Machine Translation Methods and Some Current Trends
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Дата
2023
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Анотація
This paper provides an overview of some major machine translation methods designed to speed up the rate of multilingual text translation. Machine translation is achieved by computer software transforming text from one language to another. At present, different approaches in machine translation (MT) are used: rule-based machine translation (RBMT), statistical machine translation (SMT), neural machine translation (NMT) and others. RBMT relies on linguistic rules and dictionaries to translate text and can provide accurate translations for specific domains. At the same time, this method requires much manual effort to develop and maintain the rules. SMT uses statistical models to translate text. These models are trained on large parallel corpora which consist of parallel sentences in the source and target languages. SMT can handle a wide range of languages though its translations can often be inconsistent or ungrammatical. Neural machine translation (NMT) is a recent approach that uses artificial neural networks to perform translations. This approach has shown a significant improvement in translation quality, fluency and consistency.
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Ключові слова
Rule-based machine translation (RBMT), Statistical machine translation (SMT), Neural machine translation (NMT), parallel corpora, encoder, decoder
Бібліографічний опис
Shevchenko, О. М. Machine Translation Methods and Some Current Trends / Shevchenko О. М. // SWorldJournal. – 2023. – № 19(3). – С. 115-119. – Bibliogr.: 4 ref.