DocRED: A Large-Scale Document-Level Relation Extraction Dataset

Yao, Yuan, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. “DocRED: A Large-Scale Document-Level Relation Extraction Dataset.” In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, 764–77. Florence, Italy: Association for Computational Linguistics, 2019. [1](https://doi.org/10.18653/v1/P19-1074).

Abstract

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. Empirical results show that DocRED is challenging for existing RE methods, which indicates that document-level RE remains an open problem and requires further efforts. Based on the detailed analysis on the experiments, we discuss multiple promising directions for future research. We make DocRED and the code for our baselines publicly available at https://github.com/thunlp/DocRED.

总结和评论

作者发布了一个面向文档级别关系抽取任务的大规模数据集：DocRED。该数据集是基于Wikipedia和Wikidata，同时提供了人工标注数据集和远程监督标注数据集以支持不同的使用场景。作者在DocRED数据集上评估了当时最先进的几个关系提取方法。