English Machine Reading Comprehension Datasets: A Survey
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Abstract (arXiv)
This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human performance level, and first question word. Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets.
Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 84 of its 141 references that are in the map. First posted on arXiv 25 Jan 2021.
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84 of 141 references.
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- 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
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- 2021Improving Unsupervised Question Answering via Summarization-Informed Question Generationcited by 21
- 2021MetaQA: Combining Expert Agents for Multi-Skill Question Answeringcited by 15
- 2022A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialogcited by 14
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- 2023Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering22 Mar 2023
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