Kolin Konstantin
DrS (Tech. Sci.), Professor, Chief Researcher of the Federal Research Center "Computer Science and Control”, Russian Academy of Sciences (Moscow, Russia)
Khoroshilov Alexander
DrS (Tech. Sci.), Professor of the Moscow Aviation Institute (National Research University), Leading Researcher of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Senior Researcher of the 27 Central Research Institute of the Ministry of Defense of the Russian Federation (Moscow, Russia)
Nikitin Yuri
Researcher of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Development Team Leader of the Scientific and Industrial Company “High Technologies and Strategic Systems”(Moscow, Russia)
Pshenichny Sergey
PhD (Econ. Sci.), Program Director of the Scientific and Industrial Company “High Technologies and Strategic Systems” (Moscow, Russia)
Khoroshilov Alexei
Ph.D (Engineering Sci.), Senior Researcher of the 27 Central Research Institute of the Ministry of Defense of the Russian Federation (Moscow, Russia)
Artificial intelligence in machine translation technologies
The capabilities of machine translation are closely related to the improvement of modeling the processes of understanding and generating texts in natural language, which traditionally belongs to the class of artificial intelligence problems. The article attempts to analyze the main approaches to the creation of machine translation technologies. It is concluded that these approaches have not yet provide for the formation and use of dynamic models of the world, but are moving mainly in the direction of a grammatically consistent translation of word sequences.
Keywords: machine translation; natural language; artificial Intelligence; machine translation technologies.
For citation: Artificial intelligence in machine translation technologies / Kolin K.K., Khoroshilov Al-dr A., Nikitin Yu.V., Pshenichny S.I., Khoroshilov Al-ei A. // Social Novelties and Social Sciences. – Moscow : INION RAN, 2021. – N 2. – Pp. 64–80.DOI: 10.31249/snsn/2021.02.05
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