What exactly is it about generative models that makes it difficult to deal with non-independent features? This concept has been brought up in this paper and the last and still isn't quite clear to me.

I agree with Aldebaro and found it a bit interesting that convergence was a problem. I would liked to have seen a better explaination of why CRFs converge so much slower than MEMMs.

I thought the paper did a good job of combining both theoretical and experimental information in such a short paper. In particular, I thought the combination of a couple of lesion studies as well as a real world experiment made for a nice combination in the experimental section.