Our empirical results show that both models substantially improve over the base network that is trained with only datalabel instances.
in general q performs better than p. q is more suitable when the logic rules introduce additional dependencies (e.g., span-Algorithm 1 Harnessing NN with Rules , Parameters: π -imitation parameter C -regularization strength 1: initialize neural network parameter θ 2: repeat 3: Sample a minibatch (X, Y ) ⊂ D 4: Construct teacher network q with Eq.