Artículos relacionados a Distortion Constraints in Statistical Machine Translation:...

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

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
Tapa blanda
ISBN 10: 363914550X ISBN 13: 9783639145502
Editorial: VDM Verlag, 2009

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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.

Biografía del autor

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
VDM Verlag
Año de publicación
2009
Idioma
Inglés
ISBN 10 
363914550X
ISBN 13 
9783639145502
Encuadernación
Tapa blanda
Número de páginas
112
Contacto del fabricante
no disponible
Persona responsable
no disponible