Multilingual LLM-based Teacher-Student Frameworks vs. Label Projection in Zero-Shot Cross-Lingual NER for Low-Resource Languages

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 do multilingual LLM-based teacher-student frameworks compare to traditional label projection methods in zero-shot cross-lingual NER accuracy for low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.