Abstract: Event Argument Classification (EAC) is an essential subtask of event extraction. Most previous supervised models rely on costly annotations, and reducing the demand for computa-tional and data resources in resource-constrained environments is a significant challenge within the field. We propose a Zero-Shot EAC model, ALBERT-F, which leverages the efficiency of the ALBERT architecture combined with the Flash-Attention mechanism. This novel integration aims to address the limita-tions of traditional EAC methods, which often require extensive manual annotations and significant computational resources. The ALBERT-F model simplifies the design by factorizing embedding parameters, while Flash-Attention enhances computational speed and reduces memory access overhead. With the addition of global constraints and prompting, ALBERT-F improves the generaliz-ability of the model to unseen events. Our experiments on the ACE dataset show that ALBERT-F outperforms the Zero-shot BERT baseline by achieving at least a 3.4% increase in F1 score. Moreover, the model demonstrates a substantial reduction in GPU memory consumption by 75.1% and processing time by 33.3%, underscoring its suitability for environments with constrained resources.

Tongyue Sun and Jiayi Xiao, “Optimizing Low-Resource Zero-Shot Event Argument Classification with Flash-Attention and Global Constraints Enhanced ALBERT Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150802

@article{Sun2024,
title = {Optimizing Low-Resource Zero-Shot Event Argument Classification with Flash-Attention and Global Constraints Enhanced ALBERT Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150802},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150802},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Tongyue Sun and Jiayi Xiao}
}