CHARET: Character-centered Approach to Emotion Tracking in Stories

Autonomous agents that can engage in social interactions with a human is the ultimate goal of a myriad of applications. A key challenge in the design of these applications is to define the social behavior of the agent, which requires extensive content creation. In this research, we explore how we can leverage current state-of-theart tools to make inferences about the emotional state of a character in a story as events unfold, in a coherent way. We propose a character role-labelling approach to emotion tracking that accounts for the semantics of emotions. We show that, by identifying actors and objects of events and considering the emotional state of the characters, we can achieve better performance in this task when compared to end-to-end approaches.