Tianqianjing Lin
2024
PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification
Xurui Li
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Kaisong Song
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Tianqianjing Lin
|
Yangyang Kang
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Fubang Zhao
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Changlong Sun
|
Xiaozhong Liu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
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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Co-authors
- Xurui Li 1
- Kaisong Song 1
- Yangyang Kang 1
- Fubang Zhao 1
- Changlong Sun 1
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