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Labels and epithets relating to gender, sexuality, ethnicity, color, and disability have long been divided into acceptable and offensive categories. This paper examines how differences in culture and historical contexts can cause an issue when translating English media into Arabic by examining how they are subtitled in different contexts. Examples of dialogue that used offensive or inoffensive labels were extracted from different media for the purpose of this study, which included four movies, eight series, and one TV show. The films included "Passing," "Perks of Being a Wallflower," "Gook" and "The Fundamentals of Caring"; the series comprised "Atlanta," "Breaking Bad," "Fresh of the Boat," "Derry Girls," "I Am Not Okay With This," "Dead End Paranormal Park," "Feel Good," and "Degrassi: Next Class"; and the TV show was "Atypical." The examined translations were chosen with the least potential influence of ideological manipulation and censorship; therefore, they were either Netflix or fan translations. Comparing and contrasting the degree of offense in the source and target texts and analyzing the reasoning behind this, it was found that the English and Arabic terms may have similar components but lacked the same connotations for their language speakers. What makes certain labels positive or neutral and others negative is beyond their semantic components; instead, the weight comes from the background and usage of the terms. Direct translations or even keeping the term as it was originally failed at providing the Arabic-speaking audience the same impact as that of the ST since there is still a contextual gap. This also means that having the same referent does not mean that words have the same sense. This suggests that using target language terms that refer to the same group as the source text could result in inadequate translations. The study recommends that further studies be conducted on how translators may purposely manipulate texts for ideological purposes or to meet censorship criteria.

This paper examines how flouts and violations of conversational maxims are represented in translation and how this influences the different types of translational equivalence. A number of scenes from audiovisual media where there is a gap between one character's intended meaning and another's reply were examined by comparing the employment of conversational maxims in the source and target text. Additionally, the relation between the employed translation strategies, equivalence type, and the representation of the maxims was analyzed. The results showed that audiovisual translators tend to reduce ambiguity through strategies like addition and explicitation, which alters the features of the maxims found in the source text. Moreover, linguistic differences also make retaining ambiguity a challenge. Furthermore, while functional equivalence is often seen as the ideal type to convey the sense of a text, in cases where the text contains double meanings, the rendition of sense is no longer straightforward. As a result, translators must employ compensational strategies to maintain the ambiguity or explain its existence. The study concludes that the translators must pay attention to nuance to choose the best equivalents and phrasings for each given situation since even slight changes may change the implicatures of the text. [ABSTRACT FROM AUTHOR]

This study examines the translation of 155 idioms by different machine and AI translation systems, namely Google Translate, ChatGPT, and Gemini. Various sources were utilized to collect the data, including books, magazines, interviews with native English speakers, and various websites dedicated to English idioms. This data was analyzed based on a framework built on the taxonomy of Baker (1992). The quantitative part examined the frequency of translation approaches each program used to render the idioms. The qualitative part focused on selected examples to highlight the potential issues of each approach in conveying the style and sense-based features of the idioms. The findings showed that idiom translations were done through three main approaches: literal, sense-based, and idiom-to-idiom translation. Google Translate had the highest percentage of literal translation at 76%, followed by ChatGPT at 53%, while Gemini had the lowest percentage at 21%. For sense-based translations that use nonfigurative language, Gemini was in the lead at 63%, followed by ChatGPT, with a wide gap at 35%. Google Translate had the least sense-based renditions at a mere 11%. When it came to translating idioms using figurative language, Gemini once again was in the lead with 16%, followed closely by ChatGPT at 13%, with Google Translate right behind at 12%. The study concludes that although there is vast improvement and advancement in technology, machine translation has yet to master nonliteral language such as idioms. [ABSTRACT FROM AUTHOR]