  Multimodal machine translation (MMT) aims to improve translation quality by
equipping the source sentence with its corresponding image. Despite the
promising performance, MMT models still suffer the problem of input
degradation: models focus more on textual information while visual information
is generally overlooked. In this paper, we endeavor to improve MMT performance
by increasing visual awareness from an information theoretic perspective. In
detail, we decompose the informative visual signals into two parts:
source-specific information and target-specific information. We use mutual
information to quantify them and propose two methods for objective optimization
to better leverage visual signals. Experiments on two datasets demonstrate that
our approach can effectively enhance the visual awareness of MMT model and
achieve superior results against strong baselines.
