We should note that since our model parameter A is represented and learned in the low-rank form, we only have to store and maintain the low-rank projections U φ h , V φ m and W φ h,m rather than explicitly calculate the feature tensor φ h ⊗φ m ⊗φ h,m .
updating parameters and decoding a sentence is still efficient, i.e., linear in the number of values of the feature vector.