基于数据增强和弱监督对抗训练的中文事件检测

当前的事件检测模型严重依赖于人工标注的数据，在标注数据规模有限的情况下，事件检测任务中基于完全监督方法的深度学习模型经常会出现过拟合的问题，而基于弱监督学习的使用自动标注数据代替耗时的人工标注数据的方法又常常依赖于复杂的预定义规则。为了解决上述问题，就中文事件检测任务提出了一种基于BERT的混合文本对抗训练（BMAD）方法。所提方法基于数据增强和对抗学习设定了弱监督学习场景，并采用跨度抽取模型来完成事件检测任务。首先，为改善数据不足的问题，采用回译、Mix-Text等数据增强方法来增强数据并为事件检测任务创建弱监督学习场景；然后，使用一种对抗训练机制进行噪声学习，力求最大限度地生成近似真实样本的生成样本，并最终提高整个模型的鲁棒性。在广泛使用的真实数据集自动文档抽取（ACE）2005上进行实验，结果表明相较于NPN、TLNN、HCBNN等算法，所提方法在F1分数上获取了至少0.84个百分点的提升。

Chinese event detection based on data augmentation and weakly supervised adversarial training

The existing event detection models rely heavily on human-annotated data， and supervised deep learning models for event detection task often suffer from over-fitting when there is only limited labeled data. Methods of replacing time-consuming human annotation data with auto-labeled data typically rely on sophisticated pre-defined rules. To address these issues， a BERT （Bidirectional Encoder Representations from Transformers） based Mix-text ADversarial training （BMAD） method for Chinese event detection was proposed. In the proposed method， a weakly supervised learning scene was set on the basis of data augmentation and adversarial learning， and a span extraction model was used to solve event detection task. Firstly， to relieve the problem of insufficient data， various data augmentation methods such as back-translation and Mix-Text were applied to augment data and create weakly supervised learning scene for event detection. And then an adversarial training mechanism was applied to learn with noise and improve the robustness of the whole model. Several experiments were conducted on commonly used real-world dataset Automatic Context Extraction （ACE） 2005. The results show that compared with algorithms such as Nugget Proposal Network （NPN）， Trigger-aware Lattice Neural Network （TLNN） and Hybrid-Character-Based Neural Network （HCBNN）， the proposed method has the F1 score improved by at least 0.84 percentage points.