Impact of Domain-Specific Multilingual Datasets on Zero-Shot Cross-Lingual Retrieval Performance in Hybrid Batch Training

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 incorporating domain-specific multilingual datasets (e.g., legal, medical) on the zero-shot cross-lingual retrieval performance of hybrid batch training compared to pure contrastive methods, as measured by XTREME-R F1 scores?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.