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.

翻译：事件检测是信息抽取和知识图谱中的基础任务之一。然而，实际的事件检测系统通常需要持续应对新的事件类别。由于标注大量未标注实例耗时费力，这些新类别往往仅具备少量标注样本。为此，本文提出一项新任务——类别增量式少样本事件检测。该任务面临两大挑战：旧知识遗忘与新类别过拟合。针对上述问题，本文进一步提出一种基于知识蒸馏与提示学习的创新方法——Prompt-KD。具体而言，为解决旧知识遗忘问题，Prompt-KD构建了基于注意力的多教师知识蒸馏框架：祖先教师模型基于基类预训练，并在所有学习阶段复用；父教师模型通过自适应衍生出当前学生模型。另一方面，为应对少样本学习场景并缓解新类别过拟合问题，Prompt-KD还配备了提示学习机制。在FewEvent和MAVEN两个基准数据集上的大量实验表明，Prompt-KD具有优越性能。