@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",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction
%A Ding, Guoxuan
%A Guo, Xiaobo
%A Wang, Xin
%A Wang, Lei
%A Fu, Tianshu
%A Mu, Nan
%A Zha, Daren
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ding-etal-2025-fusion
%X 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.
%U https://aclanthology.org/2025.coling-main.294/
%P 4359-4369
Markdown (Informal)
[Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction](https://aclanthology.org/2025.coling-main.294/) (Ding et al., COLING 2025)
ACL