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dc.contributor.author | Martínez Gómez, Pascual | es_ES |
dc.contributor.author | Sanchis Trilles, Germán | es_ES |
dc.contributor.author | Casacuberta Nolla, Francisco | es_ES |
dc.date.accessioned | 2014-05-08T13:04:12Z | |
dc.date.issued | 2012-09 | |
dc.identifier.issn | 0031-3203 | |
dc.identifier.uri | http://hdl.handle.net/10251/37324 | |
dc.description.abstract | [EN] One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adapta- tion is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive- aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Bayesian predictive adaptation yields the best performance. | es_ES |
dc.description.sponsorship | This paper is based upon work supported by the EC (FP7) under CasMaCat (287576) project and the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV "Consolider Ingenio 2010" (CSD2007-00018) and iTrans2 (TIN2009-14511). This work is also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under Grant Prometeo/2009/014, and by the UPV under Grant 20091027. The authors would like to thank the anonymous reviewers for their useful and constructive comments. | en_EN |
dc.format.extent | 11 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Pattern Recognition | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Pattern recognition | es_ES |
dc.subject | Machine translation | es_ES |
dc.subject | Online learning | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Online adaptation strategies for statistical machine translation in post-editing scenarios | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2012.01.011 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/287576/EU/Cognitive Analysis and Statistical Methods for Advanced Computer Aided Translation/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-14511/ES/Traduccion De Textos Y Transcripcion De Voz Interactivas/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MITURCO//TSI-020110-2009-0439/ES/ERUDITO.COM/ | 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.relation.projectID | info:eu-repo/grantAgreement/UPV//20091027/ | es_ES |
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 | Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2012). Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recognition. 45(9):3193-3203. https://doi.org/10.1016/j.patcog.2012.01.011 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.patcog.2012.01.011 | es_ES |
dc.description.upvformatpinicio | 3193 | es_ES |
dc.description.upvformatpfin | 3203 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 45 | es_ES |
dc.description.issue | 9 | es_ES |
dc.relation.senia | 234045 | |
dc.contributor.funder | European Commission | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | |
dc.contributor.funder | Ministerio de Industria, Turismo y Comercio | |
dc.contributor.funder | Generalitat Valenciana | |
dc.contributor.funder | Universitat Politècnica de València | |
dc.contributor.funder | European Regional Development Fund |