[D] Sequence to Sequence GANs

I was doing a pretty intense literature research for the sequence to sequence GANs in the past couple of days, and I was really surprised to find that there is very little that fits the bill, so I thought I would make a post here. The usual bias in machine learning seems to be that image problems have pretty out of the box solutions and everything else is a bit more complicated. With discrete sequence problems that is understandable, but with continuous sequential problems, I don't immediately see the need to jump to RL. Is there a reason people tend to avoid these kinds of solutions? Did anybody try inserting an Attention/BLSTM layer at the end of his GAN to make the output variable length?