We propose P4E, an identify-and-localize event detection framework that integrates the best of few-shot prompting and structured prediction. Our framework decomposes event detection into an unstructured identification task and a structured localization task. For the unstructured identification task, we leverage prompting to elicit knowledge from pretrained language models, allowing our model to adapt to new event types quickly. We then employ a type-agnostic sequence labeling model to localize the event trigger conditioned on the identification output. This heterogeneous model design allows P4E to make fast adaptation without sacrificing the ability to make structured predictions. Our experiments demonstrate the effectiveness of our proposed design, and P4E achieves the new state-of-the-art on few-shot entity detection across multiple datasets.

翻译：我们建议P4E, 是一个识别和定位事件检测框架, 整合了最精密的微小提示和结构化预测。 我们的框架将事件检测分解为非结构化的识别任务和结构化的本地化任务。 对于未结构化的识别任务, 我们利用激励从预先培训的语言模型中获取知识, 使我们的模型能够快速适应新事件类型。 然后我们使用类型、 不可知的序列标签模型, 将以识别输出为条件的事件触发点本地化。 这个混杂模型的设计使P4E可以在不牺牲结构化预测能力的情况下快速适应。 我们的实验展示了我们拟议设计的有效性, P4E 实现了对多个数据集的微小实体探测的新状态。