Jul 2026· Journal of Arts and Linguistics Studies· Vol 4, pp. 431-460· 0 citations· 1 references
TL;DR
This research conducted a comparative analysis of three digital translation tools, Google Translate, Lingvanex, and MateCat, evaluating their performance in translating Saadat Hasan Manto's Urdu short story "Khol Do" into English, indicating that Google Translate is the most successful tool in terms of both quantitative measures and qualitative aspects.
Abstract
This research conducted a comparative analysis of three digital translation tools, Google Translate, Lingvanex, and MateCat, evaluating their performance in translating Saadat Hasan Manto's Urdu short story "Khol Do" into English. The study utilized the BLEU (Bilingual Evaluation Understudy) metric to quantify the accuracy of each translation in relation to a human-translated reference translation by M. Umar Memon. Additionally, the research employed Molina and Albir's translation techniques to assess the quality of the translations. The goal was to identify the tool that consistently produced translations closest to the intended meaning and style of the source text or human translation. The findings indicated that Google Translate is the most successful tool in terms of both quantitative measures (having the highest BLEU scores) and qualitative aspects (having higher lexical fidelity and structural accuracy). The study highlighted the strengths and limitations of each digital translation tool in capturing the nuances of Urdu literature. The results suggested that while machine translation tools are improving, they still require human oversight to ensure accuracy and cultural relevance. The research contributed to the ongoing evaluation of machine translation tools and their applicability in translating literary texts, particularly from languages like Urdu, which posed unique challenges due to their linguistic and cultural specificities.
This study examines the extent to which machine-translated and human-translated Croatian–English phrases can be distinguished by expert evaluators by analysing inter-rater agreement. For this purpose, a parallel corpus was compiled from four Croatian research articles in the fields of Education and Psychology, together with their human translations and machine translations produced using the online tool onlinedoctranslator. com. Two professional translators, native speakers of Croatian with extensive translation experience, were asked to classify selected English translations of Croatian phrases as either machine-translated or human-translated. The analysis shows a raw inter-rater agreement of 75 % for both machine-translated and human-translated phrase sets, indicating a moderate level of agreement between the raters. These results suggest that even expert translators do not consistently reach the same conclusions when judging the origin of short, decontextualised translated phrases. The findings support the view that, at the phrase level, machine-translated output is increasingly difficult to distinguish from human translation on the basis of surface linguistic features alone.
Mirjana Borucinsky, Margareta Čanžar· Croatica et Slavica Iadertin...· 0 citations
Assessment of how GT and GPT-4o translate English VPE into Arabic focuses on the accuracy of ellipsis reconstruction and the translation strategies employed, highlighting the importance of better-quality training data for NMT and LLM tools for both discourse-level processing and context-dependent data.
Eassa Ali, Abbas Brashi, Dana Awad et al.· The International Journal of...· 0 citations
This article presents a linguistic analysis of the translation techniques used to translate a documentary film from Yakut into Russian, and classifies the stylistic errors made during the translation process. The documentary film “24 Snows” (2015, directed by M. B. Barynin) serves as the research material. The aim of the study was to identify, systematize, and analyze key translation strategies and typical stylistic errors that arise when translating the documentary from Yakut into Russian (using the film “24 Snows”). The theoretical basis of the study was formed by fundamental works on translation studies by N.K. Garbovsky, Y. I. Recker, V.S. Modestov, and other scholars, who laid the foundations for the classification of translation techniques and the analysis of translation errors. The primary method of analysis was comparative; empirical research methods such as comparison, classification, and generalization were used as auxiliary ones. The analysis utilized contextual analysis, descriptive-analytical methods, and semantic analysis techniques. A structured interview with the film’s director was used to reconstruct the context of the translation work. The study identified and systematized the following techniques for translating the original documentary text from Yakut into Russian: transcription, transliteration, adaptation, calque, and generalization. Furthermore, typical translator errors were discovered: omitted words, distorted meaning, as well as grammatical, syntactic, and logical errors.
N. A. Efremova, E. G. Nikiforova· Altaistics· 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.
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
This study investigates the translation of gender from English into Arabic, focusing on the performance of senior-level translation students in a literary context. Building on previous research on verbal and adjectival translation, the article adopts a mixed-methods approach combining quantitative frequency analysis with qualitative examination of error types. The corpus consists of selected passages from Naguib Mahfouz’s Midaq Alley, translated into Modern Standard Arabic by student translators. Gender-related renderings are classified as similar, different or unattempted in order to assess both accuracy and omission. The findings indicate that while gender is generally handled correctly at the lexical level, significant difficulties arise in maintaining agreement within phrases and clauses, particularly in cases involving inanimate or abstract nouns and structurally complex constructions. The study highlights the impact of grammatical asymmetry between English and Arabic on translation performance and underscores the need for more systematic instruction in contrastive grammar and contextual analysis. It contributes to a more nuanced understanding of gender as a key factor in English–Arabic literary translation and provides implications for translator training.
K. Mansoor, Daniel Dejica· Scientific Bulletin of the P...· 0 citations
