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.
翻译:本文介绍了一种基于语义解析的新方法,目的是改进知识库问题解答(KBQA)的性能。 具体而言,我们侧重于如何从候选人组中选择一个最佳查询图,以便从知识库(KB)中获取答案。在我们的方法中,我们首先建议将查询图线性化成一个序列,用来形成与问题相配的序列。它使我们能够使用成熟的序列模型(如BERT)来编码序列配对。然后我们使用排序方法来排序候选人查询图。与以往的研究相比,我们的方法可以有效地模拟图表与问题之间的语义互动,并从全球角度对候选人图表进行排序。实验结果显示,我们的系统在复杂问题上取得了顶级性,在网络问题上取得了第二最佳的性能。