Abstract
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.1 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.