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.
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