论文标题
P4E：很少发生事件检测作为及时引导的识别和本地化
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
论文作者
论文摘要
我们提出了P4E，这是一个识别和统一的事件检测框架，该框架集成了最佳的少量提示和结构化预测。我们的框架将事件检测分解为标识任务和本地化任务。对于我们作为多标签分类制定的标识任务，我们利用基于披肩的促使我们的目标与语言模型的预训练任务保持一致，从而使我们的模型可以快速适应新的事件类型。然后，我们采用事件类型不合时宜的序列标记模型来本地化以标识输出为条件的事件触发器。这种异质模型设计使P4E可以快速学习新事件类型，而无需牺牲进行结构化预测的能力。我们的实验证明了我们提出的设计的有效性，P4E在基准数据集上显示了较高的射击事件检测性能，很少有Event和Maven和Maven，并且与SOTA可比性的性能与ACE完全监督的事件检测。
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 identification task and a localization task. For the identification task, which we formulate as multi-label classification, we leverage cloze-based prompting to align our objective with the pre-training task of language models, allowing our model to quickly adapt to new event types. We then employ an event type-agnostic sequence labeling model to localize the event trigger conditioned on the identification output. This heterogeneous model design allows P4E to quickly learn new event types without sacrificing the ability to make structured predictions. Our experiments demonstrate the effectiveness of our proposed design, and P4E shows superior performance for few-shot event detection on benchmark datasets FewEvent and MAVEN and comparable performance to SOTA for fully-supervised event detection on ACE.
