Translation in Arabic language and literature
Mohammad RAhimi; Shokufeh Shahzeydi
Abstract
Machine translation, as a significant technology, continues to advance daily, striving to facilitate communication between cultures and individuals. In this context, Google Translate holds a unique and widespread significance among users, with its functionality expanding every day. This study, based ...
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Machine translation, as a significant technology, continues to advance daily, striving to facilitate communication between cultures and individuals. In this context, Google Translate holds a unique and widespread significance among users, with its functionality expanding every day. This study, based on the premise that artificial intelligence faces major challenges in literary translation, aims to examine the errors in AI-based translation of a literary text. To this end, Google’s Arabic translation of Mahtab, a well-known and relatively simple poem by Nima Yooshij, has been selected for analysis. In line with this, the research categorizes machine translation errors according to different linguistic levels and proposes strategies for improving such translations through human intervention. The study’s key findings indicate that, despite advancements and advantages, Google Translate still faces serious aesthetic challenges in rendering literary texts—particularly at the lexical, grammatical, semantic, and interpretive levels—necessitating human intervention through strategies such as script modification, clarification, simplification, modernization, and more.
Mohammad Rahimi Khoighani
Abstract
The purpose of this research is to investigate how the cultural elements of the story "Farsi Shekar Ast" by Mohammad Ali Jamalzadeh, is transferred from Persian to Arabic, using Peter Newmark's model for categorizing cultural categories and its translation strategies. The most important findings of the ...
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The purpose of this research is to investigate how the cultural elements of the story "Farsi Shekar Ast" by Mohammad Ali Jamalzadeh, is transferred from Persian to Arabic, using Peter Newmark's model for categorizing cultural categories and its translation strategies. The most important findings of the present study, which have been written with a descriptive-analytical method, indicate that the story of "Farsi Shekar ast" has all five categories of cultural categories that Newmark deserves, the translator translates this story more than the equivalent cultural equivalent and in this way, a translation is tailored to the culture of the target language.
