Examining the Cultural Connotations in Human and Machine Translations: A Corpus Study of Naguib Mahfouz's Zuqāq al-Midaqq Mouza H. Al-Kaabi Arabic Language and Literature Department, Mohamed bin Zayed University of Humanities, Abu Dhabi, United Arab Emirates Naji M. AlQbailat  English Language and Literature Department, Al-Balqa Applied University, Jordan Amjad Badah Linguistics, Literature and Translation Department, University of Malaga, Spain Islam A. Ismail English Language Education Department, The English and Foreign Languages University, India Khalid B. Hicham English Department, University Sultan My Slimane, Beni Mellal, Morocco Abstract—The translation of culture-specific terms constitutes a major challenge for professional translators as it necessitates a thorough understanding of both the linguistic and cultural elements. With rapid technological advancement over the past few years, machine translation has enhanced translation quality. This study investigates the transability of cultural connotations in the Arabic-English translation of Naguib Mahfouz’s Zuqāq al-Midaqq. A descriptive qualitative research design was adopted to achieve the intended goals of the study. The data comprised human and machine translations from Google Translate and ChatGPT. Through qualitative content analysis, the translations were compared for accuracy in transferring the cultural connotations prevalent in the Arabic source text. The findings revealed that the human translation showed greater naturalness and accuracy in rendering cultural connotations. Machine translation has struggled with rhetorical devices, idioms, and cultural nuances. The results also indicated that the AI-enhanced machine represented by ChatGPT captured the cultural elements more effectively than Google Translate. The study concluded that human expertise remains essential for the high-quality translation of literary works to maintain cultural significance. The findings can inform translator training and guide improvements to AI- enhanced translation for literary texts. Index Terms—machine translation, human translation, artificial intelligence, GhatGPT, corpus, literary texts I. INTRODUCTION In its broad sense, machine translation is defined as the usage of computers to translate a given text from one language into another (Wang et al., 2021, p. 143). It is a sub-field of computational linguistics through which texts are automatically translated from one text into another using a computing device (Garg & Agarwal, 2018). The core mechanism of its algorithm is to integrate the corresponding relationship between both languages into the word database beforehand (Lihua, 2022, p. 1). With the rise of technology usage in different educational sectors, there has been a growing interest in the differences between AI-based machine translations and human translations. Despite the advancements in machine translation in many aspects, including cost, speed, confidentiality, and acceptance, there is still ongoing debate about whether machine translation is as effective as human translation (Afzaal, 2022, p. 1). Therefore, using corpus-based investigations has become important in evaluating the efficacy of machine and human translation. Such examination could involve exploring extensive sets of language data to gain insights into the benefits and limitations of both methods. Traditionally, Translation Studies have focused on the linguistic aspects of the translation process, such as word choice syntax. Due to globalization, where there has been a great need to communicate across cultures, the focus has moved towards transferring the cultural elements in the given discourse. This has been known as a cultural turn in Translation Studies (see Lefevere, 1992). Newmark (1988) defines culture as "the way of life and its manifestations peculiar to a community that uses a particular language as its means of  Corresponding Author. ISSN 1798-4769 Journal of Language Teaching and Research, Vol. 15, No. 3, pp. 707-718, May 2024 DOI: https://doi.org/10.17507/jltr.1503.03 © 2024 ACADEMY PUBLICATION