  Joint entity and relation extraction (JERE) is one of the most important
tasks in information extraction. However, most existing works focus on
sentence-level coarse-grained JERE, which have limitations in real-world
scenarios. In this paper, we construct a large-scale document-level
fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained
Entity Type. Specifically, we redesign a hierarchical entity type schema
including 11 coarse-grained types and 119 fine-grained types, and then
re-annotate DocRED manually according to this schema. Through comprehensive
experiments we find that: (1) DocRED-FE is challenging to existing JERE models;
(2) Our fine-grained entity types promote relation classification. We make
DocRED-FE with instruction and the code for our baselines publicly available at
https://github.com/PKU-TANGENT/DOCRED-FE.
