DAM: Transformer-based relation detection for Question Answering over Knowledge Base

摘要

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.