Multi-view multitask learning for knowledge base relation detection

摘要

Relation detection is a key component of knowledge base question answering (KBQA). Existing methods mainly focus on learning semantic relevance between the question and the candidate relation, which is challenging due to the lexical variation (i.e. lexical gaps) especially for relations with few annotated samples. In this paper, we propose to estimate the semantic relevance from both the traditional question–relation view and a novel question–question view, which leverages the similarities among questions corresponding to the same relation. The question–question view can facilitate the learning of few-shot relations, and it supports accessing the annotated samples during inference, thus allowing new annotations to take effect on–the–fly. Moreover, a multi-task learning framework is devised to jointly optimize the models of different views. Experimental results on WebQSP and a Chinese KB relation detection dataset demonstrate the effectiveness and generalization ability of the proposal.