It can be shown (Della Pietra et al., 1995) that these are the also the values which minimize the Kullback-Liebler divergence D(p[[q) between the model and the reference distribution under the constraint that the expectations of the features (ie, the components of f) with respect to the model must equal their expectations with respect to the empirical distribution derived from the training corpus.
a simple solution to this dilemma is to let the value of a word-pair feature reflect the current position of the pair rather just its presence or absence.