A prototype evolution network for relation extraction

Abstract

Prototypical networks transform relation instances and relation types into the same semantic space, where a relation instance is assigned a type based on the nearest prototype. Traditional prototypical network methods generate relation prototypes by averaging the sentence representations from a predefined support set, which suffers from two key limitations. One limitation is sensitive to the outliers in the support set that can skew the relation prototypes. Another limitation is the lack of the necessary representational capacity to capture the full complexity of the relation extraction task. To address these limitations, we propose the Prototype Evolution Network (PEN) for relation extraction. First, we assign a type cue for each relation instance to mine the semantics of the relation type. Based on the type cues and relation instances, we then present a prototype refiner comprising a multichannel convolutional neural network and a scaling module to learn and refine the relation prototypes. Finally, we introduce historical prototypes during each episode into the current prototype learning process to enable continuous prototype evolution. We evaluate the PEN on the ACE 2005, SemEval 2010, and CoNLL2004 datasets, and the results demonstrate impressive improvements, with the PEN outperforming existing state-of-the-art methods.