Téléchargement | - Voir le manuscrit accepté : Manageable Phrase-based Statistical Machine Translation Models (PDF, 555 Kio)
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DOI | Trouver le DOI : https://doi.org/10.1007/978-3-540-75175-5_55 |
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Auteur | Rechercher : Badr, Ghada1; Rechercher : Joanis, Eric1; Rechercher : Larkin, Samuel1; Rechercher : Kuhn, Roland1 |
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Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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Format | Texte, Article |
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Conférence | 5th International Conference on Computer Recognition Systems CORES 07, Wroclaw, Poland, October 22-25, 2007 |
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Résumé | Statistical Machine Translation (SMT) is an evolving field where many techniques in Syntactic Pattern Recognition (SPR) are needed and applied. A typical phrase-based SMT system for translating from a T (target) language to an S (source) language contains one or more n-gram language models (LMs) and one or more phrase translation models (TMs). These LMs and TMs have a large memory footprint (up to several gigabytes). This paper describes novel techniques for filtering these models that ensure only relevant patterns in the LMs and TMs are loaded during translation. In experiments on a large Chinese-English task, these techniques yielded significant reductions in the amount of information loaded during translation: up to 58% reduction for LMs, and up to 75% for TMs. |
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Date de publication | 2007 |
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Dans | |
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Langue | anglais |
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Publications évaluées par des pairs | Oui |
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Numéro du CNRC | NRCC 49891 |
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Numéro NPARC | 9183591 |
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Exporter la notice | Exporter en format RIS |
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Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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Identificateur de l’enregistrement | f2a4386f-564f-44d4-9c01-c437390b8bb3 |
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Enregistrement créé | 2009-06-30 |
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Enregistrement modifié | 2020-05-10 |
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