Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.

翻译：事件检测任务能够从文本中快速检测事件，并为下游自然语言处理任务提供有力支持。此类方法大多只能检测固定预定义事件类别。若将其扩展至检测新类别而不丧失对旧类别的检测能力，则需要从零开始重新训练模型，成本高昂。增量学习可有效解决该问题，但要求新类别具有充足数据。然而实际场景中，新事件类别的高质量标注数据匮乏，导致难以获取足量数据用于模型训练。针对上述问题，我们定义了一个新任务——少样本增量事件检测，其核心是在有限数据下学习检测新事件类别的同时，尽可能保持对旧类别的检测能力。基于FewEvent数据集，我们为少样本增量事件检测任务构建了基准数据集IFSED，并提出两个基准方案IFSED-K与IFSED-KP。实验结果表明，本方法较基线方法取得更高F1值，且稳定性更优。