The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation - Couverture souple

Bi�ici, Mehmet Ergun

9783846507490: The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation

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ISBN 10 : 3846507490 ISBN 13 : 9783846507490

Editeur : LAP LAMBERT Academic Publishing, 2011

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Regression based machine translation (RegMT) model provides a learning framework for machine translation, separating learning models for training, training instance selection, feature representation, and decoding. Transductive learning approach employs training instance selection algorithms that not only make RegMT computationally more scalable but also improve the performance of standard statistical machine translation (SMT) systems. Sparse regression models for SMT are introduced and the obtained results demonstrate that sparse regression models perform better than other learning models in predicting target features, estimating word alignments, creating phrase tables, and generating translation outputs. We develop good evaluation techniques for measuring the performance of the RegMT model and the quality of the translations. We demonstrate that sparse L1 regularized regression performs better than L2 regularized regression in the German-English translation task and in the Spanish-English translation task when using small sized training sets. Graph based decoding can provide an alternative to phrase-based decoding in translation domains having low vocabulary.

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�diteurLAP LAMBERT Academic Publishing
Date d'�dition2011
ISBN 10 3846507490
ISBN 13 9783846507490
ReliureBroch�
Langueanglais
Nombre de pages172
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The Regression Model of Machine Translation

Mehmet Ergun Bi�ici

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846507490 ISBN 13 : 9783846507490

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The Regression Model of Machine Translation : Learning, Instance Selection, Decoding, and Evaluation

Mehmet Ergun Bi�ici

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846507490 ISBN 13 : 9783846507490

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Regression based machine translation (RegMT) model provides a learning framework for machine translation, separating learning models for training, training instance selection, feature representation, and decoding. Transductive learning approach employs training instance selection algorithms that not only make RegMT computationally more scalable but also improve the performance of standard statistical machine translation (SMT) systems. Sparse regression models for SMT are introduced and the obtained results demonstrate that sparse regression models perform better than other learning models in predicting target features, estimating word alignments, creating phrase tables, and generating translation outputs. We develop good evaluation techniques for measuring the performance of the RegMT model and the quality of the translations. We demonstrate that sparse L1 regularized regression performs better than L2 regularized regression in the German-English translation task and in the Spanish-English translation task when using small sized training sets. Graph based decoding can provide an alternative to phrase-based decoding in translation domains having low vocabulary. N� de r�f. du vendeur 9783846507490

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The Regression Model of Machine Translation

Mehmet Ergun Bi�ici

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846507490 ISBN 13 : 9783846507490

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Regression based machine translation (RegMT) model provides a learning framework for machine translation, separating learning models for training, training instance selection, feature representation, and decoding. Transductive learning approach employs training instance selection algorithms that not only make RegMT computationally more scalable but also improve the performance of standard statistical machine translation (SMT) systems. Sparse regression models for SMT are introduced and the obtained results demonstrate that sparse regression models perform better than other learning models in predicting target features, estimating word alignments, creating phrase tables, and generating translation outputs. We develop good evaluation techniques for measuring the performance of the RegMT model and the quality of the translations. We demonstrate that sparse L1 regularized regression performs better than L2 regularized regression in the German-English translation task and in the Spanish-English translation task when using small sized training sets. Graph based decoding can provide an alternative to phrase-based decoding in translation domains having low vocabulary. 172 pp. Englisch. N� de r�f. du vendeur 9783846507490

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The Regression Model of Machine Translation

Mehmet Ergun Bi�ici

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846507490 ISBN 13 : 9783846507490

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -Regression based machine translation (RegMT) model provides a learning framework for machine translation, separating learning models for training, training instance selection, feature representation, and decoding. Transductive learning approach employs training instance selection algorithms that not only make RegMT computationally more scalable but also improve the performance of standard statistical machine translation (SMT) systems. Sparse regression models for SMT are introduced and the obtained results demonstrate that sparse regression models perform better than other learning models in predicting target features, estimating word alignments, creating phrase tables, and generating translation outputs. We develop good evaluation techniques for measuring the performance of the RegMT model and the quality of the translations. We demonstrate that sparse L1 regularized regression performs better than L2 regularized regression in the German-English translation task and in the Spanish-English translation task when using small sized training sets. Graph based decoding can provide an alternative to phrase-based decoding in translation domains having low vocabulary.Books on Demand GmbH, �berseering 33, 22297 Hamburg 172 pp. Englisch. N� de r�f. du vendeur 9783846507490

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