This architecture could also potentially be applied to conversational response generation to relieve the safe response problem, where the generative part can be an Seq2Seq-based model that generates response utterances for given queries, and the discriminative part can evaluate the quality of the generated utterances from diverse dimensions according to human-produced responses.
unlike the image generation problems, training such a GAN for text generation here is not straightforward.