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Joint Training for Neural Machine Translation

Joint Training for Neural Machine Translation

Yong Cheng

66,62 €
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Editorial:
Springer Nature B.V.
Año de edición:
2019
Materia
Inteligencia artificial
ISBN:
9789813297470
66,62 €
IVA incluido
Disponible

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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.

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