EMNLP 2021main55 citations
English Machine Reading Comprehension Datasets: A Survey
Daria Dzendzik, Jennifer Foster, Carl Vogel
Abstract
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.
BibTeX
@inproceedings{dzendzik-etal-2021-english,
title = "{E}nglish Machine Reading Comprehension Datasets: A Survey",
author = "Dzendzik, Daria and
Foster, Jennifer and
Vogel, Carl",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.693/",
doi = "10.18653/v1/2021.emnlp-main.693",
pages = "8784--8804"
}