A notable shortcoming of this approach is that the translation model probabilities thus calculated from the training bitext can be unintuitive and unreliable (Marcu and Wong, 2002;Foster et al., 2006) as they reflect only the distribution over the phrase pairs observed in the training data.
from an SMT perspective it is important that the models reflect probability distributions which are preferred by the decoding process, i.e., phrase translations which are likely to be used frequently to achieve better translations should get higher scores and phrases which are less likely to be used should get low scores.