Various technologies described herein pertain to training and utilizing a general, statistical framework for modeling translation via Markov random fields (MRFs). An MRF-based translation model can be employed in a statistical machine translation (SMT) system. The MRF-based translation model allows for arbitrary features extracted from a phrase pair to be incorporated as evidence. The parameters of the model are estimated using a large-scale discriminative training approach based on stochastic gradient ascent and an N-best list based expected Bilingual Evaluation Understudy (BLEU) as an objective function.