Impact of Source Language Diversity versus Target Corpus Scaling on Few-Shot Cross-Lingual NER via Projection and Fine-Tuning

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 increasing the diversity of source languages compared to scaling unlabeled target corpus size affect few-shot cross-lingual NER accuracy when using projection-based data transfer versus direct multilingual fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.