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.Pre-vious 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 differ-ent languages cause variations in text representation,making it difficult to extract general semantic information.Ad-ditionally,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 set-tings.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 multilin-gual 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.
cross-lingual text classificationmeta-learningcontrastive learningfew-shot
国家科技期刊平台
NSTL
万方数据
