A Survey on Explainability in Machine Reading Comprehension
This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.
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Datasets
SNLI
HotpotQA
OpenBookQA
CommonsenseQA
MultiRC
e-SNLI
QASC
BREAK
Worldtree
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