The inference (encoder) and generative (decoder) networks are optimized jointly by maximizing a variational lower bound to the marginal distribution of input documents (corpus).
if we allow the decoder to be too expressive (e.g., a one-layer MLP), it is likely that we will end up with a very flexible similarity measure but relatively less meaningful binary representations.