One implication of this difference is that if our approach is applied to semi-supervised learning, the regularization term would be applied to labeled sentences as well (by ignoring the labels) because the target grammar shall be unambiguous on all the training sentences.
the goal of unambiguity regularization is to bias learning in favor of unambiguous grammars, which is achieved by setting the exponent in the Estep (i.e., 1 1−σ in Eq.1) to a value larger than 1, so that the distribution of the hidden variables becomes less uniform (i.e., parses become less ambiguous); in our annealing approach, the exponent is initialized to a very large value (positive infinity in our experiment) to push the learner away from the ambiguous initial grammar, and then gradually decreased to 1 to avoid inducing excessive unambiguity in the learned grammar.