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

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
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: 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? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.

Visit

doi.orgzenodo.org

Tasks

information extractionnamed entity recognitiontransfer learning

Tags

extentunlabeledtarget-languagedatavolumecorrelatecross-lingualtransfer

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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