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Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation

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Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation

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dc.contributor.advisor Casacuberta Nolla, Francisco es_ES
dc.contributor.advisor García Varea, Ismael es_ES
dc.contributor.author Ortiz Martínez, Daniel es_ES
dc.date.accessioned 2011-10-14T11:47:47Z
dc.date.available 2011-10-14T11:47:47Z
dc.date.created 2011-10-07T08:00:00Z es_ES
dc.date.issued 2011-10-14T11:47:43Z es_ES
dc.identifier.uri http://hdl.handle.net/10251/12127
dc.description.abstract This thesis presents different contributions in the fields of fully-automatic statistical machine translation and interactive statistical machine translation. In the field of statistical machine translation there are three problems that are to be addressed, namely, the modelling problem, the training problem and the search problem. In this thesis we present contributions regarding these three problems. Regarding the modelling problem, an alternative derivation of phrase-based statistical translation models is proposed. Such derivation introduces a set of statistical submodels governing different aspects of the translation process. In addition to this, the resulting submodels can be introduced as components of a log-linear model. Regarding the training problem, an alternative estimation technique for phrase-based models that tries to reduce the strong heuristic component of the standard estimation technique is proposed. The proposed estimation technique considers the phrase pairs that compose the phrase model as part of complete bisegmentations of the source and target sentences. We theoretically and empirically demonstrate that the proposed estimation technique can be efficiently executed. Experimental results obtained with the open-source THOT toolkit also presented in this thesis, show that the alternative estimation technique obtains phrase models with lower perplexity than those obtained by means of the standard estimation technique. However, the reduction in the perplexity of the model did not allow us to obtain improvements in the translation quality. To deal with the search problem, we propose a search algorithm which is based on the branch-and-bound search paradigm. The proposed algorithm generalises different search strategies that can be accessed bymodifying the input parameters. We carried out experiments to evaluate the performance of the proposed search algorithm. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.rights Reserva de todos los derechos es_ES
dc.source Riunet es_ES
dc.subject Informática es_ES
dc.subject Traducción automática es_ES
dc.subject Reconocimiento de formas es_ES
dc.subject Procesamiento de lenguaje natural es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation
dc.type Tesis doctoral es_ES
dc.identifier.doi 10.4995/Thesis/10251/12127 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 Ortiz Martínez, D. (2011). Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12127 es_ES
dc.description.accrualMethod Palancia es_ES
dc.type.version info:eu-repo/semantics/acceptedVersion es_ES
dc.relation.tesis 3666 es_ES


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