain Christen} and {A. Andreou}},
year = 2006,
month = {11},
booktitle = {2006 IEEE/NLM Life Science Systems and Applications Workshop},
url = {https://www.semanticscholar.org/paper/05cdbf3c044bc1bae5f318e24c4d97f1588819bb},
}
@inproceedings{16677717,
title = {Special issue on advances in life science systems and applications: Guest editorial},
author = {{A. Andreou} and {P. Chung} and {Guang‐Zhong Yang} and {S. Wong}},
year = 2006,
month = {11},
booktitle = {},
url = {https://www.semanticscholar.org/paper/97f8eb3bdda51a7285c049eb3a79d90da3a56f52},
}
@inproceedings{41929501,
title = {CMOS image sensors for sensor networks},
author = {{E. Culurciello} and {A. Andreou}},
year = 2006,
month = {10},
booktitle = {Analog Integrated Circuits and Signal Processing},
url = {https://www.semanticscholar.org/paper/5560fd5e58c25ce864738cf764341305ba758f90},
}
@inproceedings{111002903,
title = {Experimental results for cascadable micropower time delay estimator},
author = {{P. Julián} and {F. N. M. Pirchio} and {A. Andreou}},
year = 2006,
month = {10},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/dbb64ee184793bacc681318b35892e1fd5439e6c},
}
@inproceedings{62187386,
title = {A monolithic isolation amplifier in silicon-on-insulator CMOS: Testing and applications},
author = {{G. Marcus} and {Kim Strohben} and {S. Jaskulek} and {A. Andreou} and {E. Culurciello}},
year = 2006,
month = {10},
booktitle = {Analog Integrated Circuits and Signal Processing},
url = {https://www.semanticscholar.org/paper/0baff22901722ea21b0614228f2bee905d25d91c},
}
@InProceedings{DYNASTY-2006,
author = "Jason Eisner and Michael Kornbluh and Gordon Woodhull
and Raymond Buse and Samuel Huang and Constantinos
Michael and George Shafer",
title = "Visual Navigation Through Large Directed Graphs and
Hypergraphs",
booktitle = "Proceedings of the IEEE Symposium on Information
Visualization (InfoVis'06), Poster/Demo Session",
pages = "116--117",
year = "2006",
month = oct,
address = "Baltimore",
URL = "http://cs.jhu.edu/~jason/papers/#DYNASTY-2006",
}
@InProceedings{mason-et-al-2006,
author = "Joshua Mason and Kathryn Watkins and Jason Eisner and
Adam Stubblefield",
title = "A Natural-Language Approach to Automated Cryptanalysis
of Two-Time Pads",
booktitle = "Proceedings of the ACM Conference on Computer and
Communications Security (ACM CCS)",
pages = "235--244",
year = "2006",
month = oct,
address = "Alexandria, VA",
URL = "http://cs.jhu.edu/~jason/papers/#mason-et-al-2006",
}
@InProceedings{dreyer-eisner-2006,
aclid = "W06-1638",
author = "Markus Dreyer and Jason Eisner",
title = "Better Informed Training of Latent Syntactic
Features",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "317--326",
year = "2006",
month = jul,
address = "Sydney",
URL = "http://cs.jhu.edu/~jason/papers/#dreyer-eisner-2006",
}
@InProceedings{smith-eisner-2006-acl-risk,
aclid = "P06-2101",
author = "David A. Smith and Jason Eisner",
title = "Minimum-Risk Annealing for Training Log-Linear
Models",
booktitle = "Proceedings of the International Conference on
Computational Linguistics and the Association for
Computational Linguistics (COLING-ACL), Companion
Volume",
pages = "787--794",
year = "2006",
month = jul,
address = "Sydney",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-acl-risk",
}
@InProceedings{smith-eisner-2006-acl-sa,
aclid = "P06-1072",
author = "Noah A. Smith and Jason Eisner",
title = "Annealing Structural Bias in Multilingual Weighted
Grammar Induction",
booktitle = "Proceedings of the International Conference on
Computational Linguistics and the Association for
Computational Linguistics (COLING-ACL)",
pages = "569--576",
year = "2006",
month = jul,
address = "Sydney",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-acl-sa",
}
@inproceedings{garera-yarowsky-2006-resolving,
title = "Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora",
author = "Garera, Nikesh and
Yarowsky, David",
editor = "M\`arquez, Llu\'\i s and
Klein, Dan",
booktitle = "Proceedings of the Tenth Conference on Computational Natural Language Learning ({C}o{NLL}-X)",
month = jun,
year = "2006",
address = "New York City",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W06-2906/",
pages = "37--44"
}
@inproceedings{lin-etal-2006-generative,
title = "Generative Content Models for Structural Analysis of Medical Abstracts",
author = "Lin, Jimmy and
Karakos, Damianos and
Demner-Fushman, Dina and
Khudanpur, Sanjeev",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Goertzel, Ben and
Mani, Inderjeet",
booktitle = "Proceedings of the {HLT}-{NAACL} {B}io{NLP} Workshop on Linking Natural Language and Biology",
month = jun,
year = "2006",
address = "New York, New York",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W06-3309/",
pages = "65--72"
}
@InProceedings{eisner-tromble-2006,
author = "Jason Eisner and Roy W. Tromble",
title = "Local Search with Very Large-Scale Neighborhoods for
Optimal Permutations in Machine Translation",
booktitle = "Proceedings of the HLT-NAACL Workshop on
Computationally Hard Problems and Joint Inference in
Speech and Language Processing",
pages = "57--75",
year = "2006",
month = jun,
address = "New York",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-tromble-2006",
}
@InProceedings{smith-eisner-2006-smt,
aclid = "W06-3104",
author = "David A. Smith and Jason Eisner",
title = "Quasi-Synchronous Grammars: Alignment by Soft
Projection of Syntactic Dependencies",
booktitle = "Proceedings of the HLT-NAACL Workshop on Statistical
Machine Translation",
pages = "23--30",
year = "2006",
month = jun,
address = "New York",
note = "Nominated for 5-year retrospective Best Paper award.",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2006-smt",
}
@InProceedings{tromble-eisner-2006,
aclid = "N06-1054",
author = "Roy W. Tromble and Jason Eisner",
title = "A Fast Finite-State Relaxation Method for Enforcing
Global Constraints on Sequence Decoding",
booktitle = "Proceedings of the Human Language Technology
Conference of the North American Association for
Computational Linguistics (HLT-NAACL)",
pages = "423--430",
year = "2006",
month = jun,
address = "New York",
URL = "http://cs.jhu.edu/~jason/papers/#tromble-eisner-2006",
}
@inproceedings{18057741,
title = {Microelectromechanical systems in 3D SOI-CMOS: sensing electronics embedded in mechanical structures},
author = {{Francisco Tejada} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/ef40a56e2434aa0d9db45241354ce14d6d95c7b7},
}
@inproceedings{62152556,
title = {High-speed, address-encoding arbiter architecture},
author = {{J. Georgiou} and {A. Andreou}},
year = 2006,
month = {2},
booktitle = {Electronics Letters},
url = {https://www.semanticscholar.org/paper/bbab19fec5a003aa7223c7ef629824427bc77db8},
}
@inproceedings{61476379,
title = {Translation discovery using diverse similarity measures},
author = {{David Yarowsky} and {C. Schafer}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/cf56cd1fed212f5ad2cbdc7a07b58377df07c917},
}
@inproceedings{22413744,
title = {Dark current and noise of 100nm thick silicon on sapphire CMOS lateral PIN photodiodes},
author = {{M. Marwick} and {Francisco Tejada} and {P. Pouliquen} and {E. Culurciello} and {K. Strohbehn} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/728e8535977fe37f6754dd73f60dfbe7e1603d78},
}
@inproceedings{6132660,
title = {An Address-Event Image Sensor Network},
author = {{Thiago Teixeira} and {E. Culurciello} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/e2d47210a12160afecf78eb30ca3275b696cf9f6},
}
@inproceedings{8853781,
title = {Retinomorphic system design in three dimensional SOI-CMOS},
author = {{M. Marwick} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/962aa0ce67f8b64061c7878a9073025f118148ff},
}
@inproceedings{191944,
title = {Language Modeling with the Maximum Likelihood Set: Complexity Issues and the Back-off Formula},
author = {{Damianos G. Karakos} and {S. Khudanpur}},
year = 2006,
month = {7},
booktitle = {2006 IEEE International Symposium on Information Theory},
url = {https://www.semanticscholar.org/paper/576ef236ed4c553eaa32943ad782e11e22e0ea17},
}
@inproceedings{57804249,
title = {Multi-document statistical fact extraction and fusion},
author = {{David Yarowsky} and {Gideon S. Mann}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/3c1e8a4a290a85002c8fb997707f859088673ae6},
}
Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50\%, total vocabulary size by over 40\%, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system.
@inproceedings{riesa-yarowsky-2006-minimally,
title = "Minimally Supervised Morphological Segmentation with Applications to Machine Translation",
author = "Riesa, Jason and
Yarowsky, David",
booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = aug # " 8-12",
year = "2006",
address = "Cambridge, Massachusetts, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2006.amta-papers.21/",
pages = "185--192",
abstract = "Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50\%, total vocabulary size by over 40\%, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system."
}
@inproceedings{375582,
title = {Estimating Conditional Densities from Sparse Data for Statistical Language Modeling},
author = {{Damianos G. Karakos} and {S. Khudanpur}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b10cbdc743d8f2210685007e65101436c764fb72},
}
@inproceedings{18032935,
title = {Source Adaptation for Improved Content-Based Video Retrieval},
author = {{Arnab Ghoshal} and {S. Khudanpur}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings},
url = {https://www.semanticscholar.org/paper/58f69f3d5c124ade69d2f2fb52bd69acf77fce2e},
}
@inproceedings{69602970,
title = {Learning Structured Information in Natural Language Applications},
author = {{R. Basili} and {Nicola Cancedda} and {Marcello Federico} and {Marko Grobelink} and {Slovenia Ljubljana} and {F. Jelinek} and {D. Roth} and {J. Shawe} and {Taylor} and {Alessandro Moschitti} and {Vanessa Sandrini} and {M. Cettolo} and {S. Canisius} and {Antal van den Bosch} and {Walter Daelemans} and {Fabrizio Costa} and {Sauro Menchetti} and {Alessio Ceroni} and {Andrea Passerini} and {P. Frasconi} and {Ana Zelaia} and {I. Alegria} and {Olatz Arregi} and {B. Sierra} and {R. Subba} and {B. Di} and {Eugênio} and {Su Nam Kim} and {Sa S. Sa} and {Oliver Hasan} and {Hermann Bender} and {Ney} and {Daniele Pighin} and {C. Giuliano} and {A. Gliozzo} and {C. Strapparava} and {T. Lassen} and {T. V. Terney} and {Ana-Maria Giuglea} and {S. Nam} and {M. Bender}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/caec881d2a889a2372b35d0ba1e0fe3e58c79b39},
}
@inproceedings{18402065,
title = {A mixed analog/digital asynchronous processor for cortical computations in 3D SOI-CMOS},
author = {{J. Georgiou} and {A. Andreou} and {P. Pouliquen}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/47083729fba1bc4296ce60fc8e3fb9faf418fe02},
}
@inproceedings{6053956,
title = {A low-power correlation-derivative CMOS VLSI circuit for bearing estimation},
author = {{P. Julián} and {A. Andreou} and {David H. Goldberg}},
year = 2006,
month = {2},
booktitle = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
url = {https://www.semanticscholar.org/paper/8659e1260299d11e89c80f2201f16faee1b86c9d},
}
@inproceedings{2705729,
title = {3D integrated sensors in silicon-on-sapphire CMOS},
author = {{E. Culurciello} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/03fdd243a89c134ffd51cabca98be2fd359aa17e},
}
@inproceedings{47118668,
title = {Hybrid Silicon/Silicone (polydimethylsiloxane) Microsystem for Cell Culture},
author = {{J. B. Christen} and {A. Andreou}},
year = 2006,
booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
url = {https://www.semanticscholar.org/paper/1493f1a2d1a18286aa6a2c170ee1076b2b4cb665},
}
@inproceedings{15013898,
title = {A simplicial CNN visual processor in 3D SOI-CMOS},
author = {{P. Mandolesi} and {P. Julián} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/01144a80c37c7b79ed7841748479cd6bc8db5d9b},
}
@inproceedings{16707225,
title = {Digital phase-shift modulation for an isolation buffer in silicon-on-sapphire CMOS},
author = {{E. Culurciello} and {P. Pouliquen} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/714a9c9fadc59ebd150222f0ad9c4067c436b031},
}
@inproceedings{1094509,
title = {Hybrid silicon/silicone (polydimethylsiloxane) microsystem for cell culture},
author = {{J. B. Christen} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/2d5278f4e9b8ae905379c5008e4282ae4420919c},
}
@inproceedings{61313353,
title = {Multilingual Language Modeling},
author = {{S. Khudanpur}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/1ca79b1efec424969fb7d06d0576ba4efd2d8f7a},
}
We propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses.
@inproceedings{pytlik-yarowsky-2006-machine,
title = "Machine Translation for Languages Lacking Bitext via Multilingual Gloss Transduction",
author = "Pytlik, Brock and
Yarowsky, David",
booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = aug # " 8-12",
year = "2006",
address = "Cambridge, Massachusetts, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2006.amta-papers.18/",
pages = "156--165",
abstract = "We propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses."
}
@inproceedings{15369849,
title = {Part of Speech Tagging and Shallow Parsing of Indian Languages},
author = {{D. Rao} and {David Yarowsky}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9d3ab3e2ef54774f5bf8d505247b62d259e440a6},
}
@inproceedings{42849210,
title = {DR AF T Speech and Language Processing : An introduction to speech recognition , computational linguistics and natural language processing},
author = {{F. Jelinek}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/bc30ea1eacc49db8a01572679a0a07ee52a85cb7},
}
@inproceedings{23193472,
title = {Stacked, standing wave detectors in 3D SOI-CMOS},
author = {{Francisco Tejada} and {A. Andreou} and {P. Pouliquen}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/630a6ed484c28f2329523712460ff398f249d112},
}
@inproceedings{138266011,
title = {Integrated PDMS/CMOSMicrosystem forAutonomous Incubation and Imaging inCellCulture Studies},
author = {{Jennifer M. BlainChristen} and {A. Andreou}},
year = 2006,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9defd0f90c96bd00b0bbc1f37967df3c40a71fce},
}
@inproceedings{11906980,
title = {Imperial College and Johns Hopkins University at TRECVID},
author = {{Arnab Ghoshal} and {S. Khudanpur} and {João Magalhães} and {Simon E. Overell} and {S. Rüger} and {Alexei Yavlinsky}},
year = 2006,
booktitle = {TREC Video Retrieval Evaluation},
url = {https://www.semanticscholar.org/paper/70a5d0b63bac342c2aab9abea37cad4f2a054889},
}
@inproceedings{11496371,
title = {Chip-scale magnetic sensing and control of nanoparticles and nanorods},
author = {{E. Choi} and {Zhiyong Gu} and {D. Gracias} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/a97df2f3acf9689b414abf170266a7f45773819b},
}
@inproceedings{2861907,
title = {System for deposition and characterization of polypyrrole/gold bilayer hinges},
author = {{E. Choi} and {Yingkai Liu} and {E. Smela} and {A. Andreou}},
year = 2006,
month = {5},
booktitle = {2006 IEEE International Symposium on Circuits and Systems},
url = {https://www.semanticscholar.org/paper/2a188b09f820905a1cdc2fc973a476df9e13cea7},
}
@inproceedings{25906118,
title = {An 8-bit 800-$muhboxW$1.23-MS/s Successive Approximation ADC in SOI CMOS},
author = {{E. Culurciello} and {A. Andreou}},
year = 2006,
month = {9},
booktitle = {IEEE Transactions on Circuits and Systems - II - Express Briefs},
url = {https://www.semanticscholar.org/paper/869169a56616c156fb4b2775e2d3fd885870b1ef},
}
@inproceedings{3833221,
title = {Joint visual-text modeling for automatic retrieval of multimedia documents},
author = {{G. Iyengar} and {P. D. Sahin} and {Shaolei Feng} and {P. Ircing} and {S. Khudanpur} and {D. Klakow} and {M. R. Krause} and {R. Manmatha} and {H. Nock} and {D. Petkova} and {Brock Pytlik} and {Paola Virga}},
year = 2005,
month = {11},
booktitle = {ACM Multimedia},
url = {https://www.semanticscholar.org/paper/d55de6dcdfde200b9a975578ffe8cb5c056e2c76},
}
@InProceedings{eisner-smith-2005,
aclid = "W05-1504",
author = "Jason Eisner and Noah A. Smith",
title = "Parsing with Soft and Hard Constraints on Dependency
Length",
booktitle = "Proceedings of the International Workshop on Parsing
Technologies (IWPT)",
pages = "30--41",
year = "2005",
month = oct,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-smith-2005",
}
@InProceedings{eisner-karakos-2005,
aclid = "H05-1050",
author = "Jason Eisner and Damianos Karakos",
title = "Bootstrapping Without the Boot",
booktitle = "Proceedings of Human Language Technology Conference
and Conference on Empirical Methods in Natural Language
Processing (HLT-EMNLP)",
pages = "395--402",
year = "2005",
month = oct,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-karakos-2005",
}
@InProceedings{eisner-goldlust-smith-2005,
aclid = "H05-1036",
author = "Jason Eisner and Eric Goldlust and Noah A. Smith",
title = "Compiling Comp Ling: Weighted Dynamic Programming and
the {D}yna Language",
booktitle = "Proceedings of Human Language Technology Conference
and Conference on Empirical Methods in Natural Language
Processing (HLT-EMNLP)",
pages = "281--290",
year = "2005",
month = oct,
address = "Vancouver",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-goldlust-smith-2005",
}
@InProceedings{smith-eisner-2005-gia,
author = "Noah A. Smith and Jason Eisner",
title = "Guiding Unsupervised Grammar Induction Using
Contrastive Estimation",
booktitle = "International Joint Conference on Artificial
Intelligence (IJCAI) Workshop on Grammatical Inference
Applications",
pages = "73--82",
year = "2005",
month = jul,
address = "Edinburgh",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2005-gia",
}
@inproceedings{drabek-yarowsky-2005-induction,
title = "Induction of Fine-Grained Part-of-Speech Taggers via Classifier Combination and Crosslingual Projection",
author = "Dr\'abek, Elliott and
Yarowsky, David",
editor = "Koehn, Philipp and
Martin, Joel and
Mihalcea, Rada and
Monz, Christof and
Pedersen, Ted",
booktitle = "Proceedings of the {ACL} Workshop on Building and Using Parallel Texts",
month = jun,
year = "2005",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W05-0807/",
pages = "49--56"
}
@inproceedings{mann-yarowsky-2005-multi,
title = "Multi-Field Information Extraction and Cross-Document Fusion",
author = "Mann, Gideon and
Yarowsky, David",
editor = "Knight, Kevin and
Ng, Hwee Tou and
Oflazer, Kemal",
booktitle = "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics ({ACL}'05)",
month = jun,
year = "2005",
address = "Ann Arbor, Michigan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P05-1060/",
doi = "10.3115/1219840.1219900",
pages = "483--490"
}
@InProceedings{smith-eisner-2005-acl,
aclid = "P05-1044",
author = "Noah A. Smith and Jason Eisner",
title = "Contrastive Estimation: Training Log-Linear Models on
Unlabeled Data",
booktitle = "Proceedings of the 43rd Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "354--362",
year = "2005",
month = jun,
address = "Ann Arbor, Michigan",
note = "Nominated for Best Paper Award.",
URL = "http://cs.jhu.edu/~jason/papers/#smith-eisner-2005-acl",
}
@InProceedings{kempe-et-al-2005,
author = "Andr\'{e} Kempe and Jean-Marc Champarnaud and Jason
Eisner and Franck Guingne and Florent Nicart",
title = "A Class of Rational {$n$-WFSM} Auto-Intersections",
booktitle = "Proceedings of the Tenth International Conference on
Implementation and Application of Automata
(CIAA-2005)",
pages = "189--200",
series = "Lecture Notes in Computer Science",
number = "3845",
publisher = "Springer-Verlag",
year = "2005",
month = jun,
address = "Sophia Antipolis, France",
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author = "Damianos Karakos and Sanjeev Khudanpur and Jason
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title = "Unsupervised Classification via Decision Trees: An
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booktitle = "Proceedings of the 2005 IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP)",
volume = "5",
pages = "1081--1084",
year = "2005",
month = mar,
address = "Philadelphia",
note = "Invited talk",
URL = "http://cs.jhu.edu/~jason/papers/#karakos-et-al-2005",
}
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year = 2005,
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year = 2005,
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title = {Unsupervised classification via decision trees: an information-theoretic perspective},
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year = 2005,
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title = {Language Modeling Experiments with Random Forests},
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year = 2005,
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title = {Some of my Best Friends are Linguists},
author = {{F. Jelinek}},
year = 2005,
month = {2},
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year = 2005,
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title = {CMOS heater array for incubation environment cellular study},
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year = 2005,
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title = {A monolithic isolation amplifier in silicon-on-insulator CMOS},
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year = 2005,
month = {5},
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title = {Capacitive coupling of data and power for 3D silicon-on-insulator VLSI},
author = {{E. Culurciello} and {A. Andreou}},
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title = {Isolation charge pump fabricated in silicon on sapphire CMOS technology},
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title = {TRECVID 2005 Experiment at Johns Hopkins University: Using Hidden Markov Models for Video Retrieval},
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year = 2005,
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author = "Andr\'{e} Kempe and Jean-Marc Champarnaud and Jason
Eisner",
title = "A Note on Join and Auto-Intersection of $n$-ary
Rational Relations",
booktitle = "Proceedings of the Eindhoven FASTAR Days (Computer
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editor = "Loek Cleophas and Bruce Watson",
pages = "64--78",
year = "2004",
month = dec,
organization = "Department of Mathematics and Computer Science,
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editor = "Lin, Dekang and
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month = jul,
year = "2004",
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url = "https://aclanthology.org/W04-3242/",
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title = "Improving Bitext Word Alignments via Syntax-based Reordering of {E}nglish",
author = "Drabek, Elliott Franco and
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booktitle = "Proceedings of the {ACL} Interactive Poster and Demonstration Sessions",
month = jul,
year = "2004",
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url = "https://aclanthology.org/P04-3014/",
pages = "146--149"
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title = "Exploiting Aggregate Properties of Bilingual Dictionaries For Distinguishing Senses of {E}nglish Words and Inducing {E}nglish Sense Clusters",
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booktitle = "Proceedings of the {ACL} Interactive Poster and Demonstration Sessions",
month = jul,
year = "2004",
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aclid = "P04-3032",
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title = "Dyna: {A} Declarative Language for Implementing
Dynamic Programs",
booktitle = "Proceedings of the 42nd Annual Meeting of the
Association for Computational Linguistics (ACL),
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pages = "218--221",
year = "2004",
month = jul,
address = "Barcelona",
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booktitle = "Proceedings of the 42nd Annual Meeting of the
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Costa, Rute and
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booktitle = "Proceedings of the Fourth International Conference on Language Resources and Evaluation ({LREC}'04)",
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address = "Lisbon, Portugal",
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title = {Cross-lingual latent semantic analysis for language modeling},
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month = {5},
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title = {Joint Visual-Text Modeling for Multimedia Retrieval},
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title = {Spike communication of dynamic stimuli: rate decoding versus temporal decoding},
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url = {https://www.semanticscholar.org/paper/cf2be860f30ac5d7d621a1ceba3a7fb9340ba896},
}
@inproceedings{31449595,
title = {A 2.5-mW SOS CMOS optical receiver for chip-to-chip interconnect},
author = {{A. Apsel} and {Zhongtao Fu} and {A. Andreou}},
year = 2004,
month = {9},
booktitle = {Journal of Lightwave Technology},
url = {https://www.semanticscholar.org/paper/d4211067322dc2a0df8236d703b390b036774c4b},
}
@inproceedings{122758259,
title = {Multichannel ultrathin silicon-on-sapphire optical interconnects},
author = {{J. J. Liu} and {Z. Kalayjian} and {B. Riely} and {W. Chang} and {G. Simonis} and {A. Apsel} and {A. Andreou}},
year = 2003,
month = {10},
booktitle = {IEEE Journal of Selected Topics in Quantum Electronics},
url = {https://www.semanticscholar.org/paper/eb2ea5e1185cd3e70cffa630c12405c4c079cb1c},
}
@inproceedings{virga-khudanpur-2003-transliteration,
title = "Transliteration of Proper Names in Cross-Lingual Information Retrieval",
author = "Virga, Paola and
Khudanpur, Sanjeev",
booktitle = "Proceedings of the {ACL} 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition",
month = jul,
year = "2003",
address = "Sapporo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W03-1508/",
doi = "10.3115/1119384.1119392",
pages = "57--64"
}
@InProceedings{eisner-2003-acl,
aclid = "P03-2041",
author = "Jason Eisner",
title = "Learning Non-Isomorphic Tree Mappings for Machine
Translation",
booktitle = "Proceedings of the 41st Annual Meeting of the
Association for Computational Linguistics (ACL),
Companion Volume",
pages = "205--208",
year = "2003",
month = jul,
address = "Sapporo",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2003-acl",
}
@InProceedings{eisner-2003-hlt,
aclid = "N03-1009",
author = "Jason Eisner",
title = "Simpler and More General Minimization for Weighted
Finite-State Automata",
booktitle = "Proceedings of the Joint Meeting of the Human Language
Technology Conference and the North American Chapter of
the Association for Computational Linguistics
(HLT-NAACL)",
pages = "64--71",
year = "2003",
month = may,
address = "Edmonton",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2003-hlt",
}
@inproceedings{16056099,
title = {Maximum Entropy Modeling in Semantic Tagging},
author = {{Jia Cui} and {F. Jelinek}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/2e11d4c492b52d56bca01a21a74981b1087c444f},
}
@inproceedings{cucerzan-yarowsky-2003-minimally,
title = "Minimally Supervised Induction of Grammatical Gender",
author = "Cucerzan, Silviu and
Yarowsky, David",
booktitle = "Proceedings of the 2003 Human Language Technology Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
year = "2003",
url = "https://aclanthology.org/N03-1006/",
pages = "40--47"
}
@inproceedings{35403518,
title = {Analysis of short distance optoelectronic link architectures},
author = {{A. Apsel} and {A. Andreou}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/31473674b33933ff83740bc21936f80fbec4122b},
}
@inproceedings{58130901,
title = {Transformation based learning and data-driven lexical disambiguation: syntactic and semantic ambiguity resolution},
author = {{David Yarowsky} and {Radu Florian}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/9a5caa9c826264663dd7254dba8529b1a6748073},
}
@inproceedings{111718664,
title = {Integrated arrays of low power SOS chip-to-chip interconnects for efficient parallel communication in CMOS},
author = {{A. Apsel} and {Jiang Liu} and {A. Andreou} and {W. Chang} and {G. Simonis}},
year = 2003,
month = {6},
booktitle = {},
url = {https://www.semanticscholar.org/paper/176cfdf52c792a258161fadc2e134c981749c01e},
}
@inproceedings{oard-etal-2003-desparately,
title = "Desparately Seeking {C}ebuano",
author = "Oard, Douglas W. and
Doermann, David and
Dorr, Bonnie and
He, Daqing and
Resnik, Philip and
Weinberg, Amy and
Byrne, William and
Khudanpur, Sanjeev and
Yarowsky, David and
Leuski, Anton and
Koehn, Philipp and
Knight, Kevin",
booktitle = "Companion Volume of the Proceedings of {HLT}-{NAACL} 2003 - Short Papers",
year = "2003",
url = "https://aclanthology.org/N03-2026/",
pages = "76--78"
}
We formulate an original model for statistical machine translation (SMT) inspired by characteristics of the Arabic-English translation task. Our approach incorporates part-of-speech tags and linguistically motivated phrase chunks in a 2-level shallow syntactic model of reordering. We implement and evaluate this model, showing it to have advantageous properties and to be competitive with an existing SMT baseline. We also describe cross-categorial lexical translation coercion, an interesting component and side-effect of our approach. Finally, we discuss the novel implementation of decoding for this model which saves much development work by constructing finite-state machine (FSM) representations of translation probability distributions and using generic FSM operations for search. Algorithmic details, examples and results focus on Arabic, and the paper includes discussion on the issues and challenges of Arabic statistical machine translation.
@inproceedings{schafer-yarowsky-2003-two,
title = "A two-level syntax-based approach to {A}rabic-{E}nglish statistical machine translation",
author = "Schafer, Charles and
Yarowsky, David",
booktitle = "Workshop on Machine Translation for Semitic languages: issues and approaches",
month = sep # " 23-27",
year = "2003",
address = "New Orleans, USA",
url = "https://aclanthology.org/2003.mtsummit-semit.11/",
abstract = "We formulate an original model for statistical machine translation (SMT) inspired by characteristics of the Arabic-English translation task. Our approach incorporates part-of-speech tags and linguistically motivated phrase chunks in a 2-level shallow syntactic model of reordering. We implement and evaluate this model, showing it to have advantageous properties and to be competitive with an existing SMT baseline. We also describe cross-categorial lexical translation coercion, an interesting component and side-effect of our approach. Finally, we discuss the novel implementation of decoding for this model which saves much development work by constructing finite-state machine (FSM) representations of translation probability distributions and using generic FSM operations for search. Algorithmic details, examples and results focus on Arabic, and the paper includes discussion on the issues and challenges of Arabic statistical machine translation."
}
@inproceedings{14753351,
title = {Making MIRACLEs: Interactive translingual search for Cebuano and Hindi},
author = {{Daqing He} and {Douglas W. Oard} and {Jianqiang Wang} and {Jun Luo} and {Dina Demner-Fushman} and {Kareem Darwish} and {P. Resnik} and {S. Khudanpur} and {Michael Nossal} and {M. Subotin} and {Anton Leuski}},
year = 2003,
month = {9},
booktitle = {TALIP},
url = {https://www.semanticscholar.org/paper/79896d701850b6edb354f56dea4955c785062f33},
}
We describe a Chinese to English Machine Translation system developed at the Johns Hopkins University for the NIST 2003 MT evaluation. The system is based on a Weighted Finite State Transducer implementation of the alignment template translation model for statistical machine translation. The baseline MT system was trained using 100,000 sentence pairs selected from a static bitext training collection. Information retrieval techniques were then used to create specific training collections for each document to be translated. This document-specific training set included bitext and name entities that were then added to the baseline system by augmenting the library of alignment templates. We report translation performance of baseline and IR-based systems on two NIST MT evaluation test sets.
@inproceedings{byrne-etal-2003-johns,
title = "The {J}ohns {H}opkins {U}niversity 2003 {C}hinese-{E}nglish machine translation system",
author = "Byrne, W. and
Khudanpur, S. and
Kim, W. and
Kumar, S. and
Pecina, P. and
Virga, P. and
Xu, P. and
Yarowsky, D.",
booktitle = "Proceedings of Machine Translation Summit IX: System Presentations",
month = sep # " 23-27",
year = "2003",
address = "New Orleans, USA",
url = "https://aclanthology.org/2003.mtsummit-systems.3/",
abstract = "We describe a Chinese to English Machine Translation system developed at the Johns Hopkins University for the NIST 2003 MT evaluation. The system is based on a Weighted Finite State Transducer implementation of the alignment template translation model for statistical machine translation. The baseline MT system was trained using 100,000 sentence pairs selected from a static bitext training collection. Information retrieval techniques were then used to create specific training collections for each document to be translated. This document-specific training set included bitext and name entities that were then added to the baseline system by augmenting the library of alignment templates. We report translation performance of baseline and IR-based systems on two NIST MT evaluation test sets."
}
@inproceedings{267839748,
title = {Cross-LingualLexical Triggers in Statistical LanguageModeling},
author = {{Woosung Kim} and {S. Khudanpur}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/b32d55a3184e2f27c8d68fb82e09561484986f1c},
}
@inproceedings{deng-khudanpur-2003-latent,
title = "Latent Semantic Information in Maximum Entropy Language Models for Conversational Speech Recognition",
author = "Deng, Yonggang and
Khudanpur, Sanjeev",
booktitle = "Proceedings of the 2003 Human Language Technology Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
year = "2003",
url = "https://aclanthology.org/N03-1008/",
pages = "56--63"
}
@inproceedings{xu-etal-2003-training,
title = "Training Connectionist Models for the {S}tructured {L}anguage {M}odel",
author = "Xu, Peng and
Emami, Ahmad and
Jelinek, Frederick",
booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
year = "2003",
url = "https://aclanthology.org/W03-1021/",
pages = "160--167"
}
@inproceedings{kim-khudanpur-2003-cross,
title = "Cross-Lingual Lexical Triggers in Statistical Language Modeling",
author = "Kim, Woosung and
Khudanpur, Sanjeev",
booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
year = "2003",
url = "https://aclanthology.org/W03-1003/",
pages = "17--24"
}
@inproceedings{mann-yarowsky-2003-unsupervised,
title = "Unsupervised Personal Name Disambiguation",
author = "Mann, Gideon and
Yarowsky, David",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://aclanthology.org/W03-0405/",
pages = "33--40"
}
@inproceedings{2009128,
title = {Thin film PIN photodiodes for optoelectronic silicon on sapphire CMOS},
author = {{A. Apsel} and {E. Culurciello} and {A. Andreou} and {K. Aliberti}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/3b35f4c94262f6856d79158851b3825145c759da},
}
@inproceedings{61050622,
title = {Maximum entropy language modeling with non-local dependencies},
author = {{S. Khudanpur} and {Jun Wu}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/39c3b39e7fd10efd59a114c8ed7a02e4071ddbf5},
}
@inproceedings{41499820,
title = {A comparison of algorithms for sound localization},
author = {{P. Julián} and {A. Andreou} and {Laurence Riddle} and {S. Shamma} and {G. Cauwenberghs}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/c6959240792e8cda2e324df58f68764d312b8f4c},
}
@inproceedings{6529491,
title = {Using a connectionist model in a syntactical based language model},
author = {{Ahmad Emami} and {P. Xu} and {F. Jelinek}},
year = 2003,
month = {4},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
url = {https://www.semanticscholar.org/paper/3d6036af971c1f11ab712cc41487376a94e63673},
}
@inproceedings{62258158,
title = {Modeling and learning multilingual inflectional morphology in a minimally supervised framework},
author = {{David Yarowsky} and {R. Wicentowski}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/2fda0456d2a3a3008206f5c0ec2da0e95cb8e20d},
}
@inproceedings{30699245,
title = {A low-power CMOS integrated circuit for bearing estimation},
author = {{P. Julián} and {A. Andreou} and {P. Mandolesi} and {David H. Goldberg}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/8ed454310941f4a4a68f6941256bcd0efd90c22a},
}
@inproceedings{21766789,
title = {A comparative study of access topologies for chip-level address-event communication channels},
author = {{E. Culurciello} and {A. Andreou}},
year = 2003,
month = {9},
booktitle = {IEEE Trans. Neural Networks},
url = {https://www.semanticscholar.org/paper/8f1bfd136a506bca6be6b6b7bf427bfd37844971},
}
@inproceedings{40026731,
title = {A 7 milliwatt 1GBPS CMOS optical receiver for through wafer communication},
author = {{A. Apsel} and {A. Andreou}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/a20e20995fbe939ea3b41645daea08459d2e31d4},
}
@inproceedings{1862811,
title = {Language model adaptation using cross-lingual information},
author = {{Woosung Kim} and {S. Khudanpur}},
year = 2003,
month = {9},
booktitle = {Interspeech},
url = {https://www.semanticscholar.org/paper/23d9233dd26768acbea6c6bfe2270d313d33fef2},
}
@inproceedings{18061996,
title = {Transliteration of proper names in cross-language applications},
author = {{Paola Virga} and {S. Khudanpur}},
year = 2003,
month = {7},
booktitle = {Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
url = {https://www.semanticscholar.org/paper/b279fe7b24107a04c47fc792db3ea48bef7c532e},
}
@inproceedings{schafer-yarowsky-2003-statistical,
title = "Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction",
author = "Schafer, Charles and
Yarowsky, David",
booktitle = "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
year = "2003",
url = "https://aclanthology.org/W03-1002/",
pages = "9--16"
}
@inproceedings{5838856,
title = {Syntax for Statistical Machine Translation},
author = {{F. Och} and {D. Gildea} and {S. Khudanpur} and {Kenji Yamada} and {Alexander M. Fraser} and {Shankar Kumar} and {David A. Smith} and {Katherine Eng} and {Viren Jain} and {Zhenglin Jin} and {Dragomir R. Radev}},
year = 2003,
booktitle = {},
url = {https://www.semanticscholar.org/paper/fefd66ad4e74c333a47ed726be66f9c0e440f5e1},
}
@inproceedings{42004952,
title = {A 10 milliwatt 2 Gbps CMOS optical receiver for optoelectronic interconnect},
author = {{A. Apsel} and {A. Andreou}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/a58bd9def113605925c3e3a32c95df94a5f44223},
}
@inproceedings{36729490,
title = {Energy efficiency in a channel model for the spiking axon},
author = {{David H. Goldberg} and {A. Sripati} and {A. Andreou}},
year = 2003,
month = {6},
booktitle = {Neurocomputing},
url = {https://www.semanticscholar.org/paper/a7110da29070e2fd2b5ad8fb76ceef9f0f8056d4},
}
@inproceedings{37397472,
title = {An 8-bit, 1mW successive approximation ADC in SOI CMOS},
author = {{E. Culurciello} and {A. Andreou}},
year = 2003,
month = {5},
booktitle = {Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.},
url = {https://www.semanticscholar.org/paper/5b8e5af2063e4316296244353854ca8363f57a58},
}
@inproceedings{14571038,
title = {Compression of IP images for autostereoscopic 3D imaging applications},
author = {{N. Sgouros} and {A. Andreou} and {M. Sangriotis} and {P. Papageorgas} and {D. Maroulis} and {NG Theofanous}},
year = 2003,
month = {9},
booktitle = {3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the},
url = {https://www.semanticscholar.org/paper/8c3f905a15cad55747b223abf0c61bbc91101adc},
}
@inproceedings{39697252,
title = {Combating the Sparse Data Problem of Language Modelling},
author = {{F. Jelinek}},
year = 2003,
month = {9},
booktitle = {International Conference on Text, Speech and Dialogue},
url = {https://www.semanticscholar.org/paper/cbf801d00bf0f0020b17645874108012909e6a99},
}
@inproceedings{122879363,
title = {Polarization imaging: principles and integrated polarimeters},
author = {{A. Andreou} and {Z. Kalayjian}},
year = 2002,
month = {12},
booktitle = {IEEE Sensors Journal},
url = {https://www.semanticscholar.org/paper/08912d533bdc5a6e259bb5c3cb1e76a990be1580},
}
@inproceedings{15605004,
title = {Evaluating sense disambiguation across diverse parameter spaces},
author = {{David Yarowsky} and {Radu Florian}},
year = 2002,
month = {12},
booktitle = {Natural Language Engineering},
url = {https://www.semanticscholar.org/paper/a9b2040cc48c41cf3ccd85e4e95b3baefc1b0459},
}
@inproceedings{43025066,
title = {Combining Classifiers for word sense disambiguation},
author = {{Radu Florian} and {Silviu Cucerzan} and {C. Schafer} and {David Yarowsky}},
year = 2002,
month = {12},
booktitle = {Natural Language Engineering},
url = {https://www.semanticscholar.org/paper/b1e64153e0eccea699d01b094020f3424598cd94},
}
@inproceedings{florian-yarowsky-2002-modeling,
title = "Modeling Consensus: Classifier Combination for Word Sense Disambiguation",
author = "Florian, Radu and
Yarowsky, David",
booktitle = "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002)",
month = jul,
year = "2002",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W02-1004/",
doi = "10.3115/1118693.1118697",
pages = "25--32"
}
@inproceedings{cucerzan-yarowsky-2002-augmented,
title = "Augmented Mixture Models for Lexical Disambiguation",
author = "Cucerzan, Silviu and
Yarowsky, David",
booktitle = "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002)",
month = jul,
year = "2002",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W02-1005/",
doi = "10.3115/1118693.1118698",
pages = "33--40"
}
@inproceedings{xu-etal-2002-study,
title = "A Study on Richer Syntactic Dependencies for Structured Language Modeling",
author = "Xu, Peng and
Chelba, Ciprian and
Jelinek, Frederick",
editor = "Isabelle, Pierre and
Charniak, Eugene and
Lin, Dekang",
booktitle = "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2002",
address = "Philadelphia, Pennsylvania, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P02-1025/",
doi = "10.3115/1073083.1073116",
pages = "191--198"
}
@InProceedings{eisner-2002-acl-fst,
aclid = "P02-1001",
author = "Jason Eisner",
title = "Parameter Estimation for Probabilistic Finite-State
Transducers",
booktitle = "Proceedings of the 40th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "1--8",
year = "2002",
month = jul,
address = "Philadelphia",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-acl-fst",
}
@InProceedings{eisner-2002-acl-ot,
aclid = "P02-1008",
author = "Jason Eisner",
title = "Comprehension and Compilation in {O}ptimality
{T}heory",
booktitle = "Proceedings of the 40th Annual Meeting of the
Association for Computational Linguistics (ACL)",
pages = "56--63",
year = "2002",
month = jul,
address = "Philadelphia",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-acl-ot",
}
@InProceedings{eisner-2002-tnlp,
aclid = "W02-0102",
author = "Jason Eisner",
title = "An Interactive Spreadsheet for Teaching the
Forward-Backward Algorithm",
booktitle = "Proceedings of the ACL Workshop on Effective Tools and
Methodologies for Teaching NLP and CL",
editor = "Dragomir Radev and Chris Brew",
pages = "10--18",
year = "2002",
month = jul,
address = "Philadelphia",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-tnlp",
}
@InProceedings{eisner-2002-emnlp,
aclid = "W02-1009",
author = "Jason Eisner",
title = "Transformational Priors Over Grammars",
booktitle = "Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP)",
pages = "63--70",
year = "2002",
month = jul,
address = "Philadelphia",
note = "Nominated for Best Paper Award.",
URL = "http://cs.jhu.edu/~jason/papers/#eisner-2002-emnlp",
}
@inproceedings{cucerzan-yarowsky-2002-bootstrapping,
title = "Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day",
author = "Cucerzan, Silviu and
Yarowsky, David",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://aclanthology.org/W02-2006/"
}
@inproceedings{17923970,
title = {Maximum Entropy Language Modeling with Non-local and Syntactic Dependencies},
author = {{Jun Wu} and {S. Khudanpur}},
year = 2002,
booktitle = {},
url = {https://www.semanticscholar.org/paper/fe686c6813569d202b9cb8046822396d3c1a37cf},
}
@inproceedings{cucerzan-yarowsky-2002-language,
title = "Language Independent {NER} using a Unified Model of Internal and Contextual Evidence",
author = "Cucerzan, Silviu and
Yarowsky, David",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://aclanthology.org/W02-2007/"
}
@inproceedings{21149766,
title = {A 6 channel array of 5 milliwatt, 500 MHz optical receivers in .5 /spl mu/m SOS CMOS},
author = {{A. Apsel} and {A. Andreou} and {J. Liu}},
year = 2002,
month = {8},
booktitle = {IEEE International Symposium on Circuits and Systems proceedings},
url = {https://www.semanticscholar.org/paper/fd80450ae8cadd7d435fd065a436f636976c1d5a},
}
@inproceedings{schafer-yarowsky-2002-inducing,
title = "Inducing Translation Lexicons via Diverse Similarity Measures and Bridge Languages",
author = "Schafer, Charles and
Yarowsky, David",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://aclanthology.org/W02-2026/"
}
@inproceedings{15662068,
title = {Modeling hot-electrons effects in silicon-on-sapphire MOSFETs},
author = {{E. Culurciello} and {A. Andreou} and {P. Pouliquen}},
year = 2002,
month = {8},
booktitle = {IEEE International Symposium on Circuits and Systems proceedings},
url = {https://www.semanticscholar.org/paper/9dc4fb378b646999a9589c09f0c824b9ab38e1a7},
}
@inproceedings{14041212,
title = {Order estimation for a special class of hidden Markov sources and binary renewal processes},
author = {{S. Khudanpur} and {P. Narayan}},
year = 2002,
month = {6},
booktitle = {IEEE Transactions on Information Theory},
url = {https://www.semanticscholar.org/paper/a4282dca310b448a329eb4b2c5fb1e313e033745},
}
@inproceedings{117749207,
title = {Ultra-thin silicon-on-sapphire multi-channel optical interconnects},
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