MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction

Abstract

Document-level relation extraction aims to detect the relations within one document, which is challenging since it requires complex reasoning using mentions, entities, local and global contexts. Few previous studies have distinguished local and global reasoning explicitly, which may be problematic because they play different roles in intra-and inter-sentence relations. Moreover, the interactions between local and global contexts should be considered since they could help relation reasoning based on our observation. In this paper, we propose a novel mention-based reasoning (MRN) module based on explicitly and collaboratively local and global reasoning. Based on MRN, we design a co-predictor module to predict entity relations based on local and global entity and relation representations jointly. We evaluate our MRN model on three widelyused benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous state-ofthe-art models by a large margin.