Cross-lingual Transfer Performance of XLM-R on PAWS-X with Japanese and Chinese Source Languages

Abstract

Abstract: 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 Question

How do zero-shot cross-lingual transfer results from XLM-R on PAWS-X vary when using Japanese versus Chinese as the source language for high-resource target languages (e.g., English, Spanish) compared to low-resource ones (e.g., Turkish, Swahili) in terms of precision, recall, and inference latency?

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