COMPARATIVE ANALYSIS OF STATE-OF-THE-ART NEURAL NETWORKS FOR ART OBJECT RECOGNITION

Currently, information technology is rapidly developing and one of its branches can be called machine translation. The use of machine translation in the process of understanding each other by people from different countries is increasing every year. At the moment, Google and Yandex machine translations are among the best machine translations. The quality of machine translation from Yandex and Google is improving every year. However, according to the results of the experiment, when translating from English or Russian into Kazakh and Turkic languages, the quality of the translation decreases. This was shown by the translation result obtained from these two machine translations in March 2024. After all, translation has also shown that it is directly related to the structure of language. Since 2000, scientists from the state of Kazakhstan have been actively studying translations into the Kazakh language. The goal of the work is to improve the quality of translation from English into Kazakh. For this purpose, a transforming model was created for the Kazakh and Turkic languages for learning translation in neural machine translation OpenNMT(). The created model studied and learned an English-Kazakh parallel corpus of 180,000 words. Later, the document with a structure of 20,000 different English sentences was translated into Kazakh. The result is measured using the Blue() metric. The translation result showed a high level. It is shown that in order to improve the results of the experiment carried out in the work during model training, it is necessary to increase the number of parallel corpora created from the English-Kazakh language pair.