Performance Comparison of Teacher-Student Cross-Lingual NER Models and Baseline Projection Methods on Multilingual Benchmarks

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we pres

Research goal: How does the performance of teacher-student cross-lingual NER models compare to baseline projection methods when evaluated on standardized multilingual NER benchmarks (e.g., CoNLL-2003, Wikiner) in terms of F1-score improvements across low-resource languages?

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