Cross-lingual NER robustness in low-resource languages with source language diversity

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: What is the impact of source language diversity on the robustness of teacher-student cross-lingual NER models when evaluated on low-resource languages within the XTREME-NER suite?

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