  Data selection techniques applied to neural machine translation (NMT) aim to
increase the performance of a model by retrieving a subset of sentences for use
as training data.
  One of the possible data selection techniques are transductive learning
methods, which select the data based on the test set, i.e. the document to be
translated. A limitation of these methods to date is that using the source-side
test set does not by itself guarantee that sentences are selected with correct
translations, or translations that are suitable given the test-set domain. Some
corpora, such as subtitle corpora, may contain parallel sentences with
inaccurate translations caused by localization or length restrictions.
  In order to try to fix this problem, in this paper we propose to use an
approximated target-side in addition to the source-side when selecting suitable
sentence-pairs for training a model. This approximated target-side is built by
pre-translating the source-side.
  In this work, we explore the performance of this general idea for one
specific data selection approach called Feature Decay Algorithms (FDA).
  We train German-English NMT models on data selected by using the test set
(source), the approximated target side, and a mixture of both. Our findings
reveal that models built using a combination of outputs of FDA (using the test
set and an approximated target side) perform better than those solely using the
test set. We obtain a statistically significant improvement of more than 1.5
BLEU points over a model trained with all data, and more than 0.5 BLEU points
over a strong FDA baseline that uses source-side information only.
