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

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1. [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)*
2. [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)*
3. [Unsupervised Question Answering by Cloze Translation](https://aclanthology.org/P19-1484/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
4. [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)*
5. [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)*
6. [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)*
7. [Adversarial Evaluation of Multimodal Machine Translation](https://aclanthology.org/D18-1329/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
8. [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)*
9. [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*
10. [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*
11. [Analyzing Compositionality-Sensitivity of NLI Models](https://arxiv.org/abs/1811.07033) *— 2018 · arXiv:1811.07033*
12. [Assessing Composition in Sentence Vector Representations](https://aclanthology.org/C18-1152/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
13. [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://md.mova.elamur.ai/free/en/docs/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)*
14. [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*
15. [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://aclanthology.org/Q18-1021/) *— 2018 · Transactions of the Association for Computational Linguistics*
16. [Deep Learning: A Critical Appraisal](https://arxiv.org/abs/1801.00631) *— 2018 · arXiv:1801.00631*
17. [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*
18. [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)*
19. [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*
20. [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)*
21. [Generating Natural Language Adversarial Examples](https://arxiv.org/abs/1804.07998) *— 2018 · arXiv:1804.07998*
22. [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)*
23. [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)*
24. [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)*
25. [Learning Visually-Grounded Semantics from Contrastive Adversarial Samples](https://aclanthology.org/C18-1315/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
26. [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.mova.elamur.ai/free/en/docs/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)*
27. [Mapping to Declarative Knowledge for Word Problem Solving](https://md.mova.elamur.ai/free/en/docs/papers/roy-2018-mapping-declarative-knowledge-word-problem/) *— our card · 2018*
28. [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)*
29. [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)*
30. [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267) *— 2018 · arXiv:1809.08267*
31. [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*
32. [On Adversarial Examples for Character-Level Neural Machine Translation](https://aclanthology.org/C18-1055/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
33. [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*
34. [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://arxiv.org/abs/1804.09541) *— 2018 · arXiv:1804.09541*
35. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885*
36. [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)*
37. [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)*
38. [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)*
39. [Stress Test Evaluation for Natural Language Inference](https://aclanthology.org/C18-1198/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
40. [Structured Neural Summarization](https://arxiv.org/abs/1811.01824) *— 2018 · arXiv:1811.01824*
41. [Systematic Generalization: What Is Required and Can It Be Learned?](https://arxiv.org/abs/1811.12889) *— 2018 · arXiv:1811.12889*
42. [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*
43. [The Importance of Being Recurrent for Modeling Hierarchical Structure](https://arxiv.org/abs/1803.03585) *— 2018 · arXiv:1803.03585*
44. [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)*
45. [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) *— 2018 · arXiv:1803.05457*
46. [Towards Inference-Oriented Reading Comprehension: ParallelQA](https://aclanthology.org/W18-1001/) *— 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning*
47. [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)*
48. [Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions](https://aclanthology.org/P18-3018/) *— 2018 · Proceedings of ACL 2018, Student Research Workshop*
49. [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)*
50. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
51. [Reinforced Mnemonic Reader for Machine Reading Comprehension](https://arxiv.org/abs/1705.02798) *— 2017 · arXiv:1705.02798*
52. [Simple and Effective Multi-Paragraph Reading Comprehension](https://arxiv.org/abs/1710.10723) *— 2017 · arXiv:1710.10723*
53. [Simulating Action Dynamics with Neural Process Networks](https://arxiv.org/abs/1711.05313) *— 2017 · arXiv:1711.05313*
54. [Synthetic and Natural Noise Both Break Neural Machine Translation](https://arxiv.org/abs/1711.02173) *— 2017 · arXiv:1711.02173*
55. ackermann-2024-relationship-between-skill-neurons-robustness
56. adiba-2022-unsupervised-domain-adaptation-question-answering
57. akdemir-2020-research-task-discovery-transfer-learning
58. akimoto-2023-context-quality-matters-training-fusion
59. albilali-2021-what-does-bert-learn-arabic
60. anschutz-2024-simpler-becomes-harder-do-llms
61. aoki-2023-empirical-investigation-neural-symbolic-reasoning
62. aoki-2024-first-heuristic-then-rational-dynamic
63. araujo-2021-stress-test-evaluation-biomedical-word
64. asami-2024-what-makes-language-models-good
65. banerjee-2024-inffeed-influence-functions-as-feedback
66. bates-2025-template-is-all-you-meme
67. bayer-2024-xai-attack-utilizing-explainable-ai
68. belohlavek-2018-adversarial-examples-natural-language-processing
69. berezin-2023-no-offence-bert-i-insult
70. boreshban-2023-robustqa-framework-adversarial-text-generation
71. bui-2020-how-state-art-models-can
72. cao-2023-pay-attention-robustness-chinese-minority
73. chandra-2021-beyond-laurel-yanny-autoencoder-enabled
74. chen-2023-evaluating-enhancing-robustness-code-pre
75. chen-2024-context-aware-adversarial-attack-named
76. chen-2026-rethinking-rl-evaluation-can-benchmarks
77. choi-2025-people-will-agree-what-i
78. choudhury-2022-can-edge-probing-tests-reveal
79. choudhury-2023-implications-annotation-artifacts-edge-probing
80. christopoulou-2022-training-dynamics-curriculum-learning-study
81. cui-2023-exploring-automatically-perturbed-natural-language
82. dang-2024-curious-case-searching-correlation-between
83. ding-2026-modeling-human-adversarial-strategy-adaptation
84. djanibekov-2025-spirit-patching-speech-language-models
85. dong-2023-promptattack-probing-dialogue-state-trackers
86. dua-2021-generative-context-pair-selection-multi
87. dutt-2024-investigating-generalizability-pretrained-language-models
88. emura-2026-dual-task-paradigm-investigate-sentence
89. enhos-2025-minimizing-queries-maximizing-impact-adaptive
90. fang-2022-data-efficient-concept-extraction-pre
91. fu-2023-logic-unveils-truth-while-disguise
92. gabriel-2022-naturaladversaries-can-naturalistic-adversaries-be
93. gokhale-2022-textitgeneralized-but-not-robust-comparing
94. gor-2026-ai-take-wheel-what-drives
95. grail-2018-adversarial-networks-machine-reading
96. guo-2023-desiq-unbiased-challenging-benchmark-social
97. hai-2022-learning-invariant-representation-improves-robustness
98. huang-2019-relation-module-non-answerable-predictions
99. iglesia-2025-ranking-over-scoring-reliable-robust
100. jiang-2026-immediate-inference-missing-foundation-large

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