---
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/5/"
---
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# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 5

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

1. [Generative Data Augmentation for Commonsense Reasoning](https://aclanthology.org/2020.findings-emnlp.90/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
2. [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*
3. [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*
4. [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*
5. [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*
6. [How State-Of-The-Art Models Can Deal With Long-Form Question Answering](https://aclanthology.org/2020.paclic-1.43/) *— 2020 · Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation*
7. [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)*
8. [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*
9. [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*
10. [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*
11. [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*
12. [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)*
13. [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*
14. [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)*
15. [Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles](https://aclanthology.org/2020.findings-emnlp.272/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
16. [Look at the First Sentence: Position Bias in Question Answering](https://aclanthology.org/2020.emnlp-main.84/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
17. [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)*
18. [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://arxiv.org/abs/2010.06032) *— 2020 · arXiv:2010.06032*
19. [Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding](https://aclanthology.org/2020.emnlp-main.455/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
20. [More Bang for Your Buck: Natural Perturbation for Robust Question Answering](https://aclanthology.org/2020.emnlp-main.12/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
21. [Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/) *— our card · 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
22. [Overestimation of Syntactic Representation in Neural Language Models](https://aclanthology.org/2020.acl-main.160/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
23. [Pretrained Transformers Improve Out-of-Distribution Robustness](https://arxiv.org/abs/2004.06100) *— 2020 · arXiv:2004.06100*
24. [Priorless Recurrent Networks Learn Curiously](https://aclanthology.org/2020.coling-main.451/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
25. [Research on Task Discovery for Transfer Learning in Deep Neural Networks](https://aclanthology.org/2020.acl-srw.6/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop*
26. [Robust Encodings: A Framework for Combating Adversarial Typos](https://aclanthology.org/2020.acl-main.245/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
27. [Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training](https://aclanthology.org/2020.coling-main.374/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
28. [Robustness to Modification with Shared Words in Paraphrase Identification](https://aclanthology.org/2020.findings-emnlp.16/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
29. [SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0](https://aclanthology.org/2020.lrec-1.667/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
30. [Selective Question Answering under Domain Shift](https://aclanthology.org/2020.acl-main.503/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
31. [Shortcut Learning in Deep Neural Networks](https://arxiv.org/abs/2004.07780) *— 2020 · arXiv:2004.07780*
32. [Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks](https://aclanthology.org/2020.lrec-1.232/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
33. [Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://aclanthology.org/2020.acl-main.212/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
34. [T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack](https://aclanthology.org/2020.emnlp-main.495/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
35. [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://aclanthology.org/2020.acl-main.142/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
36. [TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP](https://arxiv.org/abs/2005.05909) *— 2020 · arXiv:2005.05909*
37. [The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions](https://aclanthology.org/2020.emnlp-main.659/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
38. [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://aclanthology.org/2020.emnlp-demos.15/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
39. [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://arxiv.org/abs/2002.06177) *— 2020 · arXiv:2002.06177*
40. [The TechQA Dataset](https://aclanthology.org/2020.acl-main.117/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
41. [Towards Debiasing NLU Models from Unknown Biases](https://aclanthology.org/2020.emnlp-main.613/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
42. [Towards Interpreting BERT for Reading Comprehension Based QA](https://aclanthology.org/2020.emnlp-main.261/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
43. [Towards Robustifying NLI Models Against Lexical Dataset Biases](https://aclanthology.org/2020.acl-main.773/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
44. [Translation Artifacts in Cross-lingual Transfer Learning](https://aclanthology.org/2020.emnlp-main.618/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
45. [Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks](https://aclanthology.org/2020.coling-industry.21/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Industry Track*
46. [Undersensitivity in Neural Reading Comprehension](https://aclanthology.org/2020.findings-emnlp.103/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
47. [Utility is in the Eye of the User: A Critique of NLP Leaderboards](https://aclanthology.org/2020.emnlp-main.393/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
48. [What Can We Do to Improve Peer Review in NLP?](https://aclanthology.org/2020.findings-emnlp.112/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
49. [What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge](https://aclanthology.org/2020.tacl-1.37/) *— 2020 · Transactions of the Association for Computational Linguistics*
50. [What Don't RNN Language Models Learn About Filler-Gap Dependencies?](https://aclanthology.org/2020.scil-1.1/) *— 2020 · Proceedings of the Society for Computation in Linguistics 2020*
51. [What Happens To BERT Embeddings During Fine-tuning?](https://aclanthology.org/2020.blackboxnlp-1.4/) *— 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
52. [What do Models Learn from Question Answering Datasets?](https://aclanthology.org/2020.emnlp-main.190/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
53. [What do we expect from Multiple-choice QA Systems?](https://aclanthology.org/2020.findings-emnlp.317/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
54. [What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?](https://aclanthology.org/2020.repl4nlp-1.24/) *— 2020 · Proceedings of the 5th Workshop on Representation Learning for NLP*
55. [Word meaning in minds and machines](https://arxiv.org/abs/2008.01766) *— 2020 · arXiv:2008.01766*
56. [Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://aclanthology.org/2020.acl-main.540/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
57. [A Lightweight Recurrent Network for Sequence Modeling](https://aclanthology.org/P19-1149/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
58. [A Logic-Driven Framework for Consistency of Neural Models](https://arxiv.org/abs/1909.00126) *— 2019 · arXiv:1909.00126*
59. [Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation](https://aclanthology.org/D19-1419/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
60. [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://aclanthology.org/D19-1253/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
61. [Analysis Methods in Neural Language Processing: A Survey](https://aclanthology.org/Q19-1004/) *— 2019 · Transactions of the Association for Computational Linguistics*
62. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
63. [Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA](https://aclanthology.org/P19-1262/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
64. [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
65. [Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack](https://aclanthology.org/D19-1461/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
66. [CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text](https://aclanthology.org/D19-1458/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
67. [Certified Robustness to Adversarial Word Substitutions](https://aclanthology.org/D19-1423/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
68. [Cognitive Graph for Multi-Hop Reading Comprehension at Scale](https://aclanthology.org/P19-1259/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
69. [Combating Adversarial Misspellings with Robust Word Recognition](https://aclanthology.org/P19-1561/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
70. [Comprehensive Multi-Dataset Evaluation of Reading Comprehension](https://aclanthology.org/D19-5820/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
71. [Deep Adversarial Learning for NLP](https://aclanthology.org/N19-5001/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials*
72. [Detecting and Reducing Bias in a High Stakes Domain](https://aclanthology.org/D19-1483/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
73. [Don”t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases](https://aclanthology.org/D19-1418/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
74. [Don”t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://aclanthology.org/P19-1084/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
75. [Effective Adversarial Regularization for Neural Machine Translation](https://aclanthology.org/P19-1020/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
76. [Errudite: Scalable, Reproducible, and Testable Error Analysis](https://aclanthology.org/P19-1073/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
77. [Evaluating adversarial attacks against multiple fact verification systems](https://aclanthology.org/D19-1292/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
78. [Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent](https://aclanthology.org/N19-1336/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
79. [Explicit Utilization of General Knowledge in Machine Reading Comprehension](https://aclanthology.org/P19-1219/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
80. [Finding Generalizable Evidence by Learning to Convince Q&A Models](https://aclanthology.org/D19-1244/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
81. [FreeLB: Enhanced Adversarial Training for Natural Language Understanding](https://arxiv.org/abs/1909.11764) *— 2019 · arXiv:1909.11764*
82. [HellaSwag: Can a Machine Really Finish Your Sentence?](https://aclanthology.org/P19-1472/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
83. [Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior](https://aclanthology.org/D19-5807/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
84. [Improving the Robustness of Question Answering Systems to Question Paraphrasing](https://aclanthology.org/P19-1610/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
85. [Incorporating Priors with Feature Attribution on Text Classification](https://aclanthology.org/P19-1631/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
86. [Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets](https://aclanthology.org/N19-1225/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
87. [Interactive Language Learning by Question Answering](https://aclanthology.org/D19-1280/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
88. [Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs](https://arxiv.org/abs/1907.09361) *— 2019 · arXiv:1907.09361*
89. [JEC-QA: A Legal-Domain Question Answering Dataset](https://arxiv.org/abs/1911.12011) *— 2019 · arXiv:1911.12011*
90. [Learning and Evaluating General Linguistic Intelligence](https://arxiv.org/abs/1901.11373) *— 2019 · arXiv:1901.11373*
91. [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://arxiv.org/abs/1909.12434) *— 2019 · arXiv:1909.12434*
92. [Learning to Ask Unanswerable Questions for Machine Reading Comprehension](https://aclanthology.org/P19-1415/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
93. [LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification](https://aclanthology.org/D19-1554/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
94. [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://arxiv.org/abs/1910.07475) *— 2019 · arXiv:1910.07475*
95. [Measuring text readability with machine comprehension: a pilot study](https://aclanthology.org/W19-4443/) *— 2019 · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications*
96. [Misleading Failures of Partial-input Baselines](https://aclanthology.org/P19-1554/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
97. [Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots](https://aclanthology.org/D19-1011/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
98. [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://aclanthology.org/P19-1485/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
99. [Natural Questions: A Benchmark for Question Answering Research](https://aclanthology.org/Q19-1026/) *— 2019*
100. [On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models](https://aclanthology.org/N19-1314/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*

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