Better Query Graph Selection for Knowledge Base Question Answering
This work addresses query graph selection in KBQA, an incremental improvement for question answering systems over knowledge bases.
The paper tackles the problem of selecting optimal query graphs for Knowledge Base Question Answering by linearizing graphs into sequences and using BERT for encoding, achieving top performance on ComplexQuestions and second best on WebQuestions.
This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA). Specifically, we focus on how to select an optimal query graph from a candidate set so as to retrieve the answer from knowledge base (KB). In our approach, we first propose to linearize the query graph into a sequence, which is used to form a sequence pair with the question. It allows us to use mature sequence modeling, such as BERT, to encode the sequence pair. Then we use a ranking method to sort candidate query graphs. In contrast to the previous studies, our approach can efficiently model semantic interactions between the graph and the question as well as rank the candidate graphs from a global view. The experimental results show that our system achieves the top performance on ComplexQuestions and the second best performance on WebQuestions.