On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
This addresses sentiment analysis for distant language pairs, offering a practical multi-lingual model approach, but it is incremental as it builds on existing XLM-R methods.
The study investigated zero-shot cross-lingual transfer learning from English to Japanese and Indonesian for sentiment classification using XLM-R, achieving the best result on one Japanese dataset and comparable results on others without target language training.
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.