Relation Detection is a core component of Knowledge Base Question Answering (KBQA). In this paper, we propose a Transformer-based deep attentive semantic matching model (DAM), to identify the KB relations corresponding to the questions. The DAM is completely based on the attention mechanism and applies the fine-grained word-level attention to enhance the matching of questions and relations. On the basis of the DAM, we build a three-stage KBQA pipeline system. The experimental results on multiple benchmarks demonstrate that our DAM model outperforms previous methods on relation detection. In addition, our DAM-based KBQA system also achieves state-of-the-art results on multiple datasets.

关系检测是知识库问答（KBQA）的核心组件。在本文中，我们提出了一种基于Transformer的深度注意力语义匹配模型（DAM），以识别与问题对应的KB关系。DAM完全基于注意力机制，并应用细粒度的单词级注意力来增强问题和关系的匹配。在DAM的基础上，我们构建了一个三级KBQA管道系统。在多个基准上的实验结果表明，我们的DAM模型优于以前的关系检测方法。此外，我们基于DAM的KBQA系统还可以在多个数据集上实现最新的结果。