  Neural Machine Translation models are brittle to input noise. Current
robustness techniques mostly adapt models to existing noisy texts, but these
models generally fail when faced with unseen noise and their performance
degrades on clean texts. In this paper, we introduce the idea of visual context
to improve translation robustness against noisy texts. In addition, we propose
a novel error correction training regime by treating error correction as an
auxiliary task to further improve robustness. Experiments on English-French and
English-German translation show that both multimodality and error correction
training are beneficial for model robustness to known and new types of errors,
while keeping the quality on clean texts.
