  In machine translation (MT) that involves translating between two languages
with significant differences in word order, determining the correct word order
of translated words is a major challenge. The dependency parse tree of a source
sentence can help to determine the correct word order of the translated words.
In this paper, we present a novel reordering approach utilizing a neural
network and dependency-based embeddings to predict whether the translations of
two source words linked by a dependency relation should remain in the same
order or should be swapped in the translated sentence. Experiments on
Chinese-to-English translation show that our approach yields a statistically
significant improvement of 0.57 BLEU point on benchmark NIST test sets,
compared to our prior state-of-the-art statistical MT system that uses sparse
dependency-based reordering features.
