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The 21st century witnesses tremendous technological and organizational advances in world's economy and societies. This has left a great impact on translation and translation studies. Corpus is a machine-readable representative collection of naturally occurring language assembled for linguistic analysis accessible with software such as, concordances that can find list and source of linguistic patterns. It lays the foundation of Corpus linguistics which makes it possible for translators and translation studies to make use of large quantity of stored data on computers for examining target language translations. Computer corpora includes spoken/ written, casual/formal, fiction/ non-fiction texts representing various demographic areas. The study aims at familiarizing student of translation and translators with the methods and practical applications of computer corpora in various fields of language use. The study reveals that Corpus data are essential for accurately describing various samples of language by showing how lexis, grammar and semantics interact to serve appropriate translation output.

Toponyms are a prerequisite for a well-written news story. Adhering to a unified transliteration pattern of toponyms significantly helps avoid confusion and improves search engine optimization. This study explores inconsistencies among human translators affiliated with Arab news websites in transliterating Arabic toponyms into English. The study attempts to find answers to the following main questions: The study attempts to find answers to the following main questions: How do human-produced toponym transliteration patterns affect the consistency of toponym transfer from Arabic to English? How could machine-aided transliteration of toponyms improve current practices? A qualitative approach is adopted to analyze the transliteration patterns of Syrian cities mentioned in 22 news articles covering the earthquake that hit Syria in February 2023. The articles were retrieved from various English websites owned by Arab media institutions. The transliteration patterns were compared to a standard transliteration system issued by the United Nations Group of Experts on Geographical Names (2007). The analysis revealed inconsistencies in human-produced transliteration patterns and conspicuous disregard for the standard system. The findings of the study were interpreted in light of the post humanist approach espoused by O' Thomas (2017), which emphasizes the importance of incorporating the machine in the translator's workflow and highlights the benefits of post humanism to enrich the field of Translation Studies. The study called for machine translation in toponym transliteration to guarantee high accuracy and consistency. Future research was recommended to test the machine's performance in this area.

Euphemism is used to soften rude expressions or words. Translating euphemisms is a very challenging task for human translators and will be more difficult for AI applications. Almost all previous studies have clustered around studying the efficiency, accuracy, coherence and cohesion of AI translation in comparison to human translation. However, these studies have not tackled the strategies used in both types of translations. Consequently, this study aims to identify the strategies utilized by AI Models with human translation when translating euphemistic expressions from Arabic into English. Additionally, it aims to evaluate how these methods influence the translation accuracy of both human translators and AI models in both directions of translation (Arabic to English and English to Arabic). The researchers have utilized some euphemistic expressions taken from the Holy Quran, Prophetic Hadiths, and some previous studies. These expressions have been inputted into AI models. The outcomes of these AI models are compared to Human translation, using Baker's (2013) translation strategies. Researchers have analysed the collected data quantitatively. The results revealed that translation by a more general word and translation by cultural substitution strategies were mostly used by human translation whereas translation using a loan word strategy is mostly used by AI models. The findings also showed that human translators outperform AI models regarding cultural substitution. It has been found that the direction of language plays a crucial role in AI outcomes, as they perform better when translating from English into Arabic.

La littérature ne vise pas la simple communication informative; elle cherche plutôt à affecter. Elle correspond des procédés spécifiques: la forme de langue, les procédés esthétiques. Par conséquent, elle pose au traducteur des problèmes attachés à sa nature. Et le traducteur doit tenir compte aux qualités esthétiques des textes à traduire. Les figures de style d'analogie sont des procédés d'écriture qui permettent de lier deux éléments pour les comparer. Elles créent des images rhétoriques en rapprochant deux éléments différents. Notre étude sera consacrée à l'analyse de la traduction de ces figures analogiques arabes vers le français dans «Je balaie le soleil des terrasses (Aknusu al-chams an al-sutuh», un recueil de nouvelles, publié en arabe en 1994, traduit en français par Yves gonzalez- Quijano, (Le cimetière des rêves) 2000. Notre problématique est de nous interroger comment les figures d'analogie arabes sont-elles rendues en français par Yves Gonzalez-Quijano et par Chat GPT (un logiciel de traduction basé sur l'intelligence artificielle (AI). Ont-ils traduit le sens et la forme de ces figures de style d'analogie ? Pour répondre à ces questions saillantes, nous allons comparer la traduction humaine avec la traduction automatique. Notre comparaison est basée sur la recherche des points de divergence et de convergence entre les deux traductions humaine et automatique.

À une époque où la technologie est en course contre la montre, les sciences dans toutes ses branches et spécialisations, qu'elles soient scientifiques ou même littéraires, doivent entrer dans ce domaine : elles n'ont plus le choix ! L'état de la traduction confirme également ce constat, du fait qu'il s'agit du moyen de communication humaine entre les peuples le plus rapide. Cette recherche vise à suivre le développement de la traduction assistée par ordinateur à travers une étude analytique de plusieurs textes de différents domaines et spécialités. L'objectif est de déterminer le niveau de la qualité de la traduction et d'indiquer ce qui est correct ou erroné, ainsi que le degré de son rapprochement du sens. Il sera question également d'aborder la différence entre la traduction automatique et la traduction à travers les mémoires de traduction, qui a connu un progrès remarquable ces derniers temps. Ainsi, nous allons s'attarder sur l'effort humain qui prépare et organise ce processus et qui est finalement le maître principal et incontestable de ce dernier

This study investigates the pivotal role of artificial intelligence in the development of translation, with a particular focus on neural translation, which relies on algorithms and deep networks to interpret texts within their full context. It examines the translation of linguistic terminology, considered one of the most challenging domains of translation due to its demand for precision and a deep awareness of specialized concepts. The study adopts an applied methodology that combines theoretical analysis with a practical comparison between human translation outputs and those produced by AI-based systems (such as Google Translate, DeepL, and ChatGPT). Using a sample of linguistic terms drawn from both Arabic and foreign academic texts, the findings reveal that artificial intelligence can enhance translation quality, reduce errors, and increase efficiency, provided it is trained on rich and diverse Arabic corpora. The study concludes that the role of the human translator must be integrated with the capabilities of intelligent systems to ensure accurate translation that preserves meaning and supports the dissemination of the Arabic language in the global digital space.

نظير أهمية مجال الأمن الصناعي والبيئة (HSE)، وباعتبار الزخم النشط للمفاهيم والمصطلحات التقنية الإنكليزية المستجدة، تسعى هذه الورقة الموسومة: نحو مقاربة تكاملية في ترجمة نصوص الأمن الصناعي إلى العربية إلى تسليط الضوء على إشكالية ترجمة النصوص المرتبطة بهذا المجال ترجمة منجزة، تحقق الغاية الوظيفية لدى المتلقي. يبرز المقال تحديات الترجمة التقنية والتي ترتبط أساسا بالمصطلح والمفهوم معا، ويوضح أهم التقنيات والأدوات الترجمية الكفيلة بتجاوز عوائقي ترجمة نصوص الأمن الصناعي. يخلص البحث إلى اقتراح استراتيجية ترجمية تكاملية، تتبنى أساليب الترجمة ونظرية الهدف، والبحث الوثائقي في مستوياته المختلفة، إلى جانب إثراء قواعد البيانات الرقمية بالمكافئات العربية، والذكاء الاصطناعي، موازنة مع الترجمة البشرية. تتيح هذه الاستراتيجية توسيع مخرجاتها إلى جميع أطياف الترجمة التقنية.

This study investigates the comparative effectiveness of human translation (HT) and machine translation (MT) in translating United Nations (UN) documents on violence against women (VAW) from English to Arabic. The research uses Na Pham's (2005) theoretical model to evaluate translations based on dynamic equivalence, cohesion, and cultural adaptability. Experiments involving terms and paragraphs analyzed semantic accuracy, cultural fidelity, and contextual coherence in translations by UN professionals and AI models, including ChatGPT-3.5 and GPT-4. Findings reveal that while HT excels in cultural nuance and textual cohesion, MT demonstrates significant improvements in term-level accuracy but struggles with contextual consistency and cultural adaptation. However, it often fails to maintain consistency between isolated term translations and their contextual use, and frequently violates Arabic grammatical norms such as adjective-noun agreement and gendered pronouns. The study emphasises the necessity of integrating advanced linguistic and cultural frameworks into AI systems to enhance translation reliability for sensitive topics. Recommendations are proposed for refining MT tools to support global advocacy efforts stressing the need for culturally aware training systems and integration of language-specific grammatical frameworks to improve semantic fidelity and contextual reliability.

تعد الترجمة من أهم الوسائل التقليدية للتواصل بين الشعوب على اختلاف لغاتها وخلفياتها الثقافية، وأداة فعالة لتعزيز التعارف ونقل المعارف والحضارات. ومع التقدم التكنولوجي ازداد الطلب -أكثر من أي وقت مضى- على الترجمة الآلية التي تستخدم التكنولوجيا الرقمية والذكاء الاصطناعي، وعلاوة على ذلك فإن الترجمة الآلية العصبية التي تستخدم شبكات عصبية اصطناعية معقدة لمحاكاة الدماغ البشري متقدمة لأنها تتفوق على الترجمة البشرية من حيث السرعة والقدرة على تخزين المعلومات واسترجاعها. ومن ثم، تحاول هذه المقالة استقصاء السؤال البحثي التالي: إلى أي مدى حققت الترجمة الآلية العصبية باستخدام DeepL Translator كمثال نموذجي دقة وجودة الترجمة البشرية؟