Statistical Machine Translation

Philipp Koehn is a lecturer in the School of Informatics at the University of Edinburgh. He is the scientific co-ordinator of the European EuroMatrix project and also involved in research funded by DARPA in the USA. He has also collaborated with leading companies in the field, such as Systran and Asia Online. He implemented the widely used decoder Pharoah, and is leading the development of the open source machine translation toolkit Moses.

• Preface; Part I. Foundations: 1. Introduction; 2. Words, sentences, corpora; 3. Probability theory; Part II. Core Methods: 4. Word-based models; 5. Phrase-based models; 6. Decoding; 7. Language models; 8. Evaluation; Part III. Advanced Topics: 9. Discriminative training; 10. Integrating linguistic information; 11. Tree-based models; Bibliography; Author index; Index.

'Philipp Koehn has provided the first comprehensive text for the rapidly growing field of statistical machine translation. This book is an invaluable resource for students, researchers, and software developers, providing a lucid and detailed presentation of all the important ideas needed to understand or create a state-of-the-art statistical machine translation system.' Robert C. Moore, Principal Researcher, Microsoft Research