Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract

• URL: http://arxiv.org/abs/2004.13842v1
• Date: Mon, 30 Mar 2020 18:03:15 GMT
• Title: Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract
• 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 propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages.
• Score: 2.3801001093799115
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages. This is in part due to the availability of curated data and research resources. One of the current challenges concerning low-resourced languages are clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creating two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and the creation of a news topic classification task from these datasets. In this study, we document our work, propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages in order to improve the performance of the classifiers.

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