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Record Nr. |
UNINA9910349303003321 |
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Autore |
Cheng Yong |
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Titolo |
Joint Training for Neural Machine Translation / / by Yong Cheng |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (90 pages) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053 |
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Disciplina |
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Soggetti |
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Natural language processing (Computer science) |
Artificial intelligence |
Computer logic |
Natural Language Processing (NLP) |
Logic in AI |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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 |
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