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.

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold.One of the challenges to learning scripts is the hierarchical nature of the knowledge.For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen.To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables.We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value.This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting.Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modelingbased method.