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Joint training for neural machine tr...

Cheng, Yong.

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Joint training for neural machine translation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Joint training for neural machine translation/ by Yong Cheng.
Author:	Cheng, Yong.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xiii, 78 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Neural Machine Translation -- 3. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation -- 4. Semi-supervised Learning for Neural Machine Translation -- 5. Joint Training for Pivot-based Neural Machine Translation -- 6. Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning -- 7. Related Work -- 8. Conclusion.
Contained By:	Springer Nature eBook
Subject:	Machine translating. -
Online resource:	https://doi.org/10.1007/978-981-32-9748-7
ISBN:	9789813297487

Joint training for neural machine translation
Cheng, Yong.

Joint training for neural machine translation[electronic resource] /by Yong Cheng. - Singapore :Springer Singapore :2019. - xiii, 78 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

1. Introduction -- 2. Neural Machine Translation -- 3. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation -- 4. Semi-supervised Learning for Neural Machine Translation -- 5. Joint Training for Pivot-based Neural Machine Translation -- 6. Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning -- 7. Related Work -- 8. Conclusion.

This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.

ISBN: 9789813297487

Standard No.: 10.1007/978-981-32-9748-7doiSubjects--Topical Terms:

659554
Machine translating.

LC Class. No.: P308 / .C44 2019

Dewey Class. No.: 418.020285

Joint training for neural machine translation
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245 1 0 $a Joint training for neural machine translation $h [electronic resource] / $c by Yong Cheng.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xiii, 78 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a 1. Introduction -- 2. Neural Machine Translation -- 3. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation -- 4. Semi-supervised Learning for Neural Machine Translation -- 5. Joint Training for Pivot-based Neural Machine Translation -- 6. Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning -- 7. Related Work -- 8. Conclusion.
520 $a This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.
650 0 $a Machine translating. $3 659554
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Natural Language Processing (NLP) $3 3381674
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