With the help of glossaries – i.e. bilingual lists of approved terminology – generic MT and AI system output can be adapted to company-specific specialised terminology. Our internal analyses show that terminology corrections account for up to 45% of post-editing changes. This post-editing effort can therefore be significantly reduced with clear and appropriate specified terminology.

Anyone who uses machine translation (MT) or AI systems for translation is familiar with this problem: AI sometimes translates "Bolzen" as "bolt", sometimes as "stud" – depending on the day of the week. This results in a lot of post-editing being required, which quickly eats up the cost savings of AI. Glossaries have proven to be a tried-and-tested tool for minimising these errors. In our projects, we have reduced terminology corrections by more than half by integrating a glossary. This may lead people to assume that the more terminology you specify, the greater the benefit, but that is not true. An overcrowded glossary may even lead to more errors. The sheer volume overwhelms the system and, in the end, individual terms overshadow the desired end result. As is so often the case, the familiar principle also applies to glossaries: less is sometimes more.