Impact of Hybrid Batch Training on Zero-Shot Cross-Lingual Retrieval Robustness Against Typological Distance in XNLI

Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual lang

Research goal: What is the impact of hybrid batch training on the robustness of zero-shot cross-lingual retrieval against typological distance variations in the XNLI dataset compared to standard multilingual fine-tuning?

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