Describing and evaluating MT results from small and resource-poor languages: A translation didactic orientated case study of Galician-German touristic texts

Abstract This article assesses the performance of four neural machine translation engines using the DQF-MQM methodology, focusing on the challenges inherent in using machine translation while working with resource-limited or minority languages such as Galician. The study examines translations between German and Galician in both directions, specifically within the context of touristic texts. The research considers different categories of errors, including accuracy, fluency and cultural references, to provide a comprehensive evaluation of the performance of the engines. By highlighting the pedagogical importance of these findings, the study aims to contribute to the training of students and future translators, equipping them with the skills needed to navigate the complexities of working with lesser-known languages. Furthermore, the results are intended to stimulate discussion on the wider implications of using machine translation in such language pairs, thereby enriching the ongoing discourse on the application of machine translation in diverse linguistic contexts.