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# Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 3

699 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/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)

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1. [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://aclanthology.org/2022.emnlp-main.598/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
2. [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*
3. [Can Explanations Be Useful for Calibrating Black Box Models?](https://aclanthology.org/2022.acl-long.429/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View](https://aclanthology.org/2022.acl-long.398/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models](https://aclanthology.org/2022.conll-1.8/) *— 2022 · Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)*
6. [CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing](https://aclanthology.org/2022.emnlp-tutorials.4/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
7. [Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory](https://aclanthology.org/2022.insights-1.14/) *— 2022 · Proceedings of the Third Workshop on Insights from Negative Results in NLP*
8. [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
9. [Combining Feature and Instance Attribution to Detect Artifacts](https://aclanthology.org/2022.findings-acl.153/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
10. [Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey](https://arxiv.org/abs/2201.12438) *— 2022 · arXiv:2201.12438*
11. [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://aclanthology.org/2022.tacl-1.73/) *— 2022 · Transactions of the Association for Computational Linguistics*
12. [Context Matters: A Pragmatic Study of PLMs'' Negation Understanding](https://aclanthology.org/2022.acl-long.315/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering](https://aclanthology.org/2022.emnlp-main.342/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
14. [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://aclanthology.org/2022.tacl-1.23/) *— 2022 · Transactions of the Association for Computational Linguistics*
15. [DataLab: A Platform for Data Analysis and Intervention](https://aclanthology.org/2022.acl-demo.18/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
16. [Debiasing Masks: A New Framework for Shortcut Mitigation in NLU](https://aclanthology.org/2022.emnlp-main.517/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
17. [Decomposing Natural Logic Inferences for Neural NLI](https://aclanthology.org/2022.blackboxnlp-1.33/) *— 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
18. [Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts](https://aclanthology.org/2022.emnlp-main.74/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
19. [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) *— 2022 · arXiv:2212.09251*
20. [Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding](https://aclanthology.org/2022.acl-long.139/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://aclanthology.org/2022.naacl-main.167/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
22. [Does Self-Rationalization Improve Robustness to Spurious Correlations?](https://aclanthology.org/2022.emnlp-main.501/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
23. [Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing](https://aclanthology.org/2022.emnlp-main.592/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
24. [DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification](https://aclanthology.org/2022.coling-1.409/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
25. [Down and Across: Introducing Crossword-Solving as a New NLP Benchmark](https://aclanthology.org/2022.acl-long.189/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [ER-Test: Evaluating Explanation Regularization Methods for Language Models](https://aclanthology.org/2022.findings-emnlp.242/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
27. [Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5](https://aclanthology.org/2022.flp-1.8/) *— 2022 · Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)*
28. [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)*
29. [Enhancing the Transformer Decoder with Transition-based Syntax](https://aclanthology.org/2022.conll-1.27/) *— 2022 · Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)*
30. [Entailment Relation Aware Paraphrase Generation](https://arxiv.org/abs/2203.10483) *— 2022 · arXiv:2203.10483*
31. [Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://aclanthology.org/2022.tacl-1.62/) *— 2022 · Transactions of the Association for Computational Linguistics*
32. [Event knowledge in large language models: the gap between the impossible and the unlikely](https://arxiv.org/abs/2212.01488) *— 2022 · arXiv:2212.01488*
33. [Exploring Length Generalization in Large Language Models](https://arxiv.org/abs/2207.04901) *— 2022 · arXiv:2207.04901*
34. [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*
35. [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*
36. [Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization](https://aclanthology.org/2022.naacl-main.199/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
37. [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)*
38. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
39. [Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation](https://aclanthology.org/2022.emnlp-main.694/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
40. [ILDAE: Instance-Level Difficulty Analysis of Evaluation Data](https://aclanthology.org/2022.acl-long.240/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [IMPLI: Investigating NLI Models'' Performance on Figurative Language](https://md.mova.elamur.ai/free/en/docs/papers/stowe-2022-impli-investigating-nli-models-performance/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://aclanthology.org/2022.findings-naacl.130/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
43. [Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://aclanthology.org/2022.findings-emnlp.59/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
44. [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*
45. [Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language](https://aclanthology.org/2022.naacl-main.321/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
46. [Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://arxiv.org/abs/2205.10782) *— 2022 · arXiv:2205.10782*
47. [Interventional Training for Out-Of-Distribution Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.799/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
48. [Investigating Reasons for Disagreement in Natural Language Inference](https://aclanthology.org/2022.tacl-1.78/) *— 2022 · Transactions of the Association for Computational Linguistics*
49. [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*
50. [Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences](https://aclanthology.org/2022.coling-1.65/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
51. [Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus?](https://aclanthology.org/2022.rocling-1.7/) *— 2022 · Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)*
52. [Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding](https://aclanthology.org/2022.emnlp-main.275/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
53. [KoBEST: Korean Balanced Evaluation of Significant Tasks](https://aclanthology.org/2022.coling-1.325/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
54. [Language Models Are Poor Learners of Directional Inference](https://aclanthology.org/2022.findings-emnlp.64/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
55. [Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations](https://arxiv.org/abs/2204.02937) *— 2022 · arXiv:2204.02937*
56. [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) *— 2022 · arXiv:2212.02199*
57. [Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models](https://aclanthology.org/2022.coling-1.143/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
58. [Lexical Generalization Improves with Larger Models and Longer Training](https://aclanthology.org/2022.findings-emnlp.323/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
59. [Life after BERT: What do Other Muppets Understand about Language?](https://aclanthology.org/2022.acl-long.227/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering](https://aclanthology.org/2022.blackboxnlp-1.35/) *— 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
61. [Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference](https://aclanthology.org/2022.emnlp-main.725/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
62. [Machine Reading, Fast and Slow: When Do Models ``Understand'''' Language?](https://aclanthology.org/2022.coling-1.8/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
63. [Measure and Improve Robustness in NLP Models: A Survey](https://aclanthology.org/2022.naacl-main.339/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
64. [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*
65. [Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference](https://aclanthology.org/2022.emnlp-main.777/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
66. [Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants](https://aclanthology.org/2022.naacl-main.275/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
67. [Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://aclanthology.org/2022.aacl-main.65/) *— 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)*
68. [On Length Divergence Bias in Textual Matching Models](https://aclanthology.org/2022.findings-acl.330/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
69. [On the data requirements of probing](https://aclanthology.org/2022.findings-acl.326/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
70. [One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes](https://aclanthology.org/2022.computel-1.5/) *— 2022 · Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages*
71. [Partial-input baselines show that NLI models can ignore context, but they don't.](https://aclanthology.org/2022.naacl-main.350/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
72. [Predicting Fine-Tuning Performance with Probing](https://aclanthology.org/2022.emnlp-main.793/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
73. [Probing for targeted syntactic knowledge through grammatical error detection](https://aclanthology.org/2022.conll-1.25/) *— 2022 · Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)*
74. [Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words](https://aclanthology.org/2022.coling-1.122/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
75. [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) *— 2022 · arXiv:2210.09150*
76. [Quantifying Adaptability in Pre-trained Language Models with 500 Tasks](https://aclanthology.org/2022.naacl-main.346/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
77. [RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation](https://aclanthology.org/2022.findings-naacl.103/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
78. [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://arxiv.org/abs/2202.06935) *— 2022 · arXiv:2202.06935*
79. [Retrieval-guided Counterfactual Generation for QA](https://aclanthology.org/2022.acl-long.117/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
80. [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)*
81. [Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering](https://aclanthology.org/2022.findings-emnlp.88/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
82. [RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners](https://aclanthology.org/2022.emnlp-main.653/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
83. [SEAL: Interactive Tool for Systematic Error Analysis and Labeling](https://aclanthology.org/2022.emnlp-demos.36/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
84. [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*
85. [Shortcut Learning of Large Language Models in Natural Language Understanding](https://arxiv.org/abs/2208.11857) *— 2022 · arXiv:2208.11857*
86. [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*
87. [State-of-the-art generalisation research in NLP: A taxonomy and review](https://arxiv.org/abs/2210.03050) *— 2022 · arXiv:2210.03050 · Nat Mach Intell 5, 1161-1174 (2023)*
88. [Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters](https://aclanthology.org/2022.findings-emnlp.28/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
89. [Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations](https://aclanthology.org/2022.tacl-1.60/) *— 2022 · Transactions of the Association for Computational Linguistics*
90. [Tailor: Generating and Perturbing Text with Semantic Controls](https://aclanthology.org/2022.acl-long.228/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
91. [Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment](https://aclanthology.org/2022.coling-1.359/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
92. [Text Characterization Toolkit (TCT)](https://aclanthology.org/2022.aacl-demo.9/) *— 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: System Demonstrations*
93. [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*
94. [The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task](https://aclanthology.org/2022.lrec-1.460/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
95. [The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail](https://aclanthology.org/2022.acl-long.516/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
96. [The Grammar-Learning Trajectories of Neural Language Models](https://aclanthology.org/2022.acl-long.568/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://aclanthology.org/2022.emnlp-main.746/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
98. [Towards Better Characterization of Paraphrases](https://aclanthology.org/2022.acl-long.588/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
99. [Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns](https://arxiv.org/abs/2201.07614) *— 2022 · arXiv:2201.07614*
100. [Unveiling Transformers with LEGO: a synthetic reasoning task](https://arxiv.org/abs/2206.04301) *— 2022 · arXiv:2206.04301*

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