Recent work in neural networks has shown that introducing latent variables leads to higher representational capacity (Kingma and Welling, 2014;Chung et al., 2015;Burda et al., 2016;Ji et al., 2016).
unlike variational autoencoders (Kingma and Ba, 2015) and related work that use continuous latent variables, our model is more similar to recent efforts that combine neural architectures with discrete latent variables and end-to-end training (Ji et al., 2016;Kim et al., 2017;Kong et al., 2017;Chen and Gimpel, 2018;Wiseman et al., 2018, inter alia).