  We target on the document-level relation extraction in an end-to-end setting,
where the model needs to jointly perform mention extraction, coreference
resolution (COREF) and relation extraction (RE) at once, and gets evaluated in
an entity-centric way. Especially, we address the two-way interaction between
COREF and RE that has not been the focus by previous work, and propose to
introduce explicit interaction namely Graph Compatibility (GC) that is
specifically designed to leverage task characteristics, bridging decisions of
two tasks for direct task interference. Our experiments are conducted on DocRED
and DWIE; in addition to GC, we implement and compare different multi-task
settings commonly adopted in previous work, including pipeline, shared
encoders, graph propagation, to examine the effectiveness of different
interactions. The result shows that GC achieves the best performance by up to
2.3/5.1 F1 improvement over the baseline.
