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Low-Resourced Machine Translation for Senegalese Wolof Language

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Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 696))

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Abstract

Natural language processing (NLP) research has made great advancements in recent years with major breakthroughs that have established new benchmarks. However, these advances have mainly benefited a certain group of languages commonly referred to as resource-rich such as English and French. Majority of other languages with weaker resources are then left behind which is the case for most African languages including Wolof. In this work, we present a parallel Wolof/French corpus of 100,000 sentences on which we conducted experiments on machine translation models based on recurrent neural networks (RNNs) in different data configurations. We noted performance gains with the models trained on subworded data as well as those trained on the French-English language pair compared to those trained on the French-Wolof pair under the same experimental conditions.

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Notes

  1. 1.

    Lexical Markup Framework.

  2. 2.

    http://pagesperso.ls2n.fr/~enguehard-c/DiLAF/index.php.

  3. 3.

    https://ai.facebook.com/research/no-language-left-behind/.

  4. 4.

    https://www.masakhane.io/.

  5. 5.

    https://www.axl.cefan.ulaval.ca/afrique/senegal.htm.

  6. 6.

    http://www.jo.gouv.sn/spip.php?article4802.

  7. 7.

    Version 2.0.0.

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Authors and Affiliations

  1. Université Cheikh Anta Diop, Dakar, Senegal

    Derguene Mbaye & Moussa Diallo

  2. Baamtu, Dakar, Senegal

    Thierno Ibrahima Diop

Authors
  1. Derguene Mbaye
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  2. Moussa Diallo
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  3. Thierno Ibrahima Diop
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Corresponding author

Correspondence to Derguene Mbaye .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, England, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International Newtown, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Mbaye, D., Diallo, M., Diop, T.I. (2024). Low-Resourced Machine Translation for Senegalese Wolof Language. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-99-3236-8_19

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  • DOI: https://doi.org/10.1007/978-981-99-3236-8_19

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