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Statistical Machine Translation
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  • Cited by 47
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Le, Ngoc Tan and Sadat, Fatiha 2018. Human Language Technology. Challenges for Computer Science and Linguistics. Vol. 10930, Issue. , p. 407.

    Besold, T. R. and Uckelman, S. L. 2018. Normative and descriptive rationality: from nature to artifice and back. Journal of Experimental & Theoretical Artificial Intelligence, Vol. 30, Issue. 2, p. 331.

    Hadj Ameur, Mohamed Seghir Khadir, Ahlem Chérifa and Guessoum, Ahmed 2018. Arabic Language Processing: From Theory to Practice. Vol. 782, Issue. , p. 3.

    Salami, Shahram and Shamsfard, Mehrnoush 2018. Phrase-Boundary Translation Model Using Shallow Syntactic Labels. Signal and Data Processing, Vol. 15, Issue. 1, p. 115.

    Saiful Islam, Md. and Purkayastha, Bipul Syam 2018. Advanced Computing and Communication Technologies. Vol. 562, Issue. , p. 207.

    Singh, Shivkaran Anand Kumar, M. Soman, K.P. Thampi, Sabu M. El-Alfy, El-Sayed M. Mitra, Sushmita and Trajkovic, Ljiljana 2018. Attention based English to Punjabi neural machine translation. Journal of Intelligent & Fuzzy Systems, Vol. 34, Issue. 3, p. 1551.

    Hayashi, Yuko and Yanagimoto, Hidekazu 2018. New Trends in E-service and Smart Computing. Vol. 742, Issue. , p. 81.

    Munigala, Vitobha Mishra, Abhijit Tamilselvam, Srikanth G. Khare, Shreya Dasgupta, Riddhiman and Sankaran, Anush 2018. PersuAIDE ! An Adaptive Persuasive Text Generation System for Fashion Domain. p. 335.

    Barmpoutis, Angelos 2018. Learning Programming Languages as Shortcuts to Natural Language Token Replacements. p. 1.

    Fujita, Atsushi and Isabelle, Pierre 2018. Expanding Paraphrase Lexicons by Exploiting Generalities. ACM Transactions on Asian and Low-Resource Language Information Processing, Vol. 17, Issue. 2, p. 1.

    Maia, Belinda and Santos, Diana 2018. Language, emotion, and the emotions: The multidisciplinary and linguistic background. Language and Linguistics Compass, Vol. 12, Issue. 6, p. e12280.

    Pathak, Amarnath Pakray, Partha and Bentham, Jereemi 2018. English–Mizo Machine Translation using neural and statistical approaches. Neural Computing and Applications,

    Farzi, Saeed Faili, Heshaam and Kianian, Sahar 2018. A preordering model based on phrasal dependency tree. Digital Scholarship in the Humanities, Vol. 33, Issue. 4, p. 748.

    Wołk, Krzysztof Zawadzka, Emilia and Wołk, Agnieszka 2018. Trends and Advances in Information Systems and Technologies. Vol. 745, Issue. , p. 797.

    Castilho, Sheila Doherty, Stephen Gaspari, Federico and Moorkens, Joss 2018. Translation Quality Assessment. Vol. 1, Issue. , p. 9.

    Wołk, Krzysztof Glinkowski, Wojciech and Żukowska, Agnieszka 2018. Trends and Advances in Information Systems and Technologies. Vol. 746, Issue. , p. 351.

    Grami, Grami Mohammad A. Alkazemi, Basim Y. Nour, Mohamed K. Naseer, Atif and Al-Doobi, Husam 2017. A Proposed Model to Address Current Errors in English into Arabic Machine Translation. p. 116.

    Nakayama, Hideki and Nishida, Noriki 2017. Zero-resource machine translation by multimodal encoder–decoder network with multimedia pivot. Machine Translation, Vol. 31, Issue. 1-2, p. 49.

    Goldberg, Yoav 2017. Neural Network Methods for Natural Language Processing. Synthesis Lectures on Human Language Technologies, Vol. 10, Issue. 1, p. 1.

    Wołk, Krzysztof and Marasek, Krzysztof 2017. Multimedia and Network Information Systems. Vol. 506, Issue. , p. 307.


Book description

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.


'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

'The book primarily represents an ideal introduction to the field of statistical machine translation, but also tackles many of the recent results in this area. It is the product of the many years of both active research and extensive teaching of the author … Each chapter is additionally endowed with a summary, further reading and exercises, achieving thus completely the proposed goal of an accessible introduction to the statistical machine translation field. Apart from its formative role for beginners, the book also stands as a complete guide for researchers in a domain of high interest and rapid expansion … For all these reasons, this book should be welcomed as a highly valuable publication.'

Source: Zentralblatt MATH

'… 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.'

Source: Target: International Journal of Translation Studies

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