Domain-Specific Data Augmentation Effects on Hybrid Batch Training Robustness in Multilingual Retrieval

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 domain-specific data augmentation influence the robustness of the hybrid batch training strategy across monolingual, cross-lingual, and multilingual retrieval tasks on the XNLI and TyDiQA benchmarks?

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