  We present a joint model for entity-level relation extraction from documents.
In contrast to other approaches - which focus on local intra-sentence mention
pairs and thus require annotations on mention level - our model operates on
entity level. To do so, a multi-task approach is followed that builds upon
coreference resolution and gathers relevant signals via multi-instance learning
with multi-level representations combining global entity and local mention
information. We achieve state-of-the-art relation extraction results on the
DocRED dataset and report the first entity-level end-to-end relation extraction
results for future reference. Finally, our experimental results suggest that a
joint approach is on par with task-specific learning, though more efficient due
to shared parameters and training steps.
