The main reason might be that MST-full follows a global graph-based dependency parsing framework, where their high order methods (in cubic time complexity) can directly analyze the relationship between any EDUs pairs in the discourse, while, we choose the transition-based local method with linear time complexity, which can only investigate the top EDUs in S and B according to the selected actions, thus usually has a lower performance than the global graph-based methods, but with a lower (linear) time complexity.
the neural network components help us maintain much fewer features than MST-full, which carefully selects 6 different sets of features that are usually obtained using extra tools and resources.