Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction
Guoxuan Ding, Xiaobo Guo, Xin Wang, Lei Wang, Tianshu Fu, Nan Mu, Daren Zha
Abstract
Event Argument Extraction is a critical task of Event Extraction, focused on identifying event arguments within text. This paper presents a novel Fusion Selection-Generation-Based Approach, by combining the precision of selective methods with the semantic generation capability of generative methods to enhance argument extraction accuracy. This synergistic integration, achieved through fusion prompt, element-based extraction, and fusion learning, addresses the challenges of input, process, and output fusion, effectively blending the unique characteristics of both methods into a cohesive model. Comprehensive evaluations on the RAMS and WikiEvents demonstrate the model’s state-of-the-art performance and efficiency.
BibTeX
@inproceedings{ding-etal-2025-fusion,
title = "Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction",
author = "Ding, Guoxuan and
Guo, Xiaobo and
Wang, Xin and
Wang, Lei and
Fu, Tianshu and
Mu, Nan and
Zha, Daren",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.294/",
pages = "4359--4369"
}