  State-of-the-art machine translation models are still not on par with human
translators. Previous work takes human interactions into the neural machine
translation process to obtain improved results in target languages. However,
not all model-translation errors are equal -- some are critical while others
are minor. In the meanwhile, the same translation mistakes occur repeatedly in
a similar context. To solve both issues, we propose CAMIT, a novel method for
translating in an interactive environment. Our proposed method works with
critical revision instructions, therefore allows human to correct arbitrary
words in model-translated sentences. In addition, CAMIT learns from and softly
memorizes revision actions based on the context, alleviating the issue of
repeating mistakes. Experiments in both ideal and real interactive translation
settings demonstrate that our proposed \method enhances machine translation
results significantly while requires fewer revision instructions from human
compared to previous methods.
