Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes to predict.They ignore information that may be conveyed by the emotion labels themselves.We propose that the semantics of emotion labels can guide a model's attention when representing the input story.Further, we observe that the emotions evoked by an event are often related: an event that evokes joy is unlikely to also evoke sadness.In this work, we explicitly model label classes via label embeddings, and add mechanisms that track label-label correlations both during training and inference.We also introduce a new semi-supervision strategy that regularizes for the correlations on unlabeled data.Our empirical evaluations show that modeling label semantics yields consistent benefits, and we advance the state-of-theart on an emotion inference task.

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.

For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them.Our goal is to extract this kind of rolebased knowledge about processes, from multiple sentence-level descriptions.This knowledge is hard to acquire; while semantic role labeling (SRL) systems can extract sentence level role information about individual mentions of a process, their results are often noisy and they do not attempt create a globally consistent characterization of a process.To overcome this, we extend standard within sentence joint inference to inference across multiple sentences.This cross sentence inference promotes role assignments that are compatible across different descriptions of the same process.When formulated as an Integer Linear Program, this leads to improvements over within-sentence inference by nearly 3% in F1.The resulting role-based knowledge is of high quality (with a F1 of nearly 82).