--- title: "Who cites Analyzing Compositionality-Sensitivity of NLI Models" description: "43 papers of the Elamur Research library cite «Analyzing Compositionality-Sensitivity of NLI Models», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Analyzing Compositionality-Sensitivity of NLI…](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) / cited by # Who cites «Analyzing Compositionality-Sensitivity of NLI Models» 43 papers of our library cite it (of the 141,840 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «Analyzing Compositionality-Sensitivity of NLI Models»](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) 1. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*2019 · 1,635 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) 2. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/) 3. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/) 4. [**Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation**Atticus Geiger, Kyle Richardson, Christopher Potts*2020 · 112 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/) **[Towards Robustifying NLI Models Against Lexical Dataset Biases](https://md.research.elamur.ai/free/en/papers/zhou-2020-robustifying-nli-models-against-lexical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiang Zhou, Mohit Bansal*2020 · 70 citations* [**IMPLI: Investigating NLI Models' Performance on Figurative Language**Kevin Stowe, Prasetya Utama, Iryna Gurevych*2022 · 68 citations*](https://md.research.elamur.ai/free/en/papers/stowe-2022-impli-investigating-nli-models-performance/) **[Posing Fair Generalization Tasks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/geiger-2019-posing-fair-generalization-tasks-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Atticus Geiger, Ignacio Cases, Lauri Karttunen, et al.*2019 · 58 citations* **[ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://md.research.elamur.ai/free/en/papers/saha-2020-conjnli-natural-language-inference-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swarnadeep Saha, Yixin Nie, Mohit Bansal*2020 · 50 citations* **[ANLIzing the Adversarial Natural Language Inference Dataset](https://md.research.elamur.ai/free/en/papers/williams-2022-anlizing-adversarial-natural-language-inference/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Adina Williams, Tristan Thrush, Douwe Kiela*2022 · 48 citations* **[An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-empirical-study-model-agnostic-debiasing/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Tianyu Liu, Xin Zheng, Xiaoan Ding, et al.*2020 · 36 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* **[HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-hyponli-exploring-artificial-patterns-hypothesis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Tianyu Liu, Xin Zheng, Baobao Chang, et al.*2020 · 27 citations* [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/) [**Adversarial Analysis of Natural Language Inference Systems arXiv:1912.03441**Tiffany Chien, Jugal Kalita*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chien-2019-adversarial-analysis-natural-language-inference/) **[Annotation Difficulties in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kalouli-2021-annotation-difficulties-natural-language-inference/) [Brazilian Symposium in Information and Human Language Technology](https://md.research.elamur.ai/free/en/venues/stil/)**Aikaterini-Lida Kalouli, Livy Real, Annebeth Buis, et al.*2021 · 5 citations* 28 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) — Analyzing Compositionality-Sensitivity of NLI Models