Comparative Analysis of Optimal Transport Distillation and Contrastive Loss for Low-Resource Cross-Lingual Retrieval on BEIR

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 goal: How does the performance of multilingual pre-trained language models on BEIR subsets compare when fine-tuned with optimal transport distillation versus contrastive loss for cross-lingual retrieval, particularly in languages with fewer than 100 labeled training pairs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.