I like this, although it is yet another example of a learned language model not capturing underlying meaning. I started a similar project last year, but using

I like this, although it is yet another example of a learned language model not capturing underlying meaning. I started a similar project last year, but using GAN (RecipeGAN). Using a GAN would learn underlying meaning, based on my experience using GANs to synthesize data that maintained univariate statistics and correlation between features of the original data. The example in the article shows how RNNs can capture structure (I have used LSTM models to synthesize JSON data, and just for fun, to model source code).