论文标题
更好的查询图选择知识基础问题回答
Better Query Graph Selection for Knowledge Base Question Answering
论文作者
论文摘要
本文介绍了一种基于语义解析的新方法，以提高知识基础问题的表现（KBQA）。具体来说，我们专注于如何从候选设置中选择最佳查询图，以从知识库（KB）检索答案。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，该序列用于与该问题形成序列对。它允许我们使用成熟的序列建模（例如BERT）编码序列对。然后，我们使用排名方法来对候选查询图进行排序。与以前的研究相反，我们的方法可以有效地对图和问题之间的语义相互作用进行建模，并从全局视图对候选图进行排名。实验结果表明，我们的系统在复杂问题上达到了最高的性能，并且是网络问题上的第二好的性能。
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.
