Impact of Hybrid Batch Training on Multilingual Representation Alignment Robustness Under Domain Shift in Zero-Shot Cross-Lingual

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: How does hybrid batch training impact the robustness of multilingual representation alignment under domain shift conditions in zero-shot cross-lingual retrieval?

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