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home / docs / papers / cited-by / Who cites «How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks» · page 2

Who cites «How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks» · page 2

133 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Pages: 1 · 2

  1. Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds — 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. Learning the Difference that Makes a Difference with Counterfactually-Augmented Data — 2019 · arXiv:1909.12434
  3. Measuring text readability with machine comprehension: a pilot study — 2019 · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
  4. Misleading Failures of Partial-input Baselines — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  5. Neural Text Summarization: A Critical Evaluation — 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)
  6. NumNet: Machine Reading Comprehension with Numerical Reasoning — 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. Question Answering for Privacy Policies: Combining Computational and Legal Perspectives — 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)
  8. Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning — our card · 2019 · arXiv:1908.05803
  9. Shifting the Baseline: Single Modality Performance on Visual Navigation & QA — 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)
  10. Towards Debiasing Fact Verification Models — 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)
  11. Understanding Dataset Design Choices for Multi-hop Reasoning — 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)
  12. 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)
  13. Unsupervised Question Answering by Cloze Translation — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  14. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension — 2018 · arXiv:1810.12885
  15. arunkumar-2023-real-time-visual-feedback-guide
  16. carlsson-2021-gandalf-general-character-name-description
  17. chinnappa-2020-wikipossessions-possession-timeline-generation-as
  18. choudhury-2022-can-edge-probing-tests-reveal
  19. he-2023-multimodal-graph-transformer-multimodal-question
  20. kocak-2025-cure-controlled-unlearning-robust-embeddings
  21. li-2019-pingan-smart-health-sjtu-coin
  22. li-2020-mrc-examples-answerable-bert-without
  23. li-2025-task-calibration-calibrating-large-language
  24. longpre-2021-transferability-minimal-prediction-preserving-inputs
  25. ludan-2023-explanation-based-finetuning-makes-models
  26. majumder-2021-model-agnostic-answer-reranking-system
  27. nolano-2024-pointing-out-shortcomings-relation-extraction
  28. reich-2022-leveraging-expert-guided-adversarial-augmentation
  29. stacey-2020-avoiding-hypothesis-only-bias-natural
  30. sugawara-2023-degrees-freedom-defining-testing-natural
  31. varanasi-2021-autoeqa-auto-encoding-questions-extractive
  32. vilares-2019-harry-potter-action-prediction-challenge
  33. watarai-2020-developing-dataset-japanese-slot-filling

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