- -

Online adaptation strategies for statistical machine translation in post-editing scenarios

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Online adaptation strategies for statistical machine translation in post-editing scenarios

Show simple item record

Files in this item

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 MICINN-FEDER/TIN2009-14511 es_ES
dc.relation MICINN-FEDER/CSD2007-00018 es_ES
dc.relation MICYT/TSI-020110-2009-439 es_ES
dc.relation GV/PROMETEO/2009/014 es_ES
dc.relation UPV/20091027 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
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


This item appears in the following Collection(s)

Show simple item record