零样本跨语言迁移学习在远距离语言对情感分类中的适用性研究
On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
AI总结：
本研究使用XLM-R预训练模型探讨从英语到日语和印尼语的零样本跨语言迁移学习，发现其在无需目标语言训练下取得优异结果，并验证了单一多语言模型的有效性。
摘要：
本研究探讨了使用XLM-R预训练模型从英语到日语和印尼语的跨语言迁移学习的适用性。通过与以往几项研究（包括采用类似零样本方法或全监督方法的模型）进行比较，概述了使用XLM-R的零样本迁移学习方法与现有模型相比的能力。我们的模型在一个日语数据集上取得了最佳结果，并在其他日语和印尼语数据集上取得了可比的结果，且无需使用目标语言进行训练。此外，结果表明，训练一个多语言模型（而不是为每种语言分别训练一个模型）并取得有前景的结果是可能的。
英文摘要：
This research explores the applicability of cross-lingual transfer learning from English to Japanese and Indonesian using the XLM-R pre-trained model. The results are compared with several previous works, either by models using a similar zero-shot approach or a fully-supervised approach, to provide an overview of the zero-shot transfer learning approach's capability using XLM-R in comparison with existing models. Our models achieve the best result in one Japanese dataset and comparable results in other datasets in Japanese and Indonesian languages without being trained using the target language. Furthermore, the results suggest that it is possible to train a multi-lingual model, instead of one model for each language, and achieve promising results.
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。
