Multilingual vs. Monolingual Encoders in Cross-Lingual NER: XTREME-NER Benchmark Analysis

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 using multilingual pre-trained language models (e.g., XLM-R, mBERT) as encoders in the teacher-student framework for cross-lingual NER on the XTREME-NER benchmark compared to monolingual models?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.