  The word order between source and target languages significantly influences
the translation quality in machine translation. Preordering can effectively
address this problem. Previous preordering methods require a manual feature
design, making language dependent design costly. In this paper, we propose a
preordering method with a recursive neural network that learns features from
raw inputs. Experiments show that the proposed method achieves comparable gain
in translation quality to the state-of-the-art method but without a manual
feature design.
