CorefDRE: Coref-Aware Document-Level Relation Extraction

Document-level Relation Extraction (Doc-level RE) aims to extract relations among entities from a document, which requires reasoning over multiple sentences. The pronouns are ubiquitous in the document, which can provide reasoning clues for Doc-level RE. However, previous works do not take the pronouns into account. In this paper, we propose Coref-aware Doc-level RE based on Graph Inference Network (CorefDRE) to infer relations. CorefDRE first dynamically constructs the heterogeneous Mention-Pronoun Affinity Graph (MPAG) by integrating coreference information of pronouns. Then, Entity Graph (EG) is aggregated from MPAG through the weight of mention-pronoun pairs, calculated by the noise suppression mechanism, and GCN. Finally, we infer relations between entities based the normalized EG. Moreover, We introduce the noise suppression mechanism via calculating affinity between pronouns and corresponding mentions to filter the noise caused by pronouns. Experimental results significantly outperform baselines by nearly 1.7-2.0 in F1 on three public datasets, DocRED, DialogRE, and MPDD. We further conduct ablation experiments to demonstrate the effectiveness of the proposed MPAG structure and the noise suppression mechanism.