Artículos relacionados a Joint Training for Neural Machine Translation (Springer...
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
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.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer
- Año de publicación2019
- ISBN 10 9813297476
- ISBN 13 9789813297470
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas94
- Contacto del fabricanteno disponible
Comprar nuevo
Ver este artículo
EUR 53,49
EUR 11,00 gastos de envío desde Alemania a España
Destinos, gastos y plazos de envíoResultados de la búsqueda para Joint Training for Neural Machine Translation (Springer...
Imagen de archivo
Joint Training for Neural Machine Translation.
Publicado por
Singapore, Springer., 2019
ISBN 10: 9813297476
ISBN 13: 9789813297470
Antiguo o usado
Tapa dura
Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
Calificación del vendedor: 4 de 5 estrellas
XIII, 78 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Springer Theses. Recognizing Outstanding Ph.D. Research. Sprache: Englisch. Nº de ref. del artículo: 10114GB
Cantidad disponible: 3 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Publicado por
Springer Nature Singapore Sep 2019, 2019
ISBN 10: 9813297476
ISBN 13: 9789813297470
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 92 pp. Englisch. Nº de ref. del artículo: 9789813297470
Cantidad disponible: 2 disponibles
Imagen de archivo
Joint Training for Neural Machine Translation (Springer Theses)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9789813297470_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Impresión bajo demandaLibrería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nominated by Tsinghua University as an outstanding Ph.D. thesisReports on current challenges and important advances in neural machine translationAddresses training jointly bidirectional neural machine translation modelsIncorporates a. Nº de ref. del artículo: 303742519
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Publicado por
Springer Nature Singapore, Springer Nature Singapore, 2019
ISBN 10: 9813297476
ISBN 13: 9789813297470
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9789813297470
Cantidad disponible: 1 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 37734954-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 37734954-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 37734954
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 37734954
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Joint Training for Neural Machine Translation
Publicado por
Springer Nature Singapore, Springer Nature Singapore Sep 2019, 2019
ISBN 10: 9813297476
ISBN 13: 9789813297470
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 92 pp. Englisch. Nº de ref. del artículo: 9789813297470
Cantidad disponible: 2 disponibles