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Genetic-based Decoder for Statistical Machine Translation

Ameur Douib 1 David Langlois 1 Kamel Smaili 1
1 SMarT - Statistical Machine Translation and Speech Modelization and Text
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
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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Submitted on : Thursday, June 23, 2016 - 11:52:45 AM
Last modification on : Tuesday, December 18, 2018 - 4:38:02 PM

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Ameur Douib, David Langlois, Kamel Smaili. Genetic-based Decoder for Statistical Machine Translation. Springer LNCS series, Lecture Notes in Computer Science, 2016. ⟨hal-01336546⟩

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