Low-resource Tibetan Text Classification Based on Prompt Learning
Text classification is one of the fundamental tasks in natural language processing.The lack of labeled data has always been an important factor limiting the development of natural language processing technologies for Tibetan and other minority languages,as traditional deep learning models have higher requirements for the scale of labeled data.To address this issue,this paper implements low-resource Tibetan text classification using prompt learning based on pre-trained language models,which involves conducting Tibetan text classification experiments using dif-ferent Tibetan pre-trained language models and prompt templates.The experimental results show that,by designing reasonable prompt templates and other methods,prompt learning can improve the effectiveness of Tibetan text clas-sification(48.3%)in the case of insufficient training data,preliminarily verifying the value and potential of prompt learning in minority language processing.However,the experimental results also indicate that the prompt learning model may underperform in specific categories,suggesting there is still potential for enhancement in the Tibetan pre-trained language model.
Tibetan text classification,pre-trained language modelprompt learningfew-shot learning