Adversarial Optimal Transport Distillation for Low-Resource Cross-Lingual Retrieval Robustness

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 adversarial training in optimal transport distillation affect the robustness of low-resource cross-lingual retrieval on the XTREME-R benchmark compared to high-resource languages, and can it be generalized to other multilingual benchmarks like PASA and XLENT?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.