  In this paper, we propose two new features for estimating phrase-based
machine translation parameters from mainly monolingual data. Our method is
based on two recently introduced neural network vector representation models
for words and sentences. It is the first time that these models have been used
in an end to end phrase-based machine translation system. Scores obtained from
our method can recover more than 80% of BLEU loss caused by removing phrase
table probabilities. We also show that our features combined with the phrase
table probabilities improve the BLEU score by absolute 0.74 points.
