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# Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 5

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/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-7/)

1. [Sometimes We Want Ungrammatical Translations](https://aclanthology.org/2021.findings-emnlp.275/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
2. [Sorting through the noise: Testing robustness of information processing in pre-trained language models](https://aclanthology.org/2021.emnlp-main.119/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
3. [Studying word order through iterative shuffling](https://aclanthology.org/2021.emnlp-main.809/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
4. [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*
5. [Targeted Adversarial Training for Natural Language Understanding](https://aclanthology.org/2021.naacl-main.424/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
6. [Textual Time Travel: A Temporally Informed Approach to Theory of Mind](https://aclanthology.org/2021.findings-emnlp.351/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
7. [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163) *— 2021 · arXiv:2106.16163*
8. [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
9. [To what extent do human explanations of model behavior align with actual model behavior?](https://aclanthology.org/2021.blackboxnlp-1.1/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
10. [Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models](https://aclanthology.org/2021.naacl-main.71/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
11. [Types of Out-of-Distribution Texts and How to Detect Them](https://aclanthology.org/2021.emnlp-main.835/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
12. [UnNatural Language Inference](https://aclanthology.org/2021.acl-long.569/) *— 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)*
13. [Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning](https://aclanthology.org/2021.acl-long.93/) *— 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)*
14. [Underreporting of errors in NLG output, and what to do about it](https://aclanthology.org/2021.inlg-1.14/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
15. [Understanding by Understanding Not: Modeling Negation in Language Models](https://aclanthology.org/2021.naacl-main.102/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
16. [Verb Knowledge Injection for Multilingual Event Processing](https://aclanthology.org/2021.acl-long.541/) *— 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)*
17. [WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge](https://aclanthology.org/2021.emnlp-main.307/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
18. [(Re)construing Meaning in NLP](https://aclanthology.org/2020.acl-main.462/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
19. [A Framework for Evaluation of Machine Reading Comprehension Gold Standards](https://aclanthology.org/2020.lrec-1.660/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
20. [A New Dataset for Natural Language Inference from Code-mixed Conversations](https://aclanthology.org/2020.calcs-1.2/) *— 2020 · Proceedings of the 4th Workshop on Computational Approaches to Code Switching*
21. [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*
22. [Adversarial Filters of Dataset Biases](https://arxiv.org/abs/2002.04108) *— 2020 · arXiv:2002.04108*
23. [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*
24. [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*
25. [An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models](https://aclanthology.org/2020.tacl-1.40/) *— 2020*
26. [An empirical analysis of existing systems and datasets toward general simple question answering](https://aclanthology.org/2020.coling-main.465/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
27. [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*
28. [Are Pretrained Language Models Symbolic Reasoners over Knowledge?](https://aclanthology.org/2020.conll-1.45/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
29. [Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options](https://aclanthology.org/2020.aacl-main.68/) *— 2020 · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*
30. [BERTnesia: Investigating the capture and forgetting of knowledge in BERT](https://aclanthology.org/2020.blackboxnlp-1.17/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
31. [BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance](https://aclanthology.org/2020.blackboxnlp-1.21/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
32. [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*
33. [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://arxiv.org/abs/2004.05986) *— 2020 · arXiv:2004.05986*
34. [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://aclanthology.org/2020.emnlp-main.731/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
35. [Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT](https://aclanthology.org/2020.emnlp-main.371/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
36. [Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://aclanthology.org/2020.acl-main.463/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
37. [Combining Event Semantics and Degree Semantics for Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/haruta-2020-combining-event-semantics-degree-semantics/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
38. [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)*
39. [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*
40. [Detecting Entailment in Code-Mixed Hindi-English Conversations](https://aclanthology.org/2020.wnut-1.22/) *— 2020 · Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)*
41. [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)*
42. [Discovering the Compositional Structure of Vector Representations with Role Learning Networks](https://aclanthology.org/2020.blackboxnlp-1.23/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
43. [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)*
44. [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*
45. [Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages](https://aclanthology.org/2020.repl4nlp-1.2/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
46. [End-to-End Bias Mitigation by Modelling Biases in Corpora](https://aclanthology.org/2020.acl-main.769/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
47. [Enhancing Generalization in Natural Language Inference by Syntax](https://aclanthology.org/2020.findings-emnlp.447/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
48. [Evaluating German Transformer Language Models with Syntactic Agreement Tests](https://arxiv.org/abs/2007.03765) *— 2020 · arXiv:2007.03765 · Proceedings of the 5th Swiss Text Analytics Conference and the 16th Conference on Natural Language Processing, SwissText/KONVENS 2020, Zurich, Switzerland, June 23-25, 2020 [online only]. CEUR Workshop Proceedings 2624*
49. [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://aclanthology.org/2020.acl-main.492/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
50. [Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data](https://aclanthology.org/2020.emnlp-main.302/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
51. [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://aclanthology.org/2020.acl-main.454/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
52. [Frequency, acceptability, and selection: A case study of clause-embedding](https://www.glossa-journal.org/article/id/5353/) *— 2020 · Glossa: a journal of general linguistics*
53. [How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://aclanthology.org/2020.acl-main.465/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
54. [How Relevant Are Selectional Preferences for Transformer-based Language Models?](https://aclanthology.org/2020.coling-main.109/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
55. [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*
56. [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*
57. [Improving QA Generalization by Concurrent Modeling of Multiple Biases](https://aclanthology.org/2020.findings-emnlp.74/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
58. [Interpreting Predictions of NLP Models](https://aclanthology.org/2020.emnlp-tutorials.3/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
59. [Intrinsic Evaluation of Summarization Datasets](https://aclanthology.org/2020.emnlp-main.649/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
60. [Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference](https://aclanthology.org/2020.repl4nlp-1.11/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
61. [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*
62. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
63. [Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)](https://aclanthology.org/2020.emnlp-main.16/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
64. [Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles](https://aclanthology.org/2020.findings-emnlp.272/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
65. [Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets](https://aclanthology.org/2020.blackboxnlp-1.12/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
66. [Logical Inferences with Comparatives and Generalized Quantifiers](https://aclanthology.org/2020.acl-srw.35/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
67. [Look at the First Sentence: Position Bias in Question Answering](https://aclanthology.org/2020.emnlp-main.84/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
68. [MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering](https://aclanthology.org/2020.emnlp-main.63/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
69. [Masking as an Efficient Alternative to Finetuning for Pretrained Language Models](https://aclanthology.org/2020.emnlp-main.174/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
70. [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://arxiv.org/abs/2010.06032) *— 2020 · arXiv:2010.06032*
71. [Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance](https://aclanthology.org/2020.acl-main.770/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
72. [MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity](https://aclanthology.org/2020.scil-1.40/) *— 2020 · Proceedings of the Society for Computation in Linguistics 2020*
73. [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://aclanthology.org/2020.acl-main.698/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
74. [OCNLI: Original Chinese Natural Language Inference](https://arxiv.org/abs/2010.05444) *— 2020 · arXiv:2010.05444*
75. [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*
76. [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://arxiv.org/abs/2006.04884) *— 2020 · arXiv:2006.04884*
77. [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*
78. [Picking BERT''s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis](https://aclanthology.org/2020.coling-main.325/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
79. [Probing Linguistic Systematicity](https://aclanthology.org/2020.acl-main.177/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
80. [Shortcut Learning in Deep Neural Networks](https://arxiv.org/abs/2004.07780) *— 2020 · arXiv:2004.07780*
81. [Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://aclanthology.org/2020.acl-main.212/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
82. [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://aclanthology.org/2020.acl-main.142/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
83. [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)*
84. [TaxiNLI: Taking a Ride up the NLU Hill](https://aclanthology.org/2020.conll-1.4/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
85. [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)*
86. [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://aclanthology.org/2020.emnlp-demos.15/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
87. [The Sensitivity of Language Models and Humans to Winograd Schema Perturbations](https://aclanthology.org/2020.acl-main.679/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
88. [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)*
89. [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*
90. [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)*
91. [What Can We Do to Improve Peer Review in NLP?](https://aclanthology.org/2020.findings-emnlp.112/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
92. [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*
93. [What Happens To BERT Embeddings During Fine-tuning?](https://aclanthology.org/2020.blackboxnlp-1.4/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
94. [What do Models Learn from Question Answering Datasets?](https://aclanthology.org/2020.emnlp-main.190/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
95. [What is More Likely to Happen Next? Video-and-Language Future Event Prediction](https://aclanthology.org/2020.emnlp-main.706/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
96. [When BERT Plays the Lottery, All Tickets Are Winning](https://aclanthology.org/2020.emnlp-main.259/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
97. [XplaiNLI: Explainable Natural Language Inference through Visual Analytics](https://aclanthology.org/2020.coling-demos.9/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations*
98. [Adversarial Analysis of Natural Language Inference Systems](https://arxiv.org/abs/1912.03441) *— 2019 · arXiv:1912.03441*
99. [An Annotated Corpus of Reference Resolution for Interpreting Common Grounding](https://arxiv.org/abs/1911.07588) *— 2019 · arXiv:1911.07588*
100. [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)*

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