Impact of Labeled Source to Unlabeled Target Data Ratio on Cross-Lingual NER Model Robustness

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 ratio of labeled source to unlabeled target data impact the F1 score robustness of cross-lingual NER models using teacher-student distillation across low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.4/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.4/10.