MIT Press
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.ContributorsSrinivas 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
Goutte / Cancedda / Dymetman
Learning Machine Translation jetzt bestellen!
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.ContributorsSrinivas 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
Autoren/Hrsg.
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