Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation

Letian Wang, Xianggen Liu, Jiancheng Lv


Abstract
We propose Label Creative Generation (LCG), a new paradigm in multi-label data augmentation. Beyond repeating data points with fixed labels, LCG creates new data by exploring innovative label combinations. Within LCG, we introduce Tail-Driven Conditional Augmentation (TDCA), combining tail-driven label sampling and label-conditioned text generation for balanced, consistent data augmentation. Our approach has demonstrated a **100.21%** increase in PSP@1 across three datasets, successfully mitigating the long-tail effect in MLTC and markedly enhancing model performance.
Anthology ID:
2024.naacl-long.49
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
855–869
Language:
URL:
https://aclanthology.org/2024.naacl-long.49
DOI:
Bibkey:
Cite (ACL):
Letian Wang, Xianggen Liu, and Jiancheng Lv. 2024. Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 855–869, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation (Wang et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.49.pdf
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 2024.naacl-long.49.copyright.pdf