Cross-lingual NER Model Robustness 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 do teacher-student cross-lingual NER models perform relative to label projection methods on low-resource languages when evaluated on adversarial or noisy inputs (e.g., perturbed text) measured by robustness metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.