  Relation classification (RC) aims to predict the relationship between a pair
of subject and object in a given context. Recently, prompt tuning approaches
have achieved high performance in RC. However, existing prompt tuning
approaches have the following issues: (1) numerous categories decrease RC
performance; (2) manually designed prompts require intensive labor. To address
these issues, a novel paradigm, Entity Pre-typing Relation Classification with
Prompt Answer Centralizing(EPPAC) is proposed in this paper. The entity
pre-tying in EPPAC is presented to address the first issue using a double-level
framework that pre-types entities before RC and prompt answer centralizing is
proposed to address the second issue. Extensive experiments show that our
proposed EPPAC outperformed state-of-the-art approaches on TACRED and TACREV by
14.4% and 11.1%, respectively. The code is provided in the Supplementary
Materials.
