Means of Identifying Grammatical and Phraseological Problems in Written Translation with the Help of Information Technologies

Abstract

This article examines the role of information technologies, particularly AI driven tools, in identifying and correcting grammatical and phraseological problems
in written translation. As globalization increases the demand for precise translations, traditional methods relying solely on human expertise face challenges in accuracy and efficiency. The integration of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) has transformed translation practices, enhancing error detection and improving overall quality. The article discusses common grammatical issues such as subject-verb agreement and tense confusion, as well as phraseological challenges like idiomatic expressions and collocations. It highlights how advanced tools like Google Translate and DeepL leverage large datasets to improve contextual understanding and translation accuracy. Furthermore, the article presents experimental findings that compare these tools’ performance, revealing strengths and limitations in handling complex translations, particularly between Uzbek and English. Ultimately, the study underscores the necessity of combining human expertise with technological advancements to achieve high-quality translations, emphasizing the evolving role of information technologies in the translation process.