  Entity-relation extraction aims to jointly solve named entity recognition
(NER) and relation extraction (RE). Recent approaches use either one-way
sequential information propagation in a pipeline manner or two-way implicit
interaction with a shared encoder. However, they still suffer from poor
information interaction due to the gap between the different task forms of NER
and RE, raising a controversial question whether RE is really beneficial to
NER. Motivated by this, we propose a novel and unified cascade framework that
combines the advantages of both sequential information propagation and implicit
interaction. Meanwhile, it eliminates the gap between the two tasks by
reformulating entity-relation extraction as unified span-extraction tasks.
Specifically, we propose a selection recurrent network as a shared encoder to
encode task-specific independent and shared representations and design two
sequential information propagation strategies to realize the sequential
information flow between NER and RE. Extensive experiments demonstrate that our
approaches can achieve state-of-the-art results on two common benchmarks, ACE05
and SciERC, and effectively model the multi-task interaction, which realizes
significant mutual benefits of NER and RE.
