PASCAL - Pattern Analysis, Statistical Modelling and
Computational Learning  Incremental Sigmoid Belief Networks for
Grammar Learning James Henderson and Ivan Titov Journal of
Machine Learning Research (JMLR) Volume 11, pp. 3541-3570, 2010.
We propose a class of Bayesian networks appropriate for
structured prediction problems where the Bayesian network’s
model structure is a function of the predicted output structure.
These incremental sigmoid belief networks (ISBNs) make decoding
possible because inference with partial output structures does
not require summing over the unboundedly many compatible model
structures, due to their directed edges and incrementally
specified model structure. ISBNs are specifically targeted at
challenging structured prediction problems such as natural
language parsing, where learning the domain’s complex
statistical dependencies benefits from large numbers of latent
variables. While exact inference in ISBNs with large numbers of
latent variables is not tractable, we propose two