The proposed RevGAN model follows a three-staged process: In Stage 1, we propose to use Self-Attentive Recursive Autoencoder for mapping the discrete user reviews and product descriptions into continuous embeddings for the advantage of capturing the ''essence" of textual information and the convenience for subsequent optimization processes.
we condition the sentiment labels on the discriminator to artificially change the rules that the discriminator works, and force it backpropagate loss functions that update generator policy correspondingly.