Robustness of Projection-Based Cross-Lingual NER Models with High-Resource to Low-Resource Language Data Ratios

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 varying the ratio of high-resource to low-resource language data on the robustness of projection-based cross-lingual NER models when tested on the WNUT-2017 and WNUT-2019 datasets?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.