To generate text using GANs, it is necessary to address the challenge posed by the discrete nature of text data. One solution is to preprocess the text by encoding it into a continuous space using a transformer-based language model. This transformation allows the GAN to treat the text as a continuous signal, rather than a series of discrete tokens, and use standard GAN architectures. After generation, the output from the generator network is decoded back into text form, producing generated text sequences.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence