(2016) present three main arguments: character level models (1) do not suffer from out-of-vocabulary issues, (2) are able to model different, rare morphological variants of a word, and (3) do not require segmentation.
text segmentation is highly non-trivial for many languages and problematic even for English as word tokenizers are either manually designed or trained on a corpus using an objective function that is unrelated to the translation task at hand, which makes the overall system sub-optimal.