Departamento de
Traducción e Interpretación


Tema:   Automática.
Autor:   Richardson, Stephen D. (ed.)
Año:   2002
Título:   Machine Translation: from Research to Real Users
Lugar:   Berlin
Editorial/Revista:   Springer
Páginas:   254
Idioma:   Inglés.
Tipo:   Libro.
ISBN/ISSN/DOI:   ISBN: 3540442820 (pbk.)
Colección:   Lecture notes in computer science, 2499 - Lecture
Disponibilidad:   Alicante BG
Índice:   1. Automatic Rule Learning for Resource-Limited MT, Jaime Carbonell, Katharina Probst, Erik Peterson, Christina Monson, Alon Lavie, Ralf Brown & Lori Levin 1-10; 2. Toward a Hybrid lntegrated Translation Environment, Michael Carl, Andy Way, & Reinhard Schäler 11.20; 3. Adaptive Bilingual Sentence Alignment, Thomas C. Chuang, GN You, & Jason S Chang 21-30; 4. DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment, Bonnie J. Dorr, Lisa Pearl, Rebecca Hwa & Nizar Habash 31-43; 5. Text Prediction with Fuzzy Alignments, George Foster, Philippe Langlais & Guy Lapalme 44-53; 6. Efficient Integration of Maximum Entropy Lexicon Models within the Training of Statistical Alignment Models Ismael Garcia-Varea, Franz J Och, Hermann Ney & Francisco Casacuberta 54-63; 7. Using Word Formation Rules to Extend MT Lexicons, Claudia Gdaniec & Esmé Manandise 64-73; 8. Example-Based Machine Translation via the Web, Nano Gough, Andy Way & Mary Hearne 74-83; 9. Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation, Nizar Habash & Bonnie Dorr 84-93; 10. Korean-Chinese Machine Translation Based on Verb Patterns, Changhyun Kim, Munpyo Hong, Yinxia Huang, Young Kil Kim, Sung Il Yang, Young Ae Seo & Sung-Kwon Choi 94-103; 11. Merging Example-Based and Statistical Machine Translation: An Experiment, Philippe Langlais & Michel Simard 104-113; 12 Classification Approach to Word Selection in Machine Translation, Hyo-Kyung Lee 114-123; 13. Better Contextual Translation Using Machine Learning, Arul Menezes 124-134; 14. Fast and Accurate Sentence Alignment of Bilingual Corpora, Robert C. Moore 135-144; 15. Deriving Semantic Knowledge from Descriptive Texts Using an MT System, Eric Nyberg, Teruko Mitamura, Kathryn Baker, David Svoboda, Brian Peterson & Jennifer Williams 145-154; 16. Using a Large Monolingual Corpus to Improve Translation Accuracy, Radie Soricut, Kevin Knight & Daniel Marcu 155-164; 17. Semi-automatic Compilation of Bilingual Lexicon Entries from PA Cross-Lingually Relevant News Articles on WWW News Sites, Takehito Utsuro, Takashi Horiuchi, Yasunobu Chiba & Takeshi Hamamoto165-176; 18. Bootstrapping the Lexicon Building Process for Machine Translation between 'New' Languages, Ruvan Weerasinghe 177-186; 19. A Report on the Experiences of Implementing an MT System for Use in a Commercial Environment, Anthony Clarke, Elisabeth Maier & Hans-Udo Stadler 187-194; 20. Getting the Message In: A Global Company's Experience with the New Generation of Low-Cost, High Performance Machine Translation Systems, Verne Morland 195-206; 21. An Assessment of Machine Translation for Vehicle Assembly Process Planning at Ford Motor Company, Nestor Rychtyckyj 207-215; 22. Fluent Machines' EliMT System, Eli Abir, Steve Klein, David Miller & Michael Steinbaum 216-219; 23. LogoMedia TRANSLATE, version 2.0, Glenn A. Akers 220-223; 24. Natural Intelligence in a Machine Translation System, Howard J. Bender 224-228; 25. Translation by the Numbers: Language Weaver, Bruce Benjamin, Kevin Knight & Daniel Marcu 229-231; 26. A New Family of the PARS Translation Systems, Michael Blekhman, Andrei Kursin, & Alla Rakova 232-236; 27. MSR-MT: The Microsoft Research Machine Translation System, William B. Dolan, Jessie Pinkham & Stephen D. Richardson 237-239; 28. The NESPOLE! Speech-to-Speech Translation System, Alon Lavie, Lori Levin, Robert Frederking & Fabio Pianesi 240-243; 29. The KANTOO MT System: Controlled Language Checker and Lexical Maintenance Tool, Teruko Mitamura, Eric Nyberg, Kathy Baker, Peter Cramer, Jeongwoo Ko, David Svoboda & Michael Duggan 248-252; 30. Approaches to Spoken Translation, Christine A. Montgomery & Naicong Li 248-252.
Resumen:   Ever since the showdown between Empiricists and Rationalists a decade ago at TMI 92, MT researchers have hotly pursued promising paradigms for MT, including datadriven approaches (e.g., statistical, example-based) and hybrids that integrate these with more traditional rule-based components. During the same period, commercial MT systems with standard transfer architectures have evolved along a parallel and almost unrelated track, increasing their coverage (primarily through manual update of their lexicons, we assume) and achieving much broader acceptance and usage, principally through the medium of the Internet. Webpage translators have become commonplace; a number of online translation services have appeared, including in their offerings both raw and postedited MT; and large corporations have been turning increasingly to MT to address the exigencies of global communication. Still, the output of the transfer-based systems employed in this expansion represents but a small drop in the ever-growing translation marketplace bucket.Now, 10 years later, we wonder if this mounting variety of MT users is any better off, and if the promise of the research technologies is being realized to any measurable degree. In this regard, the papers in this volume target responses to the following questions: • Why aren't any current commercially available MT systems primarily datadriven? • Do any commercially available systems integrate (or plan to integrate) datadriven components?• Do data-driven systems have significant performance or quality issues?• Can such systems really provide better quality to users, or is their main advantage one of fast, facilitated customization?• If any new MT technology could provide such benefits (somewhat higher quality, or facilitated customization), would that be the key to more widespread use of MT, or are there yet other more relevant unresolved issues, such as system integration?• If better quality, customization, or system integration aren't the answer, then what is it that users really need from MT in order for it to be more useful to them? The contributors to this volume have sought to shed light on these and related issues from a variety of viewpoints, including those of MT researchers, developers, end-users, professional translators, managers, and marketing experts. The jury appears still to be out, however, on whether data-driven MT, which seems to have meandered along a decade-long path of evolution (instead of revolution, as many thought it might be), will lead us to the holy grail of high-quality MT. And yet, there is a sense of progress and optimism among the practitioners of our field. [Source: editor]
Comentarios:   Proceedings of the 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002, Tiburon, CA, USA, October 8-12, 2002.
Impacto:   1i- Hutchins, W. John. 2010. 4382cit
2001-2019 Universidad de Alicante DOI: 10.14198/bitra
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