  Recent advances in large pre-trained language models (PLMs) lead to
impressive gains in natural language understanding (NLU) tasks with
task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on
sufficient labeled training instances, which are usually hard to obtain.
Prompt-based tuning on PLMs has shown to be powerful for various downstream
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 to elicit semantics from PLMs. In addition,
conventional data augmentation strategies such as synonym substitution, though
widely adopted in low-resource scenarios, only bring marginal improvements for
prompt-based few-shot learning. Thus, an important research question arises:
how to design effective data augmentation methods for prompt-based few-shot
tuning? To this end, considering the label semantics are essential in
prompt-based tuning, we propose a novel label-guided data augmentation
framework PromptDA, which exploits the enriched label semantic information for
data augmentation. Extensive experiment results on few-shot text classification
tasks demonstrate the superior performance of the proposed framework by
effectively leveraging label semantics and data augmentation for natural
language understanding. Our code is available at
https://github.com/canyuchen/PromptDA.
