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# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 4

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/)

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1. [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)*
2. [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*
3. [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*
4. [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*
5. [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*
6. [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*
7. [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://arxiv.org/abs/2112.11446) *— 2021 · arXiv:2112.11446*
8. [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*
9. [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*
10. [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*
11. [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*
12. [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://md.mova.elamur.ai/free/en/docs/papers/lal-2021-tellmewhy-dataset-answering-why-questions/) *— our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
13. [The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding](https://arxiv.org/abs/2110.10221) *— 2021 · arXiv:2110.10221*
14. [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*
15. [The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models](https://arxiv.org/abs/2108.06084) *— 2021 · arXiv:2108.06084*
16. [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*
17. [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)*
18. [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*
19. [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)*
20. [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)*
21. [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*
22. [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*
23. [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*
24. [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*
25. [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*
26. [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*
27. [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*
28. [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*
29. [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*
30. [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*
31. [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.mova.elamur.ai/free/en/docs/papers/rogers-2020-guide-dataset-explosion-qa-nli/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts*
32. [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*
33. [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*
34. [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*
35. [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://arxiv.org/abs/2004.05986) *— 2020 · arXiv:2004.05986*
36. [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*
37. [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)*
38. [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) *— 2020 · arXiv:2006.03654*
39. [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*
40. [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*
41. [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*
42. [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)*
43. [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)*
44. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
45. [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*
46. [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)*
47. [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*
48. [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*
49. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
50. [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*
51. [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://arxiv.org/abs/2007.08124) *— 2020 · arXiv:2007.08124*
52. [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)*
53. [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) *— 2020 · arXiv:2004.09297*
54. [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) *— 2020 · arXiv:2009.03300*
55. [Memory-Efficient Pipeline-Parallel DNN Training](https://arxiv.org/abs/2006.09503) *— 2020 · ICML 2021*
56. [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*
57. [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*
58. [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*
59. [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*
60. [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)*
61. [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)*
62. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
63. [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*
64. [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922) *— 2020 · arXiv:2004.13922*
65. [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*
66. [STARC: Structured Annotations for Reading Comprehension](https://md.mova.elamur.ai/free/en/docs/papers/berzak-2020-starc-structured-annotations-reading-comprehension/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
67. [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)*
68. [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*
69. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
70. [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)*
71. [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*
72. [Transformers as Soft Reasoners over Language](https://arxiv.org/abs/2002.05867) *— 2020 · arXiv:2002.05867*
73. [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*
74. [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*
75. [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*
76. [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*
77. [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*
78. [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*
79. [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)*
80. [``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)*
81. [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)*
82. [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)*
83. [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*
84. [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*
85. [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)*
86. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
87. [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://arxiv.org/abs/1904.02232) *— 2019 · arXiv:1904.02232*
88. [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)*
89. [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)*
90. [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)*
91. [CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension](https://arxiv.org/abs/1912.09156) *— 2019 · arXiv:1912.09156*
92. [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*
93. [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://arxiv.org/abs/1906.01265) *— 2019 · arXiv:1906.01265*
94. [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://md.mova.elamur.ai/free/en/docs/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)*
95. [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*
96. [Domain-agnostic Question-Answering with Adversarial Training](https://aclanthology.org/D19-5826/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
97. [Evaluating Commonsense in Pre-trained Language Models](https://arxiv.org/abs/1911.11931) *— 2019 · arXiv:1911.11931*
98. [Evidence Sentence Extraction for Machine Reading Comprehension](https://aclanthology.org/K19-1065/) *— 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
99. [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)*
100. [FreeLB: Enhanced Adversarial Training for Natural Language Understanding](https://arxiv.org/abs/1909.11764) *— 2019 · arXiv:1909.11764*

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