  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 "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 over-focusing 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.
