Joint Training for Neural Machine Translation - Rilegato

Cheng, Yong

 
9789813297470: Joint Training for Neural Machine Translation
Rilegato
ISBN 10: 9813297476 ISBN 13: 9789813297470
Casa editrice: Springer-Nature New York Inc, 2019

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Yong Cheng is currently a software engineer engaged in research at Google. Before joining Google, he worked as a senior researcher at Tencent AI Lab. He obtained his Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in 2017. His research interests focus on neural machine translation and natural language processing.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer-Nature New York Inc
Data di pubblicazione
2019
Lingua
Inglese
ISBN 10 
9813297476
ISBN 13 
9789813297470
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
92
Contatto del produttore
non disponibile
Persona responsabile
CMN Printpool Inh. Mathis Neumann
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