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The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation - Softcover
Inhaltsangabe
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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Reseña del editor
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.
Biografía del autor
Mehmet Ergun Biçici received his Bachelor of Science degree in Computer Science from Bilkent University, Ankara, Turkey in 2000. He obtained Master of Science degree in Computer Science from North Carolina State University, USA, in 2002. He obtained the Doctor of Philosophy degree in Computer Engineering at Koç University, Istanbul, Turkey in 2011.
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- VerlagLAP LAMBERT Academic Publishing
- Erscheinungsdatum2011
- ISBN 10 3846507490
- ISBN 13 9783846507490
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten172
- Kontakt zum HerstellerNicht verfügbar
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The Regression Model of Machine Translation
Verlag:
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
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783846507490
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The Regression Model of Machine Translation
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LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783846507490
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The Regression Model of Machine Translation
Verlag:
LAP LAMBERT Academic Publishing Nov 2011, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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Taschenbuch. Zustand: 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, Überseering 33, 22297 Hamburg 172 pp. Englisch. Bestandsnummer des Verkäufers 9783846507490
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The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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Beispielbild für diese ISBN
The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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Paperback
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Paperback. Zustand: Brand New. 172 pages. 8.66x5.91x0.39 inches. In Stock. Bestandsnummer des Verkäufers 3846507490
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The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846507490
ISBN 13: 9783846507490
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