Enhanced Prompt Learning for Few-shot Text Classification Method

Abstract

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. We evaluated our method 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.