---
title: "Who cites How Much Reading Does Reading Comprehension…"
description: "The papers of the Elamur Research library that cite «How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks»…"
canonical: "https://research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/cited-by/page/2/"
---
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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.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)

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

1. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
2. [Towards Debiasing NLU Models from Unknown Biases](https://aclanthology.org/2020.emnlp-main.613/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
3. [Translation Artifacts in Cross-lingual Transfer Learning](https://aclanthology.org/2020.emnlp-main.618/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
4. [What Question Answering can Learn from Trivia Nerds](https://aclanthology.org/2020.acl-main.662/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
5. [What do we expect from Multiple-choice QA Systems?](https://aclanthology.org/2020.findings-emnlp.317/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
6. [WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts](https://aclanthology.org/2020.lrec-1.140/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
7. [Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects](https://aclanthology.org/W19-1801/) *— 2019 · Proceedings of the Second Workshop on Shortcomings in Vision and Language*
8. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
9. [Bend but Don't Break? Multi-Challenge Stress Test for QA Models](https://aclanthology.org/D19-5818/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
10. [CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text](https://aclanthology.org/D19-1458/) *— 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. [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) *— our card · 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. [Don”t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://aclanthology.org/P19-1084/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
13. [Evidence Sentence Extraction for Machine Reading Comprehension](https://aclanthology.org/K19-1065/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
14. [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://aclanthology.org/P19-1092/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
15. [Harry Potter and the Action Prediction Challenge from Natural Language](https://aclanthology.org/N19-1218/) *— 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)*
16. [Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://aclanthology.org/D19-5306/) *— 2019 · Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)*
17. [Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://aclanthology.org/N19-1371/) *— 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. [Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews](https://aclanthology.org/N19-3001/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop*
19. [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)*
20. [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://arxiv.org/abs/1909.12434) *— 2019 · arXiv:1909.12434*
21. [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*
22. [Misleading Failures of Partial-input Baselines](https://aclanthology.org/P19-1554/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
23. [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)*
24. [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)*
25. [Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks](https://aclanthology.org/D19-6011/) *— 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing*
26. [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)*
27. [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/) *— our card · 2019 · arXiv:1908.05803*
28. [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)*
29. [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)*
30. [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)*
31. [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)*
32. [Unsupervised Question Answering by Cloze Translation](https://aclanthology.org/P19-1484/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
33. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885*

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