CHARET: Character-centered Approach to Emotion Tracking in Stories
This addresses the challenge of defining social behavior for autonomous agents in applications requiring emotional inference, though it appears incremental as it builds on existing tools.
The paper tackles the problem of tracking character emotions in stories by proposing a character role-labelling approach that accounts for emotional semantics, achieving better performance compared to end-to-end methods.
Autonomous agents that can engage in social interactions witha human is the ultimate goal of a myriad of applications. A keychallenge in the design of these applications is to define the socialbehavior of the agent, which requires extensive content creation.In this research, we explore how we can leverage current state-of-the-art tools to make inferences about the emotional state ofa character in a story as events unfold, in a coherent way. Wepropose a character role-labelling approach to emotion tracking thataccounts for the semantics of emotions. We show that by identifyingactors and objects of events and considering the emotional stateof the characters, we can achieve better performance in this task,when compared to end-to-end approaches.