During training, for each example, Model 1 aggregates features from all governors and objects even if they are possibly irrelevant, which may lead to a much bigger model in terms of the number of active weights.
for Model 2, Algorithm 1 uses the single highest scoring prediction of the latent variables, according to the current parameters, to refine the parameters.