Tal Linzen

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R. Thomas McCoyPostdoc in the Department of Computer Science, Princeton UniversityVerified email at princeton.edu
Ellie PavlickBrown UniversityVerified email at brown.edu
Yoav GoldbergProfessor, Bar Ilan University. Research Director, AI2-IsraelVerified email at cs.biu.ac.il
Emmanuel DupouxProfessor of Cognitive Psychology, Ecole des Hautes Etudes en Sciences Sociales, ParisVerified email at ehess.fr
Marten van SchijndelCornell UniversityVerified email at cornell.edu
Marco BaroniICREA Professor (Barcelona)Verified email at upf.edu
Alec MarantzProfessor of Linguistics and Psychology, NYUVerified email at nyu.edu
Paul SmolenskyMicrosoft Research Redmond & Krieger-Eisenhower Professor of Cognitive Science, Johns HopkinsVerified email at jhu.edu
Najoung KimBoston UniversityVerified email at bu.edu
Robert FrankProfessor of Linguistics, Yale UniversityVerified email at yale.edu
Aaron MuellerPh.D. Candidate, Johns Hopkins UniversityVerified email at jhu.edu
Grusha PrasadColgate UniversityVerified email at colgate.edu
Tim Florian JaegerBrain and Cognitive Sciences, University of RochesterVerified email at ur.rochester.edu
Kristina GulordavaPostdoc researcherVerified email at upf.edu
Edouard GraveResearch Scientist, FacebookVerified email at fb.com
Piotr BojanowskiMeta AIVerified email at fb.com
Junghyun MinMasters student at Johns Hopkins UniversityVerified email at jhu.edu
Samuel R. BowmanAssociate Professor, NYUVerified email at nyu.edu
Rebecca MarvinJohns Hopkins UniversityVerified email at jhu.edu
Grzegorz ChrupałaAssociate Professor at Tilburg UniversityVerified email at uvt.nl

Tal Linzen

New York University

Verified email at nyu.edu - Homepage

Computational Linguistics Linguistics Cognitive Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies T Linzen, E Dupoux, Y Goldberg Transactions of the Association for Computational Linguistics 4, 521-535, 2016	799	2016
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference RT McCoy, E Pavlick, T Linzen Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019	790	2019
Colorless green recurrent networks dream hierarchically K Gulordava, P Bojanowski, E Grave, T Linzen, M Baroni Proceedings of the 16th Annual Conference of the North American Chapter of …, 2018	476	2018
Targeted Syntactic Evaluation of Language Models R Marvin, T Linzen Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018	319	2018
Issues in evaluating semantic spaces using word analogies T Linzen Proceedings of the First Workshop on Evaluating Vector Space Representations …, 2016	161	2016
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation N Kim, T Linzen EMNLP, 2020	123	2020
How Can We Accelerate Progress Towards Human-like Linguistic Generalization? T Linzen Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020	115	2020
Syntactic Structure from Deep Learning T Linzen, M Baroni Annual Reviews of Linguistics, 2021	111	2021
Uncertainty and expectation in sentence processing: evidence from subcategorization distributions T Linzen, TF Jaeger Cognitive Science 40 (6), 1382-1411, 2016	111	2016
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance RT McCoy, J Min, T Linzen Proceedings of BlackboxNLP 2020, 2019	104	2019
Syntactic Data Augmentation Increases Robustness to Inference Heuristics J Min, RT McCoy, D Das, E Pitler, T Linzen Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020	102	2020
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022	99	2022
Human few-shot learning of compositional instructions BM Lake, T Linzen, M Baroni Proceedings of the 41st Annual Conference of the Cognitive Science Society, 2019	85	2019
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension N Kim, R Patel, A Poliak, A Wang, P Xia, RT McCoy, I Tenney, A Ross, ... arXiv preprint arXiv:1904.11544, 2019	82	2019
In Spoken Word Recognition, the Future Predicts the Past L Gwilliams, T Linzen, D Poeppel, A Marantz Journal of Neuroscience 38 (35), 7585-7599, 2018	72	2018
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks RT McCoy, R Frank, T Linzen Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018	68	2018
Quantity doesn't buy quality syntax with neural language models M van Schijndel, A Mueller, T Linzen EMNLP 2019, 2019	66	2019
Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks RT McCoy, R Frank, T Linzen Transactions of the Association for Computational Linguistics 8, 125--140, 2020	63	2020
A Neural Model of Adaptation in Reading M van Schijndel, T Linzen Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018	62	2018
The role of morphology in phoneme prediction: Evidence from MEG A Ettinger, T Linzen, A Marantz Brain and Language 129, 14-23, 2014	61	2014

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