The conditional probability over words, i.e., P (w i |f (z; θ)) is modeled by a multinomial logistic regression: The parameters of the neural network, i.e., θ, are learned by maximizing the the log likelihood of the observed sentence x.
to shallow models such as Skip-Gram (Mikolov et al., 2013) which also embeds into latent spaces, deep generative models have been shown to capture the implicit semantics and structure of the underlying data more effectively.