Improving Zero-Shot Cross-Lingual Retrieval for Low-Resource Niger-Congo Languages via Curriculum Learning

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 incorporating curriculum learning into hybrid batch training improve the zero-shot cross-lingual retrieval performance for low-resource Niger-Congo languages on the MIRACL benchmark compared to random sampling, as measured by NDCG@10?

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