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# 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](https://md.mova.elamur.ai/free/en/docs/papers/kaushik-2018-how-much-reading-does-reading/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/kaushik-2018-how-much-reading-does-reading/) · **2**

1. [Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds](https://aclanthology.org/D19-1434/) *— 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](https://arxiv.org/abs/1909.12434) *— 2019 · arXiv:1909.12434*
3. [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*
4. [Misleading Failures of Partial-input Baselines](https://aclanthology.org/P19-1554/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
5. [Neural Text Summarization: A Critical Evaluation](https://aclanthology.org/D19-1051/) *— 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](https://aclanthology.org/D19-1251/) *— 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](https://aclanthology.org/D19-1500/) *— 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](https://md.mova.elamur.ai/free/en/docs/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/) *— our card · 2019 · arXiv:1908.05803*
9. [Shifting the Baseline: Single Modality Performance on Visual Navigation & QA](https://aclanthology.org/N19-1197/) *— 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](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)*
11. [Understanding Dataset Design Choices for Multi-hop Reasoning](https://aclanthology.org/N19-1405/) *— 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](https://aclanthology.org/D19-6115/) *— 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)*
13. [Unsupervised Question Answering by Cloze Translation](https://aclanthology.org/P19-1484/) *— 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](https://arxiv.org/abs/1810.12885) *— 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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