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GREAT: open source software for statistical machine translation

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GREAT: open source software for statistical machine translation

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dc.contributor.author González Mollá, Jorge es_ES
dc.contributor.author Casacuberta Nolla, Francisco es_ES
dc.date.accessioned 2014-03-24T10:34:23Z
dc.date.issued 2011-06-01
dc.identifier.issn 0922-6567
dc.identifier.uri http://hdl.handle.net/10251/36594
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-011-9097-6 es_ES
dc.description.abstract [EN] In this article, the first public release of GREAT as an open-source, statistical machine translation (SMT) software toolkit is described. GREAT is based on a bilingual language modelling approach for SMT, which is so far implemented for n-gram models based on the framework of stochastic finite-state transducers. The use of finite-state models is motivated by their simplicity, their versatility, and the fact that they present a lower computational cost, if compared with other more expressive models. Moreover, if translation is assumed to be a subsequential process, finite-state models are enough for modelling the existing relations between a source and a target language. GREAT includes some characteristics usually present in state-of-the-art SMT, such as phrase-based translation models or a log-linear framework for local features. Experimental results on a well-known corpus such as Europarl are reported in order to validate this software. A competitive translation quality is achieved, yet using both a lower number of model parameters and a lower response time than the widely-used, state-of-the-art SMT system Moses. © 2011 Springer Science+Business Media B.V. es_ES
dc.description.sponsorship Study was supported by the EC (FEDER, FSE), the Spanish government (MICINN, MITyC, “Plan E”, under Grants MIPRCV “Consolider Ingenio 2010”, iTrans2 TIN2009-14511, and erudito.com TSI-020110-2009-439), and the Generalitat Valenciana (Grant Prometeo/2009/014).
dc.format.extent 16 es_ES
dc.language Inglés es_ES
dc.publisher Springer Netherlands es_ES
dc.relation MICINN/TIN2009-14511 es_ES
dc.relation MICINN/TSI-020110-2009-439 es_ES
dc.relation GV/PROMETEO/2009/014 es_ES
dc.relation.ispartof Machine Translation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Grammatical inference es_ES
dc.subject Language modelling es_ES
dc.subject Monotonic bilingual segmentation es_ES
dc.subject Statistical machine translation es_ES
dc.subject Stochastic finite-state transducers es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title GREAT: open source software for statistical machine translation es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/s10590-011-9097-6
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation González Mollá, J.; Casacuberta Nolla, F. (2011). GREAT: open source software for statistical machine translation. Machine Translation. 25(2):145-160. doi:10.1007/s10590-011-9097-6 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://link.springer.com/article/10.1007%2Fs10590-011-9097-6 es_ES
dc.description.upvformatpinicio 145 es_ES
dc.description.upvformatpfin 160 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 25 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 201860
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Generalitat Valenciana


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