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
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George Foster is a researcher in the Interactive Language Technologies Group at the Canadian National Research Council's Institute for Information Technology.
Marc Dymetman is a researcher in the Cross-Language Technologies Research Group at the Xerox Research Centre Europe.
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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multilingual corpora, composite subsequences, lexical overlapping, word alignments based, discriminative language modeling, training sentence pairs, current source word, improved lexical choice, lexical choice model, phrase translation model, factored kernel, multiple remappings, consonant signature, noisy alignments, current target word, transliteration model, transliteration candidates, best single system, patent corpus, confusion network, consensus translation, individual perceptron, sentence alignments, cosine angle distance, permutation automaton Key Phrases - Capitalized Phrases (CAPs): (learn more)
Statistical Machine Translation Primer, Combination of Statistical Word Alignments Based, Exploiting Local, Omar Hassan, Combine Machine Translation Systems, Related Work, Automatic Construction of Multilingual Name Dictionaries, Linguistic Data Consortium, Multiple Preprocessing Schemes, Chinese Gigaword, Emile Lahoud, Defense Advanced Research Projects Agency, Kernel-Based Machine Translation, Condoleezza Rice, Omar Al Beshir, Linguistically Enriched Word-Sequence Kernels, United States, Experimental Validation, Evaluation of Machine Translation, Local Phrase Translation Table, Vladimir Putin, European Parliament, English Gigaword, Target Sentence, Name Variants Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
multilingual corpora, composite subsequences, lexical overlapping, word alignments based, discriminative language modeling, training sentence pairs, current source word, improved lexical choice, lexical choice model, phrase translation model, factored kernel, multiple remappings, consonant signature, noisy alignments, current target word, transliteration model, transliteration candidates, best single system, patent corpus, confusion network, consensus translation, individual perceptron, sentence alignments, cosine angle distance, permutation automaton Key Phrases - Capitalized Phrases (CAPs): (learn more)
Statistical Machine Translation Primer, Combination of Statistical Word Alignments Based, Exploiting Local, Omar Hassan, Combine Machine Translation Systems, Related Work, Automatic Construction of Multilingual Name Dictionaries, Linguistic Data Consortium, Multiple Preprocessing Schemes, Chinese Gigaword, Emile Lahoud, Defense Advanced Research Projects Agency, Kernel-Based Machine Translation, Condoleezza Rice, Omar Al Beshir, Linguistically Enriched Word-Sequence Kernels, United States, Experimental Validation, Evaluation of Machine Translation, Local Phrase Translation Table, Vladimir Putin, European Parliament, English Gigaword, Target Sentence, Name Variants Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
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