In this work, we incorporate stochastic latent variables (Cao and Clark, 2017;Serban et al., 2017) to the sequence-to-sequence model in order to increase question diversity.
we introduce a latent variable z ∼ p(z), which is a standard Gaussian distribution N (0, I n ) in our case, and calculate the likelihood of a target sentence y as follows: We maximize the evidence lower bound (ELBO), which decomposes the loss into two parts, including (1) the KL divergence between the posterior distribution and the prior distribution, and (2) a cross-entropy loss between the generated question and the ground truth.