Cross-lingual NER Transfer Accuracy and Unlabeled Target-Language Data Volume in Low-Resource Languages

Abstract

Abstract: 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 Question

To what extent does unlabeled target-language data volume correlate with the cross-lingual transfer accuracy of teacher-student NER models across low-resource language families in XTREME?

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