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# Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 3

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** · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lai-2017-race-large-scale-reading-comprehension-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. [``World Knowledge'''' in Multiple Choice Reading Comprehension](https://aclanthology.org/2023.fever-1.5/) *— 2023 · Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)*
2. [An MRC Framework for Semantic Role Labeling](https://aclanthology.org/2022.coling-1.191/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
3. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
4. [Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension](https://aclanthology.org/2022.findings-acl.82/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
5. [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) *— 2022 · arXiv:2208.03299*
6. [BBQ: A hand-built bias benchmark for question answering](https://aclanthology.org/2022.findings-acl.165/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
7. [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
8. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
9. [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://aclanthology.org/2022.emnlp-main.598/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
10. [Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models](https://aclanthology.org/2022.emnlp-main.438/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
11. [Case-Based Abductive Natural Language Inference](https://aclanthology.org/2022.coling-1.134/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
12. [Compliance Checking with NLI: Privacy Policies vs. Regulations](https://arxiv.org/abs/2204.01845) *— 2022 · arXiv:2204.01845*
13. [ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence](https://aclanthology.org/2022.naacl-main.400/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
14. [Data Augmentation for Biomedical Factoid Question Answering](https://aclanthology.org/2022.bionlp-1.6/) *— 2022 · Proceedings of the 21st Workshop on Biomedical Language Processing*
15. [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://arxiv.org/abs/2201.05596) *— 2022 · arXiv:2201.05596*
16. [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) *— 2022 · arXiv:2212.09251*
17. [Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning](https://aclanthology.org/2022.coling-1.231/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
18. [E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning](https://aclanthology.org/2022.findings-acl.311/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
19. [Editing Models with Task Arithmetic](https://arxiv.org/abs/2212.04089) *— 2022 · arXiv:2212.04089*
20. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
21. [Evaluating the Knowledge Dependency of Questions](https://aclanthology.org/2022.emnlp-main.718/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
22. [Exploring The Landscape of Distributional Robustness for Question Answering Models](https://aclanthology.org/2022.findings-emnlp.441/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
23. [FQuAD2.0: French Question Answering and Learning When You Don''t Know](https://aclanthology.org/2022.lrec-1.237/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
24. [Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.mova.elamur.ai/free/en/docs/papers/xu-2022-fantastic-questions-where-find-them/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [Feeding What You Need by Understanding What You Learned](https://aclanthology.org/2022.acl-long.403/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT](https://aclanthology.org/2022.acl-long.330/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [Hyperdecoders: Instance-specific decoders for multi-task NLP](https://aclanthology.org/2022.findings-emnlp.124/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
28. [KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering](https://aclanthology.org/2022.emnlp-main.206/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
29. [Leveraging Large Language Models for Multiple Choice Question Answering](https://arxiv.org/abs/2210.12353) *— 2022 · arXiv:2210.12353*
30. [Life after BERT: What do Other Muppets Understand about Language?](https://aclanthology.org/2022.acl-long.227/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [LogiTorch: A PyTorch-based library for logical reasoning on natural language](https://aclanthology.org/2022.emnlp-demos.25/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
32. [Logic Traps in Evaluating Attribution Scores](https://aclanthology.org/2022.acl-long.407/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://aclanthology.org/2022.findings-acl.71/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
34. [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://aclanthology.org/2022.coling-1.126/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
35. [OPERA: Operation-Pivoted Discrete Reasoning over Text](https://aclanthology.org/2022.naacl-main.119/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
36. [PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation](https://aclanthology.org/2022.emnlp-industry.60/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track*
37. [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/abs/2204.02311) *— 2022 · arXiv:2204.02311*
39. [Perturbation Augmentation for Fairer NLP](https://aclanthology.org/2022.emnlp-main.646/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
40. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
41. [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://aclanthology.org/2022.naacl-main.313/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
42. [QAConv: Question Answering on Informative Conversations](https://aclanthology.org/2022.acl-long.370/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [QuALITY: Question Answering with Long Input Texts, Yes!](https://aclanthology.org/2022.naacl-main.391/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
44. [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://aclanthology.org/2022.findings-acl.59/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
45. [RealTime QA: What''s the Answer Right Now?](https://arxiv.org/abs/2207.13332) *— 2022 · arXiv:2207.13332*
46. [Retrieval Augmentation for Commonsense Reasoning: A Unified Approach](https://aclanthology.org/2022.emnlp-main.294/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
47. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments](https://aclanthology.org/2022.findings-emnlp.544/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
49. [Self-critiquing models for assisting human evaluators](https://arxiv.org/abs/2206.05802) *— 2022 · arXiv:2206.05802*
50. [Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems](https://aclanthology.org/2022.eamt-1.18/) *— 2022 · Proceedings of the 23rd Annual Conference of the European Association for Machine Translation*
51. [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://aclanthology.org/2022.naacl-main.132/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
52. [To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://aclanthology.org/2022.findings-naacl.96/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
53. [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556) *— 2022 · arXiv:2203.15556*
54. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
55. [UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131) *— 2022 · arXiv:2205.05131*
56. [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/abs/2201.11990) *— 2022 · arXiv:2201.11990*
57. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
58. [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/abs/2204.05832) *— 2022 · arXiv:2204.05832*
59. [What Language Model to Train if You Have One Million GPU Hours?](https://arxiv.org/abs/2210.15424) *— 2022 · arXiv:2210.15424*
60. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
62. [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://arxiv.org/abs/2206.01861) *— 2022 · arXiv:2206.01861*
63. [`Am I the Bad One''? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models](https://aclanthology.org/2022.lrec-1.28/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
64. [基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)](https://aclanthology.org/2022.ccl-1.51/) *— 2022 · Proceedings of the 21st Chinese National Conference on Computational Linguistics*
65. [A Robustly Optimized BERT Pre-training Approach with Post-training](https://aclanthology.org/2021.ccl-1.108/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
66. [Arabic Offensive Language on Twitter: Analysis and Experiments](https://aclanthology.org/2021.wanlp-1.13/) *— 2021 · Proceedings of the Sixth Arabic Natural Language Processing Workshop*
67. [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*
68. [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*
69. [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*
70. [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*
71. [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*
72. [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*
73. [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)*
74. [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)*
75. [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*
76. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
77. [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://arxiv.org/abs/2111.09543) *— 2021 · arXiv:2111.09543*
78. [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*
79. [English Machine Reading Comprehension Datasets: A Survey](https://md.mova.elamur.ai/free/en/docs/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
80. [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*
81. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
82. [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*
83. [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*
84. [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*
85. [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*
86. [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) *— 2021 · arXiv:2112.06905*
87. [General-Purpose Question-Answering with Macaw](https://arxiv.org/abs/2109.02593) *— 2021 · arXiv:2109.02593*
88. [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*
89. [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*
90. [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*
91. [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*
92. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
93. [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)*
94. [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*
95. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
96. [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*
97. [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*
98. [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*
99. [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*
100. [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)*

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