PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
This work addresses the problem of limited labeled data for researchers and practitioners using prompt-based tuning in few-shot NLU, offering a novel augmentation method that improves performance, though it is incremental as it builds on existing prompt-tuning approaches.
The paper tackles the challenge of designing effective data augmentation for prompt-based few-shot learning in natural language understanding, proposing PromptDA, a label-guided framework that leverages enriched label semantics, and demonstrates superior performance on few-shot text classification tasks.
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.