We can achieve the desired bias with a constraint on model posteriors during learning, using the posterior regularization (PR) framework (Graça et al., 2007).
to implement PR we augment the maximum marginal likelihood objective of the dependency model with a term that penalizes head-dependent tag distributions that are too permissive.