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：句子级关系抽取旨在识别给定句子中两个实体之间的关系。现有工作主要侧重于获取更优的实体表示，并采用多标签分类器进行关系抽取。这些方法的一个主要局限性在于忽略了背景关系知识以及实体类型与候选关系之间的关联性。本文提出了一种新范式——基于描述性关系提示的对比学习（CTL-DRP），联合考量实体信息、关系知识及实体类型约束。具体而言，我们在生成上下文嵌入时引入改进的实体标记与描述性关系提示，并利用对比学习对受限候选关系进行排序。CTL-DRP在TACRED数据集上取得了具有竞争力的76.7% F1分数。此外，该新范式在TACREV和Re-TACRED上分别达到85.8%和91.6%的F1分数，均为当前最优性能。