  Machine translation models have discrete vocabularies and commonly use
subword segmentation techniques to achieve an 'open-vocabulary.' This approach
relies on consistent and correct underlying unicode sequences, and makes models
susceptible to degradation from common types of noise and variation. Motivated
by the robustness of human language processing, we propose the use of visual
text representations, which dispense with a finite set of text embeddings in
favor of continuous vocabularies created by processing visually rendered text.
We show that models using visual text representations approach or match
performance of text baselines on clean TED datasets. More importantly, models
with visual embeddings demonstrate significant robustness to varied types of
noise, achieving e.g., 25.9 BLEU on a character permuted German--English task
where subword models degrade to 1.9.
