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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Anna Rogers
- sl:arxiv_num : 2107.12708
- sl:arxiv_published : 2021-07-27T10:09:13Z
- sl:arxiv_summary : Alongside huge volumes of research on deep learning models in NLP in the
recent years, there has been also much work on benchmark datasets needed to
track modeling progress. Question answering and reading comprehension have been
particularly prolific in this regard, with over 80 new datasets appearing in
the past two 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 ``reasoning types\" in question answering and
propose a new taxonomy. We also discuss the implications of over-focusing on
English, and survey the current monolingual resources for other languages and
multilingual resources. The study is aimed at both practitioners looking for
pointers to the wealth of existing data, and at researchers working on new
resources.@en
- sl:arxiv_title : QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension@en
- sl:arxiv_updated : 2021-07-27T10:09:13Z
- sl:bookmarkOf : https://arxiv.org/abs/2107.12708
- sl:creationDate : 2021-08-06
- sl:creationTime : 2021-08-06T22:01:16Z
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