Cross-lingual Contrastive Loss Effects on Multilingual Embedding Alignment Quality

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

To what extent does incorporating cross-lingual contrastive loss during robust training improve alignment quality in multilingual embedding spaces for zero-shot transfer on XTREME-R, as measured by bilingual lexical similarity scores?

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