Journal of Textual and Translation Analysis in Islamic Studies (TTAIS)

The language of the Qur’ān is highly eloquent and rich in rhetoric. Transferring features of languages from different origins is tricky. Although throughout history, scholars have made significant contributions to understanding Qur’ānic idioms, we still face challenges in conveying their meanings. There are language-specific idioms in Qur’ānic Arabic, reflecting Arab culture and environment. The fact that different cultures conceptualize reality in varying ways causes idioms to be shaped by cultural factors. This is why some translations of Qur’ānic idioms fail to convey their full depth of meaning. This article aims to investigate various interpretations and English translations of the idioms in the selected verses of Surahs Al-Isra and Al-Ma’idah. To this end, the translations of twelve prominent translators were compared. Considering the descriptive nature of the work, the researchers analyzed the methods used by the selected translators. The theoretical framework underpinning this study is the translation methods of Schleiermacher. Furthermore, the translations were analyzed and ranked based on Waddington’s holistic model of translation quality assessment. Analysis reveals a spectrum of translational strategies, defying strict categorization within Schleiermacher’s binary framework of ‘naturalizing’ and ‘alienating’ translation. A significant number of translations exhibit hybrid characteristics, displaying a preponderance towards either naturalization or alienation, yet failing to conform exclusively to either pole.

The translation of religious texts plays a crucial role within the translators’ community, prompting many translators to strive for the most appropriate equivalents in order to convey the themes and ideas as comprehensively as possible. The primary objective of the present study was to investigate the differences in equivalence among translators while translating twenty conveniently selected letters from Nahj al-Balagha from the original to the target text. Specifically, it aimed to determine whether the three English translations of Nahj al-Balagha by Mohammad Askari Jafari, Morteza Motahhari, and Sayed Ali Reza convey the same message. In this context, the research sought to identify any significant differences in equivalence among the three Persian-to-English translations by these translators. Nida’s (1964) model of equivalence served as the framework for the study, allowing a comparison of the three English translations of letters 40 to 60. This analysis focused on identifying the types of equivalence used based on Nida’s distinctions between dynamic and formal equivalence. The results indicated significant differences among the three translations: Sayed Ali Reza’s and Motahhari’s translations were notably more formal in tone, while Askari Jafari's translation was characterized as dynamic. These findings can be informative for readers interested in further research on the translation of religious texts, including Nahj al-Balagha.

The extensive training data of ChatGPT has facilitated theorizing in the field of Translation Studies, particularly during the cultural turn. Following this theorizing, a standardization framework was proposed. This study employed a qualitative and interdisciplinary approach, utilizing a descriptive method to interpret the data. The validated questions were based on the works of theorists associated with the cultural turn. Two scholars participated in structured and semi-structured interviews, employing a three-point Likert scale to capture their opinions on the proposed theory. Data analysis focused on the accuracy of ChatGPT-4’s responses in relation to the scholars’ opinions and references. The findings associated with the Likert scale were linked to task-oriented benchmarks, including factual/contextual understanding, coherence, and resolution of ambiguity. The results indicated a 29.3% weakness in ChatGPT-4’s data analysis. The resolution of ambiguity resulted in a total of neutral responses at 44.8%. Scholars unanimously endorsed the cultural interaction theory, demonstrating a 100% capacity for theorizing by ChatGPT-4. In terms of coherence and summarization, the data suggested a stronger correlation with prompt engineering. The potential for theorizing concerning existing theory was found to be 77.7%, while the standard of theorizing by ChatGPT-4 was assessed at 68.9%. Surah Al-Fatiha was selected to exemplify cultural translation according to CIT, illustrating the theory’s effectiveness in translating cultural-ideological texts. A comparison of ChatGPT-4’s translations with those of six Quranic translators underscored its synthesizing abilities, with the incorporation of cultural interaction theory into prompts significantly enhancing its translation skills.

This study is concerned with coherence in seven translations of the Surah al-Naba’ in the Holy Qur’an. Considering Charroles’ (1983) descriptive model that establishing coherence may involve adding new information to the target text, it aimed to find the frequency of supplemental and explanatory coherence and reveal whether translating coherence may result in an explicated translation or not. For this purpose, Charolles’ (1983) dichotomous modes of coherence in translation, namely supplemental (implicitation) and explanatory (explicitation) was considered as the framework of the study. The data to be analyzed were selected according to the method of purposive data collection in sacred scriptures. Also, seven renditions of the Holy Surah al-Naba’ by seven celebrated translators were taken into account, and the coherence relations were discovered by the interpretations to unravel the frequency and essence of the translated verses. The results indicated that most renditions had the same coherence level compared with the source text, by providing only minimal information existing in the verse, which was most often in accordance with the mode of supplemental coherence. As the nature of explicitation is still hypothetical in translation, the results of the study would offer a great opportunity to improve renditions in case of Qur’anic translations.

Each culture encompasses unique concepts that can be challenging for outsiders to grasp, often due to their deep-rooted connotations within the source language and culture. Various strategies exist for translating culture-specific items. This descriptive-comparative study examines the strategies employed in the Arabic-to-Persian and Arabic-to-English translations of Surah Al-An’am. This surah is one of the Tiwal suras, which are seven lengthy surahs in the Holy Qur’ān. Its complete recitation in one sitting, known as Khatme Anaam (complete recitation of Al-An’am Surah), is a common practice in Iran, performed with the hope of having one’s prayers answered by God. The Persian translation analyzed in this study was by Mohammad Mehdi Fooladvand, one of the top eight translators of the Holy Qur’ān, recognized for his balanced approach between literal and content-based translation. The English version studied was by Mustafa Khattab, a Canadian-Egyptian translator renowned for The Clear Qur’ān. The translation strategies for culture-specific items were evaluated using frameworks proposed by Vinay and Darbelnet, Newmark, and Nida. The analysis identified 50 culture-specific items in the surah. The findings revealed that both translations predominantly employed the strategy of literal translation, while footnotes and modulation were the least utilized. Statistical tests, including log-likelihood for significance and Bayes Factor for effect size, indicated significant differences between the two translations regarding the use of seven strategies. Notably, the strategies of borrowing and functional equivalence provided strong evidence against the null hypothesis.

The Qur’ān is not only rich in linguistic construction but also deeply embedded with cultural and religious meanings, making its translation a challenging task. Among the many intricate linguistic features of the Qur’ān, proverbs, especially hidden proverbs, present one of the most significant challenges for translators. These proverbs carry culture-specific meanings that are often difficult to render accurately in other languages and are hidden due to their indirect nature, which can be challenging for readers to understand (Esmaeili, 1986). This study aims to identify and analyze hidden proverbs in the Qur’ān through translations generated by artificial intelligence (GPT-3.5) and by a human translator, Qarai’s English translation. The research adopts a descriptive-qualitative method, analyzing twenty-one Qur’ānic verses containing hidden proverbs. These 21 hidden proverbs were extracted from a technical source on Qur’ānic proverbs (Esmaeili, 1986). The study evaluated the translation strategies used by both GPT-3.5 and Qarai, examining how effectively each approach conveys the figurative and cultural meanings of the proverbs. Through comparative analysis, this research investigated the strengths and limitations of AI-generated translations versus human translations in handling cultural-specific linguistic elements. The findings reveal that GPT-3.5 demonstrated strong performance in applying Beekman and Callow’s (1974) translation strategies, particularly in cases where the source language proverbs were untranslatable and required a non-figurative explanation. Compared to the human translator, GPT-3.5 provided more consistent and contextually appropriate solutions to the challenge of translating hidden proverbs. These results highlight the growing potential of AI-assisted translation tools in addressing complex linguistic and cultural challenges, suggesting promising advancements in the field of Qur’ānic translation.