AbstractData selection techniques applied to neural machine trans-lation (NMT) aim to increase the performance of a model byretrieving a subset of sentences for use as training data.One of the possible data selection techniques are trans-ductive learning methods, which select the data based on thetest set, i.e. the document to be translated. A limitation ofthese methods to date is that using the source-side test setdoes not by itself guarantee that sentences are selected withcorrect translations, or translations that are suitable given thetest-set domain. Some corpora, such as subtitle corpora, maycontain parallel sentences with inaccurate translations causedby localization or length restrictions.In order to try to fix this problem, in this paper we pro-pose to use an approximated target-side in addition to thesource-side when selecting suitable sentence-pairs for train-ing a model. This approximated target-side is built by pre-translating the source-side.In this work, we explore the performance of this generalidea for one specific data selection approach called FeatureDecay Algorithms (FDA).We train German-English NMT models on data selectedby using the test set (source), the approximated target side,and a mixture of both. Our findings reveal that models builtusing a combination of outputs of FDA (using the test setand an approximated target side) perform better than thosesolely using the test set. We obtain a statistically significantimprovement of more than 1.5 BLEU points over a modeltrained with all data, and more than 0.5 BLEU points over astrong FDA baseline that uses source-side information only.