  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.
