Hybrid Batch Training for Multimodal Alignment in Zero-Shot Cross-Lingual Image-Text 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: Does the hybrid batch training strategy proposed for information retrieval improve multimodal alignment accuracy on zero-shot cross-lingual image-text retrieval benchmarks like XM3600 compared to separate task fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.