Optimal Transport Distillation with Contrastive Learning for Low-Resource Cross-Lingual Retrieval in Multi30k

Abstract

Abstract: Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso

Research Question

How does the integration of optimal transport distillation with contrastive learning objectives impact the cross-lingual retrieval performance of low-resource language pairs in the Multi30k dataset compared to standard contrastive learning baselines?

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