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03139nam a2200481 4500 |
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190826s2019 si | s |||| 0|eng d |
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|a 9789813297487
|9 978-981-32-9748-7
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|a 10.1007/978-981-32-9748-7
|2 doi
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|a QA76.9.N38
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|a COM073000
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|a 006.35
|2 23
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|a Cheng, Yong.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Joint Training for Neural Machine Translation
|h [electronic resource] /
|c by Yong Cheng.
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|a 1st ed. 2019.
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|a Singapore :
|b Springer Singapore :
|b Imprint: Springer,
|c 2019.
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|a XIII, 78 p. 23 illus., 9 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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|a 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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|a 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.
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|a Natural language processing (Computer science).
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|a Artificial intelligence.
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|a Computer logic.
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|a Natural Language Processing (NLP).
|0 http://scigraph.springernature.com/things/product-market-codes/I21040
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|a Logic in AI.
|0 http://scigraph.springernature.com/things/product-market-codes/I21020
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9789813297470
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|i Printed edition:
|z 9789813297494
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053
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|u https://doi.org/10.1007/978-981-32-9748-7
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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|a Computer Science (Springer-11645)
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