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

ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs