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.

翻译：关系三元组抽取（RTE）是信息抽取与知识图谱构建中的核心任务。尽管近年来取得进展，现有方法仍存在若干局限：它们仅采用通用预训练模型，而未考虑RTE任务的特殊性；同时，现有基于标记的方法通常将RTE任务分解为两个子任务——先识别主体再识别客体与关系，仅从主体到客体方向抽取关系三元组，一旦主体抽取失败，与该主体相关的所有三元组抽取将完全失效。为解决这些问题，我们提出比特币（BitCoin）——一种创新的基于双向标记与监督对比学习的联合关系三元组抽取框架。具体而言，我们设计了充分考虑每个锚点对应多个正样本（而非仅限单个正样本）的监督对比学习方法，通过引入惩罚项防止主体与客体间过度相似。该框架通过双向标记器实现从主体到客体及从客体到主体的双向三元组抽取。实验结果表明，比特币（BitCoin）在基准数据集上达到最优性能，在常规关系（Normal）、单实体参与多关系（SEO）、实体对重叠（EPO）及多关系抽取任务中显著提升了F1值。