Joint Training for Neural Machine Translation

Joint Training for Neural Machine Translation Front Cover
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78 pages

Book Description

This book presents four approaches to jointly bidirectional neural machine translation (NMT) . First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary 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 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.

Book Details

  • Title: Joint Training for Neural Machine Translation
  • Author:
  • Length: 78 pages
  • Edition: 1st ed. 2019
  • Language: English
  • Publisher:
  • Publication Date: 2019-11-05
  • ISBN-10: 9813297476
  • ISBN-13: 9789813297470
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