Philipp Koehn (The Johns Hopkins University)

Neural Machine Translation (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press (Virtual Publishing)
• Erscheinungstermin: 10. Juni 2020
• ISBN-13: 9781108601764
• Artikelnr.: 70909762

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.