Scrutinizing Label: Contrastive Learning on Label Semantics and Enriched Representation for Relation Extraction

Abstract

Sentence-level relation extraction is a technique for extracting factual information about relationships between entities from a sentence. However, the customary method overlooks the semantic information conveyed by the label itself, thereby compromising the efficacy of rare types. Furthermore, there is a growing interest in exploring the use of textual information as a crucial resource to enhance RE models for more effectiveness. To address these two issues, CLERE (Contrastive Learning and Enriched Representation for Relation Extraction) based on contrastive learning and enriched representation of context is proposed. Firstly, by contrastive learning to incorporate semantic information of labels, CLERE is able to effectively convey and exploit the underlying semantics of various sample categories. Thereby enhancing its semantics understanding and classification capabilities, the issue of misclassification due to data imbalance is alleviated. Secondly, both semantics of context and positional information of tagged entities are enhanced by employing weighted layer pooling on pre-trained language models, which improves the representation of context and entity mentions. Experiments are conducted on three public dataset to authenticate the effectiveness of CLERE. The results demonstrate that the proposed model outperforms existing mainstream baseline methods significantly.