This study examines whether AI can replace human translators in poetry rendition using Guillaume Apollinaire’s poem “Le Pont Mirabeau” as a case study. The paper draws on Katharina Reiss’s (2000) theory of text typology to categorize poetry as “expressive” text-type. The researcher conducts a comparative translation study between machine-generated translations, via neural machine translation systems such as Bing, Google Translate, and DeepL, and human translations by three translators, including Shen Baochi, Dai Wangshu and Min-Hua Wu. The results show that human translations tend to be more conscious of traditional poetics and are better at representing the original poetic features of the French poem, such as line length, rhyming pattern, and poetic diction. Human translations also tend to have greater semantic accuracy and profundity compared to machine translations, which may commit gross errors and fail to represent the poem’s poetic features. Thus, human translators outperform machine translators in both aesthetic and semantic dimensions of poetry rendition.
