TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation

To address the issue about non-differentiablity, researchers and practitioners used score function-based gradient estimators such as REINFORCE to train GANs for NLG, where the discriminator is cast as a reward function for the generator. These methods suffer from poor sample efficiency, high variance, and credit assignment problems.