human translation

This study aimed at investigating the Persian human and machine (Google) renderings of The Kite Runner by Khalid Hosseini, through comparing and contrasting culture specific items (CSI) as well as the exploited translation strategies. Thus, the relevant problematic forms of the cultural differences were identified and the procedures suggested by Newmark (1988) were examined to see how they fit into the translation of cultural differences in English and Persian. The results of this qualitative study revealed that Literal Translation, Naturalization and Transposition strategies were used most frequently in both human and machine translations. However, machine translation could not present a comprehensible translation due to overuse of these strategies (75%). It was also revealed that the spirit of source text was kept intact in both renderings due to closeness of Iranian and Afghan cultures. It was further discovered that the human translated version carried the real beauty and creativities of the original work. In facty, the terms that were transformed and localized were able to carry over the flavor of the author’s work. Finally, it was shown that the regretful theme of the source text was reserved to a great extent in the human rendering of the novel, while machine translation failed to get it. Generally-speaking, based on the findings of this study, culture-specific terms make it difficult for machines to achieve complete word-for-word equivalence, and at the same time, human translator must have a broad knowledge of the literature and traditions of both the source and target languages to come up with a faithful translation in terms of both form and content.

The present descriptive study aimed at investigating the human and machine Persian translations of The Kite Runner by Khalid Hosseini, and comparing the applied translation strategies in the translated texts for culture-specific items (CSI). To this end, based on Newmark’s (1988) category, the applied strategies were identified in the two translations and compared. The obtained results showed that Naturalization and Transposition strategies were the most frequently-used strategies by both human translators and machine translation. The results also showed that machine translation could not present a comprehensible translation due to overuse of these strategies (75%). It was further revealed that the spirit of the original text was not lost in the translated versions due to the closeness of Iranian and Afghan cultures. In fact, the translated versions kept the real beauty and creativity of the original work. However, the remorseful theme of the source text was kept intact to a great extent in the human translation of the novel, while machine translation lost it. Thus, the general impression is that culture-specific terms make it difficult for the machine translation to achieve complete word-for-word and semantic equivalence, and that the human translator must have a broad knowledge of the literature and traditions of both the source and target languages.

Google Translate , a free multilingual machine translation service, developed by Google has attracted the attention of countless users due to its ease of use through modern means of mass communication, and has become the only translation tool in some areas. However, compared to human translation, these machine tools have not yet been able to deliver high-quality translations due to the complexity of translation process. Therefore, studying the modifications of machine translated texts is of great importance. Therefore, the current study aimed to explore the types of linguistic modifications of the texts translated from Persian into English through Google Translate. To this end, the abstracts of ten unpublished Persian literary articles intended to be submitted to Iranian journals were selected for the analysis. The selected abstracts were initially translated into English (target language) through Google Tra n slate from Persian (source language). To identify the kinds of changes needed to make them academically acceptable, the machine translated texts were all post edited. Then, the original Google translated texts and their post edited versions were compared to figure out the types of the applied modifications. The results of this qualitative study indicated that the linguistic post edition modification of the texts included tense, literal translation, redundancy, collocations, deletion of the main verb, word-choice and proper nouns.

Crowdsourcing Translation as a Post-Editing Method (CTPE) has emerged as a rapid and inexpensive method for translation and has drawn significant attention in recent years. This qualitative study aims to analyze and synthesize the approaches and aspects underpinning CTPE research and to identify its potential that is yet to be discovered. Through a systematic literature review focused on empirical papers, we examined the limited literature thematically and identified recurring central themes. Our review reveals that the topic of CTPE requires further attention and that its potential benefits are yet to be fully discovered. We discuss the eight core concepts that emerged during our analysis, including the purpose of CTPE, CTPE areas of application, ongoing CTPE processes, platform and crowd characteristics, motivation, CTPE domains, and future perspectives. By highlighting the strengths of CTPE, we conclude that it has the potential to be a highly effective translation method in various domains.

Linguistic issues are important in the textual analysis of translated texts. Among the most sensitive and significant texts translated into different languages, the Holy Qur’ān stands out. The text and texture of the Qur’ān are so unique that one cannot easily understand it without prior knowledge of its linguistic and extralinguistic aspects. One of the most challenging linguistic issues in the Qur’ān is proverbs, especially hidden proverbs that carry culture-specific meanings. The translator’s role in explicating the meanings of these culture-specific items is crucial. This research aims to identify and analyze Qur’ānic hidden proverbs using a technical reference (Esmaeeli, 1986) and to assess translation quality with Na Pham’s (2005) translation quality assessment model. In this study, two translation forms, AI and human (Qarai), were compared for their treatment of Qur’ānic hidden proverbs. Data collection and analysis followed a descriptive-qualitative design. Twenty-one verses containing hidden proverbs and their translations by GPT 3.5 and Qarai were analyzed. The study results indicated that, in terms of translation quality, GPT 3.5 performed better than Qarai.

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.