@inproceedings{liu-etal-2024-beyond-single,
title = "Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction",
author = "Liu, Wanlong and
Zhou, Li and
Zeng, DingYi and
Xiao, Yichen and
Cheng, Shaohuan and
Zhang, Chen and
Lee, Grandee and
Zhang, Malu and
Chen, Wenyu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.564",
pages = "9470--9487",
abstract = "Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.",
}
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<abstract>Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.</abstract>
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%0 Conference Proceedings
%T Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
%A Liu, Wanlong
%A Zhou, Li
%A Zeng, DingYi
%A Xiao, Yichen
%A Cheng, Shaohuan
%A Zhang, Chen
%A Lee, Grandee
%A Zhang, Malu
%A Chen, Wenyu
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F liu-etal-2024-beyond-single
%X Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.
%U https://aclanthology.org/2024.findings-acl.564
%P 9470-9487
Markdown (Informal)
[Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction](https://aclanthology.org/2024.findings-acl.564) (Liu et al., Findings 2024)
ACL
- Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, and Wenyu Chen. 2024. Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction. In Findings of the Association for Computational Linguistics ACL 2024, pages 9470–9487, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.