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        <title>Phrase-based language modelling for statistical machine translation</title>
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        <namePart type="given">Achraf</namePart>
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        <namePart type="given">Salma</namePart>
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    <name type="personal">
        <namePart type="given">Abdelmajid</namePart>
        <namePart type="given">Ben</namePart>
        <namePart type="family">Hamadou</namePart>
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    <name type="personal">
        <namePart type="given">Kamel</namePart>
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            <title>Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign</title>
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            <namePart type="given">Marcello</namePart>
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        <name type="personal">
            <namePart type="given">François</namePart>
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    <abstract>In this paper, we present our submitted MT system for the IWSLT2014 Evaluation Campaign. We participated in the English-French translation task. In this article we focus on one of the most important component of SMT: the language model. The idea is to use a phrase-based language model. For that, sequences from the source and the target language models are retrieved and used to calculate a phrase n-gram language model. These phrases are used to rewrite the parallel corpus which is then used to calculate a new translation model.</abstract>
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%0 Conference Proceedings
%T Phrase-based language modelling for statistical machine translation
%A Romdhane, Achraf Ben
%A Jamoussi, Salma
%A Hamadou, Abdelmajid Ben
%A Smaïli, Kamel
%Y Federico, Marcello
%Y Stüker, Sebastian
%Y Yvon, François
%S Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2014
%8 dec 4 5
%C Lake Tahoe, California
%F romdhane-etal-2014-phrase
%X In this paper, we present our submitted MT system for the IWSLT2014 Evaluation Campaign. We participated in the English-French translation task. In this article we focus on one of the most important component of SMT: the language model. The idea is to use a phrase-based language model. For that, sequences from the source and the target language models are retrieved and used to calculate a phrase n-gram language model. These phrases are used to rewrite the parallel corpus which is then used to calculate a new translation model.
%U https://aclanthology.org/2014.iwslt-evaluation.13
%P 96-99

Markdown (Informal)

[Phrase-based language modelling for statistical machine translation](https://aclanthology.org/2014.iwslt-evaluation.13) (Romdhane et al., IWSLT 2014)

• Phrase-based language modelling for statistical machine translation (Romdhane et al., IWSLT 2014)

ACL

• Achraf Ben Romdhane, Salma Jamoussi, Abdelmajid Ben Hamadou, and Kamel Smaïli. 2014. Phrase-based language modelling for statistical machine translation. In Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 96–99, Lake Tahoe, California.