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Philipp Koehn: Neural Machine Translation

Jooste, Wandri, Haque, Rejwanul and Way, Andy (2021) Philipp Koehn: Neural Machine Translation. Machine Translation, 35 (2). pp. 289-299. ISSN 1573-0573

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Neural machine translation (NMT) is an approach to machine translation (MT) that uses deep learning techniques, a broad area of machine learning based on deep artificial neural networks (NNs). The book Neural Machine Translation by Philipp Koehn targets a broad range of readers including researchers, scientists, academics, advanced undergraduate or postgraduate students, and users of MT, covering wider topics including fundamental and advanced neural network-based learning techniques and methodologies used to develop NMT systems. The book demonstrates different linguistic and computational aspects in terms of NMT with the latest practices and standards and investigates problems relating to NMT. Having read this book, the reader should be able to formulate, design, implement, critically assess and evaluate some of the fundamental and advanced deep learning techniques and methods used for MT. Koehn himself notes that he was somewhat overtaken by events, as originally this book was envisaged only as a chapter in a revised, extended version of his 2009 book Statistical Machine Translation. However, in the interim, NMT completely overtook this previously dominant paradigm, and this new book is likely to serve as the reference of note for the field for some time to come, despite the fact that new techniques are coming onstream all the time.

Item Type: Article
Additional Information: This is a book review of Neural Machine Translation by Philipp Koehn. Publisher: Cambridge University Press; Online publication date: May 2020; Online ISBN: 9781108608480; DOI:
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
P Language and Literature > P Philology. Linguistics > Language Services
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 07 Sep 2021 11:58
Last Modified: 08 Sep 2021 09:23

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