Enhancing Question Understanding and Representation for Knowledge Base Relation Detection

Abstract

Relation detection is a key step in Knowledge Base Question Answering (KBQA), but far from solved due to the significant differences between questions and relations. Previous studies usually treat relation detection as a text matching task, and mainly focus on reducing the detection error with better representations of KB relations. However, the understanding of questions is also important since they are generally more varied. And the text pair representation requires improvement because KB relations are not always counterparts of questions. In this paper, we propose a novel system with enhanced question understanding and representation processes for KB relation detection (QURRD). We design a KBQA-specific slot filling module based on Bi-LSTM-CRF for question understanding. Besides, with two CNNs for modeling and matching text pairs respectively, QURRD obtains richer question-relation representations for semantic analysis, and achieves better performance through learning from multiple tasks. We conduct experiments on both single-relation (Simple-Questions) and multi-relation (WebQSP) benchmarks. Results show that QURRD is robust against the diversity of questions and outperforms the state-of-the-art system on both tasks.