Double Graph Based Reasoning for Document-level Relation Extraction

Abstract

Document-level relation extraction aims to extract relations among entities within a document.Different from sentence-level relation extraction, it requires reasoning over multiple sentences across paragraphs.In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs.GAIN constructs two graphs, a heterogeneous mentionlevel graph (MG) and an entity-level graph (EG).The former captures complex interaction among different mentions and the latter aggregates mentions underlying for the same entities.Based on the graphs we propose a novel path reasoning mechanism to infer relations between entities.Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art.Our code is available at https://github.com/