More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering
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.
PDF Abstract
SQuAD
Natural Questions
MS MARCO
ConceptNet
TriviaQA
HotpotQA
RACE
CommonsenseQA
NewsQA
CoQA
WebQuestions
QuAC
MLQA
NarrativeQA
SearchQA
ELI5
MCTest
KILT
ReCoRD
CBT
CosmosQA
MovieQA
DuReader
ShARC
WikiMovies
CODAH
TweetQA
XQA
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