---
title: "Who cites Breaking NLI Systems with Sentences that Require…"
description: "202 papers of the Elamur Research library cite «Breaking NLI Systems with Sentences that Require Simple Lexical Inferences», found in their…"
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---
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# Who cites «Breaking NLI Systems with Sentences that Require Simple Lexical Inferences»

202 papers of our library cite it (of the 140,884 whose bibliographies we read) · 34 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)

1. [**Multi-Task Deep Neural Networks for Natural Language Understanding**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,501 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/)
2. [**BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,299 citations*](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/)
3. [**A Survey of Data Augmentation Approaches for NLP**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 935 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
4. [**BERT-ATTACK: Adversarial Attack Against BERT Using BERT**Linyang Li, Ruotian Ma, Qipeng Guo, et al.*2020 · 840 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-bert-attack-adversarial-attack-against/)
5. [**Analysis Methods in Neural Language Processing: A Survey**Yonatan Belinkov, James Glass*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
6. [**e-SNLI: Natural Language Inference with Natural Language Explanations**Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, et al.*2018 · 612 citations*](https://md.research.elamur.ai/free/en/papers/camburu-2018-e-snli-natural-language-inference/)
7. [**PAWS: Paraphrase Adversaries from Word Scrambling**Yuan Zhang, Jason Baldridge, Luheng He*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/)
8. [**Neural Text Summarization: A Critical Evaluation**Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/)
9. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 520 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
10. [**Experience Grounds Language**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 429 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/)
11. [**Combating Adversarial Misspellings with Robust Word Recognition**Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton*2019 · 388 citations*](https://md.research.elamur.ai/free/en/papers/pruthi-2019-combating-adversarial-misspellings-robust-word/)
12. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 361 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)
13. [**Word-level Textual Adversarial Attacking as Combinatorial Optimization**Yuan Zang, Fanchao Qi, Chenghao Yang, et al.*2020 · 338 citations*](https://md.research.elamur.ai/free/en/papers/zang-2020-word-level-textual-adversarial-attacking/)
14. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 312 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
15. [**PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification**Yinfei Yang, Yuan Zhang, Chris Tar, et al.*2019 · 283 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-paws-x-cross-lingual-adversarial/)
16. [**Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/)
17. [**Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual**He He, Sheng Zha, Haohan Wang*2019 · 254 citations*](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/)
18. [**HateCheck: Functional Tests for Hate Speech Detection Models**Paul Röttger, Bertie Vidgen, Dong Nguyen, et al.*2021 · 202 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/)
19. [**Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation**Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al.*2018 · 190 citations*](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/)
20. [**Translation Artifacts in Cross-lingual Transfer Learning**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2020 · 175 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2020-translation-artifacts-cross-lingual-transfer/)
21. [**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/)
22. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
23. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/)
24. [**An Analysis of Natural Language Inference Benchmarks through the Lens of Negation**Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, et al.*2020 · 130 citations*](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
25. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
26. [**Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 121 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/)
27. [**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/)
28. [**Probing Natural Language Inference Models through Semantic Fragments**Kyle Richardson, Hai Hu, Lawrence S. Moss, et al.*2019 · 102 citations*](https://md.research.elamur.ai/free/en/papers/richardson-2019-probing-natural-language-inference-models/)
29. [**BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language**Philip A. Huebner, Elior Sulem, Fisher Cynthia, et al.*2021 · 91 citations*](https://md.research.elamur.ai/free/en/papers/huebner-2021-babyberta-learning-more-grammar-small/)
30. [**Mining Discourse Markers for Unsupervised Sentence Representation Learning**Damien Sileo, Tim Van De Cruys, Camille Pradel, et al.*2019 · 83 citations*](https://md.research.elamur.ai/free/en/papers/sileo-2019-mining-discourse-markers-unsupervised-sentence/)
31. [**SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference**Martin Schmitt, Hinrich Schütze*2019 · 28 citations*](https://md.research.elamur.ai/free/en/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
32. [**How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?**Neha Srikanth, Marine Carpuat, Rachel Rudinger*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/)
33. [**A guide to the dataset explosion in QA, NLI, and commonsense reasoning**Anna Rogers, Anna Rumshisky*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
34. [**MultiCoS: A Multilingual Dataset of Connective Semantics with Context–Sentence Compatibility**Anne Mucha, Ciyang Qing, Wataru Uegaki*2026*](https://md.research.elamur.ai/free/en/papers/mucha-2026-multicos-multilingual-dataset-connective-semantics/)

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