Zero-shot cross-lingual transfer of domain-diverse pre-trained models on XTREME-R for low-resource versus high-resource languages

Abstract

Abstract: Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex

Research Question

Do models pre-trained with domain-diverse intermediate tasks retain better zero-shot cross-lingual transfer performance on XTREME-R when fine-tuned for low-resource languages compared to high-resource languages?

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