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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.
| Autorius: | Yong Cheng |
| Serija: | Springer Theses |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2019 |
| Knygos puslapių skaičius: | 92 |
| ISBN-10: | 9813297476 |
| ISBN-13: | 9789813297470 |
| Formatas: | 241 x 160 x 10 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Joint Training for Neural Machine Translation“
