Native speakers and experts of any natural language can easily tell whether a sentence or expression is composed accurately. Machine translation usually performs functional translation between languages. But often cultural nuances are missed, and expressions are applied where inappropriate. Also, literal word-to-word translation becomes something funny or nonsensical at time. Human translators and interpreters are likely to make more appropriate and sensible translations, because they have deeper expertise about the language, the culture as well as the context of any information.
Hence, DeepL has done a lot of work on their neural network architecture, their datasets and the mathematics used for implementing the models. They also collaborate with language experts, and conduct blind tests. This helps them to objectively compare the quality of their translations with existing popular translation systems.