Document-level relation extraction aims to extract relations among multiple
entity pairs from a document. Previously proposed graph-based or
transformer-based models utilize the entities independently, regardless of
global information among relational triples. This paper approaches the problem
by predicting an entity-level relation matrix to capture local and global
information, parallel to the semantic segmentation task in computer vision.
Herein, we propose a Document U-shaped Network for document-level relation
extraction. Specifically, we leverage an encoder module to capture the context
information of entities and a U-shaped segmentation module over the image-style
feature map to capture global interdependency among triples. Experimental
results show that our approach can obtain state-of-the-art performance on three
benchmark datasets DocRED, CDR, and GDA.

本研究提出基于 Entity-level Relation Matrix 与 U-shaped Network 的 Document-level Relation Extraction 模型，通过结合上下文信息以及全局关系，实现了在 DocRED，CDR 和 GDA 三个基准数据集上的最优性能。