Source Language Resource Variations in Teacher-Student Cross-Lingual NER Robustness on CoNLL-2003 Datasets

Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language. These capabilities find practical applications in well-established Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). This study aims to investigate the effectiveness of a source language when applied to a target language, particularly in the context of perturbing the input test set. We evaluate on 13 pairs of languages, each including one high-resource language (HRL) and one

Research goal: How do variations in the source language selection (e.g., high-resource vs. medium-resource languages) affect the robustness of cross-lingual NER models trained via teacher-student learning, evaluated using F1-score consistency across different target language datasets from CoNLL-2003?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.