---
title: "Who cites MCTest: A Challenge Dataset for the Open-Domain…"
description: "263 papers of the Elamur Research library cite «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text», found in their…"
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---
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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 (of the 140,884 whose bibliographies we read) · 53 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/richardson-2013-mctest-challenge-dataset-open-domain/)

1. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,488 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
2. [**Natural Questions: A Benchmark for Question Answering Research**Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.*2019 · 4,031 citations*](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
3. [**Teaching Machines to Read and Comprehend**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,806 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
4. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
5. [**Know What You Don't Know: Unanswerable Questions for SQuAD**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,360 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/)
6. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
7. [**Bidirectional Attention Flow for Machine Comprehension**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,956 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
8. [**RACE: Large-scale ReAding Comprehension Dataset From Examinations**Guokun Lai, Qizhe Xie, Hanxiao Liu, et al.*2017 · 1,619 citations*](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/)
9. [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,026 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
10. [**Learning to Ask: Neural Question Generation for Reading Comprehension**Xinya Du, Junru Shao, Claire Cardie*2017 · 913 citations*](https://md.research.elamur.ai/free/en/papers/du-2017-learning-ask-neural-question-generation/)
11. [**UNIFIEDQA: Crossing Format Boundaries with a Single QA System**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 843 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/)
12. [**Gated Self-Matching Networks for Reading Comprehension and Question Answering**Wenhui Wang, Nan Yang, Furu Wei, et al.*2017 · 823 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/)
13. [**MLQA: Evaluating Cross-lingual Extractive Question Answering**Patrick Lewis, Barlas Oğuz, Ruty Rinott, et al.*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2019-mlqa-evaluating-cross-lingual-extractive/)
14. [**The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations**Felix Hill, Antoine Bordes, Sumit Chopra, et al.*2015 · 693 citations*](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
15. [**A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task**Danqi Chen, Jason Bolton, Christopher D. Manning*2016 · 689 citations*](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/)
16. [**Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences**Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/)
17. [**TVQA: Localized, Compositional Video Question Answering**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/)
18. [**Dynamic Coattention Networks For Question Answering**Caiming Xiong, Victor Zhong, Richard Socher*2016 · 578 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/)
19. [**BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis**Hu Xu, Bing Liu, Lei Shu, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
20. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
21. [**Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks**Jason Weston, Antoine Bordes, Sumit Chopra, et al.*2015 · 529 citations*](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)
22. [**Machine Comprehension Using Match-LSTM and Answer Pointer**Shuohang Wang, Jing Jiang*2016 · 528 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/)
23. [**Fact Checking: Task definition and dataset construction**Andreas Vlachos, Sebastian Riedel*2014 · 523 citations*](https://md.research.elamur.ai/free/en/papers/vlachos-2014-fact-checking-task-definition-dataset/)
24. [**The LAMBADA dataset: Word prediction requiring a broad discourse context**Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, et al.*2016 · 506 citations*](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/)
25. [**LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning**Jian Liu, Leyang Cui, Hanmeng Liu, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/liu-2020-logiqa-challenge-dataset-machine-reading/)
26. [**Text Understanding with the Attention Sum Reader Network**Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, et al.*2016 · 379 citations*](https://md.research.elamur.ai/free/en/papers/kadlec-2016-text-understanding-attention-sum-reader/)
27. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 361 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)
28. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/)
29. [**Modeling Biological Processes for Reading Comprehension**Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, et al.*2014 · 288 citations*](https://md.research.elamur.ai/free/en/papers/berant-2014-modeling-biological-processes-reading-comprehension/)
30. [**emrQA: A Large Corpus for Question Answering on Electronic Medical Records**Anusri Pampari, Preethi Raghavan, Jennifer Liang, et al.*2018 · 285 citations*](https://md.research.elamur.ai/free/en/papers/pampari-2018-emrqa-large-corpus-question-answering/)
31. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 283 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
32. [**Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge**Todor Mihaylov, Anette Frank*2018 · 260 citations*](https://md.research.elamur.ai/free/en/papers/mihaylov-2018-knowledgeable-reader-enhancing-cloze-style/)
33. [**The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants**Lucas Bandarkar, Davis Liang, Benjamin Muller, et al.*2023 · 259 citations*](https://md.research.elamur.ai/free/en/papers/bandarkar-2023-belebele-benchmark-parallel-reading-comprehension/)
34. [**Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/)
35. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
36. [**MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension**Alon Talmor, Jonathan Berant*2019 · 234 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-multiqa-empirical-investigation-generalization-transfer/)
37. [**Dialog State Tracking: A Neural Reading Comprehension Approach**Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/gao-2019-dialog-state-tracking-neural-reading/)
38. [**A Diagram Is Worth A Dozen Images**Aniruddha Kembhavi, Mike Salvato, Eric Kolve, et al.*2016 · 207 citations*](https://md.research.elamur.ai/free/en/papers/kembhavi-2016-diagram-worth-dozen-images/)
39. [**DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension**Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, et al.*2018 · 188 citations*](https://md.research.elamur.ai/free/en/papers/saha-2018-duorc-complex-language-understanding-paraphrased/)
40. [**Learning To Use Formulas To Solve Simple Arithmetic Problems**Arindam Mitra, Chitta Baral*2016 · 178 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2016-learning-use-formulas-solve-simple/)
41. [**RealTime QA: What's the Answer Right Now?**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/)
42. [**Reasoning Over Paragraph Effects in Situations**Kevin Lin, Oyvind Tafjord, Peter Clark, et al.*2019 · 171 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-reasoning-over-paragraph-effects-situations/)
43. [**DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications**Wei He, Kai Liu, Jing Liu, et al.*2017 · 163 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-dureader-chinese-machine-reading/)
44. [**QuALITY: Question Answering with Long Input Texts, Yes!**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/)
45. [**A Span-Extraction Dataset for Chinese Machine Reading Comprehension**Yiming Cui, Ting Liu, Wanxiang Che, et al.*2018 · 156 citations*](https://md.research.elamur.ai/free/en/papers/cui-2018-span-extraction-dataset-chinese/)
46. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
47. [**DocNLI: A Large-scale Dataset for Document-level Natural Language Inference**Wenpeng Yin, Dragomir Radev, Caiming Xiong*2021 · 121 citations*](https://md.research.elamur.ai/free/en/papers/yin-2021-docnli-large-scale-dataset-document/)
48. [**Crowdsourcing Question-Answer Meaning Representations**Julian Michael, Gabriel Stanovsky, Luheng He, et al.*2018 · 112 citations*](https://md.research.elamur.ai/free/en/papers/michael-2018-crowdsourcing-question-answer-meaning-representations/)
49. [**Machine Comprehension with Discourse Relations**Karthik Narasimhan, Regina Barzilay*2015 · 91 citations*](https://md.research.elamur.ai/free/en/papers/narasimhan-2015-machine-comprehension-discourse-relations/)
50. [**English Machine Reading Comprehension Datasets: A Survey**Daria Dzendzik, Jennifer Foster, Carl Vogel*2021 · 53 citations*](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
51. [**STARC: Structured Annotations for Reading Comprehension**Yevgeni Berzak, Jonathan Malmaud, Roger Levy*2020 · 38 citations*](https://md.research.elamur.ai/free/en/papers/berzak-2020-starc-structured-annotations-reading-comprehension/)
52. [**Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension**Shaoru Guo, Yong Guan, Ru Li, et al.*2020 · 30 citations*](https://md.research.elamur.ai/free/en/papers/guo-2020-incorporating-syntax-frame-semantics-neural/)
53. [**Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds**Igor Labutov, Bishan Yang, Anusha Prakash, et al.*2018 · 17 citations*](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/)

210 more papers of our library cite it; they have no page here yet.

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