PePe: Personalized Post-editing Model utilizing User-generated Post-edits

Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we introduce a personalized automatic post-editing framework to address this challenge, which effectively generates sentences considering distinct Figure 1: Example of a personal post-editing triplet personal behaviors. To build this framework, (i.e., source (src), machine translation (mt), and postedit we first collect post-editing data that connotes (pe)) given the source text in English and the translated the user preference from a live machine translation text in Korean. A post-edited sentence does not system. Specifically, real-world users enter only contain error correction of an initial machine translation source sentences for translation and edit result but also reflects individual preference. For the machine-translated outputs according to instance, a human post-editor modifies the word "primarily" the user's preferred style. We then propose to "primary," but also change " 공헌 " to its synonym a model that combines a discriminator module " 기여 " while keeping the rest as it is (e.g., "research").