  Event extraction (EE) plays an important role in many industrial application
scenarios, and high-quality EE methods require a large amount of manual
annotation data to train supervised learning models. However, the cost of
obtaining annotation data is very high, especially for annotation of domain
events, which requires the participation of experts from corresponding domain.
So we introduce active learning (AL) technology to reduce the cost of event
annotation. But the existing AL methods have two main problems, which make them
not well used for event extraction. Firstly, the existing pool-based selection
strategies have limitations in terms of computational cost and sample validity.
Secondly, the existing evaluation of sample importance lacks the use of local
sample information. In this paper, we present a novel deep AL method for EE. We
propose a batch-based selection strategy and a Memory-Based Loss Prediction
model (MBLP) to select unlabeled samples efficiently. During the selection
process, we use an internal-external sample loss ranking method to evaluate the
sample importance by using local information. Finally, we propose a delayed
training strategy to train the MBLP model. Extensive experiments are performed
on three domain datasets, and our method outperforms other state-of-the-art
methods.
