  Neural machine translation (NMT) is typically domain-dependent and
style-dependent, and it requires lots of training data. State-of-the-art NMT
models often fall short in handling colloquial variations of its source
language and the lack of parallel data in this regard is a challenging hurdle
in systematically improving the existing models. In this work, we develop a
novel colloquial Indonesian-English test-set collected from YouTube transcript
and Twitter. We perform synthetic style augmentation to the source of formal
Indonesian language and show that it improves the baseline Id-En models (in
BLEU) over the new test data.
