Hybrid Batch Training for Zero-Shot Cross-Lingual Transfer in XTREME-R

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 hybrid batch training (combining monolingual English intermediate tasks with multilingual pretraining objectives) improve zero-shot cross-lingual transfer performance on XTREME-R compared to using only English intermediate tasks, as measured by average score and per-language accuracy?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.4/10.