  In this paper, we offer an in-depth analysis about the modeling and search
performance. We address the question if a more complex search algorithm is
necessary. Furthermore, we investigate the question if more complex models
which might only be applicable during rescoring are promising.
  By separating the search space and the modeling using $n$-best list
reranking, we analyze the influence of both parts of an NMT system
independently. By comparing differently performing NMT systems, we show that
the better translation is already in the search space of the translation
systems with less performance. This results indicate that the current search
algorithms are sufficient for the NMT systems. Furthermore, we could show that
even a relatively small $n$-best list of $50$ hypotheses already contain
notably better translations.
