Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning

TL;DR

This paper introduces a contrastive learning framework that enhances language model embeddings to better encode relational and entity information, improving performance on relation extraction and NER tasks.

Contribution

It proposes a novel contrastive learning method to impose relation structures on sentence embeddings, achieving state-of-the-art results and enabling combined entity-relation representations.

Findings

State-of-the-art relation extraction with simple KNN classifier

Effective visualization of learned relation-aware embeddings

Successful integration of relation and entity spaces

Abstract

Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token-level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al.,2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.