Our model trained within the proposed framework provides significant improvement, showing the benefits of utilizing implicit connectives at training time.
we achieve objective (b) by minimizing the discriminator's chance of correctly telling apart the features: The parameters of the augmented network a-CNN, denoted as θ A , can be learned by simply fitting to the data, i.e., minimizing the cross-entropy loss as follows: As mentioned above, here we use the same classifier C as for the implicit network, forcing a unified feature space of both networks.