Dense features are generally easier to optimize, both from a computational point of view because the smaller number of features reduces computational and memory requirements, and because the smaller number of parameters reduces the risk of overfitting.
sparse features allow for more flexibility, as their parameters can be directly optimized to increase translation accuracy, so if optimization is performed well they have the potential to greatly increase translation accuracy.