Fine-tuned mXLM-R versus Annotation Projection for Entity Boundary Precision in Low-Resource Cross-Lingual NER

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 fine-tuned mXLM-R models compare to annotation projection-based methods in low-resource cross-lingual NER when evaluated on entity boundary precision across diverse linguistic domains?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.