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Machine Translation: State of the Art, Trends and User Perspective.

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Title: Machine Translation: State of the Art, Trends and User Perspective.
Language: English
Authors: Krauwer, Steven
Peer Reviewed: N
Page Count: 8
Publication Date: 1995
Document Type: Reports - Descriptive
Speeches/Meeting Papers
Descriptors: Computational Linguistics, Computer Software, Discourse Analysis, Foreign Countries, Language Research, Languages, Linguistic Theory, Machine Translation, Research Methodology, Research Needs, Structural Analysis (Linguistics), Translation, Trend Analysis, Users (Information)
Geographic Terms: Europe
Abstract: Currently, no machine translation (MT) system is capable of successfully imitating the behavior of a human translator, and there exists no formal description of what an MT system is supposed to do. The biggest problem in practice is disambiguation. However, various types of existing systems do help reduce language barriers, even if they are poor imitations of the human translator. Users of such systems determine the utility of the systems. Three groups of users are distinguished: big companies and institutions; professional and occasional translators; and monolingual users. Big MT systems are becoming more cost-effective. Smaller systems providing high-quality output for restricted tasks and domains and multilingual systems based on spoken dialogue are being developed. Whereas market developments show a trend toward more specialized and restricted systems, the trend in research is to widen the scope of machine translation, moving from sentence to text, focusing on disambiguation based on domain restrictions, and using non-traditional statistical methods. Work is needed to provide lexicological and terminological resources for East European languages, and to create large collections of real-life data, including monolingual and bilingual text corpora. (MSE)
Entry Date: 1998
Accession Number: ED413745
Database: ERIC
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  Data: Machine Translation: State of the Art, Trends and User Perspective.
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  Data: 8
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  Data: 1995
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  Data: Reports - Descriptive<br />Speeches/Meeting Papers
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  Data: <searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Discourse+Analysis%22">Discourse Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Research%22">Language Research</searchLink><br /><searchLink fieldCode="DE" term="%22Languages%22">Languages</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistic+Theory%22">Linguistic Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Translation%22">Machine Translation</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Needs%22">Research Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+Analysis+%28Linguistics%29%22">Structural Analysis (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Translation%22">Translation</searchLink><br /><searchLink fieldCode="DE" term="%22Trend+Analysis%22">Trend Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Users+%28Information%29%22">Users (Information)</searchLink>
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  Data: Currently, no machine translation (MT) system is capable of successfully imitating the behavior of a human translator, and there exists no formal description of what an MT system is supposed to do. The biggest problem in practice is disambiguation. However, various types of existing systems do help reduce language barriers, even if they are poor imitations of the human translator. Users of such systems determine the utility of the systems. Three groups of users are distinguished: big companies and institutions; professional and occasional translators; and monolingual users. Big MT systems are becoming more cost-effective. Smaller systems providing high-quality output for restricted tasks and domains and multilingual systems based on spoken dialogue are being developed. Whereas market developments show a trend toward more specialized and restricted systems, the trend in research is to widen the scope of machine translation, moving from sentence to text, focusing on disambiguation based on domain restrictions, and using non-traditional statistical methods. Work is needed to provide lexicological and terminological resources for East European languages, and to create large collections of real-life data, including monolingual and bilingual text corpora. (MSE)
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  Data: 1998
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  Data: ED413745
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
    Subjects:
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Discourse Analysis
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Language Research
        Type: general
      – SubjectFull: Languages
        Type: general
      – SubjectFull: Linguistic Theory
        Type: general
      – SubjectFull: Machine Translation
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Research Needs
        Type: general
      – SubjectFull: Structural Analysis (Linguistics)
        Type: general
      – SubjectFull: Translation
        Type: general
      – SubjectFull: Trend Analysis
        Type: general
      – SubjectFull: Users (Information)
        Type: general
      – SubjectFull: Europe
        Type: general
    Titles:
      – TitleFull: Machine Translation: State of the Art, Trends and User Perspective.
        Type: main
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            NameFull: Krauwer, Steven
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              M: 01
              Type: published
              Y: 1995
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