Sukhoverkhov A.V., DeWitt D., Manasidi I.I., Nitta K., Krstić V. Lost in Machine Translation: Contextual Linguistic Uncertainty
DOI: https://doi.org/10.15688/jvolsu2.2019.4.10
Anton V. Sukhoverkhov
PhD, Associate Professor, Department of Philosophy, Kuban State Agrarian University
Kalinina St. 13, 350044 Krasnodar, Russia
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https://orcid.org/0000-0002-0357-4013
Dorothy DeWitt
PhD, Associate Professor, Department of Curriculum and Instructional Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
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https://orcid.org/0000-0003-3123-7150
Ioannis I. Manasidi
Student, Faculty of Applied Informatics, Kuban State Agrarian University
Kalinina St. 13, 350044 Krasnodar, Russia
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https://orcid.org/0000-0002-2090-9970
Keiko Nitta
PhD, Professor, Department of Letters, College of Arts, Rikkyo University, 3-34-1 Nishi Ikebukuro
171-8501 Toshima City, Tokyo, Japan
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https://orcid.org/0000-0002-6963-711X
Vladimir Krstić
PhD, Honorary Research Associate, Department of Philosophy, University of Auckland
Private Bag 92019, 1142 Auckland, New Zealand
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https://orcid.org/0000-0003-1953-2675
Abstract. 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.
Key words: machine translation, untranslatability, contextual translation, linguistic relativity, lexical ambiguity, syntactic ambiguity.
Citation. Sukhoverkhov A.V., DeWitt D., Manasidi I.I., Nitta K., Krstić V. Lost in Machine Translation: Contextual Linguistic Uncertainty. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 2. Yazykoznanie [Science Journal of Volgograd State University. Linguistics], 2019, vol. 18, no. 4, pp. 129-144. DOI: https://doi.org/10.15688/jvolsu2.2019.4.10
Lost in Machine Translation: Contextual Linguistic Uncertainty by Sukhoverkhov A.V., DeWitt D., Manasidi I.I., Nitta K., Krstić V. is licensed under a Creative Commons Attribution 4.0 International License.
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