Hybrid Batch Training for Robust Zero-Shot Cross-Lingual Retrieval with Multilingual Contrastive Learning

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: To what extent does the hybrid batch training strategy with multilingual contrastive learning objectives improve the robustness of zero-shot cross-lingual retrieval in the presence of noise or adversarial perturbations, as evaluated using the LASER or mBERT models on the WMT or OPUS benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.