• Corpus ID: 248512779

DEGREE: A Data-Efficient Generation-Based Event Extraction Model

@inproceedings{Hsu2021DEGREEAD,
  title={DEGREE: A Data-Efficient Generation-Based Event Extraction Model},
  author={I-Hung Hsu and Kuan-Hao Huang and Elizabeth Boschee and Scott Miller and Premkumar Natarajan and Kai-Wei Chang and Nanyun Peng},
  year={2021}
}
Event extraction requires high-quality expert human annotations, which are usually expensive. Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge. In this paper, we focus on low-resource end-to-end event extraction and propose D EGREE , a data-efficient model that for-mulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, D EGREE learns to summarize… 
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