  Neural Machine Translation (MT) has reached state-of-the-art results.
However, one of the main challenges that neural MT still faces is dealing with
very large vocabularies and morphologically rich languages. In this paper, we
propose a neural MT system using character-based embeddings in combination with
convolutional and highway layers to replace the standard lookup-based word
representations. The resulting unlimited-vocabulary and affix-aware source word
embeddings are tested in a state-of-the-art neural MT based on an
attention-based bidirectional recurrent neural network. The proposed MT scheme
provides improved results even when the source language is not morphologically
rich. Improvements up to 3 BLEU points are obtained in the German-English WMT
task.
