The DocRED dataset is one of the most popular and widely used benchmarks for
document-level relation extraction (RE). It adopts a recommend-revise
annotation scheme so as to have a large-scale annotated dataset. However, we
find that the annotation of DocRED is incomplete, i.e., false negative samples
are prevalent. We analyze the causes and effects of the overwhelming false
negative problem in the DocRED dataset. To address the shortcoming, we
re-annotate 4,053 documents in the DocRED dataset by adding the missed relation
triples back to the original DocRED. We name our revised DocRED dataset
Re-DocRED. We conduct extensive experiments with state-of-the-art neural models
on both datasets, and the experimental results show that the models trained and
evaluated on our Re-DocRED achieve performance improvements of around 13 F1
points. Moreover, we conduct a comprehensive analysis to identify the potential
areas for further improvement. Our dataset is publicly available at
this https URL.

本文针对 DocRED 数据集中占主导地位的漏报问题进行了研究，并通过重新注释数据集来修复该问题，重新命名为 Re-DocRED 数据集，同时实验结果表明使用 Re-DocRED 数据集进行训练和评估可以获得 13 个 F1 分数的性能提升。