---
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/6/"
---
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# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 6

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

1. [On Making Reading Comprehension More Comprehensive](https://aclanthology.org/D19-5815/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
2. [On the Robustness of Self-Attentive Models](https://aclanthology.org/P19-1147/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
3. [One Size Does Not Fit All: Comparing NMT Representations of Different Granularities](https://aclanthology.org/N19-1154/) *— 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)*
4. [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) *— 2019 · arXiv:1908.11828*
5. [PAWS: Paraphrase Adversaries from Word Scrambling](https://aclanthology.org/N19-1131/) *— 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)*
6. [Posing Fair Generalization Tasks for Natural Language Inference](https://aclanthology.org/D19-1456/) *— 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)*
7. [Probing Neural Network Comprehension of Natural Language Arguments](https://aclanthology.org/P19-1459/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
8. [Procedural Reasoning Networks for Understanding Multimodal Procedures](https://aclanthology.org/K19-1041/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
9. [QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization](https://aclanthology.org/D19-1333/) *— 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)*
10. [Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension](https://aclanthology.org/P19-1543/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
11. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
12. [Relation Module for Non-Answerable Predictions on Reading Comprehension](https://aclanthology.org/K19-1070/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
13. [Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension](https://aclanthology.org/P19-1221/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
14. [Retrofitting Contextualized Word Embeddings with Paraphrases](https://aclanthology.org/D19-1113/) *— 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)*
15. [Robust Neural Machine Translation with Doubly Adversarial Inputs](https://aclanthology.org/P19-1425/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
16. [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://arxiv.org/abs/1905.00537) *— 2019 · arXiv:1905.00537*
17. [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://aclanthology.org/N19-1165/) *— 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)*
18. [To Annotate or Not? Predicting Performance Drop under Domain Shift](https://aclanthology.org/D19-1222/) *— 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)*
19. [Towards Debiasing Fact Verification Models](https://aclanthology.org/D19-1341/) *— 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)*
20. [Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples](https://aclanthology.org/N19-1396/) *— 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)*
21. [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://aclanthology.org/D19-1221/) *— 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)*
22. [Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://aclanthology.org/D19-6115/) *— 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)*
23. [Unsupervised Question Answering by Cloze Translation](https://aclanthology.org/P19-1484/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
24. [White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks](https://aclanthology.org/N19-1139/) *— 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)*
25. [pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference](https://aclanthology.org/N19-1362/) *— 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)*
26. [AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https://aclanthology.org/P18-1225/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [Adversarial Evaluation of Multimodal Machine Translation](https://aclanthology.org/D18-1329/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
28. [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://aclanthology.org/N18-1170/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
29. [Adversarial Networks for Machine Reading](https://aclanthology.org/2018.tal-2.4/) *— 2018*
30. [Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models](https://aclanthology.org/K18-1047/) *— 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning*
31. [Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge](https://aclanthology.org/K18-1007/) *— 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning*
32. [An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM](https://aclanthology.org/D18-2006/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
33. [Analyzing Compositionality-Sensitivity of NLI Models](https://arxiv.org/abs/1811.07033) *— 2018 · arXiv:1811.07033*
34. [Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension](https://aclanthology.org/C18-1083/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
35. [Assessing Composition in Sentence Vector Representations](https://aclanthology.org/C18-1152/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
36. [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
37. [Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension](https://aclanthology.org/K18-1011/) *— 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning*
38. [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://aclanthology.org/Q18-1021/) *— 2018 · Transactions of the Association for Computational Linguistics*
39. [Deep Learning: A Critical Appraisal](https://arxiv.org/abs/1801.00631) *— 2018 · arXiv:1801.00631*
40. [Deep learning evaluation using deep linguistic processing](https://aclanthology.org/W18-1003/) *— 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning*
41. [Did the Model Understand the Question?](https://aclanthology.org/P18-1176/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System](https://aclanthology.org/W18-5436/) *— 2018 · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP*
43. [Efficient and Robust Question Answering from Minimal Context over Documents](https://aclanthology.org/P18-1160/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [Generating Natural Language Adversarial Examples](https://arxiv.org/abs/1804.07998) *— 2018 · arXiv:1804.07998*
45. [HotFlip: White-Box Adversarial Examples for Text Classification](https://aclanthology.org/P18-2006/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
46. [How much should you ask? On the question structure in QA systems.](https://aclanthology.org/W18-5435/) *— 2018 · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP*
47. [Know What You Don”t Know: Unanswerable Questions for SQuAD](https://aclanthology.org/P18-2124/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
48. [LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better](https://aclanthology.org/P18-1132/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Learning Visually-Grounded Semantics from Contrastive Adversarial Samples](https://aclanthology.org/C18-1315/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
50. [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/) *— our card · 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
51. [Mapping to Declarative Knowledge for Word Problem Solving](https://md.research.elamur.ai/free/en/papers/roy-2018-mapping-declarative-knowledge-word-problem/) *— our card · 2018*
52. [Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering](https://aclanthology.org/P18-1158/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification](https://aclanthology.org/P18-1178/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267) *— 2018 · arXiv:1809.08267*
55. [Nightmare at test time: How punctuation prevents parsers from generalizing](https://aclanthology.org/W18-5404/) *— 2018 · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP*
56. [On Adversarial Examples for Character-Level Neural Machine Translation](https://aclanthology.org/C18-1055/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
57. [Pathologies of Neural Models Make Interpretations Difficult](https://aclanthology.org/D18-1407/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
58. [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://arxiv.org/abs/1804.09541) *— 2018 · arXiv:1804.09541*
59. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885*
60. [Robust Multilingual Part-of-Speech Tagging via Adversarial Training](https://aclanthology.org/N18-1089/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
61. [Semantically Equivalent Adversarial Rules for Debugging NLP models](https://aclanthology.org/P18-1079/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Stochastic Answer Networks for Machine Reading Comprehension](https://aclanthology.org/P18-1157/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [Stress Test Evaluation for Natural Language Inference](https://aclanthology.org/C18-1198/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
64. [Structured Neural Summarization](https://arxiv.org/abs/1811.01824) *— 2018 · arXiv:1811.01824*
65. [Systematic Generalization: What Is Required and Can It Be Learned?](https://arxiv.org/abs/1811.12889) *— 2018 · arXiv:1811.12889*
66. [Tackling Adversarial Examples in QA via Answer Sentence Selection](https://aclanthology.org/W18-2604/) *— 2018 · Proceedings of the Workshop on Machine Reading for Question Answering*
67. [The Importance of Being Recurrent for Modeling Hierarchical Structure](https://arxiv.org/abs/1803.03585) *— 2018 · arXiv:1803.03585*
68. [The Web as a Knowledge-Base for Answering Complex Questions](https://aclanthology.org/N18-1059/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
69. [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) *— 2018 · arXiv:1803.05457*
70. [Towards Inference-Oriented Reading Comprehension: ParallelQA](https://aclanthology.org/W18-1001/) *— 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning*
71. [Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension](https://aclanthology.org/N18-1144/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
72. [Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions](https://aclanthology.org/P18-3018/) *— 2018 · Proceedings of ACL 2018, Student Research Workshop*
73. [Using Adversarial Examples in Natural Language Processing](https://aclanthology.org/L18-1584/) *— 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)*
74. [Visual Referring Expression Recognition: What Do Systems Actually Learn?](https://aclanthology.org/N18-2123/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)*
75. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
76. [Reinforced Mnemonic Reader for Machine Reading Comprehension](https://arxiv.org/abs/1705.02798) *— 2017 · arXiv:1705.02798*
77. [Simple and Effective Multi-Paragraph Reading Comprehension](https://arxiv.org/abs/1710.10723) *— 2017 · arXiv:1710.10723*
78. [Simulating Action Dynamics with Neural Process Networks](https://arxiv.org/abs/1711.05313) *— 2017 · arXiv:1711.05313*
79. [Synthetic and Natural Noise Both Break Neural Machine Translation](https://arxiv.org/abs/1711.02173) *— 2017 · arXiv:1711.02173*

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