Cross-lingual Transfer Performance of XLM-R and mBERT in XTREME-R with Modality Variations

Abstract

Abstract: Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss,

Research Question

How does the performance of XLM-R on zero-shot cross-lingual transfer tasks compare to mBERT when evaluated on languages from different typological families in XTREME-R, and what is the effect of varying the modality of intermediate tasks (text-only vs. multimodal) on this performance?

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