Latent Dependency Forest Models
  Probabilistic modeling is one of the foundations of modern machine learning
and artificial intelligence. In this paper, we propose a novel type of
probabilistic models named latent dependency forest models (LDFMs). A LDFM
models the dependencies between random variables with a forest structure that
can change dynamically based on the variable values. It is therefore capable of
modeling context-specific independence. We parameterize a LDFM using a
first-order non-projective dependency grammar. Learning LDFMs from data can be
formulated purely as a parameter learning problem, and hence the difficult
problem of model structure learning is circumvented. Our experimental results
show that LDFMs are competitive with existing probabilistic models.
