  A good translation should not only translate the original content
semantically, but also incarnate personal traits of the original text. For a
real-world neural machine translation (NMT) system, these user traits (e.g.,
topic preference, stylistic characteristics and expression habits) can be
preserved in user behavior (e.g., historical inputs). However, current NMT
systems marginally consider the user behavior due to: 1) the difficulty of
modeling user portraits in zero-shot scenarios, and 2) the lack of
user-behavior annotated parallel dataset. To fill this gap, we introduce a
novel framework called user-driven NMT. Specifically, a cache-based module and
a user-driven contrastive learning method are proposed to offer NMT the ability
to capture potential user traits from their historical inputs under a zero-shot
learning fashion. Furthermore, we contribute the first Chinese-English parallel
corpus annotated with user behavior called UDT-Corpus. Experimental results
confirm that the proposed user-driven NMT can generate user-specific
translations.
