Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

‘This book can essentially be viewed as an important contribution to the increasingly important area of neural MT, which will be a great help to NLP researchers, scientists, academics, undergraduate or postgraduate students, and MT researchers and users in particular.’

Wandri Jooste, Rejwanul Haque, and Andy Way Source: Machine Translation

‘This book can essentially be viewed as an important contribution to the increasingly important area of neural MT, which will be a great help to NLP researchers, scientists, academics, undergraduate or postgraduate students, and MT researchers and users in particular.’

Wandri Jooste, Rejwanul Haque,·Andy Way Source: Machine Translation