Answering while Summarizing: Multi-task Learningfor Multi-hop QA with Evidence Extraction
Abstract
Question answering (QA) using textual
sources for purposes such as reading comprehension (RC) has attracted much attention.
This study focuses on the task of explainable
multi-hop QA, which requires the system to
return the answer with evidence sentences
by reasoning and gathering disjoint pieces
of the reference texts. It proposes the Query
Focused Extractor (QFE) model for evidence
extraction and uses multi-task learning with
the QA model. QFE is inspired by extractive
summarization models; compared with the
existing method, which extracts each evidence
sentence independently, it sequentially extracts evidence sentences by using an RNN
with an attention mechanism on the question
sentence. It enables QFE to consider the dependency among the evidence sentences and
cover important information in the question
sentence. Experimental results show that QFE
with a simple RC baseline model achieves a
state-of-the-art evidence extraction score on
HotpotQA. Although designed for RC, 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.