Continual Few-shot Event Detection via Hierarchical Augmentation
  Networks
Chenlong Zhang Pengfei Cao Yubo Chen Kang Liu Zhiqiang Zhang Mengshu Sun Jun Zhao
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.