Inspired by the success of self attention mechanism and Transformer architecture
 in sequence transduction and image generation applications, we propose novel self
 attention-based architectures to improve the performance of adversarial latent code-
 based schemes in text generation. Adversarial latent code-based text generation
 has recently gained a lot of attention due to their promising results. In this paper,
 we take a step to fortify the architectures used in these setups, specifically AAE
 and ARAE. We benchmark two latent code-based methods (AAE and ARAE)
 designed based on adversarial setups. In our experiments, the Google sentence
 compression dataset is utilized to compare our method with these methods using
 various objective and subjective measures. The experiments demonstrate the
 proposed (self) attention-based models outperform the state-of-the-art in adversarial
 code-based text generation.
We propose a self-attention based GAN architecture for unconditional text generation and improve on previous adversarial code-based results.