Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

作     者：Jun Yin Wayne Xin Zhao Xiao-Ming Li 

作者机构：School of Electronic Engineering and Computer Science Peking University Beijing 100871 China School of Information Renmin University of China Beijing 100872 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2017年第32卷第4期

页      面：805-813页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：supported by the National Basic Research 973 Program of China 国家自然科学基金 Wayne Xin Zhao was partially supported by Beijing Natural Science Foundation the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing 

主　　题：question answering deep neural network knowledge base 

摘      要：Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.