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Joint Training for Neural Machine Translation

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 matric...

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Main Author: Cheng, Yong. (Author, http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Singapore : Springer Singapore : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Springer Theses, Recognizing Outstanding Ph.D. Research,
Subjects:
Natural language processing (Computer science).
Artificial intelligence.
Computer logic.
Natural Language Processing (NLP).
Logic in AI.
Online Access:https://doi.org/10.1007/978-981-32-9748-7
Table of Contents:
  • 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.

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