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ACL 2025longmain

The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages

Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto, Dale Dunbar, Graham Morrissey, Fadel Thior, Luandrie Potgieter, Jessica Ojo, Atnafu Lambebo Tonja, Maushami Chetty, Wilhelmina NdapewaOnyothi Nekoto, Pelonomi Moiloa, Jade Abbott, Vukosi Marivate, Benjamin Rosman

Lelapa AI · Way With Words · Lelapa AI and University of Pretoria · Mohamed bin Zayed University of Artificial Intelligence · Retro Rabbit · University of Pretoria · University of the Witwatersrand

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2025.acl-long.1487 ↗

摘要

This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their linguistic resource. This framework is supported by the Esethu license, a novel community-centric data license. As a proof of concept, we introduce the Vuk’uzenzele isiXhosa Speech Dataset (ViXSD), an open-source corpus developed under the Esethu Framework and License. The dataset, containing read speech from native isiXhosa speakers enriched with demographic and linguistic metadata, demonstrates how community-driven licensing and curation principles can bridge resource gaps in automatic speech recognition (ASR) for African languages while safeguarding the interests of data creators. We describe the framework guiding dataset development, outline the Esethu license provisions, present the methodology for ViXSD, and present ASR experiments validating ViXSD’s usability in building and refining voice-driven applications for isiXhosa.