  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.
