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# 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.mova.elamur.ai/free/en/docs/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

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1. [Evaluating Neural Model Robustness for Machine Comprehension](https://aclanthology.org/2021.eacl-main.210/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
2. [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*
3. [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*
4. [Explanation-Based Human Debugging of NLP Models: A Survey](https://aclanthology.org/2021.tacl-1.90/) *— 2021 · Transactions of the Association for Computational Linguistics*
5. [Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models](https://aclanthology.org/2021.emnlp-main.445/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
6. [Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples](https://aclanthology.org/2021.eacl-main.13/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
7. [If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering](https://aclanthology.org/2021.naacl-main.363/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
8. [Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling](https://aclanthology.org/2021.sigdial-1.6/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
9. [Improving Paraphrase Detection with the Adversarial Paraphrasing Task](https://aclanthology.org/2021.acl-long.552/) *— 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)*
10. [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://aclanthology.org/2021.emnlp-main.696/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
11. [Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation](https://aclanthology.org/2021.acl-srw.21/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop*
12. [Increasing Robustness to Spurious Correlations using Forgettable Examples](https://aclanthology.org/2021.eacl-main.291/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
13. [Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates](https://aclanthology.org/2021.findings-emnlp.374/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
14. [Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?](https://aclanthology.org/2021.eacl-main.311/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
15. [Knowing More About Questions Can Help: Improving Calibration in Question Answering](https://aclanthology.org/2021.findings-acl.172/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
16. [Learning to Perturb Word Embeddings for Out-of-distribution QA](https://aclanthology.org/2021.acl-long.434/) *— 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. [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*
18. [Measuring and Improving Consistency in Pretrained Language Models](https://aclanthology.org/2021.tacl-1.60/) *— 2021*
19. [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://aclanthology.org/2021.emnlp-main.374/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
20. [Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning](https://aclanthology.org/2021.acl-short.110/) *— 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)*
21. [Multi-granularity Textual Adversarial Attack with Behavior Cloning](https://aclanthology.org/2021.emnlp-main.371/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
22. [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://aclanthology.org/2021.eacl-main.259/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
23. [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*
24. [On Robustness of Neural Semantic Parsers](https://aclanthology.org/2021.eacl-main.292/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
25. [On the Interaction of Belief Bias and Explanations](https://aclanthology.org/2021.findings-acl.259/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
26. [On the Interplay Between Fine-tuning and Composition in Transformers](https://aclanthology.org/2021.findings-acl.201/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
27. [On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise](https://aclanthology.org/2021.nlp4convai-1.7/) *— 2021 · Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI*
28. [On the Transferability of Adversarial Attacks against Neural Text Classifier](https://aclanthology.org/2021.emnlp-main.121/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
29. [OpenAttack: An Open-source Textual Adversarial Attack Toolkit](https://aclanthology.org/2021.acl-demo.43/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations*
30. [Posterior Differential Regularization with f-divergence for Improving Model Robustness](https://aclanthology.org/2021.naacl-main.85/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
31. [Promoting Graph Awareness in Linearized Graph-to-Text Generation](https://aclanthology.org/2021.findings-acl.82/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
32. [RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering](https://aclanthology.org/2021.naacl-main.100/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
33. [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)*
34. [Robust Question Answering Through Sub-part Alignment](https://aclanthology.org/2021.naacl-main.98/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
35. [Robustness Analysis of Grover for Machine-Generated News Detection](https://aclanthology.org/2021.alta-1.12/) *— 2021 · Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association*
36. [Robustness Gym: Unifying the NLP Evaluation Landscape](https://aclanthology.org/2021.naacl-demos.6/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations*
37. [Robustness and Adversarial Examples in Natural Language Processing](https://aclanthology.org/2021.emnlp-tutorials.5/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
38. [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://aclanthology.org/2021.emnlp-main.302/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
39. [Self-Supervised Test-Time Learning for Reading Comprehension](https://aclanthology.org/2021.naacl-main.95/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
40. [Sensitivity as a Complexity Measure for Sequence Classification Tasks](https://aclanthology.org/2021.tacl-1.53/) *— 2021 · Transactions of the Association for Computational Linguistics*
41. [Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization](https://aclanthology.org/2021.emnlp-main.530/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
42. [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*
43. [Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models](https://aclanthology.org/2021.repl4nlp-1.27/) *— 2021 · Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)*
44. [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*
45. [Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.](https://aclanthology.org/2021.wassa-1.19/) *— 2021 · Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis*
46. [Test Harder than You Train: Probing with Extrapolation Splits](https://aclanthology.org/2021.blackboxnlp-1.2/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
47. [Towards Improving Adversarial Training of NLP Models](https://aclanthology.org/2021.findings-emnlp.81/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
48. [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*
49. [Universal Adversarial Attacks with Natural Triggers for Text Classification](https://aclanthology.org/2021.naacl-main.291/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
50. [Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models](https://aclanthology.org/2021.acl-short.43/) *— 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)*
51. [Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models](https://aclanthology.org/2021.emnlp-main.315/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
52. [Why Machine Reading Comprehension Models Learn Shortcuts?](https://aclanthology.org/2021.findings-acl.85/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
53. [基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)](https://aclanthology.org/2021.ccl-1.41/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
54. [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*
55. [A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples](https://aclanthology.org/2020.coling-main.585/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
56. [A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering](https://aclanthology.org/2020.scai-1.2/) *— 2020 · Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)*
57. [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*
58. [Advancing Seq2seq with Joint Paraphrase Learning](https://aclanthology.org/2020.clinicalnlp-1.30/) *— 2020 · Proceedings of the 3rd Clinical Natural Language Processing Workshop*
59. [Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension](https://aclanthology.org/2020.findings-emnlp.333/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
60. [Adversarial Filters of Dataset Biases](https://arxiv.org/abs/2002.04108) *— 2020 · arXiv:2002.04108*
61. [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*
62. [Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots](https://aclanthology.org/2020.emnlp-main.490/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
63. [Adversarial Subword Regularization for Robust Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.175/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
64. [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*
65. [Are we Estimating or Guesstimating Translation Quality?](https://aclanthology.org/2020.acl-main.558/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
66. [Ask to Learn: A Study on Curiosity-driven Question Generation](https://aclanthology.org/2020.coling-main.202/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
67. [Asking without Telling: Exploring Latent Ontologies in Contextual Representations](https://aclanthology.org/2020.emnlp-main.552/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
68. [Assessing Robustness of Text Classification through Maximal Safe Radius Computation](https://aclanthology.org/2020.findings-emnlp.266/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
69. [Assessing the ability of Transformer-based Neural Models to represent structurally unbounded dependencies](https://aclanthology.org/2020.scil-1.2/) *— 2020 · Proceedings of the Society for Computation in Linguistics 2020*
70. [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://aclanthology.org/2020.emnlp-main.500/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
71. [BLEURT: Learning Robust Metrics for Text Generation](https://aclanthology.org/2020.acl-main.704/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
72. [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://aclanthology.org/2020.tacl-1.43/) *— 2020*
73. [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*
74. [CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation](https://aclanthology.org/2020.emnlp-main.417/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
75. [CharBERT: Character-aware Pre-trained Language Model](https://aclanthology.org/2020.coling-main.4/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
76. [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)*
77. [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://aclanthology.org/2020.coling-main.580/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
78. [Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation](https://aclanthology.org/2020.acl-main.595/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
79. [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://aclanthology.org/2020.emnlp-main.746/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
80. [DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking](https://aclanthology.org/2020.acl-main.761/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications](https://arxiv.org/abs/2004.11142) *— 2020 · arXiv:2004.11142*
82. [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*
83. [Enhancing Transformer with Sememe Knowledge](https://aclanthology.org/2020.repl4nlp-1.21/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
84. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
85. [Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples](https://md.mova.elamur.ai/free/en/docs/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
86. [From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks](https://aclanthology.org/2020.aacl-main.79/) *— 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*
87. [GenAug: Data Augmentation for Finetuning Text Generators](https://aclanthology.org/2020.deelio-1.4/) *— 2020 · Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures*
88. [Generative Data Augmentation for Commonsense Reasoning](https://aclanthology.org/2020.findings-emnlp.90/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
89. [Gradient-based Analysis of NLP Models is Manipulable](https://aclanthology.org/2020.findings-emnlp.24/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
90. [Graph-Based Knowledge Integration for Question Answering over Dialogue](https://aclanthology.org/2020.coling-main.219/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
91. [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*
92. [How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization](https://aclanthology.org/2020.findings-emnlp.229/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
93. [Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples](https://aclanthology.org/2020.emnlp-main.228/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
94. [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*
95. [Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension](https://aclanthology.org/2020.conll-1.2/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
96. [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*
97. [It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations](https://aclanthology.org/2020.acl-main.263/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
98. [Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness](https://aclanthology.org/2020.emnlp-main.650/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
99. [Learning Robust Models for e-Commerce Product Search](https://aclanthology.org/2020.acl-main.614/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
100. [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)*

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