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-resoResearch goal: How does hybrid batch training compare to adversarial training in improving zero-shot cross-lingual retrieval accuracy on XNLI for low-resource language pairs?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.