Generative adversarial networks (GANs) have shown considerable success,
especially in the realistic generation of images. In this work, we apply
similar techniques for the generation of text. We propose a novel approach to
handle the discrete nature of text, during training, using word embeddings. Our
method is agnostic to vocabulary size and achieves competitive results relative
to methods with various discrete gradient estimators.