Neural Machine Translation

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.

• 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.

'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, Machine Translation