  Existing persona-grounded dialog models often fail to capture simple
implications of given persona descriptions, something which humans are able to
do seamlessly. For example, state-of-the-art models cannot infer that interest
in hiking might imply love for nature or longing for a break. In this paper, we
propose to expand available persona sentences using existing commonsense
knowledge bases and paraphrasing resources to imbue dialog models with access
to an expanded and richer set of persona descriptions. Additionally, we
introduce fine-grained grounding on personas by encouraging the model to make a
discrete choice among persona sentences while synthesizing a dialog response.
Since such a choice is not observed in the data, we model it using a discrete
latent random variable and use variational learning to sample from hundreds of
persona expansions. Our model outperforms competitive baselines on the
PersonaChat dataset in terms of dialog quality and diversity while achieving
persona-consistent and controllable dialog generation.
