Hybrid Batch Training vs Contrastive Learning for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages with XLM-R

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 does the hybrid batch training approach compare to contrastive learning methods in improving zero-shot cross-lingual retrieval accuracy on low-resource language pairs using the XLM-R model in the XTREME benchmark?

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