In the literature, adversarial learning has the difficulty in learning latent representations from discrete structures (e.g., sequence of word embeddings).
most of existing studies in NLP apply adversarial learning with autoencoder-based models, which map a discrete word sequence into a continuous code space beforehand (Makhzani et al., 2015).