Zero-shot cross-lingual transfer performance with Japanese vs. English on PAWS-X

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 languag

Research goal: What is the impact of using Japanese as a source language for zero-shot cross-lingual transfer on the PAWS-X benchmark compared to English, measured by accuracy across different language pairs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.