Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance.

翻译：事件检测（Event Detection, ED）旨在识别非结构化文本中的关键触发词并据此预测事件类型。传统ED模型对数据需求过高，难以适应标注数据稀缺的真实应用场景。此外，典型ED模型面临因ED数据集中的触发词偏差导致的上下文绕过与泛化能力受限问题。为此，我们聚焦于真正少样本范式以满足低资源场景需求。具体而言，提出一种用于少样本事件检测去偏的多步提示学习模型（MsPrompt），包含以下三个组件：采用欠采样模块构建适配真正少样本设置的新型训练集；配备知识增强本体的多步提示模块，充分利用预训练语言模型（PLMs）中的事件语义与潜在先验知识以解决上下文绕过问题；原型模块补偿稀疏数据下事件分类的不足并提升泛化性能。在ACE-2005和FewEvent两个公开数据集上的实验表明，MsPrompt能够超越现有最优模型，尤其在严格低资源场景下，其加权F1分数相较最佳基线模型提升11.43%，并实现了优异的去偏性能。