Abstract
Text-based question answering (TBQA) has
been studied extensively in recent years. Most
existing approaches focus on finding the answer to a question within a single paragraph.
However, many difficult questions require
multiple supporting evidence from scattered
text across two or more documents. In this paper, we propose the Dynamically Fused Graph
Network (DFGN), a novel method to answer
those questions requiring multiple scattered
evidence and reasoning over them. Inspired
by human’s step-by-step reasoning behavior,
DFGN includes a dynamic fusion layer that
starts from the entities mentioned in the given
query, explores along the entity graph dynamically built from the text, and gradually finds
relevant supporting entities from the given
documents. We evaluate DFGN on HotpotQA,
a public TBQA dataset requiring multi-hop
reasoning. DFGN achieves competitive results
on the public board. Furthermore, our analysis shows DFGN could produce interpretable
reasoning chains.