Optimal Transport Distillation Performance in Low-Resource Cross-Lingual Retrieval with Varying Labeled Data

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: What is the impact of varying the amount of labeled training data on the effectiveness of optimal transport distillation for cross-lingual retrieval tasks in low-resource languages, measured by mean reciprocal rank (MRR) on benchmarks like BEIR? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.