  In this paper we address the problem of fine-tuned text generation with a
limited computational budget. For that, we use a well-performing text
generative adversarial network (GAN) architecture - Diversity-Promoting GAN
(DPGAN), and attempted a drop-in replacement of the LSTM layer with a
self-attention-based Transformer layer in order to leverage their efficiency.
The resulting Self-Attention DPGAN (SADPGAN) was evaluated for performance,
quality and diversity of generated text and stability. Computational
experiments suggested that a transformer architecture is unable to drop-in
replace the LSTM layer, under-performing during the pre-training phase and
undergoing a complete mode collapse during the GAN tuning phase. Our results
suggest that the transformer architecture need to be adapted before it can be
used as a replacement for RNNs in text-generating GANs.
