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

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/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/glockner-2018-breaking-nli-systems-sentences-that-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/glockner-2018-breaking-nli-systems-sentences-that-3/)

1. [MultiCoS: A Multilingual Dataset of Connective Semantics with Context–Sentence Compatibility](https://aclanthology.org/2026.lrec-1.381/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [Matina: A Large-Scale 73B Token Persian Text Corpus](https://aclanthology.org/2025.naacl-long.462/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
3. [Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://aclanthology.org/2024.acl-long.555/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Benchmark Transparency: Measuring the Impact of Data on Evaluation](https://aclanthology.org/2024.naacl-long.86/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
5. [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://aclanthology.org/2024.eacl-long.138/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [Generating Benchmarks for Factuality Evaluation of Language Models](https://aclanthology.org/2024.eacl-long.4/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics](https://aclanthology.org/2024.emnlp-main.175/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
8. [How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://aclanthology.org/2024.tacl-1.63/) *— 2024 · Transactions of the Association for Computational Linguistics*
9. [LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study](https://aclanthology.org/2024.findings-emnlp.870/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
10. [Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals](https://aclanthology.org/2024.findings-emnlp.205/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
11. [Natural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models](https://aclanthology.org/2024.acl-long.815/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models](https://aclanthology.org/2024.eacl-long.27/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step](https://aclanthology.org/2024.acl-long.515/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis](https://aclanthology.org/2024.findings-eacl.68/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
15. [Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness](https://aclanthology.org/2024.naacl-long.310/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
16. [A Multilingual Evaluation of NER Robustness to Adversarial Inputs](https://aclanthology.org/2023.repl4nlp-1.4/) *— 2023 · Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)*
17. [A Simple, Yet Effective Approach to Finding Biases in Code Generation](https://aclanthology.org/2023.findings-acl.718/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
18. [CREST: A Joint Framework for Rationalization and Counterfactual Text Generation](https://aclanthology.org/2023.acl-long.842/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Curing the SICK and Other NLI Maladies](https://aclanthology.org/2023.cl-1.5/) *— 2023 · Computational Linguistics*
20. [DISCO: Distilling Counterfactuals with Large Language Models](https://aclanthology.org/2023.acl-long.302/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction](https://aclanthology.org/2023.acl-long.354/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification](https://aclanthology.org/2023.emnlp-main.826/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
23. [Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases](https://aclanthology.org/2023.findings-acl.833/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
24. [Improving the robustness of NLI models with minimax training](https://aclanthology.org/2023.acl-long.801/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [SETI: Systematicity Evaluation of Textual Inference](https://aclanthology.org/2023.findings-acl.252/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
26. [A Report on the FigLang 2022 Shared Task on Understanding Figurative Language](https://aclanthology.org/2022.flp-1.26/) *— 2022 · Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)*
27. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
28. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
29. [AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning](https://aclanthology.org/2022.findings-emnlp.170/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
30. [CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation](https://aclanthology.org/2022.findings-emnlp.216/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
31. [Can Transformers Reason in Fragments of Natural Language?](https://aclanthology.org/2022.emnlp-main.768/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
32. [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://aclanthology.org/2022.tacl-1.73/) *— 2022 · Transactions of the Association for Computational Linguistics*
33. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
34. [Enhancing Tabular Reasoning with Pattern Exploiting Training](https://aclanthology.org/2022.aacl-main.54/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
35. [Entailment Relation Aware Paraphrase Generation](https://arxiv.org/abs/2203.10483) *— 2022 · arXiv:2203.10483*
36. [Exposing the Limits of Video-Text Models through Contrast Sets](https://aclanthology.org/2022.naacl-main.261/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
37. [FLUTE: Figurative Language Understanding through Textual Explanations](https://aclanthology.org/2022.emnlp-main.481/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
38. [Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets](https://aclanthology.org/2022.acl-long.190/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples](https://aclanthology.org/2022.coling-1.340/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
40. [Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings](https://aclanthology.org/2022.findings-acl.158/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
41. [Language Technologies for Low Resource Languages: Sociolinguistic and Multilingual Insights](https://aclanthology.org/2022.sigul-1.12/) *— 2022 · Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages*
42. [Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization](https://aclanthology.org/2022.lrec-1.45/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
43. [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://aclanthology.org/2022.findings-naacl.168/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
44. [Paraphrasing, textual entailment, and semantic similarity above word level](https://arxiv.org/abs/2208.05387) *— 2022 · arXiv:2208.05387*
45. [Realistic Data Augmentation Framework for Enhancing Tabular Reasoning](https://aclanthology.org/2022.findings-emnlp.324/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
46. [Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning](https://aclanthology.org/2022.acl-long.231/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [Semantically Distributed Robust Optimization for Vision-and-Language Inference](https://aclanthology.org/2022.findings-acl.118/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
48. [Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences](https://aclanthology.org/2022.findings-emnlp.252/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
49. [Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective](https://aclanthology.org/2022.findings-acl.185/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
50. [TestAug: A Framework for Augmenting Capability-based NLP Tests](https://aclanthology.org/2022.coling-1.307/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
51. [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning](https://aclanthology.org/2022.findings-naacl.187/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
52. [Unsupervised Natural Language Inference Using PHL Triplet Generation](https://aclanthology.org/2022.findings-acl.159/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
53. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
54. [A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist](https://aclanthology.org/2021.humeval-1.14/) *— 2021 · Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)*
55. [A Logic-Based Framework for Natural Language Inference in Dutch](https://arxiv.org/abs/2110.03323) *— 2021 · arXiv:2110.03323*
56. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075) *— 2021 · arXiv:2105.03075*
57. [Alignment Rationale for Natural Language Inference](https://aclanthology.org/2021.acl-long.417/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
58. [Annotation Difficulties in Natural Language Inference](https://aclanthology.org/2021.stil-1.27/) *— 2021 · Proceedings of the 13th Brazilian Symposium in Information and Human Language Technology*
59. [Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions](https://aclanthology.org/2021.findings-acl.181/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
60. [Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference](https://aclanthology.org/2021.blackboxnlp-1.26/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
61. [Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning](https://aclanthology.org/2021.emnlp-main.713/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
62. [BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language](https://aclanthology.org/2021.conll-1.49/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
63. [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://aclanthology.org/2021.emnlp-main.819/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
64. [COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences](https://aclanthology.org/2021.findings-acl.78/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
65. [DynaSent: A Dynamic Benchmark for Sentiment Analysis](https://aclanthology.org/2021.acl-long.186/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
66. [Explaining Contextualization in Language Models using Visual Analytics](https://aclanthology.org/2021.acl-long.39/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
67. [Exploring Transitivity in Neural NLI Models through Veridicality](https://aclanthology.org/2021.eacl-main.78/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
68. [FANCY: A Diagnostic Data-Set for NLI Models](https://aclanthology.org/2021.clicit-1.44/) *— 2021 · Proceedings of the Eighth Italian Conference on Computational Linguistics (CLiC-it 2021)*
69. [Figurative Language in Recognizing Textual Entailment](https://aclanthology.org/2021.findings-acl.297/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
70. [Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples](https://aclanthology.org/2021.eacl-main.13/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
71. [HateCheck: Functional Tests for Hate Speech Detection Models](https://aclanthology.org/2021.acl-long.4/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
72. [How Fast can BERT Learn Simple Natural Language Inference?](https://aclanthology.org/2021.eacl-main.51/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
73. [Increasing Robustness to Spurious Correlations using Forgettable Examples](https://aclanthology.org/2021.eacl-main.291/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
74. [IndoNLI: A Natural Language Inference Dataset for Indonesian](https://aclanthology.org/2021.emnlp-main.821/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
75. [Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates](https://aclanthology.org/2021.findings-emnlp.374/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
76. [Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference](https://aclanthology.org/2021.findings-acl.331/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
77. [Language Models for Lexical Inference in Context](https://aclanthology.org/2021.eacl-main.108/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
78. [NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance](https://aclanthology.org/2021.nodalida-main.28/) *— 2021 · Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)*
79. [NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning](https://aclanthology.org/2021.starsem-1.7/) *— 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
80. [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://aclanthology.org/2021.eacl-main.259/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
81. [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://aclanthology.org/2021.acl-long.523/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
82. [Probing Pre-Trained Language Models for Disease Knowledge](https://aclanthology.org/2021.findings-acl.266/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
83. [Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://aclanthology.org/2021.eacl-main.295/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
84. [Robustness Gym: Unifying the NLP Evaluation Landscape](https://aclanthology.org/2021.naacl-demos.6/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations*
85. [Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text](https://aclanthology.org/2021.wnut-1.37/) *— 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)*
86. [SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics](https://aclanthology.org/2021.findings-acl.10/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
87. [TabPert : An Effective Platform for Tabular Perturbation](https://aclanthology.org/2021.emnlp-demo.39/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
88. [Understanding Model Robustness to User-generated Noisy Texts](https://aclanthology.org/2021.wnut-1.38/) *— 2021 · Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)*
89. [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.mova.elamur.ai/free/en/docs/papers/rogers-2020-guide-dataset-explosion-qa-nli/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts*
90. [Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension](https://aclanthology.org/2020.findings-emnlp.333/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
91. [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://aclanthology.org/2020.acl-main.441/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
92. [An Analysis of Natural Language Inference Benchmarks through the Lens of Negation](https://md.mova.elamur.ai/free/en/docs/papers/hossain-2020-analysis-natural-language-inference-benchmarks/) *— our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
93. [An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference](https://aclanthology.org/2020.conll-1.48/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
94. [An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models](https://aclanthology.org/2020.tacl-1.40/) *— 2020*
95. [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://md.mova.elamur.ai/free/en/docs/papers/jeretic-2020-are-natural-language-inference-models/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
96. [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://aclanthology.org/2020.emnlp-main.500/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
97. [Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models](https://arxiv.org/abs/2005.14709) *— 2020 · arXiv:2005.14709*
98. [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://aclanthology.org/2020.emnlp-main.661/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
99. [ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation](https://aclanthology.org/2020.coling-main.417/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
100. [DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking](https://aclanthology.org/2020.acl-main.761/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*

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