  Neural machine translation has achieved remarkable empirical performance over
standard benchmark datasets, yet recent evidence suggests that the models can
still fail easily dealing with substandard inputs such as misspelled words, To
overcome this issue, we introduce a new encoding heuristic of the input symbols
for character-level NLP models: it encodes the shape of each character through
the images depicting the letters when printed. We name this new strategy visual
embedding and it is expected to improve the robustness of NLP models because
humans also process the corpus visually through printed letters, instead of
machinery one-hot vectors. Empirically, our method improves models' robustness
against substandard inputs, even in the test scenario where the models are
tested with the noises that are beyond what is available during the training
phase.
