When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes by Tucker, Mycal, Eisape, Tiwalayo, Qian, Peng, Levy, Roger, Shah, Julie

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Language Learning and Processing in People and Machines by Nematzadeh, Aida, Futrell, Richard, Levy, Roger P

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Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B by Luo, Jiaming, Cao, Yuan, Barzilay, Regina

“…© 2019 Association for Computational Linguistics In this paper we propose a novel neural approach for automatic decipherment of lost languages. …”
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Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing by Schuster, Tal, Ram, Ori, Barzilay, Regina, Globerson, Amir

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Assessing Language Proficiency from Eye Movements in Reading by Berzak, Yevgeni, Katz, Boris, Levy, Roger P

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Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention by Yin, Pengcheng, Fang, Hao, Neubig, Graham, Pauls, Adam, Platanios, Emmanouil Antonios, Su, Yu, Thomson, Sam, Andreas, Jacob

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Implicit Representations of Meaning in Neural Language Models by Li, Belinda Z, Nye, Maxwell, Andreas, Jacob

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Value-Agnostic Conversational Semantic Parsing by Platanios, Emmanouil Antonios, Pauls, Adam, Roy, Subhro, Zhang, Yuchen, Kyte, Alexander, Guo, Alan, Thomson, Sam, Krishnamurthy, Jayant, Wolfe, Jason, Andreas, Jacob, Klein, Dan

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Unsupervised Lexicon Discovery from Acoustic Input by Lee, Chia-ying, O'Donnell, Timothy John, Glass, James R.

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Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B by Luo, Jiaming, Cao, Yuan, Barzilay, Regina

“…© 2019 Association for Computational Linguistics In this paper we propose a novel neural approach for automatic decipherment of lost languages. …”
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An unsupervised method for uncovering morphological chains by Narasimhan, Karthik Rajagopal, Barzilay, Regina, Jaakkola, Tommi S.

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Cross-lingual parse disambiguation based on semantic correspondence by Frermann, Lea, Bond, Francis

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Neural language models as psycholinguistic subjects: Representations of syntactic state by Qian, Peng, Levy, Roger P

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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies

“…© 2019 Association for Computational Linguistics State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success. …”
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Inferring Which Medical Treatments Work from Reports of Clinical Trials by Lehman, Eric, DeYoung, Jay, Barzilay, Regina, Wallace, Byron C

“…© 2019 Association for Computational Linguistics How do we know if a particular medical treatment actually works? …”
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Neural Networks for Joint Sentence Classification in Medical Paper Abstracts by Dernoncourt, Franck, Lee, Ji Young, Szolovits, Peter

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Deriving Machine Attention from Human Rationales by Bao, Yujia, Chang, Shiyu, Yu, Mo, Barzilay, Regina

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Learning the countability of English nouns from corpus data by Baldwin, Timothy, Bond, Francis

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Graphie: A graph-based framework for information extraction by Qian, Y, Santus, E, Jin, Z, Guo, J, Barzilay, R

“…© 2019 Association for Computational Linguistics Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. …”
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SyntaxGym: An Online Platform for Targeted Evaluation of Language Models by Gauthier, Jon, Hu, Jennifer, Wilcox, Ethan, Qian, Peng, Levy, Roger

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