BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based
  Joint Relational Triple Extraction Framework
Luyao He Zhongbao Zhang Sen Su Yuxin Chen
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.