  Dialogue relation extraction (DRE) that identifies the relations between
argument pairs in dialogue text, suffers much from the frequent occurrence of
personal pronouns, or entity and speaker coreference. This work introduces a
new benchmark dataset DialogRE^C+, introducing coreference resolution into the
DRE scenario. With the aid of high-quality coreference knowledge, the reasoning
of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we
manually annotate total 5,068 coreference chains over 36,369 argument mentions
based on the existing DialogRE data, where four different coreference chain
types namely speaker chain, person chain, location chain and organization chain
are explicitly marked. We further develop 4 coreference-enhanced graph-based
DRE models, which learn effective coreference representations for improving the
DRE task. We also train a coreference resolution model based on our annotations
and evaluate the effect of automatically extracted coreference chains
demonstrating the practicality of our dataset and its potential to other
domains and tasks.
