For our concrete experiments, we picked a model based on Lemma 1 and used h( Using this setup, parameter estimation for the new model can be accomplished via a slight modification of the EM algorithm for IBM Model 1.
we have that the posterior probabilities of this model factor just as those of the standard Model 1 and we have an M step that requires optimizing � a (k) q(a (k) |e (k) , f (k) ) log p(f (k) , a (k) |e (k) ) 1: Input: Define E, F , L, M , (e (k) , f (k) , l k , m k ) for k = 1 .