Sentence-level relation extraction aims to identify the relation between two entities for a given sentence. The existing works mostly focus on obtaining a better entity representation and adopting a multi-label classifier for relation extraction. A major limitation of these works is that they ignore background relational knowledge and the interrelation between entity types and candidate relations. In this work, we propose a new paradigm, Contrastive Learning with Descriptive Relation Prompts(CTL-DRP), to jointly consider entity information, relational knowledge and entity type restrictions. In particular, we introduce an improved entity marker and descriptive relation prompts when generating contextual embedding, and utilize contrastive learning to rank the restricted candidate relations. The CTL-DRP obtains a competitive F1-score of 76.7% on TACRED. Furthermore, the new presented paradigm achieves F1-scores of 85.8% and 91.6% on TACREV and Re-TACRED respectively, which are both the state-of-the-art performance.
翻译:句子级关系提取旨在识别给定句子中两个实体之间的关系。现有研究大多专注于获得更好的实体表示,并采用多标签分类器进行关系提取。这些工作的一个主要局限性是忽略背景关系知识和实体类型与候选关系之间的相互关系。本文提出了一种新的范式,即基于描述性关系提示的对比学习(CTL-DRP),以共同考虑实体信息,关系知识和实体类型限制。特别地,在生成上下文嵌入时,我们引入了改进的实体标记和描述性关系提示,并利用对比学习来排列受限制的候选关系。 CTL-DRP在TACRED上获得了竞争力的F1分数76.7%。此外,新提出的范式分别在TACREV和Re-TACRED上分别获得了85.8%和91.6%的F1分数,这两个分数均是最先进的表现。