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.
@article{arxiv.2507.11634,
title = {Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation},
author = {Farideh Majidi and Ziaeddin Beheshtifard},
journal= {arXiv preprint arXiv:2507.11634},
year = {2025}
}
Comments
Proceedings of the First National Conference on Artificial Intelligence and Emerging Research: Convergence of Humans and Intelligent Systems