Comparative Performance of Optimal Transport Distillation and Contrastive Alignment in Low-Resource Cross-Lingual Retrieval on

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 compare to contrastive alignment in cross-lingual image-text retrieval for low-resource language pairs when evaluated using the MILAN benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.