Error Analysis of a Machine-Translated Poem: Towards a Teaching Guide
Abstract
The article is a suggested three-hour practical class on teaching error analysis in a machine-translated poem in the French to English direction. The poem Femmes de France by Léopold Sédar Senghor was used in the step-by-step error analysis teaching. The article shows how four machine translation (MT) systems (Amazon, DeepL, Google and Microsoft) output a typical poem by an African author and how the analysis of the errors from the four machine translations can be taught to students of the literary MT class. The poem was first analysed for errors against the original work, which is in French, and then analysed again for errors against Melvin Dixon‟s English translation (human translation). An adaptation of Kopenen‟s (2010) error classification model for MT was chosen for this demonstration because the model was found more suitable for analysing meanings and intentions in the literary genre of poetry than the other available models. In the end, it is revealed that the error class of mistranslated concept, after analysis against the human translation (HT), had a more significant number of errors than the same class of mistranslated concept when analysed in the source poem. This result shows the students that the human translator is a more critical reference than the poem‟s author when evaluating the MT of poetry.
