DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource
  Event Extraction
Gang Zhao Xiaocheng Gong Xinjie Yang Guanting Dong Shudong Lu Si Li
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.