Phrase-level attention network for few-shot inverse relation classification in knowledge graph

Abstract

Relation classification aims to recognize semantic relation between two given entities mentioned in the given text. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops significantly for few-shot learning. In this paper, we propose a Phrase-level Attention Network, function words adaptively enhanced attention framework (FAEA+), to attend class-related function words by the designed hybrid attention for few-shot inverse relation classification in Knowledge Graph. Then, an instance-aware prototype network is present to adaptively capture relation information associated with query instances and eliminate intra-class redundancy due to function words introduced. We theoretically prove that the introduction of function words will increase intra-class differences, and the designed instance-aware prototype network is competent for reducing redundancy. Experimental results show that FAEA+ significantly improved over strong baselines on two few-shot relation classification datasets. Moreover, our model has a distinct advantage in solving inverse relations, which outperforms state-of-the-art results by 16.82% under a 1-shot setting in FewRel1.0.