---
title: "Who cites RACE: Large-scale ReAding Comprehension Dataset…"
description: "The papers of the Elamur Research library that cite «RACE: Large-scale ReAding Comprehension Dataset From Examinations», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/"
---
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# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 6

619 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/lai-2017-race-large-scale-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/) · … · [5](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/5/) · **6** · [7](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/)

1. [Hierarchical Graph Network for Multi-hop Question Answering](https://aclanthology.org/2020.emnlp-main.710/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
2. [Improving QA Generalization by Concurrent Modeling of Multiple Biases](https://aclanthology.org/2020.findings-emnlp.74/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
3. [LEGAL-BERT: The Muppets straight out of Law School](https://aclanthology.org/2020.findings-emnlp.261/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
4. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
5. [LiveQA: A Question Answering Dataset over Sports Live](https://aclanthology.org/2020.ccl-1.98/) *— 2020 · Proceedings of the 19th Chinese National Conference on Computational Linguistics*
6. [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://arxiv.org/abs/2007.08124) *— 2020 · arXiv:2007.08124*
7. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
8. [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) *— 2020 · arXiv:2004.09297*
9. [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) *— 2020 · arXiv:2009.03300*
10. [Memory-Efficient Pipeline-Parallel DNN Training](https://arxiv.org/abs/2006.09503) *— 2020 · ICML 2021*
11. [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](https://aclanthology.org/2020.coling-main.238/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
12. [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://aclanthology.org/2020.acl-main.130/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
13. [Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering](https://aclanthology.org/2020.acl-main.654/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
14. [NUT-RC: Noisy User-generated Text-oriented Reading Comprehension](https://aclanthology.org/2020.coling-main.242/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
15. [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) *— 2020 · arXiv:2010.10499*
16. [PRover: Proof Generation for Interpretable Reasoning over Rules](https://aclanthology.org/2020.emnlp-main.9/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
17. [Question Directed Graph Attention Network for Numerical Reasoning over Text](https://aclanthology.org/2020.emnlp-main.549/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
18. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
19. [Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian](https://aclanthology.org/2020.coling-main.570/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
20. [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922) *— 2020 · arXiv:2004.13922*
21. [SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations](https://aclanthology.org/2020.acl-main.502/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
22. [STARC: Structured Annotations for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/berzak-2020-starc-structured-annotations-reading-comprehension/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
23. [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://aclanthology.org/2020.emnlp-main.99/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
24. [ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension](https://aclanthology.org/2020.lrec-1.675/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
25. [Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension](https://aclanthology.org/2020.lantern-1.3/) *— 2020 · Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)*
26. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
27. [Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text](https://aclanthology.org/2020.emnlp-main.111/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
28. [Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task](https://aclanthology.org/2020.lrec-1.152/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
29. [Transformers as Soft Reasoners over Language](https://arxiv.org/abs/2002.05867) *— 2020 · arXiv:2002.05867*
30. [UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://aclanthology.org/2020.findings-emnlp.171/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
31. [UPB at SemEval-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis](https://aclanthology.org/2020.semeval-1.160/) *— 2020 · Proceedings of the Fourteenth Workshop on Semantic Evaluation*
32. [Unsupervised Domain Adaptation of Language Models for Reading Comprehension](https://aclanthology.org/2020.lrec-1.663/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
33. [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://arxiv.org/abs/2009.13081) *— 2020 · arXiv:2009.13081*
34. [What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge](https://aclanthology.org/2020.tacl-1.37/) *— 2020 · Transactions of the Association for Computational Linguistics*
35. [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*
36. [Which *BERT? A Survey Organizing Contextualized Encoders](https://aclanthology.org/2020.emnlp-main.608/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
37. [“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models](https://aclanthology.org/2020.emnlp-main.556/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
38. [基于深度学习的语言模型研究进展](https://jos.org.cn/jos/article/abstract/6169) *— 2020 · 软件学报 (Journal of Software)*
39. [基于神经网络的机器阅读理解综述](https://jos.org.cn/jos/article/abstract/6048) *— 2020 · 软件学报 (Journal of Software)*
40. [多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)](https://aclanthology.org/2020.ccl-1.23/) *— 2020 · Proceedings of the 19th Chinese National Conference on Computational Linguistics*
41. [An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering](https://aclanthology.org/D19-5829/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
42. [Answering questions by learning to rank - Learning to rank by answering questions](https://aclanthology.org/D19-1256/) *— 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)*
43. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
44. [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://arxiv.org/abs/1904.02232) *— 2019 · arXiv:1904.02232*
45. [Beyond English-Only Reading Comprehension: Experiments in Zero-shot Multilingual Transfer for Bulgarian](https://aclanthology.org/R19-1053/) *— 2019 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)*
46. [BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels](https://aclanthology.org/D19-1249/) *— 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)*
47. [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) *— 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)*
48. [CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension](https://arxiv.org/abs/1912.09156) *— 2019 · arXiv:1912.09156*
49. [CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding](https://aclanthology.org/D19-5824/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
50. [Careful Selection of Knowledge to Solve Open Book Question Answering](https://aclanthology.org/P19-1615/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
51. [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://arxiv.org/abs/1906.01265) *— 2019 · arXiv:1906.01265*
52. [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/) *— our card · 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)*
53. [Dialog State Tracking: A Neural Reading Comprehension Approach](https://aclanthology.org/W19-5932/) *— 2019 · Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue*
54. [Domain-agnostic Question-Answering with Adversarial Training](https://aclanthology.org/D19-5826/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
55. [Evaluating Commonsense in Pre-trained Language Models](https://arxiv.org/abs/1911.11931) *— 2019 · arXiv:1911.11931*
56. [Evidence Sentence Extraction for Machine Reading Comprehension](https://aclanthology.org/K19-1065/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
57. [Finding Generalizable Evidence by Learning to Convince Q&A Models](https://aclanthology.org/D19-1244/) *— 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)*
58. [FreeLB: Enhanced Adversarial Training for Natural Language Understanding](https://arxiv.org/abs/1909.11764) *— 2019 · arXiv:1909.11764*
59. [FriendsQA: Open-Domain Question Answering on TV Show Transcripts](https://aclanthology.org/W19-5923/) *— 2019 · Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue*
60. [From ”F” to ”A” on the N.Y. Regents Science Exams: An Overview of the Aristo Project](https://arxiv.org/abs/1909.01958) *— 2019 · arXiv:1909.01958*
61. [GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://aclanthology.org/D19-1597/) *— 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)*
62. [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*
63. [IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension](https://aclanthology.org/D19-6009/) *— 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing*
64. [Improving Machine Reading Comprehension with General Reading Strategies](https://aclanthology.org/N19-1270/) *— 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)*
65. [Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text](https://aclanthology.org/P19-1217/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
66. [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://arxiv.org/abs/1904.09679) *— 2019 · arXiv:1904.09679*
67. [JEC-QA: A Legal-Domain Question Answering Dataset](https://arxiv.org/abs/1911.12011) *— 2019 · arXiv:1911.12011*
68. [Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge](https://aclanthology.org/D19-6010/) *— 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing*
69. [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://aclanthology.org/D19-1282/) *— 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)*
70. [Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering](https://aclanthology.org/N19-1030/) *— 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)*
71. [MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants](https://aclanthology.org/S19-1012/) *— 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)*
72. [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) *— 2019 · arXiv:1909.08053*
73. [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://aclanthology.org/P19-1485/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
74. [Multilingual Question Answering from Formatted Text applied to Conversational Agents](https://arxiv.org/abs/1910.04659) *— 2019 · arXiv:1910.04659*
75. [Natural Questions: A Benchmark for Question Answering Research](https://aclanthology.org/Q19-1026/) *— 2019*
76. [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)*
77. [Patient Knowledge Distillation for BERT Model Compression](https://arxiv.org/abs/1908.09355) *— 2019 · arXiv:1908.09355*
78. [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*
79. [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473) *— 2019 · arXiv:1910.11473*
80. [QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions](https://arxiv.org/abs/1909.03553) *— 2019 · arXiv:1909.03553*
81. [Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://aclanthology.org/N19-1240/) *— 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)*
82. [Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering](https://aclanthology.org/D19-1260/) *— 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)*
83. [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*
84. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
85. [Repurposing Entailment for Multi-Hop Question Answering Tasks](https://aclanthology.org/N19-1302/) *— 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)*
86. [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) *— 2019 · arXiv:1907.11692*
87. [TWEETQA: A Social Media Focused Question Answering Dataset](https://aclanthology.org/P19-1496/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
88. [Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering](https://aclanthology.org/D19-6003/) *— 2019 · Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing*
89. [What”s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering](https://aclanthology.org/D19-1281/) *— 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)*
90. [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) *— 2019 · arXiv:1906.08237*
91. [基於特徵粒度之訓練策略於中文口語問答系統之應用 (A Feature-granularity Training Strategy for Chinese Spoken Question Answering)](https://aclanthology.org/2019.ijclclp-2.1/) *— 2019*
92. [A Co-Matching Model for Multi-choice Reading Comprehension](https://aclanthology.org/P18-2118/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
93. [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://arxiv.org/abs/1810.07366) *— 2018 · arXiv:1810.07366 · EMNLP 2019 5886-5891*
94. [Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog](https://aclanthology.org/N18-1185/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
95. [DARC-IT: a DAtaset for Reading Comprehension in ITalian](https://aclanthology.org/2018.clicit-1.13/) *— 2018 · Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018)*
96. [DRCD: a Chinese Machine Reading Comprehension Dataset](https://arxiv.org/abs/1806.00920) *— 2018 · arXiv:1806.00920*
97. [DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension](https://aclanthology.org/P18-1156/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task](https://aclanthology.org/S18-1175/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
99. [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)*
100. [How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/) *— our card · 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*

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