There's interesting research out of Deepmind on two parts of this, which are using the Transformer model in Reinforcement Learning contexts[0] and creating textual GANs[1]. As you are probably aware, GANs are one of the important tools that have driven forward image synthesis and until recently it was impossible to apply them to text, so I expect this to push us quite a bit forward. There's also ongoing work in the selection of the metric to use to evaluate the generated text, and discriminate between human and machine-generated text.

[0] https://arxiv.org/abs/1910.06764

[1] https://arxiv.org/abs/1905.09922