Learning machine translation /
Goutte, Cyril.
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, Jesƒus Gimňez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mah,̌ Llus̕ Mr̉quez, 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.
Description based upon print version of record.
Contents; Series Foreword; Preface; 1 A Statistical Machine Translation Primer; I Enabling Technologies; 2 Mining Patents for Parallel Corpora; 3 Automatic Construction of Multilingual Name Dictionaries; 4 Named Entity Transliteration and Discovery in Multilingual Corpora; 5 Combination of Statistical Word Alignments Based on Multiple Preprocessing Schemes; 6 Linguistically Enriched Word-Sequence Kernels for Discriminative Language Modeling; II Machine Translation; 7 Toward Purely Discriminative Training for Tree-Structured Translation Models
8 Reranking for Large-Scale Statistical Machine Translation9 Kernel-Based Machine Translation; 10 Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction; 11 Discriminative Phrase Selection for SMT; 12 Semisupervised Learning for Machine Translation; 13 Learning to Combine Machine Translation Systems; References; Contributors; Index
Mode of access: World Wide Web
Made available online by Ebrary.
Includes bibliographical references (p. [277]-306) and index.
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, Jesƒus Gimňez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mah,̌ Llus̕ Mr̉quez, 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.
Description based on PDF viewed 12/23/2015.
Machine translating
Translating and interpreting
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