Abstract
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.
Highlights
Part I of the book serves as an introduction to MT in general and Neural machine translation (NMT) in particular
Part I of the book serves as an introduction to MT in general and NMT in particular
MT systems were trained with the use of neural networks (NNs)-based end-to-end learning protocols and with the addition of the attention mechanism the same translation quality could be achieved with NMT systems as with statistical MT (SMT)
Summary
Part I of the book serves as an introduction to MT in general and NMT in particular. In four chapters, it covers some of the important issues and concepts, how MT is used today, the history of MT and how MT can be evaluated. The short first chapter entitled “The Translation Problem” begins with examples as to why MT can be so difficult It lists some of the Natural Language Processing (NLP) abstractions to have emerged over the years which have proven useful to explain these difficulties. Human assessments are a lot more accurate than automatic evaluation methods, but are slow and costly so cannot be used as frequently as one might like In this part of the chapter BLEU (Papineni et al 2002), METEOR (Banerjee and Lavie 2005), TER (and HTER) (Snover et al 2006) and characTER (Wang et al 2016) are described and compared to one another in terms of advantages and disadvantages. Some of the more recently proposed automatic evaluation metrics are briefly covered
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