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# Who cites «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text»

263 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/missing-2013-richardson-mctest-challenge-dataset-open-domain/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/missing-2013-richardson-mctest-challenge-dataset-open-domain-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/missing-2013-richardson-mctest-challenge-dataset-open-domain-3/)

1. [Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling](https://aclanthology.org/2026.acl-srw.119/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
2. [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://arxiv.org/abs/2604.12374) *— 2026 · arXiv:2604.12374*
3. [MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning](https://aclanthology.org/2025.coling-main.111/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
4. [NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
5. [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://aclanthology.org/2025.acl-long.169/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://aclanthology.org/2025.acl-long.675/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [Are self-explanations from Large Language Models faithful?](https://aclanthology.org/2024.findings-acl.19/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
8. [Context-Aware Question Answering in Urdu](https://aclanthology.org/2024.icnlsp-1.26/) *— 2024 · Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)*
9. [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://aclanthology.org/2024.emnlp-main.799/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
10. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
11. [LFED: A Literary Fiction Evaluation Dataset for Large Language Models](https://aclanthology.org/2024.lrec-main.915/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
12. [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://aclanthology.org/2024.acl-short.55/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
13. [TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya](https://aclanthology.org/2024.lrec-main.1404/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
14. [大语言模型故事理解能力评价数据集(Benchmarking story comprehension ability of large language model)](https://aclanthology.org/2024.ccl-1.60/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
15. [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://arxiv.org/abs/2308.00002) *— 2023 · arXiv:2308.00002*
16. [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
17. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
18. [Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory](https://aclanthology.org/2023.bea-1.10/) *— 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)*
19. [Domain Incremental Lifelong Learning in an Open World](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
20. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision](https://aclanthology.org/2023.emnlp-main.1001/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
22. [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://aclanthology.org/2023.jlcl-2.1/) *— 2023 · Journal for Language Technology and Computational Linguistics, Vol. 36 No. 2*
23. [KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding](https://aclanthology.org/2023.findings-acl.743/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
24. [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://arxiv.org/abs/2308.11483) *— 2023 · arXiv:2308.11483*
25. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.343/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
27. [Personality Understanding of Fictional Characters during Book Reading](https://aclanthology.org/2023.acl-long.826/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
29. [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) *— 2023 · arXiv:2308.16884 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024*
30. [Using contradictions improves question answering systems](https://aclanthology.org/2023.acl-short.72/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
31. [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*
32. [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) *— 2022 · arXiv:2208.03299*
33. [DTW at Qur''an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://aclanthology.org/2022.osact-1.10/) *— 2022 · Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection*
34. [DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection](https://aclanthology.org/2022.emnlp-main.51/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
35. [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)*
36. [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://aclanthology.org/2022.louhi-1.5/) *— 2022 · Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)*
37. [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://aclanthology.org/2022.acl-long.501/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [MixQG: Neural Question Generation with Mixed Answer Types](https://aclanthology.org/2022.findings-naacl.111/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
39. [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://aclanthology.org/2022.findings-emnlp.14/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
40. [Narrative Datasets through the Lenses of NLP and HCI](https://aclanthology.org/2022.hcinlp-1.7/) *— 2022 · Proceedings of the Second Workshop on Bridging Human–Computer Interaction and Natural Language Processing*
41. [Narrative Why-Question Answering: A Review of Challenges and Datasets](https://aclanthology.org/2022.gem-1.48/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
42. [Polyglot Prompt: Multilingual Multitask Prompt Training](https://aclanthology.org/2022.emnlp-main.674/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
43. [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*
44. [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*
45. [RealTime QA: What''s the Answer Right Now?](https://arxiv.org/abs/2207.13332) *— 2022 · arXiv:2207.13332*
46. [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://aclanthology.org/2022.naacl-main.317/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
47. [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)*
48. [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*
49. [Zero-shot cross-lingual open domain question answering](https://aclanthology.org/2022.mia-1.9/) *— 2022 · Proceedings of the Workshop on Multilingual Information Access (MIA)*
50. [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*
51. [Can NLI Models Verify QA Systems' Predictions?](https://aclanthology.org/2021.findings-emnlp.324/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
52. [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*
53. [DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://aclanthology.org/2021.findings-acl.435/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
54. [Does Structure Matter? Encoding Documents for Machine Reading Comprehension](https://aclanthology.org/2021.naacl-main.367/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
55. [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*
56. [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*
57. [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*
58. [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*
59. [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*
60. [MIMOQA: Multimodal Input Multimodal Output Question Answering](https://aclanthology.org/2021.naacl-main.418/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
61. [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*
62. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
63. [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)*
64. [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*
65. [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://aclanthology.org/2021.tacl-1.68/) *— 2021 · Transactions of the Association for Computational Linguistics*
66. [Sorting through the noise: Testing robustness of information processing in pre-trained language models](https://aclanthology.org/2021.emnlp-main.119/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
67. [Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification](https://aclanthology.org/2021.emnlp-main.207/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
68. [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)*
69. [Winnowing Knowledge for Multi-choice Question Answering](https://aclanthology.org/2021.findings-emnlp.100/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
70. [A Frame-based Sentence Representation for Machine Reading Comprehension](https://aclanthology.org/2020.acl-main.83/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
71. [A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection](https://aclanthology.org/2020.coling-main.457/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
72. [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*
73. [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://aclanthology.org/2020.tacl-1.43/) *— 2020*
74. [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access](https://aclanthology.org/2020.sigdial-1.35/) *— 2020 · Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
75. [FQuAD: French Question Answering Dataset](https://aclanthology.org/2020.findings-emnlp.107/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
76. [Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.237/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
77. [LifeQA: A Real-life Dataset for Video Question Answering](https://aclanthology.org/2020.lrec-1.536/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
78. [Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions](https://aclanthology.org/2020.lrec-1.217/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
79. [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*
80. [LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning](https://arxiv.org/abs/2007.08124) *— 2020 · arXiv:2007.08124*
81. [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)*
82. [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) *— 2020 · arXiv:2009.03300*
83. [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*
84. [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*
85. [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*
86. [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) *— 2020 · arXiv:2002.04326*
87. [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*
88. [Scene Restoring for Narrative Machine Reading Comprehension](https://aclanthology.org/2020.emnlp-main.247/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
89. [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*
90. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
91. [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*
92. [Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start](https://aclanthology.org/2020.emnlp-main.660/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
93. [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)*
94. [A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC](https://aclanthology.org/N19-1241/) *— 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)*
95. [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://aclanthology.org/D19-1107/) *— 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)*
96. [Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives](https://aclanthology.org/N19-1052/) *— 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)*
97. [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://arxiv.org/abs/1911.09241) *— 2019 · arXiv:1911.09241*
98. [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://arxiv.org/abs/1904.02232) *— 2019 · arXiv:1904.02232*
99. [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)*
100. [CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text](https://aclanthology.org/D19-1458/) *— 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)*

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