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Alongside huge volumes of research on deep learning models in NLP in therecent years, there has been also much work on benchmark datasets needed totrack modeling progress. Question answering and reading comprehension have beenparticularly prolific in this regard, with over 80 new datasets appearing inthe past two years. This study is the largest survey of the field to date. Weprovide an overview of the various formats and domains of the currentresources, highlighting the current lacunae for future work. We further discussthe current classifications of "skills" that question answering/readingcomprehension systems are supposed to acquire, and propose a new taxonomy. Thesupplementary materials survey the current multilingual resources andmonolingual resources for languages other than English, and we discuss theimplications of over-focusing on English. The study is aimed at bothpractitioners looking for pointers to the wealth of existing data, and atresearchers working on new resources.

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