---
title: "Who cites Adversarial Examples for Evaluating Reading…"
description: "The papers of the Elamur Research library that cite «Adversarial Examples for Evaluating Reading Comprehension Systems», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Adversarial Examples for Evaluating Reading…](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/) / cited by

# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 3

579 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/2/) · **3** · [4](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/5/) · [6](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/6/)

1. [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*
2. [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*
3. [DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models](https://aclanthology.org/2022.emnlp-main.531/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
4. [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*
5. [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*
6. [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*
7. [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)*
8. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
9. [Identifying Human Strategies for Generating Word-Level Adversarial Examples](https://aclanthology.org/2022.findings-emnlp.454/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
10. [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*
11. [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*
12. [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*
13. [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*
14. [Learning Invariant Representation Improves Robustness for MRC Models](https://aclanthology.org/2022.findings-emnlp.241/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
15. [Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition](https://aclanthology.org/2022.findings-acl.154/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
16. [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*
17. [Logical Inference for Counting on Semi-structured Tables](https://aclanthology.org/2022.acl-srw.8/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
18. [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*
19. [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*
20. [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*
21. [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*
22. [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*
23. [NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?](https://aclanthology.org/2022.findings-emnlp.413/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
24. [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*
25. [On the Robustness of Cognate Generation Models](https://aclanthology.org/2022.lrec-1.458/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
26. [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*
27. [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*
28. [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*
29. [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*
30. [PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack](https://aclanthology.org/2022.coling-1.253/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
31. [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*
32. [Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation](https://aclanthology.org/2022.emnlp-main.482/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
33. [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) *— 2022 · arXiv:2210.09150*
34. [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://aclanthology.org/2022.tacl-1.59/) *— 2022 · Transactions of the Association for Computational Linguistics*
35. [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*
36. [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*
37. [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858) *— 2022 · arXiv:2209.07858*
38. [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*
39. [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*
40. [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)*
41. [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*
42. [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*
43. [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) *— 2022 · arXiv:2212.04356*
44. [SHARP: Search-Based Adversarial Attack for Structured Prediction](https://aclanthology.org/2022.findings-naacl.71/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
45. [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*
46. [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*
47. [Semantically Distributed Robust Optimization for Vision-and-Language Inference](https://aclanthology.org/2022.findings-acl.118/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
48. [Spa: On the Sparsity of Virtual Adversarial Training for Dependency Parsing](https://aclanthology.org/2022.findings-aacl.2/) *— 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022*
49. [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)*
50. [TAPE: Assessing Few-shot Russian Language Understanding](https://aclanthology.org/2022.findings-emnlp.183/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
51. [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*
52. [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*
53. [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*
54. [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)*
55. [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*
56. [TopiOCQA: Open-domain Conversational Question Answering with Topic Switching](https://aclanthology.org/2022.tacl-1.27/) *— 2022 · Transactions of the Association for Computational Linguistics*
57. [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*
58. [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)*
59. [Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU](https://aclanthology.org/2022.emnlp-main.167/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
60. [Unsupervised Domain Adaptation on Question-Answering System with Conversation Data](https://aclanthology.org/2022.sigdial-1.42/) *— 2022 · Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
61. [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)*
62. [VALUE: Understanding Dialect Disparity in NLU](https://md.research.elamur.ai/free/en/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)*
63. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
64. [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*
65. [\textitGeneralized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness](https://aclanthology.org/2022.findings-acl.213/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
66. [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*
67. [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*
68. [Adversarial Attack against Cross-lingual Knowledge Graph Alignment](https://aclanthology.org/2021.emnlp-main.432/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
69. [Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models](https://arxiv.org/abs/2111.02840) *— 2021 · arXiv:2111.02840*
70. [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*
71. [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*
72. [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*
73. [Attacks against Ranking Algorithms with Text Embeddings: A Case Study on Recruitment Algorithms](https://aclanthology.org/2021.blackboxnlp-1.36/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
74. [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*
75. [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*
76. [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*
77. [Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking](https://aclanthology.org/2021.emnlp-demo.14/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
78. [Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio](https://aclanthology.org/2021.acl-short.75/) *— 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)*
79. [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*
80. [Can NLI Models Verify QA Systems' Predictions?](https://aclanthology.org/2021.findings-emnlp.324/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
81. [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*
82. [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*
83. [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)*
84. [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*
85. [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*
86. [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*
87. [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*
88. [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*
89. [Contribution d'informations syntaxiques aux capacités de généralisation compositionelle des modèles seq2seq convolutifs (Assessing the Contribution of Syntactic Information for Compositional Generalization of seq2seq Convolutional Networks)](https://aclanthology.org/2021.jeptalnrecital-taln.12/) *— 2021 · Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale*
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*

Pages: [1](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/2/) · **3** · [4](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/5/) · [6](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/6/)