Optimal Transport-Based Knowledge Distillation for Multilingual Retrieval Performance Gaps Under Training Data Noise

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 gap between high and low-resource languages in multilingual retrieval tasks change when using different optimal transport-based knowledge distillation techniques under varying degrees of training data noise?

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