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# Who cites «Evaluating Models' Local Decision Boundaries via Contrast Sets» · page 2

264 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/gardner-2020-evaluating-models-local-decision-boundaries/)

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1. [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*
2. [Exploring The Landscape of Distributional Robustness for Question Answering Models](https://aclanthology.org/2022.findings-emnlp.441/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
3. [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*
4. [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://aclanthology.org/2022.acl-long.592/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://aclanthology.org/2022.lrec-1.575/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
6. [Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate](https://aclanthology.org/2022.naacl-main.97/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
7. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
8. [Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach](https://aclanthology.org/2022.naacl-main.410/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
9. [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*
10. [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*
11. [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*
12. [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*
13. [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*
14. [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*
15. [Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models](https://aclanthology.org/2022.woah-1.15/) *— 2022 · Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)*
16. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
17. [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) *— 2022 · arXiv:2210.09150*
18. [ROSE: Robust Selective Fine-tuning for Pre-trained Language Models](https://aclanthology.org/2022.emnlp-main.186/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
19. [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)*
20. [Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://aclanthology.org/2022.emnlp-main.146/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
21. [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*
22. [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*
23. [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)*
24. [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)*
25. [Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts](https://aclanthology.org/2022.emnlp-main.439/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
26. [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)*
27. [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://aclanthology.org/2022.emnlp-main.131/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
28. [VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena](https://aclanthology.org/2022.acl-long.567/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [VALUE: Understanding Dialect Disparity in NLU](https://md.mova.elamur.ai/free/en/docs/papers/ziems-2022-value-understanding-dialect-disparity-nlu/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation](https://aclanthology.org/2022.findings-acl.84/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
32. [A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs](https://aclanthology.org/2021.conll-1.19/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
33. [Active Learning by Acquiring Contrastive Examples](https://aclanthology.org/2021.emnlp-main.51/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
34. [Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization](https://aclanthology.org/2021.findings-emnlp.179/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
35. [Are NLP Models really able to Solve Simple Math Word Problems?](https://md.mova.elamur.ai/free/en/docs/papers/patel-2021-are-nlp-models-really-able/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
36. [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*
37. [Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA](https://aclanthology.org/2021.naacl-main.9/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
38. [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*
39. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
40. [Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning](https://aclanthology.org/2021.findings-acl.137/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
41. [CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding](https://aclanthology.org/2021.acl-long.181/) *— 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)*
42. [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*
43. [Can NLI Models Verify QA Systems' Predictions?](https://aclanthology.org/2021.findings-emnlp.324/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
44. [CausaLM: Causal Model Explanation Through Counterfactual Language Models](https://aclanthology.org/2021.cl-2.13/) *— 2021 · Computational Linguistics*
45. [Causal Augmentation for Causal Sentence Classification](https://aclanthology.org/2021.cinlp-1.1/) *— 2021 · Proceedings of the First Workshop on Causal Inference and NLP*
46. [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://arxiv.org/abs/2109.00725) *— 2021 · arXiv:2109.00725*
47. [Competency Problems: On Finding and Removing Artifacts in Language Data](https://aclanthology.org/2021.emnlp-main.135/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
48. [Connecting Attributions and QA Model Behavior on Realistic Counterfactuals](https://aclanthology.org/2021.emnlp-main.447/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
49. [Contrastive Explanations for Model Interpretability](https://aclanthology.org/2021.emnlp-main.120/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
50. [Coreference Reasoning in Machine Reading Comprehension](https://aclanthology.org/2021.acl-long.448/) *— 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)*
51. [CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization](https://aclanthology.org/2021.emnlp-main.164/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
52. [Detecting over/under-translation errors for determining adequacy in human translations](https://arxiv.org/abs/2104.00267) *— 2021 · arXiv:2104.00267*
53. [Does Putting a Linguist in the Loop Improve NLU Data Collection?](https://aclanthology.org/2021.findings-emnlp.421/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
54. [Dynabench: Rethinking Benchmarking in NLP](https://aclanthology.org/2021.naacl-main.324/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
55. [Entity-Based Knowledge Conflicts in Question Answering](https://arxiv.org/abs/2109.05052) *— 2021 · arXiv:2109.05052*
56. [Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?](https://aclanthology.org/2021.findings-acl.364/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
57. [Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?](https://aclanthology.org/2021.acl-long.346/) *— 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. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
59. [Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://aclanthology.org/2021.findings-acl.336/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
60. [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://arxiv.org/abs/2104.09570) *— 2021 · arXiv:2104.09570*
61. [Generalization in Instruction Following Systems](https://aclanthology.org/2021.naacl-main.76/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
62. [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
63. [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)*
64. [HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability](https://aclanthology.org/2021.acl-long.338/) *— 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)*
65. [How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?](https://aclanthology.org/2021.emnlp-main.28/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
66. [I Wish I Would Have Loved This One, But I Didn't – A Multilingual Dataset for Counterfactual Detection in Product Review](https://aclanthology.org/2021.emnlp-main.568/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
67. [I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://aclanthology.org/2021.acl-long.134/) *— 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)*
68. [Implicitly Abusive Language – What does it actually look like and why are we not getting there?](https://aclanthology.org/2021.naacl-main.48/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
69. [Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection](https://aclanthology.org/2021.naacl-main.475/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
70. [Joint Universal Syntactic and Semantic Parsing](https://aclanthology.org/2021.tacl-1.46/) *— 2021*
71. [Learning to Recognize Dialect Features](https://aclanthology.org/2021.naacl-main.184/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
72. [Learning with Instance Bundles for Reading Comprehension](https://aclanthology.org/2021.emnlp-main.584/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
73. [Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs](https://aclanthology.org/2021.tacl-1.58/) *— 2021 · Transactions of the Association for Computational Linguistics*
74. [NOPE: A Corpus of Naturally-Occurring Presuppositions in English](https://md.mova.elamur.ai/free/en/docs/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/) *— our card · 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
75. [Numerical reasoning in machine reading comprehension tasks: are we there yet?](https://aclanthology.org/2021.emnlp-main.759/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
76. [Paired Examples as Indirect Supervision in Latent Decision Models](https://aclanthology.org/2021.emnlp-main.466/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
77. [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)*
78. [Prompting Contrastive Explanations for Commonsense Reasoning Tasks](https://aclanthology.org/2021.findings-acl.366/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
79. [Reliability Testing for Natural Language Processing Systems](https://aclanthology.org/2021.acl-long.321/) *— 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)*
80. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
81. [Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation](https://aclanthology.org/2021.findings-acl.338/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
82. [TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training](https://aclanthology.org/2021.semeval-1.51/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
83. [Temporal Reasoning on Implicit Events from Distant Supervision](https://aclanthology.org/2021.naacl-main.107/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
84. [Tribrid: Stance Classification with Neural Inconsistency Detection](https://aclanthology.org/2021.emnlp-main.547/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
85. [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://aclanthology.org/2021.acl-long.98/) *— 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)*
86. [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
87. [(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*
88. [A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions](https://aclanthology.org/2020.findings-emnlp.67/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
89. [Aligning AI With Shared Human Values](https://arxiv.org/abs/2008.02275) *— 2020 · arXiv:2008.02275*
90. [Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer](https://aclanthology.org/2020.figlang-1.25/) *— 2020 · Proceedings of the Second Workshop on Figurative Language Processing*
91. [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://arxiv.org/abs/2012.05345) *— 2020 · arXiv:2012.05345 · Patterns, Volume 2, Issue 11, 100336. 2021*
92. [Experience Grounds Language](https://aclanthology.org/2020.emnlp-main.703/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
93. [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)*
94. [Improving Compositional Generalization in Semantic Parsing](https://aclanthology.org/2020.findings-emnlp.225/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
95. [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*
96. [Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning](https://aclanthology.org/2020.emnlp-main.712/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
97. [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*
98. [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
99. [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*
100. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*

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