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.

翻译：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。通过将结果与先前若干研究进行比较——包括采用类似零样本方法的模型或全监督方法的模型——以概述使用XLM-R的零样本迁移学习方法相较于现有模型的能力。我们的模型在未经目标语言训练的情况下，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获得了可比的结果。此外，结果表明，训练一个多语言模型（而非为每种语言单独训练模型）并取得良好结果是可行的。