An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering

Abstract

Question Answering (QA) requires understanding of queries expressed in natural languages and identification of relevant information content to provide an answer. For closed-world QAs, information access is obtained by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. Our study of multiple question answering methods finds that the neural networks are able to produce state-of-art results in different question answering domains, and inclusion of additional information via KB embeddings further improve the performance of such approaches. Further progress in QA can be improved by incorporating more powerful representations of KBs.