BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework

TL;DR

This paper introduces BitCoin, a novel joint relational triple extraction framework using bidirectional tagging and supervised contrastive learning, significantly improving extraction accuracy and F1 scores over existing methods.

Contribution

The paper proposes a bidirectional tagging approach combined with supervised contrastive learning and a penalty term, addressing limitations of previous RTE methods and enhancing extraction performance.

Findings

Achieves state-of-the-art results on benchmark datasets.

Significantly improves F1 scores on multiple relation extraction tasks.

Effectively considers multiple positives and prevents excessive similarity in contrastive learning.

Abstract

Relation triple extraction (RTE) is an essential task in information extraction and knowledge graph construction. Despite recent advancements, existing methods still exhibit certain limitations. They just employ generalized pre-trained models and do not consider the specificity of RTE tasks. Moreover, existing tagging-based approaches typically decompose the RTE task into two subtasks, initially identifying subjects and subsequently identifying objects and relations. They solely focus on extracting relational triples from subject to object, neglecting that once the extraction of a subject fails, it fails in extracting all triples associated with that subject. To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. Specifically, we design a supervised contrastive learning method that considers multiple positives per anchor rather than restricting it to just one positive. Furthermore, a penalty term is introduced to prevent excessive similarity between the subject and object. Our framework implements taggers in two directions, enabling triples extraction from subject to object and object to subject. Experimental results show that BitCoin achieves state-of-the-art results on the benchmark datasets and significantly improves the F1 score on Normal, SEO, EPO, and multiple relation extraction tasks.