Scaling Teacher-Student Models for Cross-Lingual NER in 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: What is the impact of scaling the size of the teacher and student models on the accuracy of cross-lingual NER in low-resource languages, and how does this compare to direct transfer methods when evaluated on out-of-domain datasets?

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