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Advances in neural variational inference have facilitated the learning ofpowerful directed graphical models with continuous latent variables, such asvariational autoencoders. The hope is that such models will learn to representrich, multi-modal latent factors in real-world data, such as natural languagetext. However, current models often assume simplistic priors on the latentvariables - such as the uni-modal Gaussian distribution - which are incapableof representing complex latent factors efficiently. To overcome thisrestriction, we propose the simple, but highly flexible, piecewise constantdistribution. This distribution has the capacity to represent an exponentialnumber of modes of a latent target distribution, while remaining mathematicallytractable. Our results demonstrate that incorporating this new latentdistribution into different models yields substantial improvements in naturallanguage processing tasks such as document modeling and natural languagegeneration for dialogue.

Piecewise Latent Variables for Neural Variational Text Processing