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Dimensionality reduction methods for machine translation quality estimation

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Dimensionality reduction methods for machine translation quality estimation

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dc.contributor.author González Rubio, Jesús es_ES
dc.contributor.author Navarro Cerdán, José Ramón es_ES
dc.contributor.author Casacuberta Nolla, Francisco es_ES
dc.date.accessioned 2014-07-24T15:25:03Z
dc.date.issued 2013-12
dc.identifier.issn 0922-6567
dc.identifier.uri http://hdl.handle.net/10251/39000
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-013-9139-3 es_ES
dc.description.abstract [EN] Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets. es_ES
dc.description.sponsorship This work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no. 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Machine Translation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Machine translation es_ES
dc.subject Quality estimation es_ES
dc.subject Dimensionality reduction es_ES
dc.subject Partial least squares regression es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Dimensionality reduction methods for machine translation quality estimation es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/s10590-013-9139-3
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/287576/EU/Cognitive Analysis and Statistical Methods for Advanced Computer Aided Translation/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2009-14205-C04-02/ES/Tecnicas Interactivas Y Adaptativas Para Sistemas Automaticos De Reconocimiento, Aprendizaje Y Percepcion/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO09%2F2009%2F014/ES/Adaptive learning and multimodality in pattern recognition (Almapater)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat 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 Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Dimensionality reduction methods for machine translation quality estimation. Machine Translation. 27(3-4):281-301. https://doi.org/10.1007/s10590-013-9139-3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/article/10.1007/s10590-013-9139-3 es_ES
dc.description.upvformatpinicio 281 es_ES
dc.description.upvformatpfin 301 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 27 es_ES
dc.description.issue 3-4 es_ES
dc.relation.senia 254547
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Generalitat Valenciana
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