LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY

LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY

The article considers the issues related to the semantic, grammatical, stylistic and technical difficulties currently present in machine translation and compares its four main approaches: Rule-based (RBMT), Corpora-based (CBMT), Neural (NMT), and Hybrid (HMT). It also examines some “open systems”,...

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Bibliographic Details
Main Authors: Anton V. Sukhoverkhov, Dorothy DeWitt, Ioannis I. Manasidi, Keiko Nitta, Vladimir Krstić
Format: Article
Language:English
Published: Volgograd State University 2019-12-01
Series:Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie
Subjects:
machine translation
untranslatability
contextual translation
linguistic relativity
lexical ambiguity
syntactic ambiguity
Online Access:https://l.jvolsu.com/index.php/en/archive-en/591-science-journal-of-volsu-linguistics-2019-vol-18-no-4/intercultural-communication-and-comparative-studies-of-languages/1975-sukhoverkhov-a-v-dewitt-d-manasidi-i-i-nitta-k-krsti-v-lost-in-machine-translation-contextual-linguistic-uncertainty

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https://l.jvolsu.com/index.php/en/archive-en/591-science-journal-of-volsu-linguistics-2019-vol-18-no-4/intercultural-communication-and-comparative-studies-of-languages/1975-sukhoverkhov-a-v-dewitt-d-manasidi-i-i-nitta-k-krsti-v-lost-in-machine-translation-contextual-linguistic-uncertainty

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