The Effectiveness of AI-Human Mediation in Recreating Literary Style: A Case of English/Arabic Poetry Translation
Keywords:
AI-driven English/Arabic translation; stylistic recreation; poetry translation; AI-human mediation; ChatGPT; human prompt engineeringAbstract
Over the last three decades, computerized translation has witnessed accelerating developments in its forms, effectiveness, and terminology. Considering their ability to facilitate AI-human cooperation and evolving adaptability to diverse contextual nuances, emerging AI-driven translation tools have demonstrated remarkable progress in translation output quality. Despite the successive evolution in the types and performance of algorithmic translation software, these AI-powered translation tools continue to be challenged by the stylistic features of different text types, but most remarkably literary texts. This study addresses one of the least researched aspects in connection with the effectiveness of AI-driven translation software. The study aims to assess the quality and limitations of AI-powered translation enhanced with human prompt engineering in producing a stylistic translation of a poem from English into Arabic. The research methodology is guided by a hybrid methodology based on an experimental comparison between a professional human translation and a translation produced via AI-human interaction. The results show that LLMs’ shortcomings that disrupt the effectiveness of AI-HT mediation are related to AI’s decision-making limitations in applying translation strategies, algorithmic bias, sequential processing bias, and the lack of emotional intelligence. The results also imply that an effective AI translation requires independent human postediting rather than interactive AI-HT mediation. The study has profound implications for translation education, professional translation practices, and machine translation engineering programs.
https://doi.org/10.26803/ijlter.24.12.9
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