  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.
