Dzendzik, Daria, Vogel, Carl ORCID: 0000-0001-8928-8546 and Foster, Jennifer ORCID: 0000-0002-7789-4853 (2021) English machine reading comprehension datasets: a survey. In: 2021 Conference on Empirical Methods in Natural Language Processing, 7-11 Nov 2021, Punta Cana, Dominican Republic & Online.
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.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Subjects: | Computer Science > Artificial intelligence Computer Science > Computational linguistics Computer Science > Machine learning |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Institutes and Centres > ADAPT |
Published in: | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. . Association for Computational Linguistics (ACL). |
Publisher: | Association for Computational Linguistics (ACL) |
Official URL: | https://doi.org/10.18653/v1/2021.emnlp-main.693 |
Copyright Information: | © 2021 Association for Computational Linguistics |
Funders: | Science Foundation Ireland in the ADAPT Centre for Digital Content Technology, SFI Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund |
ID Code: | 29145 |
Deposited On: | 19 Oct 2023 13:35 by Jennifer Foster . Last Modified 19 Oct 2023 13:35 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution-Noncommercial 4.0 1MB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record