In this paper we use Markov random fields (MRFs) to model the joint distribution ܲ ‫ܟ‬ (, ‫)܍‬ over a source-target translation phrase pair (, ‫,)܍‬ parameterized by ‫.ܟ‬ Different from the directional translation models, as in Equation 1, the MRF model is undirected, which we believe upholds the spirit of the use of bi-directional translation probabilities under the log-linear framework.
the agreement or the compatibility of a phrase pair is more effective to score translation quality than a directional translation probability which is modeled based on an imagined generative story does.