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# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 2

579 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/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

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1. [XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models](https://aclanthology.org/2023.acl-demo.25/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
2. [``World Knowledge'''' in Multiple Choice Reading Comprehension](https://aclanthology.org/2023.fever-1.5/) *— 2023 · Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)*
3. [基于推理链的多跳问答对抗攻击和对抗增强训练方法(Reasoning Chain Based Adversarial Attack and Adversarial Augmentation Training for Multi-hop Question Answering)](https://aclanthology.org/2023.ccl-1.1/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
4. [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://aclanthology.org/2022.acl-long.464/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction](https://aclanthology.org/2022.naacl-main.43/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
6. [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.mova.elamur.ai/free/en/docs/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
7. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
8. [ASQA: Factoid Questions Meet Long-Form Answers](https://aclanthology.org/2022.emnlp-main.566/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
9. [Benchmarking the Covariate Shift Robustness of Open-world Intent Classification Approaches](https://aclanthology.org/2022.aacl-short.3/) *— 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 2: Short Papers)*
10. [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)*
11. [Can Rationalization Improve Robustness?](https://aclanthology.org/2022.naacl-main.278/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
12. [Challenges in Generalization in Open Domain Question Answering](https://aclanthology.org/2022.findings-naacl.155/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
13. [Claim Extraction and Law Matching for COVID-19-related Legislation](https://aclanthology.org/2022.lrec-1.50/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
14. [CommonsenseQA 2.0: Exposing the Limits of AI through Gamification](https://arxiv.org/abs/2201.05320) *— 2022 · arXiv:2201.05320*
15. [Consistency Training with Virtual Adversarial Discrete Perturbation](https://aclanthology.org/2022.naacl-main.414/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
16. [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*
17. [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*
18. [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*
19. [Deep Learning for Text Style Transfer: A Survey](https://aclanthology.org/2022.cl-1.6/) *— 2022 · Computational Linguistics*
20. [Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations](https://aclanthology.org/2022.repl4nlp-1.9/) *— 2022 · Proceedings of the 7th Workshop on Representation Learning for NLP*
21. [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*
22. [Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis](https://aclanthology.org/2022.naacl-main.430/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
23. [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*
24. [FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness](https://aclanthology.org/2022.findings-emnlp.267/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
25. [Feeding What You Need by Understanding What You Learned](https://aclanthology.org/2022.acl-long.403/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
27. [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*
28. [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*
29. [Improving the Adversarial Robustness of NLP Models by Information Bottleneck](https://aclanthology.org/2022.findings-acl.284/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
30. [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://arxiv.org/abs/2202.12205) *— 2022 · arXiv:2202.12205 · Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-42, 2022*
31. [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*
32. [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*
33. [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*
34. [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*
35. [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*
36. [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*
37. [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*
38. [On the Robustness of Reading Comprehension Models to Entity Renaming](https://aclanthology.org/2022.naacl-main.37/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
39. [PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks](https://aclanthology.org/2022.coling-1.423/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
40. [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*
41. [Phrase-level Textual Adversarial Attack with Label Preservation](https://aclanthology.org/2022.findings-naacl.83/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
42. [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*
43. [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) *— 2022 · arXiv:2210.09150*
44. [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://aclanthology.org/2022.tacl-1.59/) *— 2022 · Transactions of the Association for Computational Linguistics*
45. [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*
46. [Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and English](https://aclanthology.org/2022.coling-1.459/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
47. [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858) *— 2022 · arXiv:2209.07858*
48. [Red Teaming Language Models with Language Models](https://aclanthology.org/2022.emnlp-main.225/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
49. [Residue-Based Natural Language Adversarial Attack Detection](https://aclanthology.org/2022.naacl-main.281/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
50. [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)*
51. [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*
52. [Robust Question Answering against Distribution Shifts with Test-Time Adaption: An Empirical Study](https://aclanthology.org/2022.findings-emnlp.460/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
53. [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) *— 2022 · arXiv:2212.04356*
54. [Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks](https://aclanthology.org/2022.findings-naacl.8/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
55. [SemAttack: Natural Textual Attacks via Different Semantic Spaces](https://aclanthology.org/2022.findings-naacl.14/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
56. [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*
57. [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)*
58. [TAPE: Assessing Few-shot Russian Language Understanding](https://aclanthology.org/2022.findings-emnlp.183/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
59. [TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack](https://aclanthology.org/2022.emnlp-main.821/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
60. [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*
61. [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*
62. [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)*
63. [To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://aclanthology.org/2022.findings-naacl.96/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
64. [TopiOCQA: Open-domain Conversational Question Answering with Topic Switching](https://aclanthology.org/2022.tacl-1.27/) *— 2022 · Transactions of the Association for Computational Linguistics*
65. [Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples](https://aclanthology.org/2022.findings-acl.134/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
66. [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://aclanthology.org/2022.acl-long.142/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
67. [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)*
68. [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)*
69. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
70. [Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP](https://aclanthology.org/2022.emnlp-main.771/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
71. [AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples](https://aclanthology.org/2021.emnlp-main.571/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
72. [Adv-OLM: Generating Textual Adversaries via OLM](https://aclanthology.org/2021.eacl-main.71/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
73. [Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models](https://arxiv.org/abs/2111.02840) *— 2021 · arXiv:2111.02840*
74. [Adversarial Self-Supervised Learning for Out-of-Domain Detection](https://aclanthology.org/2021.naacl-main.447/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
75. [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*
76. [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*
77. [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*
78. [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*
79. [Benchmarking Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2021.findings-acl.56/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
80. [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*
81. [Can NLI Models Verify QA Systems' Predictions?](https://aclanthology.org/2021.findings-emnlp.324/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
82. [Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap](https://aclanthology.org/2021.mrqa-1.6/) *— 2021 · Proceedings of the 3rd Workshop on Machine Reading for Question Answering*
83. [Can We Improve Model Robustness through Secondary Attribute Counterfactuals?](https://aclanthology.org/2021.emnlp-main.386/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
84. [Changing the World by Changing the Data](https://aclanthology.org/2021.acl-long.170/) *— 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)*
85. [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*
86. [Concealed Data Poisoning Attacks on NLP Models](https://aclanthology.org/2021.naacl-main.13/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
87. [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*
88. [Contextualized Perturbation for Textual Adversarial Attack](https://aclanthology.org/2021.naacl-main.400/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
89. [Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification](https://aclanthology.org/2021.emnlp-main.651/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
90. [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)*
91. [Crafting Adversarial Examples for Neural Machine Translation](https://aclanthology.org/2021.acl-long.153/) *— 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)*
92. [Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble](https://aclanthology.org/2021.acl-long.426/) *— 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)*
93. [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://aclanthology.org/2021.findings-acl.293/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
94. [Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation](https://aclanthology.org/2021.emnlp-main.756/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
95. [Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification](https://aclanthology.org/2021.findings-acl.294/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
96. [Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models](https://aclanthology.org/2021.acl-short.20/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
97. [Don''t Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques](https://aclanthology.org/2021.findings-emnlp.405/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
98. [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)*
99. [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*
100. [Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack](https://aclanthology.org/2021.naacl-main.293/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*

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