  Generative Adversarial Networks (GANs) have seen steep ascension to the peak
of ML research zeitgeist in recent years. Mostly catalyzed by its success in
the domain of image generation, the technique has seen wide range of adoption
in a variety of other problem domains. Although GANs have had a lot of success
in producing more realistic images than other approaches, they have only seen
limited use for text sequences. Generation of longer sequences compounds this
problem. Most recently, SeqGAN (Yu et al., 2017) has shown improvements in
adversarial evaluation and results with human evaluation compared to a MLE
based trained baseline. The main contributions of this paper are three-fold: 1.
We show results for sequence generation using a GAN architecture with efficient
policy gradient estimators, 2. We attain improved training stability, and 3. We
perform a comparative study of recent unbiased low variance gradient estimation
techniques such as REBAR (Tucker et al., 2017), RELAX (Grathwohl et al., 2018)
and REINFORCE (Williams, 1992). Using a simple grammar on synthetic datasets
with varying length, we indicate the quality of sequences generated by the
model.
