Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
Weiwen Xu, Huihui Zhang, Deng Cai, Wai Pang Lam · 2021
Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA) at web scale.Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process.In this paper, we propose a new framework to exploit more valid facts while obtaining explainability for multi-hop QA by dynamically constructing a semantic graph and reasoning over it.We employ Abstract Meaning Representation (AMR) as semantic graph representation.Our framework contains three new ideas: (a) AMR-SG, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.(b) A novel path-based fact analytics approach exploiting AMR-SG to extract active facts from a large fact pool to answer questions.(c) A fact-level relation modeling leveraging graph convolution network (GCN) to guide the reasoning process.Results on two scientific multi-hop QA datasets show that we can surpass recent approaches including those using additional knowledge graphs while maintaining high explainability on OpenBookQA and achieve a new state-ofthe-art result on ARC-Challenge in a computationally practicable setting.