基于语言学扰动的事件检测数据增强方法

Linguistic Perturbation Based Data Augmentation for Event Detection

近年来,深度学习在事件检测领域取得了长足进展。但是,现有方法通常受制于事件检测标注数据的规模和训练阶段的不稳定性。针对上述问题,本文提出了基于语言学扰动的事件检测数据增强方法,从语法和语义两个角度生成伪数据来提升事件检测的性能。为了有效的利用生成的伪数据,该文探索了数据增加和多实例学习两个训练策略。在KBP 2017事件检测数据集上的实验验证了我们方法的有效性。此外,在人工构造的少量ACE2005数据集上的实验结果证明该文方法可以大幅度提升小数据情况下的模型学习性能。

Deep learning recently applied in the event detection task is limited by the scarcity of the annotated data and the instability during the training phase.This paper proposes a data augmentation method based on linguistic perturbation for event detection,which generates pseudo data from both syntactic and semantic perspectives to improve the performance of event detection systems.In order to effectively exploit generated pseudo data,this paper explores two training strategies:data addition and multi-instance learning.Experiments on the KBP 2017 event detection dataset demonstrate the effectiveness of our approach.Furthermore,the empirical results on a manual constructed portion of ACE2005 dataset show that the proposed method can significantly improve the model performance on small training data.