Applications of Language Studies
A semiannual International Research Journal

Translating Persian Poetic Puns into English: A Comparative Study of Generative AI and Conventional Machine Translation Systems

Articles in Press

Document Type : Original Article

Authors

Niloufar Alimohammadi 1
Leila Nik Nasab 2

1 BA degree, Foreign Languages Department, Hazrat-e Masoumeh University

2 Foreign Languages Department, Hazrat-e Masoumeh University

10.22034/jals.2026.2067526.1096
Abstract
Translation has long been recognized as a challenging process, particularly in the fields of literature and poetry, where meaning is often conveyed through stylistic and rhetorical devices. Puns constitute a significant translation challenge because their effectiveness often stems from semantic ambiguity or phonological resemblance, features that rarely have direct equivalents in the target language. This paper explores the effectiveness of AI-based and machine translation tools in translating puns, focusing on the challenges arising from the structural features of Persian verse. 82 poems containing puns were selected, compiled, and translated using two machine translation tools—Google and Bing—and three AI tools: ChatGPT, Gemini, and Copilot. The translations were then analyzed and compared to evaluate the tools' ability to identify and translate the puns, based on Delabastita's framework. The results revealed that the three conversational AI models outperformed the two machine translation services; in terms of successful translations, ChatGPT ranked first, followed by Copilot in second place and Gemini in third. Bing Translator and Google Translate tied for last. However, these improvements seem attainable, as many of the tools were able to recognize, translate, and differentiate between the words forming the puns with only minor differences between successful and unsuccessful detection.

Keywords

AI-based Translation
Machin Translation
Pun
Subjects
Applications of Language Studies

Articles in Press, Accepted Manuscript
Available Online from 31 July 2026

  • Receive Date 29 September 2025
  • Revise Date 19 July 2026
  • Accept Date 31 July 2026

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