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

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/) · … · [4](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/4/) · **5** · [6](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/) · [7](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/)

1. [Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data](https://aclanthology.org/2021.emnlp-main.202/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
2. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
3. [Benchmarking Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2021.findings-acl.56/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
4. [BiQuAD: Towards QA based on deeper text understanding](https://aclanthology.org/2021.starsem-1.10/) *— 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
5. [CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images](https://aclanthology.org/2021.naacl-main.289/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
6. [CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations](https://aclanthology.org/2021.emnlp-main.323/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
7. [CodeQA: A Question Answering Dataset for Source Code Comprehension](https://aclanthology.org/2021.findings-emnlp.223/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
8. [Comparing Test Sets with Item Response Theory](https://aclanthology.org/2021.acl-long.92/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
9. [Controllable Open-ended Question Generation with A New Question Type Ontology](https://aclanthology.org/2021.acl-long.502/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
10. [Controlling Text Edition by Changing Answers of Specific Questions](https://aclanthology.org/2021.findings-acl.110/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
11. [Corporate Bankruptcy Prediction with Domain-Adapted BERT](https://aclanthology.org/2021.econlp-1.4/) *— 2021 · Proceedings of the Third Workshop on Economics and Natural Language Processing*
12. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
13. [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://arxiv.org/abs/2111.09543) *— 2021 · arXiv:2111.09543*
14. [Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI](https://aclanthology.org/2021.emnlp-main.303/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
15. [Encoding Explanatory Knowledge for Zero-shot Science Question Answering](https://aclanthology.org/2021.iwcs-1.5/) *— 2021 · Proceedings of the 14th International Conference on Computational Semantics (IWCS)*
16. [English Machine Reading Comprehension Datasets: A Survey](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
17. [Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations](https://aclanthology.org/2021.emnlp-main.295/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
18. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
19. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
20. [Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://aclanthology.org/2021.findings-acl.336/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
21. [Extract, Integrate, Compete: Towards Verification Style Reading Comprehension](https://aclanthology.org/2021.findings-emnlp.255/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
22. [FQuAD2.0: French Question Answering and knowing that you know nothing](https://arxiv.org/abs/2109.13209) *— 2021 · arXiv:2109.13209*
23. [Few Shot Dialogue State Tracking using Meta-learning](https://aclanthology.org/2021.eacl-main.148/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
24. [GANDALF: a General Character Name Description Dataset for Long Fiction](https://aclanthology.org/2021.mrqa-1.13/) *— 2021 · Proceedings of the 3rd Workshop on Machine Reading for Question Answering*
25. [GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation](https://aclanthology.org/2021.findings-acl.113/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
26. [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) *— 2021 · arXiv:2112.06905*
27. [General-Purpose Question-Answering with Macaw](https://arxiv.org/abs/2109.02593) *— 2021 · arXiv:2109.02593*
28. [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://aclanthology.org/2021.tacl-1.57/) *— 2021*
29. [Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources](https://aclanthology.org/2021.findings-acl.197/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
30. [Learning from Limited Labels for Long Legal Dialogue](https://aclanthology.org/2021.nllp-1.20/) *— 2021 · Proceedings of the Natural Legal Language Processing Workshop 2021*
31. [Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization](https://aclanthology.org/2021.naacl-main.104/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
32. [MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering](https://aclanthology.org/2021.findings-emnlp.196/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
33. [Memory-efficient Transformers via Top-k Attention](https://aclanthology.org/2021.sustainlp-1.5/) *— 2021 · Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing*
34. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
35. [MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion](https://aclanthology.org/2021.semeval-1.150/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
36. [Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension](https://aclanthology.org/2021.ccl-1.91/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
37. [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://aclanthology.org/2021.paclic-1.58/) *— 2021 · Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation*
38. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
39. [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
40. [NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension](https://aclanthology.org/2021.semeval-1.110/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
41. [NLQuAD: A Non-Factoid Long Question Answering Data Set](https://aclanthology.org/2021.eacl-main.106/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
42. [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://aclanthology.org/2021.findings-emnlp.350/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
43. [On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings](https://aclanthology.org/2021.emnlp-main.596/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
44. [On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study](https://aclanthology.org/2021.acl-long.517/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
45. [On the Transferability of Minimal Prediction Preserving Inputs in Question Answering](https://aclanthology.org/2021.naacl-main.101/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
46. [Optimizing Deeper Transformers on Small Datasets](https://aclanthology.org/2021.acl-long.163/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
47. [PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning](https://aclanthology.org/2021.semeval-1.109/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
48. [PROST: Physical Reasoning about Objects through Space and Time](https://aclanthology.org/2021.findings-acl.404/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
49. [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) *— 2021 · arXiv:2107.13586*
50. [Prediction or Comparison: Toward Interpretable Qualitative Reasoning](https://aclanthology.org/2021.findings-acl.59/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
51. [REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training](https://aclanthology.org/2021.findings-acl.13/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
52. [Relation-aware Video Reading Comprehension for Temporal Language Grounding](https://aclanthology.org/2021.emnlp-main.324/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
53. [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://aclanthology.org/2021.naacl-main.47/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
54. [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://arxiv.org/abs/2112.11446) *— 2021 · arXiv:2112.11446*
55. [Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension](https://aclanthology.org/2021.findings-emnlp.176/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
56. [Single-dataset Experts for Multi-dataset Question Answering](https://aclanthology.org/2021.emnlp-main.495/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
57. [Surface Form Competition: Why the Highest Probability Answer Isn”t Always Right](https://aclanthology.org/2021.emnlp-main.564/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
58. [TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference](https://aclanthology.org/2021.naacl-main.463/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
59. [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://md.research.elamur.ai/free/en/papers/lal-2021-tellmewhy-dataset-answering-why-questions/) *— our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
60. [The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding](https://arxiv.org/abs/2110.10221) *— 2021 · arXiv:2110.10221*
61. [The Power of Scale for Parameter-Efficient Prompt Tuning](https://aclanthology.org/2021.emnlp-main.243/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
62. [The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models](https://arxiv.org/abs/2108.06084) *— 2021 · arXiv:2108.06084*
63. [Ti-Reader: 基于注意力机制的藏文机器阅读理解端到端网络模型(Ti-Reader: An End-to-End Network Model Based on Attention Mechanisms for Tibetan Machine Reading Comprehension)](https://aclanthology.org/2021.ccl-1.21/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
64. [Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text](https://aclanthology.org/2021.ranlp-1.97/) *— 2021 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)*
65. [Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification](https://aclanthology.org/2021.findings-acl.103/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
66. [Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge](https://aclanthology.org/2021.mrqa-1.12/) *— 2021 · Proceedings of the 3rd Workshop on Machine Reading for Question Answering*
67. [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://aclanthology.org/2021.acl-short.117/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
68. [UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning](https://aclanthology.org/2021.semeval-1.106/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
69. [Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models](https://aclanthology.org/2021.acl-short.43/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
70. [Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation](https://aclanthology.org/2021.sigdial-1.14/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
71. [WebSRC: A Dataset for Web-Based Structural Reading Comprehension](https://aclanthology.org/2021.emnlp-main.343/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
72. [What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension](https://aclanthology.org/2021.findings-emnlp.202/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
73. [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
74. [Zero-Shot Dialogue State Tracking via Cross-Task Transfer](https://aclanthology.org/2021.emnlp-main.622/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
75. [jurBERT: A Romanian BERT Model for Legal Judgement Prediction](https://aclanthology.org/2021.nllp-1.8/) *— 2021 · Proceedings of the Natural Legal Language Processing Workshop 2021*
76. [基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)](https://aclanthology.org/2021.ccl-1.41/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
77. [A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.](https://aclanthology.org/2020.findings-emnlp.393/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
78. [A BERT-based Dual Embedding Model for Chinese Idiom Prediction](https://aclanthology.org/2020.coling-main.113/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
79. [A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction](https://aclanthology.org/2020.acl-main.361/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
80. [A Sentence Cloze Dataset for Chinese Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.589/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
81. [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.233/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
82. [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts*
83. [Automatic Distractor Generation for Multiple Choice Questions in Standard Tests](https://aclanthology.org/2020.coling-main.189/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
84. [Bi-directional CognitiveThinking Network for Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.235/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
85. [BioMRC: A Dataset for Biomedical Machine Reading Comprehension](https://aclanthology.org/2020.bionlp-1.15/) *— 2020 · Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing*
86. [Bridging Information-Seeking Human Gaze and Machine Reading Comprehension](https://aclanthology.org/2020.conll-1.11/) *— 2020 · Proceedings of the 24th Conference on Computational Natural Language Learning*
87. [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://arxiv.org/abs/2004.05986) *— 2020 · arXiv:2004.05986*
88. [Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings](https://aclanthology.org/2020.findings-emnlp.423/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
89. [Constrained Fact Verification for FEVER](https://aclanthology.org/2020.emnlp-main.629/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
90. [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://aclanthology.org/2020.emnlp-main.154/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
91. [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) *— 2020 · arXiv:2006.03654*
92. [DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering](https://aclanthology.org/2020.acl-main.411/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
93. [Deeply Embedded Knowledge Representation & Reasoning For Natural Language Question Answering: A Practitioner”s Perspective](https://aclanthology.org/2020.spnlp-1.12/) *— 2020 · Proceedings of the Fourth Workshop on Structured Prediction for NLP*
94. [Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension](https://aclanthology.org/2020.acl-main.599/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
95. [ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding](https://arxiv.org/abs/2010.12148) *— 2020 · arXiv:2010.12148*
96. [ETC: Encoding Long and Structured Inputs in Transformers](https://aclanthology.org/2020.emnlp-main.19/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
97. [EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering](https://aclanthology.org/2020.emnlp-main.438/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
98. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
99. [Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition](https://aclanthology.org/2020.coling-main.334/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
100. [Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity](https://aclanthology.org/2020.bionlp-1.11/) *— 2020 · Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing*

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