Zero-shot Text Classification Method with Self-supervised Knowledge Enhancement

To alleviate the class imbalance issue in unlabeled data for zero-shot text classification task and fully utilize the reasoning ability of pre-trained language models,this paper proposes a Knowledge Enhanced Zero-shot Text Classification(KE0TC)method.This method uses prompt templates to guide large pre-trained language model to extend category labels and builds a knowledge graph,using the graph structure for denoising and self-supervised data generation.Paragraph sampling and other methods are used to map the extracted labeled data into the parameter space of the classifier,thus achieving modeling of the classification space without collecting unlabeled training data.Compared with four baseline models on three text classification datasets,the proposed method achieves higher classification performance with lower time-consuming and less corpus.