Scaling In-Domain Examples for Cross-Lingual NER Performance 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: How does scaling the number of in-domain examples during fine-tuning affect the entity-level precision and recall of projection-based cross-lingual NER models for low-resource languages, and what is the optimal trade-off between data size and model performance?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.