This is because DDTM is globally normalized with edges parameterized as log-bilinear weights, which results in the mean-field updates taking the form of matrix operations followed by nonlinearities.
a single iteration of mean-field is equivalent to a forward pass through a recursive neural network, whose architecture is defined by the tree structure of the thread.