Paper: Conditional Random Field Autoencoders for Unsupervised Structured Prediction

CRF autoencoders (just now presented at NIPS) seem pretty neat. The central idea is to add an autoencoding layer to the typical latent-variable chain-structured conditional random field. This imposes the constraint that the model be able to accurately reproduce the input features given the latent labels, which translates to a slightly different objective when learning. It seems like a nice trick, to insist that the latent labels actually be able to generate the features.

However, that said, it does seem like a step back towards markov random fields. CRFs work so well because they are discriminitive rather than generative - no effort is wasted modeling the uncertainty over the entire problem space, and only the conditional distribution of the labels is important. CRF autoencoders seem to move more towards a generative approach, because the ability of the labels to generate the features is a factor in the objective. If a CRF autoencoder performs well on a task, how well would a markov random field work?

edit: I just spoke with author / presenter Waleed Ammar (nice guy).
He pointed out that learning the corresponding MRF would be *very
slow*, which makes sense.