Coarse-grained decomposition and fine-grained interaction for multi-hop question answering

In recent years, question answering (QA) and reading comprehension (RC) has attracted much attention, and most research on QA has focused on multi-hop QA task which requires connecting multiple pieces of evidence scattered in a long context to answer the question. The key to the multi-hop QA 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 well as ignoring parts of contexts related to the question and failing to 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-Grained complex question Decomposition (CGDe) strategy is introduced to decompose complex questions into simple ones without any additional annotations; (2) Fine-Grained Interaction (FGIn) strategy is introduced to explicitly represent each word in documents 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.