@inproceedings{tuo-etal-2024-shot,
title = "Few-Shot Event Argument Extraction Based on a Meta-Learning Approach",
author = "Tuo, Aboubacar and
Besan{\c{c}}on, Romaric and
Ferret, Olivier and
Tourille, Julien",
editor = "Cao, Yang (Trista) and
Papadimitriou, Isabel and
Ovalle, Anaelia",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-srw.17",
pages = "146--153",
abstract = "Few-shot learning techniques for Event Extraction are developed to alleviate the cost of data annotation. However, most studies on few-shot event extraction only focus on event trigger detection and no study has been proposed on argument extraction in a meta-learning context. In this paper, we investigate few-shot event argument extraction using prototypical networks, casting the task as a relation classification problem. Furthermore, we propose to enhance the relation embeddings by injecting syntactic knowledge into the model using graph convolutional networks. Our experimental results show that our proposed approach achieves strong performance on ACE 2005 in several few-shot configurations, and highlight the importance of syntactic knowledge for this task. More generally, our paper provides a unified evaluation framework for meta-learning approaches for argument extraction.",
}
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<abstract>Few-shot learning techniques for Event Extraction are developed to alleviate the cost of data annotation. However, most studies on few-shot event extraction only focus on event trigger detection and no study has been proposed on argument extraction in a meta-learning context. In this paper, we investigate few-shot event argument extraction using prototypical networks, casting the task as a relation classification problem. Furthermore, we propose to enhance the relation embeddings by injecting syntactic knowledge into the model using graph convolutional networks. Our experimental results show that our proposed approach achieves strong performance on ACE 2005 in several few-shot configurations, and highlight the importance of syntactic knowledge for this task. More generally, our paper provides a unified evaluation framework for meta-learning approaches for argument extraction.</abstract>
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%0 Conference Proceedings
%T Few-Shot Event Argument Extraction Based on a Meta-Learning Approach
%A Tuo, Aboubacar
%A Besançon, Romaric
%A Ferret, Olivier
%A Tourille, Julien
%Y Cao, Yang (Trista)
%Y Papadimitriou, Isabel
%Y Ovalle, Anaelia
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F tuo-etal-2024-shot
%X Few-shot learning techniques for Event Extraction are developed to alleviate the cost of data annotation. However, most studies on few-shot event extraction only focus on event trigger detection and no study has been proposed on argument extraction in a meta-learning context. In this paper, we investigate few-shot event argument extraction using prototypical networks, casting the task as a relation classification problem. Furthermore, we propose to enhance the relation embeddings by injecting syntactic knowledge into the model using graph convolutional networks. Our experimental results show that our proposed approach achieves strong performance on ACE 2005 in several few-shot configurations, and highlight the importance of syntactic knowledge for this task. More generally, our paper provides a unified evaluation framework for meta-learning approaches for argument extraction.
%U https://aclanthology.org/2024.naacl-srw.17
%P 146-153
Markdown (Informal)
[Few-Shot Event Argument Extraction Based on a Meta-Learning Approach](https://aclanthology.org/2024.naacl-srw.17) (Tuo et al., NAACL 2024)
ACL
- Aboubacar Tuo, Romaric Besançon, Olivier Ferret, and Julien Tourille. 2024. Few-Shot Event Argument Extraction Based on a Meta-Learning Approach. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 146–153, Mexico City, Mexico. Association for Computational Linguistics.