  Most of the recent work on terminology integration in machine translation has
assumed that terminology translations are given already inflected in forms that
are suitable for the target language sentence. In day-to-day work of
professional translators, however, it is seldom the case as translators work
with bilingual glossaries where terms are given in their dictionary forms;
finding the right target language form is part of the translation process. We
argue that the requirement for apriori specified target language forms is
unrealistic and impedes the practical applicability of previous work. In this
work, we propose to train machine translation systems using a source-side data
augmentation method that annotates randomly selected source language words with
their target language lemmas. We show that systems trained on such augmented
data are readily usable for terminology integration in real-life translation
scenarios. Our experiments on terminology translation into the morphologically
complex Baltic and Uralic languages show an improvement of up to 7 BLEU points
over baseline systems with no means for terminology integration and an average
improvement of 4 BLEU points over the previous work. Results of the human
evaluation indicate a 47.7% absolute improvement over the previous work in term
translation accuracy when translating into Latvian.
