earticle

영어

Neural Machine Translation(NMT) has greatly improved machine translation quality, further signaling wider use of MT in the human translation process. As the localization industry evolves, translation production process becomes segmented into draft translation by translator, review by external reviewer and editing by client. In particular, changes in translation ecosystem have led to switching translation service object from technical contents to creative web contents. Among text types that currently flow in, commercial advertizement is assumed to shed light on style difference between machine translation and human translation. Therefore, this study explores style shifts in cosmetic commercials by translation mode such as machine translation, human translation and trans-creation, employing methodological approach from systemic functional language(SFL) - transitivity and register. Except for information freshly added or deleted, trans-created version finalized by a client organization shows the same features as human translation performed by an individual translator within a range that TT can be compared to ST. Meanwhile, machine translation and human translation are distinctly different in transitivity and register, indicating that both linguistic features are two important factors to determine translation style.