Document-level relation extraction (DocRE) aims to extract semantic relations
among entity pairs in a document. Typical DocRE methods blindly take the full
document as input, while a subset of the sentences in the document, noted as
the evidence, are often sufficient for humans to predict the relation of an
entity pair. In this paper, we propose an evidence-enhanced framework, Eider,
that empowers DocRE by efficiently extracting evidence and effectively fusing
the extracted evidence in inference. We first jointly train an RE model with a
lightweight evidence extraction model, which is efficient in both memory and
runtime. Empirically, even training the evidence model on silver labels
constructed by our heuristic rules can lead to better RE performance. We
further design a simple yet effective inference process that makes RE
predictions on both extracted evidence and the full document, then fuses the
predictions through a blending layer. This allows Eider to focus on important
sentences while still having access to the complete information in the
document. Extensive experiments show that Eider outperforms state-of-the-art
methods on three benchmark datasets (e.g., by 1.37/1.26 Ign F1/F1 on DocRED).

本文提出了一种证据增强的框架 Eider，该框架采用有效提取证据的方法并通过融合层推断有效结合提取的证据和完整文档，从而优化了基于整篇文档的关系抽取方法，实验证明 Eider 在三个基准数据集上优于现有最先进方法（例如 DocRED 上 Ign F1/F1 提高了 1.37/1.26）