--- title: "Who cites LSTMs Can Learn — 71 citing papers" description: "71 papers of the Elamur Research library cite «LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them…" canonical: "https://research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LSTMs Can Learn Syntax-Sensitive Dependencies…](https://md.research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/) / cited by # 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»](https://md.research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/) 1. [**What do you learn from context? Probing for sentence structure in contextualized word representations arXiv:1905.06316**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 588 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/) **[How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tal Linzen*2020 · 258 citations* **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 221 citations* **[Compound Probabilistic Context-Free Grammars for Grammar Induction](https://md.research.elamur.ai/free/en/papers/kim-2019-compound-probabilistic-context-free-grammars/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yoon Kim, Chris Dyer, Alexander Rush*2019 · 196 citations* **[The emergence of number and syntax units in LSTM language models](https://md.research.elamur.ai/free/en/papers/lakretz-2019-emergence-number-syntax-units-lstm/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yair Lakretz, German Kruszewski, Theo Desbordes, et al.*2019 · 194 citations* **[Cross-Linguistic Syntactic Evaluation of Word Prediction Models](https://md.research.elamur.ai/free/en/papers/mueller-2020-cross-linguistic-syntactic-evaluation-word/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 speech](https://md.research.elamur.ai/free/en/papers/yedetore-2023-how-poor-is-stimulus-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aditya Yedetore, Tal Linzen, Robert Frank, et al.*2023 · 48 citations* **[Neural Generative Rhetorical Structure Parsing](https://md.research.elamur.ai/free/en/papers/mabona-2019-neural-generative-rhetorical-structure-parsing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Amandla Mabona, Laura Rimell, Stephen Clark, et al.*2019 · 35 citations* **[Exploring Transitivity in Neural NLI Models through Veridicality](https://md.research.elamur.ai/free/en/papers/yanaka-2021-exploring-transitivity-neural-nli-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hitomi Yanaka, Koji Mineshima, Kentaro Inui*2021 · 27 citations* [**Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks**Claire Hobbs, R. Thomas McCoy*2026*](https://md.research.elamur.ai/free/en/papers/hobbs-2026-collocational-bootstrapping-hypothesis-about-learning/) 60 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/) — LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better