Cross-lingual NER Recall Improvement via Annotation Projection and Multilingual Transformer Pre-training

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: Does combining annotation projection with multilingual transformer pre-training yield higher cross-lingual NER recall on low-resource languages than scaling pre-training data alone?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.