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.

提出了一种记忆性的层次增强网络 (Hierarchical Augmentation Networks, HANet) 框架，用于解决连续少样本事件检测任务，其中包括通过原型增强来记忆先前的事件类型以及通过对令牌表示进行对比性增强来学习新的事件类型。与其他方法和 ChatGPT 相比，实验证明我们的方法在多个连续少样本事件检测任务中表现显著优于所有这些方法。