QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
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QA Dataset Explosion : A Taxonomy of NLP Resources for Question Answering and Reading Comprehension. / Rogers, Anna; Gardner, Matt; Augenstein, Isabelle.
I: ACM Computing Surveys, Bind 55, Nr. 10, 197, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - QA Dataset Explosion
T2 - A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
AU - Rogers, Anna
AU - Gardner, Matt
AU - Augenstein, Isabelle
N1 - Publisher Copyright: © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - natural language understanding
KW - Reading comprehension
U2 - 10.1145/3560260
DO - 10.1145/3560260
M3 - Journal article
AN - SCOPUS:85147798618
VL - 55
JO - ACM Computing Surveys
JF - ACM Computing Surveys
SN - 0360-0300
IS - 10
M1 - 197
ER -
ID: 337589133