  Recent advances regarding question answering and reading comprehension have
resulted in models that surpass human performance when the answer is contained
in a single, continuous passage of text, requiring only single-hop reasoning.
However, in actual scenarios, lots of complex queries require multi-hop
reasoning. The key to the Question Answering task is semantic feature
interaction between documents and questions, which is widely processed by
Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the
surface semantics of words in complex questions and fails to capture implied
semantic feature of intermediate answers. As a result, Bi-DAF partially ignores
part of the contexts related to the question and cannot extract the most
important parts of multiple documents. In this paper we propose a new model
architecture for multi-hop question answering, by applying two completion
strategies: (1) Coarse-Grain complex question Decomposition (CGDe) strategy are
introduced to decompose complex question into simple ones under the condition
of without any additional annotations (2) Fine-Grained Interaction (FGIn)
strategy are introduced to better represent each word in the document and
extract more comprehensive and accurate sentences related to the inference
path. The above two strategies are combined and tested on the SQuAD and
HotpotQA datasets, and the experimental results show that our method
outperforms state-of-the-art baselines.
