Therefore, we go in the opposite direction, and propose an extended version of split-merge learning to handle the multiple branches in the taxonomy.
we split each state into all the subcategories in the lower layer in the taxonomy even if it has multiple branches, train, and then measure for every two sibling subcategories in the same layer the loss in likelihood incurred when merging them.