@inproceedings{wang-etal-2024-create,
title = "Create! Don`t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation",
author = "Wang, Letian and
Liu, Xianggen and
Lv, Jiancheng",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.49/",
doi = "10.18653/v1/2024.naacl-long.49",
pages = "855--869",
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."
}
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%0 Conference Proceedings
%T Create! Don‘t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation
%A Wang, Letian
%A Liu, Xianggen
%A Lv, Jiancheng
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-create
%X 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.
%R 10.18653/v1/2024.naacl-long.49
%U https://aclanthology.org/2024.naacl-long.49/
%U https://doi.org/10.18653/v1/2024.naacl-long.49
%P 855-869
Markdown (Informal)
[Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation](https://aclanthology.org/2024.naacl-long.49/) (Wang et al., NAACL 2024)
ACL