  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 personal behaviors.
To build this framework, we first collect post-editing data that connotes the
user preference from a live machine translation system. Specifically,
real-world users enter source sentences for translation and edit the
machine-translated outputs according to the user's preferred style. We then
propose a model that combines a discriminator module and user-specific
parameters on the APE framework. Experimental results show that the proposed
method outperforms other baseline models on four different metrics (i.e., BLEU,
TER, YiSi-1, and human evaluation).
