Robustness of Cross-Lingual NER Models via Domain Adaptation 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 domain adaptation techniques on the robustness of cross-lingual NER models trained via annotation projection when applied to low-resource languages, as measured by F1-score on domain-specific adversarial test sets?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.