A Multilingual Sentiment Analysis Model Based on Continual Learning
[Objective]This study addresses the performance degradation due to catastrophic forgetting when multilingual models handle tasks in new languages.[Methods]We proposed a multilingual sentiment analysis model,mLMs-EWC,based on continual learning.The model incorporates continual learning into multilingual models,enabling it to learn new language features while retaining the linguistic characteristics of previously learned languages.[Results]In continual sentiment analysis experiments involving three languages,the mLMs-EWC model outperformed the Multi-BERT model by approximately 5.0％in French and 4.5％in English tasks.Additionally,the mLMs-EWC model was evaluated on a lightweight distilled model,showing an improvement of up to 24.7％in English tasks.[Limitations]This study focuses on three widely used languages,and further validation is needed to assess the model's generalization capability to other languages.[Conclusions]The proposed model can alleviate catastrophic forgetting in multilingual sentiment analysis tasks and achieve continual learning on multilingual datasets.
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