The feature parameters Φ can be roughly divided into two categories: dense feature that measures the plausibility of each translation rule from a particular aspect, e.g., the rule translation probabilities p(f |e) and p(e|f ); and sparse feature that fires when certain phenomena is observed, e.g., when a frequent word pair co-occured in a rule.
to λ, feature parameters in Φ are usually modeled by generative models for dense features, or by indicator functions for sparse ones.