Statistical Machine Translation

This first textbook on statistical machine translation shows students and developers how to build an automatic language translation system.

Philipp Koehn (Author)

9780521874151, Cambridge University Press

Hardback, published 17 December 2009

446 pages, 24 b/w illus. 70 exercises
25.4 x 17.9 x 2.5 cm, 1.02 kg

'… Statistical Machine Translation provides an excellent synthesis of a vast amount of literature (the bibliography section takes up 45 double-column pages) and presents it in a well-structured and articulate way. Moreover, the book has been class-tested and contains a set of exercises at the end of each chapter, as well as numerous references to open source tools and resources which enable the diligent reader to build MT systems for any language pair.' Target: International Journal of Translation Studies

The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.

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.

Subject Areas: Natural language & machine translation [UYQL], Probability & statistics [PBT]