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PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners
Canyu Chen · Kai Shu

Recent advances in large pre-trained language models (PLMs) lead to impressive gains on natural language understanding (NLU) tasks with task-specific fine-tuning. However, direct fine-tuning PLMs heavily relies on a large amount of labeled instances, which are usually hard to obtain. Prompt-based tuning on PLMs has proven valuable for various few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU tasks mainly focus on deriving proper label words with a verbalizer or generating prompt templates for eliciting semantics from PLMs. In addition, conventional data augmentation methods have also been verified useful for few-shot tasks. However, currently there are few data augmentation methods designed for the prompt-based tuning paradigm. Therefore, we study a new problem of data augmentation for prompt-based few shot learners. Since the label semantics are essential in prompt-based tuning, we propose a novel label-guided data augmentation method PromptDA which exploits the enriched label semantic information for data augmentation. Extensive experiment results on few-shot text classification tasks show that our proposed framework achieves superior performance by effectively leveraging label semantics and data augmentation for natural language understanding.

Author Information

Canyu Chen (Illinois Institute of Technology)
Kai Shu (Illinois Institute of Technology)

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