The work of <cite>(Poon and Domingos, 2009</cite> ) models joint probability of the dependency tree and its latent semantic representation using Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) , selecting parameters (weights of first-order clauses) to maximize the probability of the observed dependency structures. For each sentence, the MLN induces a Markov network, an undirected graphical model with nodes corresponding to ground atoms and cliques corresponding to ground clauses. The MLN is a powerful formalism and allows for modeling complex interaction between features of the input (syntactic trees) and latent output (semantic representation), however, unsupervised learning of semantics with general MLNs can be prohibitively expensive. The reason for this is that MLNs are undirected models and when learned to maximize likelihood of syntactically annotated sentences, they would require marginalization over semantic representation but also over the entire space of syntactic structures and lexical units. Given the complexity of the semantic parsing task and the need to tackle large datasets, even approximate methods are likely to be infeasible.