KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt
Event Extraction (EE) is a key task in information extraction, which requires high-quality annotated data that are often costly to obtain. Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations. While recent approac...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020036 |
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| Summary: | Event Extraction (EE) is a key task in information extraction, which requires high-quality annotated data that are often costly to obtain. Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations. While recent approaches have endeavored to address EE through a more data-efficient generative process, they often overlook event keywords, which are vital for EE. To tackle these challenges, we introduce KeyEE, a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE). We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language model. With the auxiliary sub-prompt, KeyEE learns event keywords knowledge implicitly, thereby reducing the dependence on annotated data. Furthermore, we investigate and analyze various EKE sub-prompt strategies to encourage further research in this area. Our experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results. |
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| ISSN: | 2096-0654 |