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DEGREE: A Data-Efficient Generation-Based Event Extraction Model. (arXiv:2108.12724v3 [cs.CL] UPDATED)
Web: http://arxiv.org/abs/2108.12724
May 5, 2022, 1:11 a.m. | I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
cs.CL updates on arXiv.org arxiv.org
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 DEGREE, a data-efficient model that formulates event extraction as
a conditional generation problem. Given a passage and a manually designed
prompt, DEGREE learns to summarize the events mentioned in the passage into a
natural sentence …
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