Text classification is a critical and foundational task in Tibetan natural language processing, it plays crucial role various applications, such as sentiment analysis information extraction. However, the limited availability of annotated data poses significant challenge to processing. This paper proposes prompt learning-based method for low-resource text overcome this challenge. utilizes pre-tr...

Developing a high-performance text classification model in low-resource language is challenging due to the lack of labeled data. Meanwhile, collecting large amounts data cost-inefficient. One approach increase amount create synthetic using augmentation techniques. However, most available techniques work on English and are highly language-dependent as they perform at word sentence level, such re...