An enhanced prompt learning method(EPL4FTC)for few-shot text classification task is proposed.This algorithm first converts the text classification task into the form of prompt learning based on natural language inference.Thus,the implicit data enhancement is achieved based on the prior knowledge of pre-training language models and the algorithm is optimized by two losses with different granularities.Moreover,to capture the category information of specific downstream tasks,the triple loss is used for joint optimization.The masked-language model is incorporated as a regularizer to improve the generalization ability.Through the evaluation on four Chinese and three English text classification datasets,the experimental results show that the classification accuracy of the proposed EPL4FTC is significantly better than the other compared baselines.
关键词
预训练语言模型/少样本学习/文本分类/提示学习/三元组损失
Key words
pre-trained language model/few-shot learning/text classification/prompt learning/triplet loss
维普
万方数据