Addressing Untranslatability: ChatGPT’s Compensatory Strategies for Translating Verbified Proper Nouns in English–Arabic Contexts

Abstract

This research paper examines ChatGPT's methodologies for addressing the untranslatability challenges posed by verbified proper nouns in English-Arabic translation. The process of denominalization, whereby proper nouns are converted into verbal forms, presents substantial translation difficulties due to its complex intersection of linguistic structure and cultural context. Previous scholarship by Phrisan et al. (2021) has investigated verbified proper nouns specifically in English, demonstrating their unique linguistic transformations and the cultural connotations they carry. Parallel research by Sattar et al. (2023) has explored the phenomenon of verbified common nouns in both Arabic and English, providing valuable insights into the cross-linguistic dimensions of this linguistic process. However, the academic literature currently lacks a focused examination of how verbified proper nouns are handled in translation between Arabic and English, particularly within the realm of artificial intelligence-assisted translation. The present study addresses this research gap by analyzing ChatGPT's approach to this complex translation issue, utilizing Newmark's (1988) theoretical framework on translation strategies. Our findings indicate that ChatGPT employs a user-oriented approach predicated on an assumption of minimal shared cultural knowledge with the end user. This approach leads the AI system to consistently adopt semantic translation strategies, prioritizing the accurate conveyance of meaning and intended effect from the source language to the target language. While this method ensures comprehension and maintains the original text's impact for the target audience, our analysis reveals that its effectiveness remains subject to specific contextual conditions. This investigation makes a significant contribution to understanding AI capabilities in handling linguistically and culturally complex translation tasks. The study highlights both the potential of large language models in overcoming translation challenges and their current limitations when dealing with specialized linguistic phenomena like verbified proper nouns. These findings have important implications for the future development of AI translation systems and their application in cross-cultural communication contexts.

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