Translation as a linguistic act in the context of artificial intelligence: the impact of technological changes on traditional approaches

Abstract

The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities in the definition of word categories, while contextual rules are used as auxiliary methods. It is also necessary to consider the various factors affecting automatic language processing and combine statistical and linguistic methods to achieve better translation results. Conclusions - In order to improve the performance and efficiency of translation systems, it is important to use a comprehensive approach that combines various techniques and machine learning methods. The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results