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.