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arXiv · 2203.00193

EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing

Jiejun Tan·Wenbin Hu·WeiWei Liu
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Abstract

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.

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Jiejun Tan, Wenbin Hu, WeiWei Liu. 2022-03-01. EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing. https://arxiv.org/abs/2203.00193

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