  Character-based translation has several appealing advantages, but its
performance is in general worse than a carefully tuned BPE baseline. In this
paper we study the impact of character-based input and output with the
Transformer architecture. In particular, our experiments on EN-DE show that
character-based Transformer models are more robust than their BPE counterpart,
both when translating noisy text, and when translating text from a different
domain. To obtain comparable BLEU scores in clean, in-domain data and close the
gap with BPE-based models we use known techniques to train deeper Transformer
models.
