Sabina V. Pakharenko

This article analyzes translation strategies and adaptive tools used in rendering texts from British periodicals. The primary tools employed in the study are the DeepL and Reverso Context platforms, which support semi-automatic translation workflows for both literary and journalistic texts. The aim of the study is to determine the most effective translation strategies for rendering wordplay in British tabloid newspapers through the integration of online platforms and traditional adaptive translation methods. The corpus analyzed in this study consists of texts published in The Observer and The Sun, which most clearly demonstrate key aspects of translation practice. The material includes headlines and articles published between 2019 and 2025, primarily from editorial sections. The methodological framework is based on adaptive and comprehensive translation approaches, focusing on text transformation with regard to cultural specifics, social realities, linguistic features, and journalistic style. When selecting translation strategies, the authors adopt a functionalist approach grounded in Skopos theory. The study identifies translation practices in literary and journalistic discourse based on the above-mentioned platforms, and clarifies approaches to linguistic creativity, glossing, searching, analysis, and interpretation of wordplay, as well as semi-automatic translation and post-editing for the development of terminology databases and the refinement of machine translation outputs using artificial intelligence tools. The authors conclude that further methodological development is required in modeling different types of discourse (publisher, journalist, and audience perspectives) in the translation process. In addition to online platforms, this requires supplementary adaptive translation tools, including post-editing, text deconstruction, case studies, and generalization.