Does anyone have an intuitive explanation for Eq. (1) (obtained by the fundamental theorem of random fields)?

While reading the theoretical part I got the impression that convergence would not be a problem for CRF, given that the authors mention: a) the loss is convex in the case of fully observable states and b) Eq. (2) can be easily computed and c) that a single iteration of their algorithms is equivalently to Baum-Welch (mentioned after Eq. (2), where I assume they meant one iteration of Baum-Welch). But, for my surprise, the experimental results showed that convergence is a big issue.