  Textual Question Answering (QA) aims to provide precise answers to user's
questions in natural language using unstructured data. One of the most popular
approaches to this goal is machine reading comprehension(MRC). In recent years,
many novel datasets and evaluation metrics based on classical MRC tasks have
been proposed for broader textual QA tasks. In this paper, we survey 47 recent
textual QA benchmark datasets and propose a new taxonomy from an application
point of view. In addition, We summarize 8 evaluation metrics of textual QA
tasks. Finally, we discuss current trends in constructing textual QA benchmarks
and suggest directions for future work.

Computation and Language;Artificial Intelligence