TR-Net: Token Relation Inspired Table Filling Network for Joint Entity and Relation Extraction

Abstract

Recently, table filling models have achieved promising performance in jointly extracting relation triplets from complex sentences, leveraging their inherent structural advantage of delineating entities and relations as table cells. Nonetheless, these models predominantly concentrate on the cells corresponding to entity pairs within the predicted tables, neglecting the interrelations among other token pairs. This oversight can potentially lead to the exclusion of essential token information. To address these challenges, we introduce the Token Relation-Inspired Network (TR-Net), a novel framework for the joint extraction of entities and relations. It encompasses a token relation generator that adaptively constructs a token relation table, concentrating on the prominent token cells. Moreover, it also uses a structure-enhanced encoder that integrates the structural and sequential data of sentences via a highway gate mechanism. Our experimental analysis demonstrates that TR-Net delivers considerable enhancements and achieves state-of-the-art performance on four public datasets.