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.

翻译：近年来，大规模预训练语言模型在自然语言理解任务中通过任务特定微调取得了显著进展。然而，直接微调预训练语言模型严重依赖充足的标注训练实例，而这在实际中往往难以获得。基于提示的预训练语言模型调优方法已被证明在各类下游少样本任务中具有强大能力。现有针对少样本自然语言理解任务的提示调优研究主要集中于通过言语器推导合适的标签词，或生成提示模板以从预训练语言模型中激发语义信息。此外，传统数据增强策略（如同义词替换）虽在低资源场景中被广泛采用，但仅为基于提示的少样本学习带来有限改进。因此，一个重要的研究问题浮现：如何为基于提示的少样本调优设计有效的数据增强方法？针对这一问题，考虑到标签语义在提示调优中的关键作用，我们提出了一种新颖的标签引导数据增强框架PromptDA，该框架利用增强的标签语义信息进行数据增强。在少样本文本分类任务上的大量实验结果表明，该框架通过有效结合标签语义与数据增强技术，在自然语言理解任务中展现出优越性能。我们的代码已开源至https://github.com/canyuchen/PromptDA。