Document-level Relation Extraction (DocRE) is a more challenging task
compared to its sentence-level counterpart. It aims to extract relations from
multiple sentences at once. In this paper, we propose a semi-supervised
framework for DocRE with three novel components. Firstly, we use an axial
attention module for learning the interdependency among entity-pairs, which
improves the performance on two-hop relations. Secondly, we propose an adaptive
focal loss to tackle the class imbalance problem of DocRE. Lastly, we use
knowledge distillation to overcome the differences between human annotated data
and distantly supervised data. We conducted experiments on two DocRE datasets.
Our model consistently outperforms strong baselines and its performance exceeds
the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard.
Our code and data will be released at this https URL.

本文提出了一个半监督框架，包含三个新组件，针对文档级关系抽取任务进行优化，通过轴向注意力模块学习实体对之间的相互依赖关系、自适应 focal loss 解决 DocRE 的类别不平衡问题，以及使用知识蒸馏方法克服人工标注数据与远程监督数据之间的差异。在两个 DocRE 数据集上的实验证明我们的模型表现超过了强基线模型，并且在 DocRED 排行榜上的 F1 分数和 Ign_F1 分数分别增加了 1.36 和 1.46 分。