  While recent advances in deep learning led to significant improvements in
machine translation, neural machine translation is often still not able to
continuously adapt to the environment. For humans, as well as for machine
translation, bilingual dictionaries are a promising knowledge source to
continuously integrate new knowledge. However, their exploitation poses several
challenges: The system needs to be able to perform one-shot learning as well as
model the morphology of source and target language.
  In this work, we proposed an evaluation framework to assess the ability of
neural machine translation to continuously learn new phrases. We integrate
one-shot learning methods for neural machine translation with different word
representations and show that it is important to address both in order to
successfully make use of bilingual dictionaries. By addressing both challenges
we are able to improve the ability to translate new, rare words and phrases
from 30% to up to 70%. The correct lemma is even generated by more than 90%.
