Enhancing Cross-Lingual Retrieval for Low-Resource Languages via Multimodal Embeddings in Optimal Transport Distillation

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: Does integrating multimodal embeddings into the optimal transport distillation framework enhance cross-lingual retrieval performance on XTREME for low-resource languages compared to text-only models?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.