@article{Duan2024, 
author = {Junwen Duan and Xincheng Liao and Ying An and Jianxin Wang},
title = {KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt},
year = {2024},
journal = {Big Data Mining and Analytics},
volume = {7},
number = {2},
pages = {547-560},
keywords = {natural language processing, Event Extraction (EE), Multi-Prompt Learning (MPL), low-resource},
url = {https://www.sciopen.com/article/10.26599/BDMA.2023.9020036},
doi = {10.26599/BDMA.2023.9020036},
abstract = {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.}
}