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

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