Fidelity Analysis of Artificial Intelligence-Powered Poetry Translation: A Comparison of Google Translate, Deepl Translate, and Libre Translate

Abstract

The research aims to analyze the fidelity of Google Translate, DeepL Translate, and Libre Translate in conveying the meaning and intent of the poetry. Employing a mixed-method approach, data were collected from ten purposively selected lecturers at Universitas Islam Makassar and Universitas Megarezky Makassar, Quantitative data were obtained through a structured questionnaire using a five-point Likert scale to measure fidelity, while qualitative insights were gathered via thematic content analysis of the AI-powered poetry translations. Findings reveal a clear fidelity score: DeepL Translate (mean = 4.5, excellent), Google Translate (mean = 3.7, good), and Libre Translate (mean = 1.8, low). The researcher concludes that AI-powered translation tools exhibit considerable variability in their capacity to maintain the message, intent, and stylistic attributes of poetry. Of the three systems assessed, DeepL Translate shows the highest level of fidelity, supported by both quantitative evaluations and qualitative theme analyses.