# A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
###### Abstract  
Insufficiently labeled samples and low-generalization performance have become significant natural language processing problems, drawing significant concern for few-shot text classification (FSTC). Advances in prompt learning have significantly improved the performance of FSTC. However, prompt learning methods typically require the pre-trained language model and tokens of the vocabulary list for model training, while different language models have different token coding structures, making it impractical to build effective Chinese prompt learning methods from previous approaches related to English. In addition, a majority of current prompt learning methods do not make use of existing unlabeled data, thus often leading to unsatisfactory performance in real-world applications. To address the above limitations, we propose a novel Chinese FSTC method called CIPLUD that combines an improved prompt learning method and existing unlabeled data, which are used for the classification of a small amount of Chinese text data. We used the Chinese pre-trained language model to build two modules: the Multiple Masks Optimization-based Prompt Learning (MMOPL) module and the One-Class Support Vector Machine-based Unlabeled Data Leveraging (OCSVM-UDL) module. The former generates prompt prefixes with multiple masks and constructs suitable prompt templates for Chinese labels. It optimizes the random token combination problem during label prediction with joint probability and length constraints. The latter, by establishing an OCSVM model in the trained text vector space, selects reasonable pseudo-label data for each category from a large amount of unlabeled data. After selecting the pseudo-label data, we mixed them with the previous few-shot annotated data to obtain brand new training data and then repeated the steps of the two modules as an iterative semi-supervised optimization process. The experimental results on the four Chinese FSTC benchmark datasets demonstrate that our proposed solution outperformed other prompt learning methods with an average accuracy improvement of 2.3%. Chinese few-shot text classification; prompt learning; unlabeled data; pre-trained language model +
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has shown impressively in FSTC tasks. This method utilizes natural language prompts and the PLM and converts downstream tasks into masked language modeling problems. Compared to traditional approaches, prompt learning has proven to be particularly effective in extreme scenarios where there is only one training sample per type. Although much research has been conducted on prompt learning for English FSTC, its application in Chinese FSTC is still in the early stages, given the language disparity between Chinese and English. Prompt learning methods require a pre-trained language model and token of the vocabulary list for training, and various language models have varying token coding structures, making it difficult to adopt previous English-related approaches for effective Chinese prompt learning. For example, the label \"computer\" requires three tokens in Chinese, while it only needs a single token in English. This is because Chinese is a highly context-dependent language. Meaningful labels often require multiple tokens to express the full meaning and context of the label, whereas, in English, a single token can often convey the same meaning. Thus, how to design a generic prompt template for Chinese labels has become the first problem to be solved. In addition, the Chinese language characteristic results in the mask tokens being expanded to more than one when prompt learning is used. This may be a random token composition problem with the prediction labels obtained by mask decoding. Random token composition does not belong to a predefined label set, which produces an output result that is difficult to accept. Hence, how to deal with a random token composition when predicting Chinese labels is the second problem to be solved.