Synergistic Optimization of Monolingual and Cross-Lingual Tasks for Zero-Shot Retrieval in Low-Resource News Domain Adaptation

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 synergistic optimization of monolingual and cross-lingual tasks improve zero-shot retrieval performance on low-resource language pairs in news domain adaptation?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.