  Information extraction (IE) has been studied extensively. The existing
methods always follow a fixed extraction order for complex IE tasks with
multiple elements to be extracted in one instance such as event extraction.
However, we conduct experiments on several complex IE datasets and observe that
different extraction orders can significantly affect the extraction results for
a great portion of instances, and the ratio of sentences that are sensitive to
extraction orders increases dramatically with the complexity of the IE task.
Therefore, this paper proposes a novel adaptive ordered IE paradigm to find the
optimal element extraction order for different instances, so as to achieve the
best extraction results. We also propose an reinforcement learning (RL) based
framework to generate optimal extraction order for each instance dynamically.
Additionally, we propose a co-training framework adapted to RL to mitigate the
exposure bias during the extractor training phase. Extensive experiments
conducted on several public datasets demonstrate that our proposed method can
beat previous methods and effectively improve the performance of various IE
tasks, especially for complex ones.
