  In this paper, we propose a robust neural machine translation (NMT)
framework. The framework consists of a homophone noise detector and a
syllable-aware NMT model to homophone errors. The detector identifies potential
homophone errors in a textual sentence and converts them into syllables to form
a mixed sequence that is then fed into the syllable-aware NMT. Extensive
experiments on Chinese->English translation demonstrate that our proposed
method not only significantly outperforms baselines on noisy test sets with
homophone noise, but also achieves a substantial improvement on clean text.
