We begin by reviewing IBM Model 1 and introducing the necessary notation.
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 .