With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural networkbased (NN-based) methods develop, NNbased KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the crossattention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.
Hao et al. REF present an end-to-end neural network model to represent the questions, which improves the representation of questions via cross-attention mechanism.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
ACL
Computer Science