Jun 2026· Journal of Universal Science Research· Vol 4, pp. 121-131· 0 citations
TL;DR
The findings indicate that artificial intelligence can function as an effective auxiliary tool in translation, but human translators and post-editing remain essential in literary, academic, legal, medical and journalistic texts.
Abstract
This article analyzes linguistic and stylistic problems that arise in translation processes in the age of artificial intelligence, particularly through neural machine translation and large language models. The study acknowledges the advantages of AI-assisted translation, including speed, multilingual processing and the production of preliminary drafts. However, the main focus is placed on semantic ambiguity, contextual misinterpretation, literal rendering of idiomatic expressions, inconsistency in terminology, stylistic neutralization and the loss of cultural connotations. The article proposes an analytical model for assessing translation quality based on linguistic, stylistic and pragmatic criteria. The findings indicate that artificial intelligence can function as an effective auxiliary tool in translation, but human translators and post-editing remain essential in literary, academic, legal, medical and journalistic texts
The work highlights the need to take into account the stylistic and semantic features of poetic discourse in automated translation and suggests ways to improve machine translation algorithms to more accurately convey the artistic and emotional nuances of poetry.
D.T. Teshebaeva· Vestnik of the Kyrgyz-Russia...· 0 citations
Backround - The rapid advancement of Artificial Intelligence (AI) in translation studies has transformed how literary texts are processed, shifting from literal word-for-word transfer to more contextually nuanced approaches. Literary fables demand particular sensitivity to personification, onomatopoeia, and moral messaging.
Urgency of Research - Despite the proliferation of AI translation tools, previous studies have predominantly focused on macro-level quality evaluation (e.g., fluency and accuracy) rather than examining the micro-linguistic strategies AI employs. There remains a significant gap in understanding how different AI models identify and apply established translation techniques within Molina and Albir's comprehensive framework of 18 translation techniques.
Research Objectives - This study aims to evaluate and compare the performance of four AI tools—ChatGPT, Gemini, Claude, and DeepL—in identifying and applying translation techniques in the literary fable "The Clever Rabbit," specifically examining how each model utilizes Molina and Albir's 18 translation techniques to achieve dynamic equivalence.
Research Method - Adopting a qualitative descriptive approach, this study employs purposive sampling to select translation units demonstrating specific techniques. Data were collected through comparative textual analysis of one English source text and four Indonesian target texts, validated through theoretical triangulation and source triangulation.
Research Findings - The findings reveal a clear strategic polarization: generative AI models (ChatGPT, Gemini, Claude) demonstrate dominance in complex transformation techniques such as Modulation (20-23%), Equivalence (12-13%), and Explicitation, reflecting deeper contextual understanding. In contrast, DeepL shows extreme reliance on Literal Translation (>65%) with minimal cultural or stylistic adaptation. ChatGPT excels in local adaptation through generalization and particularization; Gemini stands out in narrative vitality through expressive lexical variation; and Claude offers structural efficiency through precise grammatical reduction.
Research Conclusion & Novelty - This study concludes that while all AI tools can transfer denotative meaning, generative models (LLMs) are superior in applying high-level translation techniques necessary for maintaining emotional nuance, discourse cohesion, and literary appeal. The novelty lies in its micro-linguistic analysis using Molina and Albir's comprehensive taxonomy across four distinct AI platforms, providing unprecedented insight into the "black box" of AI translation strategies. The findings offer practical guidance for educators, researchers, and translators in selecting appropriate AI tools, emphasizing that critical human post-editing remains indispensable for achieving true literary equivalence.
: This paper examines the adequacy of translating idiomatic language in Serbian media headlines into German and Russian, with particular emphasis on set expressions using artificial intelligence tools (ChatGPT, Gemini, and Google Translate). The corpus comprises twenty-one headlines collected from online news portals, with a focus on the potential and limitations of these three tools, as well as on the relationship between literalness and expressiveness in translation. The results indicate that these AI tools are highly effective at the lexical and syntactic levels, but also reveal limitations in the domain of phraseology, that is, in the translation of stylistically marked linguistic units.
Magdalena Duvnjak, V. Fedorov, Miloš Pupavac· SINTEZA· 0 citations
Machine translation has become more fluent and contextually accurate with recent advances in artificial intelligence. However, terminological consistency has been underexplored, particularly in political and electoral discourse where lexical repetition and conceptual precision are critical for cohesion and clarity. This study investigates terminological consistency in AI-generated English–Arabic political translations produced by ChatGPT and Google Gemini Advanced. The translations were generated and analyzed between January and June 2026 using the systems’ default settings to ensure comparability and avoid potential variations resulting from user-configured parameters. The study employs a mixed-methods corpus-based approach. The study analyzes 30 political and electoral texts with 60 recurring key terms. Quantitative analysis measures the degree of consistency in the form of stability percentages, and qualitative analysis studies lexical variation and its effect on discourse cohesion and clarity. The adequacy and consistency of the translation were checked against a reference translation based on the United Nations Development Programme (UNDP) Arabic Lexicon of Electoral Terminology. The results indicate that ChatGPT achieved higher terminological consistency than Google Gemini. ChatGPT’s lexical equivalents for repeated political and electoral terms were more stable than its Gemini counterpart, which showed more lexical variation, especially in context-sensitive terms such as campaign, electoral law, and judicial review. The study concludes that terminological consistency should be considered as a separate dimension of translation quality and emphasizes the importance of terminology control and human post-editing in AI-assisted political translation
Osama Bala· (Faculty of Arts Journal) مج...· 0 citations
The paper argues that NLP should operate as an interpretive assistant rather than an autonomous literary translator in translating Iraqi poetry into English, and proposes a culturally aware, human-in-the-loop framework for supporting literary translation.
Whaj Mneer Esmail· Iraqi Literary and Cultural...· 0 citations
The results show that LLMs and Google Translate consistently outperform specialized MT systems in terms of fluency, meaning preservation, and lexical-thematic alignment.
Beatriz Ribeiro Borges, P. H. R. Gabriel, E. Faria· International Journal of Dat...· 0 citations