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.

本文介绍 DocRED 数据集，该数据集为文档级别中关系抽取 (Relation Extraction) 提供了一个新的方法，并提供了大规模的远程监督数据，以满足超级 / 弱监督训练需求。同时我们实验了目前最先进的方法，结果表明文档级别中关系抽取仍需要进一步的研究。