Scaling Adversarial Multilingual Data Augmentation for Robust Zero-Shot Cross-Lingual 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: What is the impact of scaling the size of adversarial multilingual data augmentation on the robustness of hybrid batch-trained models in zero-shot cross-lingual retrieval tasks, as measured by XNLI accuracy and TyDiQA performance?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.