  Humans often refer to personal narratives, life experiences, and events to
make a conversation more engaging and rich. While persona-grounded dialog
models are able to generate responses that follow a given persona, they often
miss out on stating detailed experiences or events related to a persona, often
leaving conversations shallow and dull. In this work, we equip dialog models
with 'background stories' related to a persona by leveraging fictional
narratives from existing story datasets (e.g. ROCStories). Since current dialog
datasets do not contain such narratives as responses, we perform an
unsupervised adaptation of a retrieved story for generating a dialog response
using a gradient-based rewriting technique. Our proposed method encourages the
generated response to be fluent (i.e., highly likely) with the dialog history,
minimally different from the retrieved story to preserve event ordering and
consistent with the original persona. We demonstrate that our method can
generate responses that are more diverse, and are rated more engaging and
human-like by human evaluators, compared to outputs from existing dialog
models.
