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Genetic-Based Decoder for Statistical Machine Translation

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

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Abstract

We propose a new algorithm for decoding on machine translation process. This approach is based on an evolutionary algorithm. We hope that this new method will constitute an alternative to Moses’s decoder which is based on a beam search algorithm while the one we propose is based on the optimisation of a total solution. The results achieved are very encouraging in terms of measures and the proposed translations themselves are well built.

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Authors and Affiliations

  1. Villers-lès-Nancy, France

    Douib Ameur, Langlois David & Smaïli Kamel

Authors
  1. Douib Ameur
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  2. Langlois David
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  3. Smaïli Kamel
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Corresponding authors

Correspondence to Douib Ameur , Langlois David or Smaïli Kamel .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Ameur, D., David, L., Kamel, S. (2018). Genetic-Based Decoder for Statistical Machine Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75486-4

  • Online ISBN: 978-3-319-75487-1

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