@inproceedings{li-etal-2024-pdameta-meta,
title = "{PDAM}eta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification",
author = "Li, Xurui and
Song, Kaisong and
Lin, Tianqianjing and
Kang, Yangyang and
Zhao, Fubang and
Sun, Changlong and
Liu, Xiaozhong",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1109",
pages = "12668--12678",
abstract = "Recently, we have witnessed the breakthroughs of meta-learning for few-shot learning scenario. Data augmentation is essential for meta-learning, particularly in situations where data is extremely scarce. However, existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. Inspired by the recent success of large language models (LLMs) which demonstrate improved language comprehension abilities, we propose a Meta-learning framework with Progressive Data Augmentation (PDAMeta) for few-shot text classification, which contains a two-stage data augmentation strategy. First, the prompt-based data augmentation enriches the diversity of the training instances from a global perspective. Second, the attention-based data augmentation further improves the data quality from a local perspective. Last, we propose a dual-stream contrastive meta-learning strategy to learn discriminative text representations from both original and augmented instances. Extensive experiments conducted on four public few-shot text classification datasets show that PDAMeta significantly outperforms several state-of-the-art models and shows better robustness.",
}
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<abstract>Recently, we have witnessed the breakthroughs of meta-learning for few-shot learning scenario. Data augmentation is essential for meta-learning, particularly in situations where data is extremely scarce. However, existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. Inspired by the recent success of large language models (LLMs) which demonstrate improved language comprehension abilities, we propose a Meta-learning framework with Progressive Data Augmentation (PDAMeta) for few-shot text classification, which contains a two-stage data augmentation strategy. First, the prompt-based data augmentation enriches the diversity of the training instances from a global perspective. Second, the attention-based data augmentation further improves the data quality from a local perspective. Last, we propose a dual-stream contrastive meta-learning strategy to learn discriminative text representations from both original and augmented instances. Extensive experiments conducted on four public few-shot text classification datasets show that PDAMeta significantly outperforms several state-of-the-art models and shows better robustness.</abstract>
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%0 Conference Proceedings
%T PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification
%A Li, Xurui
%A Song, Kaisong
%A Lin, Tianqianjing
%A Kang, Yangyang
%A Zhao, Fubang
%A Sun, Changlong
%A Liu, Xiaozhong
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F li-etal-2024-pdameta-meta
%X Recently, we have witnessed the breakthroughs of meta-learning for few-shot learning scenario. Data augmentation is essential for meta-learning, particularly in situations where data is extremely scarce. However, existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. Inspired by the recent success of large language models (LLMs) which demonstrate improved language comprehension abilities, we propose a Meta-learning framework with Progressive Data Augmentation (PDAMeta) for few-shot text classification, which contains a two-stage data augmentation strategy. First, the prompt-based data augmentation enriches the diversity of the training instances from a global perspective. Second, the attention-based data augmentation further improves the data quality from a local perspective. Last, we propose a dual-stream contrastive meta-learning strategy to learn discriminative text representations from both original and augmented instances. Extensive experiments conducted on four public few-shot text classification datasets show that PDAMeta significantly outperforms several state-of-the-art models and shows better robustness.
%U https://aclanthology.org/2024.lrec-main.1109
%P 12668-12678
Markdown (Informal)
[PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification](https://aclanthology.org/2024.lrec-main.1109) (Li et al., LREC-COLING 2024)
ACL
- Xurui Li, Kaisong Song, Tianqianjing Lin, Yangyang Kang, Fubang Zhao, Changlong Sun, and Xiaozhong Liu. 2024. PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12668–12678, Torino, Italia. ELRA and ICCL.