🔗 Who cites «LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better»

71 papers of our library cite it (of the 141,840 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better»

A Structural Probe for Finding Syntax in Word RepresentationsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)John Hewitt, Christopher D. Manning2019 · 1,232 citations
1. What do you learn from context? Probing for sentence structure in contextualized word representationsarXiv:1905.06316Ian Tenney, Patrick Xia, Berlin Chen, et al.2019 · 588 citations
   How Can We Accelerate Progress Towards Human-like Linguistic Generalization?Annual Meeting of the Association for Computational Linguistics (ACL)Tal Linzen2020 · 258 citations
   Visualizing and Understanding the Effectiveness of BERTConference on Empirical Methods in Natural Language Processing (EMNLP)Yaru Hao, Li Dong, Furu Wei, et al.2019 · 221 citations
   Compound Probabilistic Context-Free Grammars for Grammar InductionAnnual Meeting of the Association for Computational Linguistics (ACL)Yoon Kim, Chris Dyer, Alexander Rush2019 · 196 citations
   The emergence of number and syntax units in LSTM language modelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Yair Lakretz, German Kruszewski, Theo Desbordes, et al.2019 · 194 citations
   Cross-Linguistic Syntactic Evaluation of Word Prediction ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, et al.2020 · 97 citations
   How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speechAnnual Meeting of the Association for Computational Linguistics (ACL)Aditya Yedetore, Tal Linzen, Robert Frank, et al.2023 · 48 citations
   Neural Generative Rhetorical Structure ParsingConference on Empirical Methods in Natural Language Processing (EMNLP)Amandla Mabona, Laura Rimell, Stephen Clark, et al.2019 · 35 citations
   Exploring Transitivity in Neural NLI Models through VeridicalityConference of the European Chapter of the Association for Computational Linguistics (EACL)Hitomi Yanaka, Koji Mineshima, Kentaro Inui2021 · 27 citations
2. Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networksClaire Hobbs, R. Thomas McCoy2026

60 more papers of our library cite it; they have no page here yet.

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