Prototypical attention network for few-shot relation classification with entity-aware embedding module

Relation classification (RC) identifies the semantic relation between entity pairs and plays a critical role in knowledge graph construction and knowledge graph completion. However, insufficient labeled instances of long-tail relations make the training of supervised and distant supervised (DS) relation classification models difficult. Few-shot RC is an effective solution to this problem. At present, metric-based few-shot RC models focus on the representation of relation prototypes and the interaction between instances, ignoring meaningful entity representation and the association of entities and other words in the instance. We propose a prototypical attention network with an entity-aware embedding module (PAN-EAEM) to solve this problem. Firstly, the entity-aware embedding module (EAEM) draws more attention to entity-related words to capture key features. This plug-and-play module can improve the performance of other metric-based models as well. Secondly, the prototypical attention network (PAN) decreases the influence of noise on relation prototype representation by reducing intra-class differences and inter-class ambiguities. Extensive experiments prove that our proposed model obtains state-of-the-art performance on the FewRel dataset.