论文标题
EPPAC：实体前类型的关系分类，并及时答案
EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing
论文作者
论文摘要
关系分类（RC）旨在预测给定上下文中一对主体和对象之间的关系。最近，及时的调整方法在RC中取得了高性能。但是，现有的及时调整方法存在以下问题：（1）许多类别降低了RC性能； （2）手动设计的提示需要大量劳动。为了解决这些问题，本文提出了一种新颖的范式，实体前的关系分类（EPPAC）。提出了EPPAC中的实体tying tying，以使用双层框架来解决第一个问题，该框架在RC之前预先类型实体并提出提示回答集体化以解决第二期。广泛的实验表明，我们提出的EPPAC在Tacred和Tacrev上的最先进方法分别超过了14.4％和11.1％。该代码在补充材料中提供。
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.
