  Neural Machine Translation (NMT) models have been proved strong when
translating clean texts, but they are very sensitive to noise in the input.
Improving NMT models robustness can be seen as a form of "domain" adaption to
noise. The recently created Machine Translation on Noisy Text task corpus
provides noisy-clean parallel data for a few language pairs, but this data is
very limited in size and diversity. The state-of-the-art approaches are heavily
dependent on large volumes of back-translated data. This paper has two main
contributions: Firstly, we propose new data augmentation methods to extend
limited noisy data and further improve NMT robustness to noise while keeping
the models small. Secondly, we explore the effect of utilizing noise from
external data in the form of speech transcripts and show that it could help
robustness.
