  Generative models for text have substantially contributed to tasks like
machine translation and language modeling, using maximum likelihood
optimization (MLE). However, for creative text generation, where multiple
outputs are possible and originality and uniqueness are encouraged, MLE falls
short. Methods optimized for MLE lead to outputs that can be generic,
repetitive and incoherent. In this work, we use a Generative Adversarial
Network framework to alleviate this problem. We evaluate our framework on
poetry, lyrics and metaphor datasets, each with widely different
characteristics, and report better performance of our objective function over
other generative models.
