  We introduce a neural machine translation model that views the input and
output sentences as sequences of characters rather than words. Since word-level
information provides a crucial source of bias, our input model composes
representations of character sequences into representations of words (as
determined by whitespace boundaries), and then these are translated using a
joint attention/translation model. In the target language, the translation is
modeled as a sequence of word vectors, but each word is generated one character
at a time, conditional on the previous character generations in each word. As
the representation and generation of words is performed at the character level,
our model is capable of interpreting and generating unseen word forms. A
secondary benefit of this approach is that it alleviates much of the challenges
associated with preprocessing/tokenization of the source and target languages.
We show that our model can achieve translation results that are on par with
conventional word-based models.
