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Distortion Constraints in Statistical Machine Translation: Constraining word reordering in Machine Translation with rules and knowledge outside of the Statistical MT model - Couverture souple

Olteanu, Marian Gelu

 
9783639145502: Distortion Constraints in Statistical Machine Translation: Constraining word reordering in Machine Translation with rules and knowledge outside of the Statistical MT model
Couverture souple
ISBN 10 : 363914550X ISBN 13 : 9783639145502
Editeur : VDM Verlag, 2009

Synopsis

Statistical machine translation (SMT) offers many benefits over rule based and example based machine translation especially ease to train and robustness. It represents state-of-the-art in machine translation (MT) but it has to deal with certain issues that are not trivial to solve in a statistical framework: correct distortion, correct agreement, morphology issues, etc. In order to solve distortion issues, different models were proposed: syntax-based MT, enhanced distortion models, clause restructuring. All of these define more complex distortion models than monotonic distortion models (which favor lack of word reordering). These proposed models don't completely solve the issue of easily adding linguistic knowledge into the MT decoder. The work proposes a model designed to augment SMT models with linguistic knowledge, either in a rule-based fashion or in a probabilistic fashion. The theoretical framework proposed in this work was implemented in Phramer statistical phrase-based decoder and tested using various levels of knowledge - surface , part of speech, constituency parse trees. The experimental results show improvement in the quality of the translation.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Statistical machine translation (SMT) offers many benefits over rule based and example based machine translation especially ease to train and robustness. It represents state-of-the-art in machine translation (MT) but it has to deal with certain issues that are not trivial to solve in a statistical framework: correct distortion, correct agreement, morphology issues, etc. In order to solve distortion issues, different models were proposed: syntax-based MT, enhanced distortion models, clause restructuring. All of these define more complex distortion models than monotonic distortion models (which favor lack of word reordering). These proposed models don't completely solve the issue of easily adding linguistic knowledge into the MT decoder. The work proposes a model designed to augment SMT models with linguistic knowledge, either in a rule-based fashion or in a probabilistic fashion. The theoretical framework proposed in this work was implemented in Phramer statistical phrase-based decoder and tested using various levels of knowledge - surface , part of speech, constituency parse trees. The experimental results show improvement in the quality of the translation.

Biographie de l'auteur

Marian Gelu Olteanu was born on October 30, 1978 in Pite?ti, Romania. Marian attended Politehnica University of Bucharest where he received his B.S. in Computer Science in 2002. He also attended The University of Texas at Dallas, where he received his M.S. in Computer Science in 2004 and his PhD in Computer Science in 2007.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
363914550X
ISBN 13 
9783639145502
Reliure
Broché
Nombre de pages
112
Coordonnées du fabricant
non disponible
Personne responsable
non disponible