Optimal Transport Distillation for Multimodal Cross-Lingual Retrieval

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: Can the optimal transport distillation framework be extended to multimodal cross-lingual retrieval tasks, and how does its performance compare to existing multimodal models on benchmarks like M3C and LXMERT when evaluating low and high-resource language pairs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.5/10.