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Cost-sensitive active learning for computer-assisted translation

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Cost-sensitive active learning for computer-assisted translation

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dc.contributor.author González Rubio, Jesús es_ES
dc.contributor.author Casacuberta Nolla, Francisco es_ES
dc.date.accessioned 2014-09-26T18:12:17Z
dc.date.available 2014-09-26T18:12:17Z
dc.date.issued 2014-02
dc.identifier.issn 0167-8655
dc.identifier.uri http://hdl.handle.net/10251/40333
dc.description This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 37, 1 February 2014, Pages 124–134] DOI: 10.1016/j.patrec.2013.06.007 es_ES
dc.description.abstract [EN] Machine translation technology is not perfect. To be successfully embedded in real-world applications, it must compensate for its imperfections by interacting intelligently with the user within a computer-assisted translation framework. The interactive¿predictive paradigm, where both a statistical translation model and a human expert collaborate to generate the translation, has been shown to be an effective computer-assisted translation approach. However, the exhaustive supervision of all translations and the use of non-incremental translation models penalizes the productivity of conventional interactive¿predictive systems. We propose a cost-sensitive active learning framework for computer-assisted translation whose goal is to make the translation process as painless as possible. In contrast to conventional active learning scenarios, the proposed active learning framework is designed to minimize not only how many translations the user must supervise but also how difficult each translation is to supervise. To do that, we address the two potential drawbacks of the interactive-predictive translation paradigm. On the one hand, user effort is focused to those translations whose user supervision is considered more ¿informative¿, thus, maximizing the utility of each user interaction. On the other hand, we use a dynamic machine translation model that is continually updated with user feedback after deployment. We empirically validated each of the technical components in simulation and quantify the user effort saved. We conclude that both selective translation supervision and translation model updating lead to important user-effort reductions, and consequently to improved translation productivity. es_ES
dc.description.sponsorship Work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat Project (Grants agreement No. 287576), by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/014), and by the Spanish Government under Grant TIN2012-31723. The authors thank Daniel Ortiz-Martinez for providing us with the log-linear SMT model with incremental features and the corresponding online learning algorithms. The authors also thank the anonymous reviewers for their criticisms and suggestions. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation GV/PROMETEO/2009/014) es_ES
dc.relation MICINN/TIN2012-31723 es_ES
dc.relation.ispartof Pattern Recognition Letters es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Computer-assisted translation es_ES
dc.subject Interactive machine translation es_ES
dc.subject Active learning es_ES
dc.subject Online learning es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Cost-sensitive active learning for computer-assisted translation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.patrec.2013.06.007
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/287576 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 González Rubio, J.; Casacuberta Nolla, F. (2014). Cost-sensitive active learning for computer-assisted translation. Pattern Recognition Letters. 37(1):124-134. doi:10.1016/j.patrec.2013.06.007 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.patrec.2013.06.007 es_ES
dc.description.upvformatpinicio 124 es_ES
dc.description.upvformatpfin 134 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 37 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 254549
dc.contributor.funder European Commission
dc.contributor.funder Generalitat Valenciana (GV)
dc.contributor.funder Ministerio de Ciencia e Innovación (MICINN)


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