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

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          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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          Author and article information

          Journal
          18 February 2020
          Article
          2003.04986
          434a0c81-04d0-4095-976e-1bdb15c46063

          http://creativecommons.org/licenses/by-sa/4.0/

          History
          Custom metadata
          Submitted to Resources for African Indigenous Languages (RAIL) at LREC 2020
          cs.CL cs.LG stat.ML

          Theoretical computer science,Machine learning,Artificial intelligence
          Theoretical computer science, Machine learning, Artificial intelligence

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