Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation
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:
- 10.18653/v1/2024.naacl-long.49
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.49.pdf