Comparative Analysis of Hybrid Batch Training for Zero-Shot Cross-Lingual Image-Text Retrieval Against State-of-the-Art

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 the hybrid batch training strategy proposed in this paper compare to state-of-the-art multilingual models (e.g., mBART, XLM-R, LASER) in zero-shot cross-lingual image-text retrieval on benchmarks like MULTI30K and COCO-CN?

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