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# Who cites «Breaking NLI Systems with Sentences that Require Simple Lexical Inferences» · page 2

202 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/glockner-2018-breaking-nli-systems-sentences-that/)

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1. [Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity](https://aclanthology.org/2020.lrec-1.709/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
2. [Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks](https://aclanthology.org/2020.emnlp-main.616/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
3. [Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference](https://aclanthology.org/2020.emnlp-main.657/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
4. [Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?](https://aclanthology.org/2020.acl-main.543/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
5. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
6. [Evaluation of Coreference Resolution Systems Under Adversarial Attacks](https://aclanthology.org/2020.codi-1.16/) *— 2020 · Proceedings of the First Workshop on Computational Approaches to Discourse*
7. [Experience Grounds Language](https://aclanthology.org/2020.emnlp-main.703/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
8. [GenAug: Data Augmentation for Finetuning Text Generators](https://aclanthology.org/2020.deelio-1.4/) *— 2020 · Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures*
9. [How does Punctuation Affect Neural Models in Natural Language Inference](https://aclanthology.org/2020.pam-1.15/) *— 2020 · Proceedings of the Probability and Meaning Conference (PaM 2020)*
10. [Hy-NLI: a Hybrid system for Natural Language Inference](https://aclanthology.org/2020.coling-main.459/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
11. [HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference](https://aclanthology.org/2020.lrec-1.846/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
12. [INFOTABS: Inference on Tables as Semi-structured Data](https://aclanthology.org/2020.acl-main.210/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
13. [Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness](https://aclanthology.org/2020.emnlp-main.650/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
14. [Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs](https://aclanthology.org/2020.coling-main.571/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
15. [Language (Re)modelling: Towards Embodied Language Understanding](https://aclanthology.org/2020.acl-main.559/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
16. [Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference](https://aclanthology.org/2020.starsem-1.3/) *— 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics*
17. [Mining Knowledge for Natural Language Inference from Wikipedia Categories](https://aclanthology.org/2020.findings-emnlp.313/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
18. [More Bang for Your Buck: Natural Perturbation for Robust Question Answering](https://aclanthology.org/2020.emnlp-main.12/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
19. [Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation](https://md.mova.elamur.ai/free/en/docs/papers/geiger-2020-neural-natural-language-inference-models/) *— our card · 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
20. [On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks](https://aclanthology.org/2020.findings-emnlp.305/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
21. [On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT](https://aclanthology.org/2020.starsem-1.10/) *— 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics*
22. [Robustness to Modification with Shared Words in Paraphrase Identification](https://aclanthology.org/2020.findings-emnlp.16/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
23. [Shortcut Learning in Deep Neural Networks](https://arxiv.org/abs/2004.07780) *— 2020 · arXiv:2004.07780*
24. [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://arxiv.org/abs/2001.01565) *— 2020 · arXiv:2001.01565*
25. [Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis](https://aclanthology.org/2020.emnlp-main.292/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
26. [The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions](https://aclanthology.org/2020.emnlp-main.659/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
27. [Towards Debiasing NLU Models from Unknown Biases](https://aclanthology.org/2020.emnlp-main.613/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
28. [Towards Robustifying NLI Models Against Lexical Dataset Biases](https://aclanthology.org/2020.acl-main.773/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
29. [Translation Artifacts in Cross-lingual Transfer Learning](https://aclanthology.org/2020.emnlp-main.618/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
30. [What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge](https://aclanthology.org/2020.tacl-1.37/) *— 2020 · Transactions of the Association for Computational Linguistics*
31. [Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://aclanthology.org/2020.acl-main.540/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
32. [A Logic-Driven Framework for Consistency of Neural Models](https://arxiv.org/abs/1909.00126) *— 2019 · arXiv:1909.00126*
33. [A logical-based corpus for cross-lingual evaluation](https://aclanthology.org/D19-6103/) *— 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)*
34. [Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation](https://aclanthology.org/D19-1419/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
35. [Adversarial Analysis of Natural Language Inference Systems](https://arxiv.org/abs/1912.03441) *— 2019 · arXiv:1912.03441*
36. [Analysis Methods in Neural Language Processing: A Survey](https://aclanthology.org/Q19-1004/) *— 2019 · Transactions of the Association for Computational Linguistics*
37. [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://aclanthology.org/D19-1107/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
38. [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
39. [Can Neural Networks Understand Monotonicity Reasoning?](https://aclanthology.org/W19-4804/) *— 2019 · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP*
40. [Combating Adversarial Misspellings with Robust Word Recognition](https://aclanthology.org/P19-1561/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
41. [Composition of Sentence Embeddings: Lessons from Statistical Relational Learning](https://aclanthology.org/S19-1004/) *— 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)*
42. [Deep Learning for Natural Language Inference](https://aclanthology.org/N19-5002/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials*
43. [Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets](https://aclanthology.org/K19-1019/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
44. [Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects](https://aclanthology.org/D19-1625/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
45. [Don''t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases](https://aclanthology.org/D19-1418/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
46. [Don''t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://aclanthology.org/P19-1084/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
47. [EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/) *— our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
48. [Explaining Simple Natural Language Inference](https://aclanthology.org/W19-4016/) *— 2019 · Proceedings of the 13th Linguistic Annotation Workshop*
49. [HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning](https://aclanthology.org/S19-1027/) *— 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)*
50. [HellaSwag: Can a Machine Really Finish Your Sentence?](https://aclanthology.org/P19-1472/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
51. [How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG](https://aclanthology.org/D19-1335/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
52. [Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets](https://aclanthology.org/N19-1225/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
53. [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://arxiv.org/abs/1909.12434) *— 2019 · arXiv:1909.12434*
54. [Mining Discourse Markers for Unsupervised Sentence Representation Learning](https://aclanthology.org/N19-1351/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
55. [Misleading Failures of Partial-input Baselines](https://aclanthology.org/P19-1554/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
56. [Multi-Task Deep Neural Networks for Natural Language Understanding](https://aclanthology.org/P19-1441/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
57. [Neural Text Summarization: A Critical Evaluation](https://aclanthology.org/D19-1051/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
58. [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) *— 2019 · arXiv:1908.11828*
59. [PAWS: Paraphrase Adversaries from Word Scrambling](https://aclanthology.org/N19-1131/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
60. [Posing Fair Generalization Tasks for Natural Language Inference](https://aclanthology.org/D19-1456/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
61. [Probing Natural Language Inference Models through Semantic Fragments](https://arxiv.org/abs/1909.07521) *— 2019 · arXiv:1909.07521*
62. [Probing Neural Network Comprehension of Natural Language Arguments](https://aclanthology.org/P19-1459/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
63. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
64. [SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/schmitt-2019-sherliic-typed-event-focused-lexical/) *— our card · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
65. [Shifting the Baseline: Single Modality Performance on Visual Navigation & QA](https://aclanthology.org/N19-1197/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
66. [Understanding Roles and Entities: Datasets and Models for Natural Language Inference](https://arxiv.org/abs/1904.09720) *— 2019 · arXiv:1904.09720*
67. [Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://aclanthology.org/D19-6115/) *— 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)*
68. [pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference](https://aclanthology.org/N19-1362/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
69. [AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https://aclanthology.org/P18-1225/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
70. [Analyzing Compositionality-Sensitivity of NLI Models](https://arxiv.org/abs/1811.07033) *— 2018 · arXiv:1811.07033*
71. [Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://aclanthology.org/D18-1007/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
72. [Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning](https://aclanthology.org/W18-5211/) *— 2018 · Proceedings of the 5th Workshop on Argument Mining*
73. [How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks](https://md.mova.elamur.ai/free/en/docs/papers/kaushik-2018-how-much-reading-does-reading/) *— our card · 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
74. [Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging](https://aclanthology.org/D18-1143/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
75. [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://aclanthology.org/D18-1009/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
76. [Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences](https://arxiv.org/abs/1810.13033) *— 2018 · arXiv:1810.13033*
77. [The Importance of Being Recurrent for Modeling Hierarchical Structure](https://arxiv.org/abs/1803.03585) *— 2018 · arXiv:1803.03585*
78. [e-SNLI: Natural Language Inference with Natural Language Explanations](https://arxiv.org/abs/1812.01193) *— 2018 · arXiv:1812.01193*
79. abzianidze-2023-formal-proofs-structured-explanations-proposing
80. abzianidze-2023-spacenli-evaluating-consistency-predicting-inferences
81. banerjee-2024-inffeed-influence-functions-as-feedback
82. chung-2025-divlogiceval-framework-benchmarking-logical-reasoning
83. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
84. fiacco-2019-deep-neural-model-inspection-comparison
85. gabriel-2022-naturaladversaries-can-naturalistic-adversaries-be
86. gemechu-2024-external-knowledge-driven-argument-mining
87. gokhale-2022-textitgeneralized-but-not-robust-comparing
88. gupta-2024-evaluating-concurrent-robustness-language-models
89. howard-2022-neurocounterfactuals-beyond-minimal-edit-counterfactuals
90. k-2022-multilingual-checklist-generation-evaluation
91. kogkalidis-2024-oyxoy-modern-nlp-test-suite
92. li-2025-persona-aware-alignment-framework-personalized
93. li-2025-task-calibration-calibrating-large-language
94. lin-2024-sentence-space-metrics-ssm-evaluation
95. madusanka-2025-unravelling-logic-investigating-generalisation-transformers
96. murugesan-2023-mismatch-fine-grained-evaluation-machine
97. nguyen-2023-firo-finite-context-indexing-restricted
98. reich-2022-leveraging-expert-guided-adversarial-augmentation
99. schmitt-2021-continuous-entailment-patterns-lexical-inference
100. skandalis-2025-neurosymbolic-ai-natural-language-inference

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