Graph-based KB and Text Fusion Interaction Network for Open Domain Question Answering

Abstract

The incompleteness of the knowledge base (KB) limits the performance of open domain question answering (QA).Represent the incomplete KB with graph attention network (GAT) and complement the incomplete KB by extra text achieves great success to boost the QA system when the KB is incomplete. In this paper, we propose a Graph-based KB and Text Fusion Interaction Network (GTFIN) to improve the performance of the incomplete QA system by utilizing the KB and text information.In GTFIN, to reduce the influence of the query-unrelated noisy information of GAT on final answer prediction, we first design a global-normalization graph attention network (GGAT) by determining the query-related edge weights from the global perspective, and then a coarse-to-fine text reader (CFReader) is proposed to both exploit the relation information and obtain the entity mention representation in the text to enhance the incomplete KB. We further incorporate a bi-attention mechanism to enhance the interaction between question and entity representation which could find more query-related entities for final answer prediction. On the widely used KBQA benchmark WebQSP, our model achieves state-of-the-art performance.