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.

本研究提出一种基于强化学习的自适应有序信息提取框架，能够动态生成最佳提取顺序，并采用一种基于强化学习的协同训练框架来缓解提取器训练阶段的曝光偏差，并通过多个公共数据集的广泛实验表明，我们提出的方法能够击败以前的方法，特别是对于复杂的信息提取任务。

基于深度强化学习的自适应有序信息提取