  Rule-based machine translation is a machine translation paradigm where
linguistic knowledge is encoded by an expert in the form of rules that
translate text from source to target language. While this approach grants
extensive control over the output of the system, the cost of formalising the
needed linguistic knowledge is much higher than training a corpus-based system,
where a machine learning approach is used to automatically learn to translate
from examples. In this paper, we describe different approaches to leverage the
information contained in rule-based machine translation systems to improve a
corpus-based one, namely, a neural machine translation model, with a focus on a
low-resource scenario. Three different kinds of information were used:
morphological information, named entities and terminology. In addition to
evaluating the general performance of the system, we systematically analysed
the performance of the proposed approaches when dealing with the targeted
phenomena. Our results suggest that the proposed models have limited ability to
learn from external information, and most approaches do not significantly alter
the results of the automatic evaluation, but our preliminary qualitative
evaluation shows that in certain cases the hypothesis generated by our system
exhibit favourable behaviour such as keeping the use of passive voice.
