Joint Training for Neural Machine Translation / / by Yong Cheng
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| Autore: |
Cheng Yong
|
| Titolo: |
Joint Training for Neural Machine Translation / / by Yong Cheng
|
| Pubblicazione: | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Edizione: | 1st ed. 2019. |
| Descrizione fisica: | 1 online resource (90 pages) |
| Disciplina: | 418.020285 |
| Soggetto topico: | Natural language processing (Computer science) |
| Artificial intelligence | |
| Computer logic | |
| Natural Language Processing (NLP) | |
| Logic in AI | |
| Nota di contenuto: | 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. |
| 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. |
| Titolo autorizzato: | Joint Training for Neural Machine Translation |
| ISBN: | 981-329-748-4 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910349303003321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Springer Theses, Recognizing Outstanding Ph.D. Research, . 2190-5053