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| Reihe | Springer Theses | 
|---|---|
| ISBN | 9789813297470 | 
| Sprache | Englisch | 
| Erscheinungsdatum | 06.09.2019 | 
| Genre | Informatik, EDV/Informatik | 
| Verlag | Springer Singapore | 
| Lieferzeit | Lieferbar in 11 Werktagen | 
| Herstellerangaben | Anzeigen  Springer Nature Customer Service Center GmbH ProductSafety@springernature.com | 
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.
| Reihe | Springer Theses | 
|---|---|
| ISBN | 9789813297470 | 
| Sprache | Englisch | 
| Erscheinungsdatum | 06.09.2019 | 
| Genre | Informatik, EDV/Informatik | 
| Verlag | Springer Singapore | 
| Lieferzeit | Lieferbar in 11 Werktagen | 
| Herstellerangaben | Anzeigen  Springer Nature Customer Service Center GmbH ProductSafety@springernature.com | 
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