Relation classification aims to predict a relation between two entities in a sentence. The existing methods regard all relations as the candidate relations for the two entities in a sentence. These methods neglect the restrictions on candidate relations by entity types, which leads to some inappropriate relations being candidate relations. In this paper, we propose a novel paradigm, RElation Classification with ENtity Type restriction (RECENT), which exploits entity types to restrict candidate relations. Specially, the mutual restrictions of relations and entity types are formalized and introduced into relation classification. Besides, the proposed paradigm, RECENT, is model-agnostic. Based on two representative models GCN and SpanBERT respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT. Experimental results on a standard dataset indicate that RECENT improves the performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively. Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.

翻译：现有方法将所有关系视为两个实体在某一句中的候选关系。这些方法忽视了按实体类型对候选人关系的限制,从而导致一些不适当的关系成为候选关系。在本文件中,我们提出了一个新的范式,即将分类与ENTITY类型限制(RECENT)挂钩,利用实体类型限制候选人关系。特别是,将关系和实体类型的相互限制正式化,并引入关系分类。此外,拟议的模式,RECENT和SpanBERT是示范性。根据两个具有代表性的模式,分别是GCN和SpanBERT,REENT和RECENT_SpanBERT在RECENT接受培训。标准数据集的实验结果显示,RECENT分别通过6.9和4.4 F1点改进了GCN和SpanBERT的性能。特别是RECENT在TRED上取得了一个新的状态。