Targeted Augmentation for Low-Resource Event Extraction

Sijia Wang, Lifu Huang


Abstract
Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential solutions, struggle to strike a balance between weak augmentation (e.g., synonym augmentation) and drastic augmentation (e.g., conditional generation without proper guidance). This paper introduces a novel paradigm that employs targeted augmentation and back validation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. Extensive experimental results demonstrate the effectiveness of the proposed paradigm. Furthermore, identified limitations are discussed, shedding light on areas for future improvement.
Anthology ID:
2024.findings-naacl.275
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4414–4428
Language:
URL:
https://aclanthology.org/2024.findings-naacl.275
DOI:
Bibkey:
Cite (ACL):
Sijia Wang and Lifu Huang. 2024. Targeted Augmentation for Low-Resource Event Extraction. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4414–4428, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Targeted Augmentation for Low-Resource Event Extraction (Wang & Huang, Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-naacl.275.pdf
Copyright:
 2024.findings-naacl.275.copyright.pdf