AI-Based processing of poetic language and human translation in literary contexts

Abstract

As artificial intelligence (AI) continues to redefine the boundaries of linguistic research, this study examines the extent to which machine translation (MT) and AI tools can go beyond literal meaning, push beyond surface-level syntax and semantics to process context-sensitive issues in literary translation. While traditional MT systems such as Google Translate and Microsoft Translator are optimized for direct source-to-target mapping, AI language models like ChatGPT represent a broader category of tools designed for general-purpose language generation, including but not limited to translation. Using a 14-line Arabic poem, translations were generated by three MT systems, one AI model (ChatGPT), and two certified human translators. These outputs were evaluated against ten linguistic and stylistic dimensions: punctuation, layout, rhyme, mood, theme, logico-semantics, transitivity, field, tenor, and mode. The six translation versions were compared using a framework grounded in systemic functional linguistics (SFL). The analysis also considers how humans process cognitive-linguistic features when rendering poetic language. Results indicate that ChatGPT outperformed both MT systems and human translators in structural and semantic coherence, as well as in preserving poetic features such as rhyme and mood. However, all automated systems struggled with context-rich dimensions like tenor and mode, underscoring the enduring value of human interpretive depth. The findings highlight the potential of AI language models to complement, rather than replace, human expertise in literary translation and advocate for hybrid approaches that integrate computational efficiency with poetic language and cultural sensitivity. Acknowledgements We would like to express our sincere appreciation to the human translators who contributed to this study by translating the selected poem from Arabic into English. We equally thank the two translation experts who contributed to the analysis of translations. Their careful attention to linguistic nuance and cultural context played a significant role in comparing the machine-generated and human-produced translations. The authors are thankful to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program. Disclosure Statement The authors reported no potential conflict of interests.

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