%0 Conference Proceedings %T English Machine Reading Comprehension Datasets: A Survey %A Dzendzik, Daria %A Foster, Jennifer %A Vogel, Carl %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing %D 2021 %8 November %I Association for Computational Linguistics %C Online and Punta Cana, Dominican Republic %F dzendzik-etal-2021-english %X 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. %R 10.18653/v1/2021.emnlp-main.693 %U https://aclanthology.org/2021.emnlp-main.693 %U https://doi.org/10.18653/v1/2021.emnlp-main.693 %P 8784-8804