We find that our approach systematically outperforms GIZA++ and BERKELEY on all NIST datasets.
generalizing the agreement to model the discrepancy between word alignment and phrase segmentation is consistently beneficial for improving translation quality, suggesting that it is important to introduce structural constraints into word alignment to increase the correlation between alignment and translation.