Two-Phase Semantic Retrieval for Explainable Multi-Hop Question Answering

Qin Wang, Jianzhou Feng, Ganlin Xu, Lei Huang

Abstract: Explainable Multi-Hop Question Answering (MHQA) requires an ability to reason explicitly across facts to arrive at the answer. The majority of multi-hop reasoning methods concentrate on semantic similarity to obtain the next hops or act as entity-centric inference. However, approaches that ignore the rationales required for problems can easily lead to blindness in reasoning. In this paper, we propose a two-Phase text Retrieval method with an entity Mask mechanism (PRM), which focuses on the rationale from global semantics along with entity consideration. Specifically, it consists of two components: 1) The rationale-aware retriever is pre-trained via a dual encoder framework with an entity mask mechanism. The learned representations of hypotheses and facts are utilized to obtain top K candidate core facts by a sentence-level dense retrieval. 2) The entity-aware validator determines the reachability of hypotheses and core facts with an entity granularity sparse matrix. Our experiments on three public datasets in the scientific domain (i.e., OpenbookQA, Worldtree, and ARC-Challenge) demonstrate that the proposed model has achieved remarkable performance over the existing methods.