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.
Highlights
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)
The generative-linguistic theory, Russian formalists, French structuralists and others have all contributed to language formalisation in recent years, the ecological, process and system approaches to language nature questioned the possibility and effectiveness of such formalisation
We review existing translation algorithms, comparing outcomes of the most popular machine translation systems (PROMT, Yandex and Google) with, respectively, Rule-based (RBMT), Hybrid (HMT) and Neural (NMT) algorithms
Summary
Антон Владимирович Суховерхов Кубанский государственный аграрный университет, г. Иоаннис Игоревич Манасиди Кубанский государственный аграрный университет, г. Коллективом авторов статьи, носителями языка разных стран (России, Греции, Малайзии, Японии и Сербии), проведено тестирование качества перевода наиболее показательных фраз на английском, русском, греческом, малайском и японском языках с использованием различных систем машинного перевода: PROMT (RBMT), Яндекс.Переводчик (HMT) и Google Translate (NMT). Что для совершенствования современных систем машинного перевода требуется объединение буквальной, концептуальной и контентно-контекстной форм обработки смыслов текста, улучшение корпусов метафор и контекстологических словарей Де Витт), разработка различных типов и стилей перевода, включающих специфические диалекты и идиолекты пользователей, а также гендерные особенности языка И. Манасиди показано, что без участия человека невозможен перевод бурно развивающегося интернет-сленга, характеризующегося смешением национальных языков с английским.
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