An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering
Sabin Kafle, Nisansa de Silva, Dejing Dou · 2019
Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is 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.