  The standard approach to incorporate linguistic information to neural machine
translation systems consists in maintaining separate vocabularies for each of
the annotated features to be incorporated (e.g. POS tags, dependency relation
label), embed them, and then aggregate them with each subword in the word they
belong to. This approach, however, cannot easily accommodate annotation schemes
that are not dense for every word.
  We propose a method suited for such a case, showing large improvements in
out-of-domain data, and comparable quality for the in-domain data. Experiments
are performed in morphologically-rich languages like Basque and German, for the
case of low-resource scenarios.
