Evaluating Intralingual Machine Translation Quality: Application of an Adapted MQM Scheme to German Plain Language
Silvana Deilen, Sergio Hernández Garrido, Ekaterina Lapshinova‐Koltunski, Christiane Maaß, Annie Werner · Information · 2026
This paper presents the results of a study in which we conducted a fine-grained error analysis for intralingual machine translations into Plain Language. As there are no established error schemes for intralingual translations, we adapted the MQM scheme to fit the purposes of intralingual translation and expanded the scheme by error categories that are only relevant to intralingual translation. Our study has revealed that substantial differences exist between general-purpose and domain-specific models, with fine-tuned systems achieving notably higher accuracy and fewer severe errors across most categories. We found that across all four models, most errors occurred in the “Accuracy” category, closely followed by errors in the “Linguistic conventions” category and that all evaluated models produced persistent issues, particularly in terms of accuracy, linguistic conventions, and alignment with the target audience. In addition, we identified subcategories from the MQM scheme that are primarily relevant to interlingual translation, such as “Textual conventions”. Furthermore, we found that manual error annotation is resource-intensive and subjective, highlighting the urgent need for the development of automatic or semi-automatic error annotation tools. We also discuss difficulties that arose in the annotation process and show how methodological limitations might be overcome in future studies. Our findings provide practical directions for improving both machine translation technology and quality assurance frameworks for intralingual translation into Plain Language.