  Early work on narrative modeling used explicit plans and goals to generate
stories, but the language generation itself was restricted and inflexible.
Modern methods use language models for more robust generation, but often lack
an explicit representation of the scaffolding and dynamics that guide a
coherent narrative. This paper introduces a new model that integrates explicit
narrative structure with neural language models, formalizing narrative modeling
as a Switching Linear Dynamical System (SLDS). A SLDS is a dynamical system in
which the latent dynamics of the system (i.e. how the state vector transforms
over time) is controlled by top-level discrete switching variables. The
switching variables represent narrative structure (e.g., sentiment or discourse
states), while the latent state vector encodes information on the current state
of the narrative. This probabilistic formulation allows us to control
generation, and can be learned in a semi-supervised fashion using both labeled
and unlabeled data. Additionally, we derive a Gibbs sampler for our model that
can fill in arbitrary parts of the narrative, guided by the switching
variables. Our filled-in (English language) narratives outperform several
baselines on both automatic and human evaluations.
