  Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.
