Researchers claim that AI-translated text is less 'lexically' rich than human translations

Human interpreters make choices unique to them, consciously or unconsciously, when translating one language into another. They might explicate, normalize, or condense and summarize, creating fingerprints known informally as "translationese." In machine learning, generating accurate translations has been the main objective thus far. But this might be coming at the expense of translation richness and diversity. In a new study, researchers at Tilburg University and the University of Maryland attempt to quantify the lexical and grammatical diversity of "machine translationese" -- i.e., the fingerprints made by AI translation algorithms.