基于增量适应的波斯语情感分析跨语言少样本学习
Cross-lingual Few-shot Learning for Persian Sentiment Analysis with Incremental Adaptation
AI总结：
本研究针对波斯语情感分析数据有限的问题，结合少样本学习与增量学习对XLM-RoBERTa等多语言模型进行微调，实验表明mDeBERTa和XLM-RoBERTa达到了96%的准确率，验证了该方法的有效性。
摘要：
本研究使用少样本学习和增量学习方法，探讨波斯语的跨语言情感分析。主要目标是开发一个能在数据有限的情况下进行波斯语情感分析的模型，同时利用高资源语言的先验知识。为此，采用了三个预训练多语言模型（XLM-RoBERTa、mDeBERTa和DistilBERT），通过少样本和增量学习方法，在来自X、Instagram、Digikala、Snappfood和Taaghche等多种来源的少量波斯语数据样本上进行微调。这种多样性使模型能够从广泛的语境中学习。实验结果表明，mDeBERTa和XLM-RoBERTa取得了高性能，在波斯语情感分析上达到96%的准确率。这些发现突显了将少样本学习、增量学习与多语言预训练模型相结合的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
