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home / docs / papers / cited-by / Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 5

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

Pages: 1 · 2 · 3 · 4 · 5 · 6

  1. Universal Adversarial Triggers for Attacking and Analyzing NLP — 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 — 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
  3. Unsupervised Question Answering by Cloze Translation — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  4. White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks — 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 — 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 — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  7. Adversarial Evaluation of Multimodal Machine Translation — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  8. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks — 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 — 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning
  10. Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge — 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning
  11. Analyzing Compositionality-Sensitivity of NLI Models — 2018 · arXiv:1811.07033
  12. Assessing Composition in Sentence Vector Representations — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
  13. Breaking NLI Systems with Sentences that Require Simple Lexical Inferences — 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 — 2018 · Proceedings of the 22nd Conference on Computational Natural Language Learning
  15. Constructing Datasets for Multi-hop Reading Comprehension Across Documents — 2018 · Transactions of the Association for Computational Linguistics
  16. Deep Learning: A Critical Appraisal — 2018 · arXiv:1801.00631
  17. Deep learning evaluation using deep linguistic processing — 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning
  18. Did the Model Understand the Question? — 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 — 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 — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  21. Generating Natural Language Adversarial Examples — 2018 · arXiv:1804.07998
  22. HotFlip: White-Box Adversarial Examples for Text Classification — 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 — 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 — 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 — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
  26. Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences — 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 — our card · 2018
  28. Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering — 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 — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  30. Neural Approaches to Conversational AI — 2018 · arXiv:1809.08267
  31. Nightmare at test time: How punctuation prevents parsers from generalizing — 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 — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
  33. Pathologies of Neural Models Make Interpretations Difficult — 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 — 2018 · arXiv:1804.09541
  35. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension — 2018 · arXiv:1810.12885
  36. Robust Multilingual Part-of-Speech Tagging via Adversarial Training — 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 — 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 — 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 — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
  40. Structured Neural Summarization — 2018 · arXiv:1811.01824
  41. Systematic Generalization: What Is Required and Can It Be Learned? — 2018 · arXiv:1811.12889
  42. Tackling Adversarial Examples in QA via Answer Sentence Selection — 2018 · Proceedings of the Workshop on Machine Reading for Question Answering
  43. The Importance of Being Recurrent for Modeling Hierarchical Structure — 2018 · arXiv:1803.03585
  44. The Web as a Knowledge-Base for Answering Complex Questions — 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 — 2018 · arXiv:1803.05457
  46. Towards Inference-Oriented Reading Comprehension: ParallelQA — 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 — 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 — 2018 · Proceedings of ACL 2018, Student Research Workshop
  49. Visual Referring Expression Recognition: What Do Systems Actually Learn? — 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? — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  51. Reinforced Mnemonic Reader for Machine Reading Comprehension — 2017 · arXiv:1705.02798
  52. Simple and Effective Multi-Paragraph Reading Comprehension — 2017 · arXiv:1710.10723
  53. Simulating Action Dynamics with Neural Process Networks — 2017 · arXiv:1711.05313
  54. Synthetic and Natural Noise Both Break Neural Machine Translation — 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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