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”,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Volgograd State University
2019-12-01
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| Series: | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
| Subjects: | |
| 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 |
| _version_ | 1797794136996184064 |
|---|---|
| author | Anton V. Sukhoverkhov Dorothy DeWitt Ioannis I. Manasidi Keiko Nitta Vladimir Krstić |
| author_facet | Anton V. Sukhoverkhov Dorothy DeWitt Ioannis I. Manasidi Keiko Nitta Vladimir Krstić |
| author_sort | Anton V. Sukhoverkhov |
| collection | DOAJ |
| description | 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”, which allow
the correction or augmentation of content by the users themselves (“crowdsourced translation”). The authors
of the article, native speakers presenting different countries (Russia, Greece, Malaysia, Japan and Serbia), tested
the translation quality of the most representative phrases from the English, Russian, Greek, Malay and Japanese
languages by using different machine translation systems: PROMT (RBMT), Yandex.Translate (HMT) and
Google Translate (NMT). The test results presented by the authors show low “comprehension level” of semantic,
linguistic and pragmatic contexts of translated texts, mistranslations of rare and culture-specific words,
unnecessary translation of proper names, as well as a low rate of idiomatic phrase and metaphor recognition. It
is argued that the development of machine translation requires incorporation of literal, conceptual, and content-
and-contextual forms of meaning processing into text translation expansion of metaphor corpora and
contextological dictionaries, and implementation of different types and styles of translation, which take into
account gender peculiarities, specific dialects and idiolects of users. The problem of untranslatability (‘linguistic
relativity’) of the concepts, unique to a particular culture, has been reviewed from the perspective of machine
translation. It has also been shown, that the translation of booming Internet slang, where national languages
merge with English, is almost impossible without human correction. |
| first_indexed | 2024-03-13T02:58:26Z |
| format | Article |
| id | doaj.art-40400a606e944757bf0f2598b3131db0 |
| institution | Directory Open Access Journal |
| issn | 1998-9911 2409-1979 |
| language | English |
| last_indexed | 2024-03-13T02:58:26Z |
| publishDate | 2019-12-01 |
| publisher | Volgograd State University |
| record_format | Article |
| series | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
| spelling | doaj.art-40400a606e944757bf0f2598b3131db02023-06-27T21:19:59ZengVolgograd State UniversityVestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie1998-99112409-19792019-12-0118412914410.15688/jvolsu2.2019.4.10LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTYAnton V. Sukhoverkhov0https://orcid.org/0000-0002-0357-4013Dorothy DeWitt1https://orcid.org/0000-0003-3123-7150Ioannis I. Manasidi2https://orcid.org/0000-0002-2090-9970Keiko Nitta3https://orcid.org/0000-0002-6963-711XVladimir Krstić4https://orcid.org/0000-0003-1953-2675Kuban State Agrarian University, Krasnodar, RussiaUniversity of Malaya, Kuala Lumpur, MalaysiaKuban State Agrarian University, Krasnodar, RussiaCollege of Arts, Rikkyo University, Toshima City, Tokyo, JapanUniversity of Auckland, Auckland, New ZealandThe 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”, which allow the correction or augmentation of content by the users themselves (“crowdsourced translation”). The authors of the article, native speakers presenting different countries (Russia, Greece, Malaysia, Japan and Serbia), tested the translation quality of the most representative phrases from the English, Russian, Greek, Malay and Japanese languages by using different machine translation systems: PROMT (RBMT), Yandex.Translate (HMT) and Google Translate (NMT). The test results presented by the authors show low “comprehension level” of semantic, linguistic and pragmatic contexts of translated texts, mistranslations of rare and culture-specific words, unnecessary translation of proper names, as well as a low rate of idiomatic phrase and metaphor recognition. It is argued that the development of machine translation requires incorporation of literal, conceptual, and content- and-contextual forms of meaning processing into text translation expansion of metaphor corpora and contextological dictionaries, and implementation of different types and styles of translation, which take into account gender peculiarities, specific dialects and idiolects of users. The problem of untranslatability (‘linguistic relativity’) of the concepts, unique to a particular culture, has been reviewed from the perspective of machine translation. It has also been shown, that the translation of booming Internet slang, where national languages merge with English, is almost impossible without human correction.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-uncertaintymachine translationuntranslatabilitycontextual translationlinguistic relativitylexical ambiguitysyntactic ambiguity |
| spellingShingle | Anton V. Sukhoverkhov Dorothy DeWitt Ioannis I. Manasidi Keiko Nitta Vladimir Krstić LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie machine translation untranslatability contextual translation linguistic relativity lexical ambiguity syntactic ambiguity |
| title | LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY |
| title_full | LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY |
| title_fullStr | LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY |
| title_full_unstemmed | LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY |
| title_short | LOST IN MACHINE TRANSLATION: CONTEXTUAL LINGUISTIC UNCERTAINTY |
| title_sort | lost in machine translation contextual linguistic uncertainty |
| topic | machine translation untranslatability contextual translation linguistic relativity lexical ambiguity syntactic ambiguity |
| url | 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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