To tackle this problem, generative adversarial networks (GAN) with reinforcement learning (RL) training approaches have been introduced to text generation tasks Che et al., 2017;Lin et al., 2017;Shi et al., 2018;, where the discriminator is trained to distinguish real and generated text samples to provide reward signals for the generator, and the generator is optimized via policy gradient .
recent studies have shown that potential issues of training GANs on discrete data are more severe than exposure bias (Semeniuta1 et al., 2018;Caccia et al., 2018).