Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples. To mitigate these challenges, we propose a memory-based framework: Hierarchical Augmentation Networks (HANet). To memorize previous event types with limited memory, we incorporate prototypical augmentation into the memory set. For the issue of learning new event types in few-shot scenarios, we propose a contrastive augmentation module for token representations. Despite comparing with previous state-of-the-art methods, we also conduct comparisons with ChatGPT. Experiment results demonstrate that our method significantly outperforms all of these methods in multiple continual few-shot event detection tasks.

翻译：传统持续事件检测依赖大量标注数据进行训练，这在现实应用中往往难以实现。本文引入持续少样本事件检测（CFED）这一更常见的场景，即无法获取大量标注样本的情况。CFED任务具有挑战性，因为它需要记忆先前的事件类型，同时利用少量样本学习新的事件类型。为缓解这些难题，我们提出了一种基于记忆的框架：层级增强网络（HANet）。为在有限记忆容量下记忆先前事件类型，我们将原型增强引入记忆集。针对少样本场景下学习新事件类型的问题，我们提出了一种用于令牌表示的对比增强模块。除与先前最先进方法进行比较外，我们还与ChatGPT进行了对比。实验结果表明，我们的方法在多项持续少样本事件检测任务中显著优于所有这些方法。