  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.
