We investigated two methods for utilizing heterogeneous annotations for neural network models, showing that they have respective advantages compared to their discrete counterparts.
neural stacking allows tighter feature integration compared to discrete stacking, and neural multi-view training is free from the feature and efficiency constraints of discrete one.