Document-level Relation Extraction (DRE) aims to recognize the relations
between two entities. The entity may correspond to multiple mentions that span
beyond sentence boundary. Few previous studies have investigated the mention
integration, which may be problematic because coreferential mentions do not
equally contribute to a specific relation. Moreover, prior efforts mainly focus
on reasoning at entity-level rather than capturing the global interactions
between entity pairs. In this paper, we propose two novel techniques, Context
Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve the
DRE. Instead of simply applying average pooling, the contexts are utilized to
guide the integration of coreferential mentions in a weighted sum manner.
Additionally, inter-pair reasoning executes an iterative algorithm on the
entity pair graph, so as to model the interdependency of relations. We evaluate
our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR,
and GDA. Experimental results show that our model outperforms previous
state-of-the-art models.

本文提出了基于上下文的提及整合和实体间推理两种技术，以改善文档级关系抽取。在三个基准数据集上的实验结果表明，我们的模型胜过了先前的最先进模型。