Contrastive meta-learning framework for few-shot cross-lingual text classification

Abstract

Many security risk control issues, such as public opinion analysis in international scenarios, have been identified as text classification problems, which are challenging due to the involvement of multiple languages. Previous studies have demonstrated that the performance of few-shot text classification tasks can be enhanced through cross-lingual semantic knowledge transfer. However, the advancement of cross-lingual text classification is faced with several challenges. Firstly, it has been found difficult to obtain language-agnostic representations that perform well in cross-lingual transfer. Moreover, the differences in grammatical structure and syntactic rules between different languages cause variations in text representation, making it difficult to extract general semantic information. Additionally, the scarcity of labeled data has been identified as a severe constraint on the performance of most existing methods. In many real-world scenarios, only a small amount of labeled data is available, which has been found to severely degrade the performance of many methods. Therefore, effective methods are needed to accurately transfer knowledge in few-shot situations and improve the generalization ability of classification models. To tackle these challenges, a novel framework was proposed that integrates contrastive learning and meta-learning. Within the framework, contrastive learning was utilized to extract general language-agnostic semantic information, while the rapid generalization advantages of meta-learning were leveraged to improve knowledge transfer in few-shot settings. Furthermore, a task-based data augmentation method was proposed to further improve the performance of the framework in few-shot cross-lingual classification. Extensive experiments conducted on two widely used multilingual text classification datasets show that the proposed method outperforms several strong baselines. This indicates that the method can be effectively applied in the field of risk control and security.

Keywords

• cross-lingual text classification;meta-learning;contrastive learning;few-shot