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[null,"1.","Record Nr.",null,"UNINA9910349303003321"]
[null,null,"Autore",null,"Cheng Yong"]
[null,null,"Titolo",null,"Joint Training for Neural Machine Translation / / by Yong Cheng"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019"]
[null,null,"ISBN",null,"981-329-748-4"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (90 pages)"]
[null,null,"Collana",null,"Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053"]
[null,null,"Disciplina",null,"418.020285"]
[null,null,"Soggetti",null,"Natural language processing (Computer science)"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer logic"]
[{"$nulls":4},"Natural Language Processing (NLP)"]
[{"$nulls":4},"Logic in AI"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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."]