Document-enhanced Question Answering over Knowledge-Bases

Abstract

Recently,knowledge base(KB) has been widely adopted to the task of question answering(QA) to provide a proper answer for a given question,known as the KBQA problem.However,knowledge base itself may be incomplete(e.g.KB does not contain the answer to the question,or some of the entities and relationships in the question),limiting the overall performance of existing KBQA models.To resolve this issue,this paper proposes a new model to leverage textual documents for KBQA task by providing additional answers to enhance knowledge base coverage and background information to enhance the representation of questions.Specifically,the proposed model consists of three modules,namely entity and question representation module,document and enhanced-question representation module and answer prediction module.The first module aims to learn the representations of entities from the retrieved subgraph of knowledge base.Then,the question representation can be updated with the fusion of seed entities.The second module attempts to learn a proper representation of the document that is relevant to the given question.Then,the question representation can be further improved by fusing the document information.Finally,the last module makes an answer prediction based on the information of knowledge base,updated question and documents.Extensive experiments are conducted on the WebQuestionsSP dataset,and the results show that better accuracy can be obtained in comparison with other counterparts.

Keywords

• kb-qa|co-attention|end-to-end|neural network|fusion gate function