Philipp Koehn (The Johns Hopkins University)

Neural Machine Translation

• Gebundenes Buch

Produktbeschreibung

Deep learning is revolutionizing how machine translation systems are built today. This introduction to machine translation starts from the basics of neural network methods and reaches the state of the art, while giving illuminating historical, linguistic, and applied context. Code examples in Python give a hands-on blueprint for implementation.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 408
• Erscheinungstermin: 18. Juni 2020
• Englisch
• Abmessung: 250mm x 175mm x 26mm
• Gewicht: 854g
• ISBN-13: 9781108497329
• ISBN-10: 1108497322
• Artikelnr.: 58818674

Autorenporträt

Philipp Koehn is a leading researcher in the field of machine translation and Professor of Computer Science at Johns Hopkins University. In 2010 he authored the textbook Statistical Machine Translation (Cambridge). He received the Award of Honor from the International Association for Machine Translation and was one of three finalists for the European Inventor Award of the European Patent Office in 2013. Professor Koehn also works actively in industry as Chief Scientist for Omniscien Technology and as a consultant for Facebook.

Inhaltsangabe

Part I. Introduction: 1. The Translation Problem
2. Uses of Machine Translation
3. History
4. Evaluation
Part II. Basics: 5. Neural Networks
6. Computation Graphs
7. Neural Language Models
8. Neural Translation Models
9. Decoding
Part III. Refinements: 10. Machine Learning Tricks
11. Alternate Architectures
12. Revisiting Words
13. Adaptations
14. Beyond Parallel Corpora
15. Linguistic Structure
16. Current Challenges
17. Analysis and Visualization.