Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation

• URL: http://arxiv.org/abs/2507.11634v1
• Date: Tue, 15 Jul 2025 18:13:25 GMT
• Title: Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation
• Authors: Farideh Majidi, Ziaeddin Beheshtifard,
• Abstract summary: This research examines cross-lingual sentiment analysis using few-shot learning and incremental learning methods in Persian.<n>Three pre-trained multilingual models were employed, which were fine-tuned using few-shot and incremental learning approaches.<n> Experimental results show that the mDeBERTa and XLM-RoBERTa achieved high performances, reaching 96% accuracy on Persian sentiment analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research examines cross-lingual sentiment analysis using few-shot learning and incremental learning methods in Persian. The main objective is to develop a model capable of performing sentiment analysis in Persian using limited data, while getting prior knowledge from high-resource languages. To achieve this, three pre-trained multilingual models (XLM-RoBERTa, mDeBERTa, and DistilBERT) were employed, which were fine-tuned using few-shot and incremental learning approaches on small samples of Persian data from diverse sources, including X, Instagram, Digikala, Snappfood, and Taaghche. This variety enabled the models to learn from a broad range of contexts. Experimental results show that the mDeBERTa and XLM-RoBERTa achieved high performances, reaching 96% accuracy on Persian sentiment analysis. These findings highlight the effectiveness of combining few-shot learning and incremental learning with multilingual pre-trained models.

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