Papers with low-resource end-to-end event extraction
DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
| Approach: | They propose a data-efficient event extraction model that formulates event extraction as a conditional generation problem. |
| Outcome: | The proposed model can be trained with only a few labeled examples. |