Most current Event Extraction (EE) methods focus on the high-resource scenario, which requires a large amount of annotated data and can hardly be applied to low-resource domains. To address EE more effectively with limited resources, we propose the Demonstration-enhanced Schema-guided Generation (DemoSG) model, which benefits low-resource EE from two aspects: Firstly, we propose the demonstration-based learning paradigm for EE to fully use the annotated data, which transforms them into demonstrations to illustrate the extraction process and help the model learn effectively. Secondly, we formulate EE as a natural language generation task guided by schema-based prompts, thereby leveraging label semantics and promoting knowledge transfer in low-resource scenarios. We conduct extensive experiments under in-domain and domain adaptation low-resource settings on three datasets, and study the robustness of DemoSG. The results show that DemoSG significantly outperforms current methods in low-resource scenarios.

翻译：当前大多数事件抽取方法聚焦于高资源场景，需要大量标注数据且难以应用于低资源领域。为更有效地在资源受限条件下解决事件抽取问题，我们提出了演示增强模式引导生成（DemoSG）模型，该模型从两方面提升低资源事件抽取性能：首先，我们提出基于演示学习的事件抽取范式，通过将标注数据转化为演示实例来阐明抽取过程，从而充分挖掘标注数据价值并帮助模型高效学习；其次，我们将事件抽取建模为基于模式提示的自然语言生成任务，借助标签语义实现低资源场景下的知识迁移。我们在三个数据集上开展了领域内与领域适应低资源场景的广泛实验，并对DemoSG的鲁棒性进行了研究。结果表明，DemoSG在低资源场景下显著优于当前方法。