  Recently, the development of neural machine translation (NMT) has
significantly improved the translation quality of automatic machine
translation. While most sentences are more accurate and fluent than
translations by statistical machine translation (SMT)-based systems, in some
cases, the NMT system produces translations that have a completely different
meaning. This is especially the case when rare words occur.
  When using statistical machine translation, it has already been shown that
significant gains can be achieved by simplifying the input in a preprocessing
step. A commonly used example is the pre-reordering approach.
  In this work, we used phrase-based machine translation to pre-translate the
input into the target language. Then a neural machine translation system
generates the final hypothesis using the pre-translation. Thereby, we use
either only the output of the phrase-based machine translation (PBMT) system or
a combination of the PBMT output and the source sentence.
  We evaluate the technique on the English to German translation task. Using
this approach we are able to outperform the PBMT system as well as the baseline
neural MT system by up to 2 BLEU points. We analyzed the influence of the
quality of the initial system on the final result.
