Document-level relation extraction (DocRE) is the task of identifying all
relations between each entity pair in a document. Evidence, defined as
sentences containing clues for the relationship between an entity pair, has
been shown to help DocRE systems focus on relevant texts, thus improving
relation extraction. However, evidence retrieval (ER) in DocRE faces two major
issues: high memory consumption and limited availability of annotations. This
work aims at addressing these issues to improve the usage of ER in DocRE.
First, we propose DREEAM, a memory-efficient approach that adopts evidence
information as the supervisory signal, thereby guiding the attention modules of
the DocRE system to assign high weights to evidence. Second, we propose a
self-training strategy for DREEAM to learn ER from automatically-generated
evidence on massive data without evidence annotations. Experimental results
reveal that our approach exhibits state-of-the-art performance on the DocRED
benchmark for both DocRE and ER. To the best of our knowledge, DREEAM is the
first approach to employ ER self-training.

本文针对文档级别关系抽取中证据检索面临的内存消耗高和注释有限的问题，提出了 DREEAM 方法，其采用内存高效的方式指导 DocRE 系统将注意力集中于证据上并采用自学习策略从大量数据中自动生成证据，实验证明该方法在 DocRE 和 ER 方面取得了最先进的性能。