survey

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Authors:

Isabelle AugensteinAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 10

Article No.: 197, Pages 1 - 45

https://doi.org/10.1145/3560260

Published: 02 February 2023 Publication History

Abstract

Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been much work on benchmark datasets needed to track modeling progress. Question answering and reading comprehension have been particularly prolific in this regard, with more than 80 new datasets appearing in the past 2 years. This study is the largest survey of the field to date. We provide an overview of the various formats and domains of the current resources, highlighting the current lacunae for future work. We further discuss the current classifications of “skills” that question answering/reading comprehension systems are supposed to acquire and propose a new taxonomy. The supplementary materials survey the current multilingual resources and monolingual resources for languages other than English, and we discuss the implications of overfocusing on English. The study is aimed at both practitioners looking for pointers to the wealth of existing data and at researchers working on new resources.

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Index Terms

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Question answering

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Published In

ACM Computing Surveys Volume 55, Issue 10

October 2023

772 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567475

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 02 February 2023

Online AM: 13 September 2022

Accepted: 11 August 2022

Revised: 03 July 2022

Received: 27 July 2021

Published in CSUR Volume 55, Issue 10

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