Abstract
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 CAMIT
enhances machine translation results significantly
while requires fewer revision instructions from human compared to previous methods