Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi

• URL: http://arxiv.org/abs/2003.04986v1
• Date: Tue, 18 Feb 2020 13:58:06 GMT
• Title: Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi
• Authors: Vukosi Marivate, Tshephisho Sefara, Vongani Chabalala, Keamogetswe Makhaya, Tumisho Mokgonyane, Rethabile Mokoena, Abiodun Modupe
• Abstract summary: We create datasets that are focused on news headlines for Setswana and Sepedi. We also create a news topic classification task. We investigate an approach on data augmentation, better suited to low resource languages.
• Score: 2.3801001093799115
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges for low-resourced languages is clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creation of two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and creation of a news topic classification task. We document our work and also present baselines for classification. We investigate an approach on data augmentation, better suited to low resource languages, to improve the performance of the classifiers

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