  This paper describes NTT's submission to the WMT19 robustness task. This task
mainly focuses on translating noisy text (e.g., posts on Twitter), which
presents different difficulties from typical translation tasks such as news.
Our submission combined techniques including utilization of a synthetic corpus,
domain adaptation, and a placeholder mechanism, which significantly improved
over the previous baseline. Experimental results revealed the placeholder
mechanism, which temporarily replaces the non-standard tokens including emojis
and emoticons with special placeholder tokens during translation, improves
translation accuracy even with noisy texts.
