Effect of Domain-Specific Monolingual Data on Multilingual 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 effect of incorporating domain-specific monolingual data (e.g., legal or medical texts) into the hybrid batch training strategy on the multilingual retrieval performance of models evaluated on the XNLI benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.