  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 a document. In this paper, we
propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs.
GAIN first constructs a heterogeneous mention-level graph (hMG) to model
complex interaction among different mentions across the document. It also
constructs an entity-level graph (EG), based on which 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/DreamInvoker/GAIN .
