@inproceedings{li-etal-2024-pdameta,
    title = "{PDAM}eta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification",
    author = "Li, Xurui  and
      Song, Kaisong  and
      Lin, Tianqianjin  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.",
}

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.
Li, Xurui Song, Kaisong Lin, Tianqianjin Kang, Yangyang Zhao, Fubang Sun, Changlong Liu, Xiaozhong
PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification