  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 among two or more documents. In this
paper, we propose 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 produces interpretable reasoning chains.
