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.- Anthology ID:
- 2021.emnlp-main.693
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8784–8804
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.693
- DOI:
- 10.18653/v1/2021.emnlp-main.693
- Cite (ACL):
- Daria Dzendzik, Jennifer Foster, and Carl Vogel. 2021. English Machine Reading Comprehension Datasets: A Survey. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8784–8804, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- English Machine Reading Comprehension Datasets: A Survey (Dzendzik et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-main.693.pdf
- Code
- dariad/rczoo
- Data
- ARC, AmazonQA, BiPaR, BookCorpus, BoolQ, CliCR, CoQA, CosmosQA, DROP, DuoRC, HotpotQA, IIRC, LAMBADA, MCScript, MCTest, MS MARCO, MedHop, MovieQA, MultiRC, NarrativeQA, Natural Questions, NewsQA, OpenBookQA, QASC, QUASAR, QUASAR-S, QUASAR-T, RACE, ReCO, ReClor, ReCoRD, RecipeQA, SQuAD, SciQ, SearchQA, ShARC, SubjQA, TriviaQA, TweetQA, Who-did-What, WikiHop, WikiMovies, WikiQA, WikiReading, Worldtree, emrQA