Document-level Relation Extraction (RE) is a more challenging task than
sentence RE as it often requires reasoning over multiple sentences. Yet, human
annotators usually use a small number of sentences to identify the relationship
between a given entity pair. In this paper, we present an embarrassingly simple
but effective method to heuristically select evidence sentences for
document-level RE, which can be easily combined with BiLSTM to achieve good
performance on benchmark datasets, even better than fancy graph neural network
based methods. We have released our code at
this https URL

该论文提出了一种简单而有效的文档级关系抽取方法，可结合 BiLSTM 使用少量证据句子识别给定实体对之间的关系，并在基准数据集上取得了优异的性能，甚至优于基于图神经网络的方法。

只需三句话：本地路径增强的文档关系提取

Three Sentences Are All You Need: Local Path Enhanced Document Relation  Extraction

Document-level relation extraction (DocRE) models generally use graph
networks to implicitly model the reasoning skill (i.e., pattern recognition,
logical reasoning, coreference reasoning, etc.) related to the relation between
one entity pair in a document. In this paper, we propose a novel discriminative
reasoning framework to explicitly model the paths of these reasoning skills
between each entity pair in this document. Thus, a discriminative reasoning
network is designed to estimate the relation probability distribution of
different reasoning paths based on the constructed graph and vectorized
document contexts for each entity pair, thereby recognizing their relation.
Experimental results show that our method outperforms the previous
state-of-the-art performance on the large-scale DocRE dataset. The code is
publicly available at this https URL

本文提出了一种新颖的判别推理框架来显式地建模文档中每对实体对之间的推理路径，并利用构建的图形和每个实体对的矢量化文档上下文来估计不同的推理路径的关系概率分布，从而识别它们的关系，实验证明我们的方法在大规模 DocRE 数据集上优于之前的最优表现。