  Neural Machine Translation (NMT) models tend to achieve best performance when
larger sets of parallel sentences are provided for training. For this reason,
augmenting the training set with artificially-generated sentence pairs can
boost performance.
  Nonetheless, the performance can also be improved with a small number of
sentences if they are in the same domain as the test set. Accordingly, we want
to explore the use of artificially-generated sentences along with
data-selection algorithms to improve German-to-English NMT models trained
solely with authentic data.
  In this work, we show how artificially-generated sentences can be more
beneficial than authentic pairs, and demonstrate their advantages when used in
combination with data-selection algorithms.
