  Neural machine translation (NMT) is notoriously sensitive to noises, but
noises are almost inevitable in practice. One special kind of noise is the
homophone noise, where words are replaced by other words with similar
pronunciations. We propose to improve the robustness of NMT to homophone noises
by 1) jointly embedding both textual and phonetic information of source
sentences, and 2) augmenting the training dataset with homophone noises.
Interestingly, to achieve better translation quality and more robustness, we
found that most (though not all) weights should be put on the phonetic rather
than textual information. Experiments show that our method not only
significantly improves the robustness of NMT to homophone noises, but also
surprisingly improves the translation quality on some clean test sets.
