MaskGAN fills in text blanks more flexibly and effectively.
problem Handling complex manipulations on given tokens in conditional text generation.
method Decomposed conditional text generation into make-a-blank and fill-in-the-blank tasks, and used hierarchical multi-agent reinforcement learning.
result The model produces realistic texts without compromising quality and diversity.