In this way, our model is able to capture a wide range of information including the auxiliary features without having uncontrolled feature explosion, while still having the full accessibility to the manually engineered features that are proven useful.
we define the arc score s γ (h → m) as the combination where θ ∈ R L , U ∈ R r×n , V ∈ R r×n , and W ∈ R r×d are the model parameters to be learned.