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Description:
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The Internet gives us access to a wealth of information in languages we
don't understand. The investigation of automated or semi-automated
approaches to translation has become a thriving research field with
enormous commercial potential. This volume investigates how machine
learning techniques can improve statistical machine translation, currently
at the forefront of research in the field.
The book looks first at enabling technologies: technologies that solve
problems that are not machine translation proper but are linked closely to
the development of a machine translation system. These include the
acquisition of bilingual sentence-aligned data from comparable corpora,
automatic construction of multilingual name dictionaries, and word
alignment. The book then presents new or improved statistical machine
translation techniques, including a discriminative training framework for
leveraging syntactic information, the use of semi-supervised and
kernel-based learning methods, and the combination of multiple machine
translation outputs in order to improve overall translation quality.
Contributors:
Srinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob
Elming, George Foster, Jesús Giménez, Cyril Goutte, Nizar Habash,
Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak,
Alexandre Klementiev, Gregor Leusch, Pierre Mahé, Lluís Màrquez, Evgeny
Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan
Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian,
Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada
About the Editors:
Cyril Goutte is a researcher in the Interactive Language Technologies Group
at the Canadian National Research Council's Institute for Information
Technology.
Nicola Cancedda is a researcher in the Cross-Language Technologies Research
Group at the Xerox Research Centre Europe.
Marc Dymetman is a researcher in the Cross-Language Technologies Research
Group at the Xerox Research Centre Europe.
George Foster is a researcher in the Interactive Language Technologies
Group at the Canadian National Research Council's Institute for Information
Technology.
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