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Question answering (QA) using textual sources for purposes such as readingcomprehension (RC) has attracted much attention. This study focuses on the taskof explainable multi-hop QA, which requires the system to return the answerwith evidence sentences by reasoning and gathering disjoint pieces of thereference texts. It proposes the Query Focused Extractor (QFE) model forevidence extraction and uses multi-task learning with the QA model. QFE isinspired by extractive summarization models; compared with the existing method,which extracts each evidence sentence independently, it sequentially extractsevidence sentences by using an RNN with an attention mechanism on the questionsentence. It enables QFE to consider the dependency among the evidencesentences and cover important information in the question sentence.Experimental results show that QFE with a simple RC baseline model achieves astate-of-the-art evidence extraction score on HotpotQA. Although designed forRC, it also achieves a state-of-the-art evidence extraction score on FEVER,which is a recognizing textual entailment task on a large textual database.

Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction