# A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
###### Abstract
Additionally, with the unprecedented volume of unlabeled data generated from the practice environment, recent research on how to effectively utilize existing, unlabeled datasets within prompt learning has attracted significant attention. On the one hand, unlabeled data are easier to obtain than labeled data; on the other hand, overfitting from classification previously can be avoided to a significant extent. Semi-supervised learning using unlabeled data has emerged as an important approach to classification, data mining, and information retrieval. Pseudo-label paradigms that assign labels with high confidence to unlabeled data based on a trained model have been increasingly investigated [12]. With more unlabeled data, better classification performance can be expected by increasing the size of the trainable data. However, the convergence of prompt learning and unlabeled data is a challenging research topic that has not been extensively studied. For the issues discussed above, we propose a Chinese few-shot text classification method called CIPLUD that combines an improved prompt learning method and existing unlabeled data for Chinese FSTC. The CIPLUD model is composed of two modules, including the Multiple Masks Optimization-based Prompt Learning (MMOPL) module and the One-Class Support Vector Machine-based Unlabeled Data Leveraging (OCSVM-UDL) module. As part of the MMOPL module, our method designs universal prompt templates with multiple masks for different tasks. We then built a text classification model using universal prompt templates and a Chinese pre-trained language model. Afterward, we optimized the predicted label of the model using joint probability and length constraints, effectively eliminating the problem of a random token composition. In the OCSVM-UDL module, our method assigns pseudo-labels to the unlabeled data through a one-class support vector machine model [13] inspired by anomaly detection. With the help of few-shot-labeled data, the OCSVM model can obtain spherical constraint boundaries for different classes in the feature space through training. Then, these constraint boundaries are used to filter the unlabeled data. The unlabeled samples that do not fall into any boundary are considered low-confidence anomaly data, and the unlabeled samples that fall into multiple constraint boundaries are considered ambiguous data. This filtering approach can filter noise data from the unlabeled data and effectively screen out pseudo-labeled data with high confidence and disambiguation. Finally, the new training data are created by blending the pseudo-labeled data with the few-shot-labeled data. The flow of the above-mentioned two modules is repeated until the performance of the text classification model stabilizes, signaling the end of the iteration. The experimental results on the few-shot text classification datasets, FewCLUE [14], demonstrate the effectiveness of our approach. The proposed model is referred to as the CIPLUD model. The main contributions of our paper are summarized as follows:  
* According to our best knowledge, this is the first study to combine the prompt learning method and unlabeled data for Chinese FSTC. The proposed CIPLUD model de-signs a universal prompt template for different Chinese FSTC tasks. It uses the joint probability and length-constrained decoding method to solve the random token composition problem caused by the multiple masks' prompt templates;
* To design a pseudo-label candidate method using the OCSVM model and conduct semi-supervised training using the pseudo-labels, resulting in improved performance;
* We conducted experiments and evaluated a series of Chinese FSTC datasets. The experimental results demonstrate that the proposed CIPLUD model can gain significant improvement over other prompt learning methods. The rest of the paper is organized as follows. We review some representative work on the few-shot text classification and prompt learning in Section 2. In Section 3, we describe the architecture of the proposed model in detail.