用XLM-R实现英到日印尼零样本情感分类，无需目标语言训练数据
On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
- 基于XLM-R模型进行跨语言零样本迁移学习
- 日语数据集上达到最佳效果，印尼语表现相当
- 支持多语言统一建模，适合低资源语言场景
本研究探讨使用XLM-R预训练模型，从英语向日语和印尼语进行零样本跨语言情感分类的可行性。通过与采用类似零样本方法或全监督方法的先前工作对比，评估XLM-R在跨语言迁移学习中的表现。实验结果显示，该方法在其中一个日语数据集上取得最佳性能，在其他日语和印尼语数据集上也获得可比结果，且未使用任何目标语言标注数据。结果表明，构建一个通用多语言模型而非为每种语言单独建模，也能取得良好效果。
原文摘要 · Abstract (English)
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.
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