DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction
Gang Zhao, Xiaocheng Gong, Xinjie Yang, Guanting Dong, Shudong Lu, Si Li · 2023
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 Demonstrationenhanced Schema-guided Generation (De-moSG) 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 schemabased prompts, thereby leveraging label semantics and promoting knowledge transfer in lowresource 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.