Unlike neural DMV, however, when computing the probability of a dependency, we rely on not only local information as in DMV, but also global and contextual information from a compressed representation of the input sentence produced by neural networks.
instead of modeling the joint probability of the input sentence and its dependency parse as in a generative model, we model the conditional probability of the sentence and parse given global information of the sentence.