We propose the use of WordNet synsets in a syntax-based reordering model for hierarchical statistical machine translation (HPB-SMT) to enable the model to generalize to phrases not seen in the training data but that have equivalent meaning. We detail our methodology to incorporate synsets’ knowledge in the reordering model and evaluate the resulting WordNetenhanced SMT systems on the English-toFarsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPBSMT) by 0.6 points absolute.