Exploiting Rich Syntax for Better Knowledge Base Question Answering
This work addresses KBQA, a key task in natural language processing for improving question answering over structured knowledge bases, but it is incremental as it builds on existing encoding methods by adding syntax.
The authors tackled the problem of Knowledge Base Question Answering (KBQA) by incorporating syntactic tree information, which previous methods often ignored, and achieved state-of-the-art performance on a benchmark dataset.
Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees.In this paper, we propose an approach to learn syntax-based representations for KBQA. First, we encode path-based syntax by considering the shortest dependency paths between keywords. Then, we propose two encoding strategies to mode the information of whole syntactic trees to obtain tree-based syntax. Finally, we combine both path-based and tree-based syntax representations for KBQA. We conduct extensive experiments on a widely used benchmark dataset and the experimental results show that our syntax-aware systems can make full use of syntax information in different settings and achieve state-of-the-art performance of KBQA.