Buch, Englisch, 78 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 313 g
Reihe: Springer Theses
Buch, Englisch, 78 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 313 g
Reihe: Springer Theses
ISBN: 978-981-329-747-0
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Introduction.- 2. Neural Machine Translation.- 3. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation.- 4. Semi-supervised Learning for Neural Machine Translation.- 5. Joint Training for Pivot-based Neural Machine Translation.- 6. Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning.- 7. Related Work.- 8. Conclusion.
