Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
Abstract
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 https://github.com/tonytan48/Re-DocRED.
Keywords
Cite
@article{arxiv.2205.12696,
title = {Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction},
author = {Qingyu Tan and Lu Xu and Lidong Bing and Hwee Tou Ng and Sharifah Mahani Aljunied},
journal= {arXiv preprint arXiv:2205.12696},
year = {2023}
}
Comments
Accepted by EMNLP 2022