Prompt-based for Low-Resource Tibetan Text Classification

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-trained models learn representation generation capabilities on large-scale unsupervised corpus, enabling few-shot classification. Experimental results demonstrate that proposed significantly improves performance scenarios. work provides new research idea Hopefully, will inspire subsequent