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Towards Data-Driven Machine Translation for Lumasaaba

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Digital Science (DSIC18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 850))

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Abstract

This paper reports on results from initial efforts towards the application of data-driven machine translation for low-resourced East African languages. In particular, the paper evaluates the application of phrase-based statistical machine translation (PBSMT) and neural machine translation (NMT) for automating translation between Lumasaaba (an East African Bantu language) and English. As expected, the PBSMT approach outperforms the NMT approach on a small Bible-based corpus of parallel sentences. The parallel corpus and the respective machine translation evaluations presented in this paper can be used as baselines for future machine translation quality improvement efforts involving Lumasaaba and other related East African Bantu languages.

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Notes

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Acknowledgments

The work in this paper was supported by funds from a Google Faculty award of July 2012.

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Authors and Affiliations

  1. Department of Information Systems, School of Computing and Informatics Technology, College of Computing and Information Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda

    Peter Nabende

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  1. Peter Nabende
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Correspondence to Peter Nabende .

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Editors and Affiliations

  1. Institute of Certified Specialists, Perm, Russia

    Tatiana Antipova

  2. Information Systems and Software Engineering, University of Coimbra, Coimbra, Portugal

    Alvaro Rocha

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Nabende, P. (2019). Towards Data-Driven Machine Translation for Lumasaaba. In: Antipova, T., Rocha, A. (eds) Digital Science. DSIC18 2018. Advances in Intelligent Systems and Computing, vol 850. Springer, Cham. https://doi.org/10.1007/978-3-030-02351-5_1

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