Class-Incremental Few-Shot Event Detection
Kailin Zhao Xiaolong Jin Long Bai Jiafeng Guo Xueqi Cheng
Event detection is one of the fundamental tasks in information extraction and
knowledge graph. However, a realistic event detection system often needs to
deal with new event classes constantly. These new classes usually have only a
few labeled instances as it is time-consuming and labor-intensive to annotate a
large number of unlabeled instances. Therefore, this paper proposes a new task,
called class-incremental few-shot event detection. Nevertheless, this task
faces two problems, i.e., old knowledge forgetting and new class overfitting.
To solve these problems, this paper further presents a novel knowledge
distillation and prompt learning based method, called Prompt-KD. Specifically,
to handle the forgetting problem about old knowledge, Prompt-KD develops an
attention based multi-teacher knowledge distillation framework, where the
ancestor teacher model pre-trained on base classes is reused in all learning
sessions, and the father teacher model derives the current student model via
adaptation. On the other hand, in order to cope with the few-shot learning
scenario and alleviate the corresponding new class overfitting problem,
Prompt-KD is also equipped with a prompt learning mechanism. Extensive
experiments on two benchmark datasets, i.e., FewEvent and MAVEN, demonstrate
the superior performance of Prompt-KD.