  Data selection has proven its merit for improving Neural Machine Translation
(NMT), when applied to authentic data. But the benefit of using synthetic data
in NMT training, produced by the popular back-translation technique, raises the
question if data selection could also be useful for synthetic data?
  In this work we use Infrequent N-gram Recovery (INR) and Feature Decay
Algorithms (FDA), two transductive data selection methods to obtain subsets of
sentences from synthetic data. These methods ensure that selected sentences
share n-grams with the test set so the NMT model can be adapted to translate
it.
  Performing data selection on back-translated data creates new challenges as
the source-side may contain noise originated by the model used in the
back-translation. Hence, finding n-grams present in the test set become more
difficult. Despite that, in our work we show that adapting a model with a
selection of synthetic data is an useful approach.
