[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-5/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 5

# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 5

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

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-7/)

1. [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*
2. [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*
3. [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)*
4. [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*
5. [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)*
6. [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*
7. [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://arxiv.org/abs/1904.09679) *— 2019 · arXiv:1904.09679*
8. [JEC-QA: A Legal-Domain Question Answering Dataset](https://arxiv.org/abs/1911.12011) *— 2019 · arXiv:1911.12011*
9. [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)*
10. [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)*
11. [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)*
12. [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) *— 2019 · arXiv:1909.08053*
13. [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*
14. [Natural Questions: A Benchmark for Question Answering Research](https://aclanthology.org/Q19-1026/) *— 2019*
15. [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)*
16. [Patient Knowledge Distillation for BERT Model Compression](https://arxiv.org/abs/1908.09355) *— 2019 · arXiv:1908.09355*
17. [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473) *— 2019 · arXiv:1910.11473*
18. [QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions](https://arxiv.org/abs/1909.03553) *— 2019 · arXiv:1909.03553*
19. [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)*
20. [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)*
21. [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*
22. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
23. [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)*
24. [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) *— 2019 · arXiv:1907.11692*
25. [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*
26. [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*
27. [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)*
28. [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) *— 2019 · arXiv:1906.08237*
29. [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*
30. [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)*
31. [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://arxiv.org/abs/1810.07366) *— 2018 · arXiv:1810.07366 · EMNLP 2019 5886-5891*
32. [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)*
33. [DRCD: a Chinese Machine Reading Comprehension Dataset](https://arxiv.org/abs/1806.00920) *— 2018 · arXiv:1806.00920*
34. [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)*
35. [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)*
36. [How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks](https://md.mova.elamur.ai/free/en/docs/papers/kaushik-2018-how-much-reading-does-reading/) *— our card · 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
37. [Know What You Don''t Know: Unanswerable Questions for SQuAD](https://aclanthology.org/P18-2124/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
38. [Large-scale Cloze Test Dataset Created by Teachers](https://aclanthology.org/D18-1257/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
39. [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.mova.elamur.ai/free/en/docs/papers/khashabi-2018-looking-beyond-surface-challenge-set/) *— 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)*
40. [MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge](https://aclanthology.org/L18-1564/) *— 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)*
41. [Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension](https://aclanthology.org/D18-1238/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
42. [Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension](https://aclanthology.org/C18-1073/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
43. [Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.mova.elamur.ai/free/en/docs/papers/labutov-2018-multi-relational-question-answering-narratives/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267) *— 2018 · arXiv:1809.08267*
45. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885*
46. [Reading Comprehension with Graph-based Temporal-Casual Reasoning](https://aclanthology.org/C18-1069/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
47. [SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge](https://aclanthology.org/S18-1119/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
48. [The Web as a Knowledge-Base for Answering Complex Questions](https://aclanthology.org/N18-1059/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
49. [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://arxiv.org/abs/1809.02922) *— 2018 · arXiv:1809.02922*
50. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
51. [Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension](https://aclanthology.org/S18-1120/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
52. [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://arxiv.org/abs/1711.05073) *— 2017 · arXiv:1711.05073*
53. [Dynamic Fusion Networks for Machine Reading Comprehension](https://arxiv.org/abs/1711.04964) *— 2017 · arXiv:1711.04964*
54. [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://aclanthology.org/P17-1147/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://arxiv.org/abs/1611.09268) *— 2016 · arXiv:1611.09268*
56. agrawal-2024-exmy-data-type-technique-arbitrary
57. aksu-2021-velocidapter-task-oriented-dialogue-comprehension
58. alrashed-2025-mix-minhash-match-cross-source
59. alsarra-2023-conflibert-arabic-pre-trained-arabic
60. alyafeai-2025-mole-metadata-extraction-validation-scientific
61. bai-2025-efficient-pretraining-data-selection-language
62. bansal-2023-few-shot-unified-question-answering
63. benedetto-2025-survey-automated-distractor-evaluation-multiple
64. berger-2026-generation-evaluation-resource-error-categorized
65. borchmann-2025-arc-challenge-is-not-that
66. brunato-2018-darc-it-dataset-reading-comprehension
67. byun-2025-textit-d-gen-automatic-distractor
68. cahyawijaya-2022-how-long-is-enough-exploring
69. carlsson-2021-gandalf-general-character-name-description
70. chen-2021-neuer-semeval-2021-task-4
71. chen-2023-boosting-inference-efficiency-unleashing-power
72. chen-2023-natural-response-generation-chinese-reading
73. chen-2025-cross-task-examples-task-prompts
74. chen-2025-simple-linear-patch-revives-layer
75. chen-2025-unveiling-uncertainty-deep-dive-into
76. chen-2026-confidence-aware-alignment-makes-reasoning
77. da-2019-jeff-da-coin-shared-task
78. djenovic-2026-development-serbian-qa-datasets-through
79. do-2023-machine-reading-comprehension-vietnamese-customer
80. dong-2025-maximum-score-routing-mixture-experts
81. elshabrawy-2025-enabling-natural-zero-shot-prompting
82. farajidizaji-2026-task-competence-instruction-following-evaluating
83. feng-2024-long-sequence-modeling-attention-tensorization
84. feng-2025-dive-into-moe-diversity-enhanced
85. fernando-2025-linguistic-entity-masking-improve-cross
86. gao-2025-bypass-back-propagation-optimization-based
87. glavas-2026-intra-prompt-parallel-decoding-common
88. goncalves-2026-luxdiagrc-diagnostic-reading-comprehension-corpus
89. grimm-2021-biquad-qa-based-deeper-text
90. groeneveld-2023-catwalk-unified-language-model-evaluation
91. hai-2022-learning-invariant-representation-improves-robustness
92. han-2024-self-adaptive-sampling-accurate-video
93. hanyu-2021-chinese-machine-reading-comprehension-dataset
94. hong-2026-gmoe-global-mixture-experts-logit
95. horton-2025-hamming-attention-distillation-binarizing-keys
96. hourri-2025-patch-learnable-tile-level-hybrid
97. hu-2025-yulan-mini-pushing-limits-open
98. huang-2023-statistically-profiling-biases-natural-language
99. huang-2025-do-robot-snakes-dream-like
100. hwang-2026-can-llms-estimate-cognitive-complexity

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-4/) · **5** · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-7/)

- [← previous Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-4/)
- [next → Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-6/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)