Quality used to be a static concept in the translation industry. Undisputedly translation buyers would expect human, such as good quality, translations. Now, in many cases, users seem to be happy to accept good enough machine translations (MTs), especially when they are delivered in real time. The TAUS Quality Dashboard with the underlying MQM/DQF metrics is getting traction as a standard approach to quality evaluation. It can help to differentiate, measure and benchmark translation quality according to agreed content profiles. It potentially takes a lot of the subjectivity out of the quality management function. DQF helps to aggregate data that support decisions on the training and selection of MT engines and the matching of resources and translation assets.
Normalizing Translation Quality Management: DQF Use Cases and Integrations