Performance Degradation of Optimal Transport Distillation for Cross-Lingual Retrieval in Extreme Low-Resource Conditions

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 optimal transport distillation for cross-lingual retrieval degrade under extreme low-resource conditions when evaluated on BEIR subsets for languages with fewer than 100 labeled training pairs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.