CHARET: Character-centered Approach to Emotion Tracking in Stories

TL;DR

This paper introduces CHARET, a character-centered method for emotion tracking in stories that leverages role-labeling and semantic understanding to improve inference of characters' emotional states during narrative unfolding.

Contribution

It proposes a novel role-labeling approach that incorporates semantic context for more accurate emotion tracking compared to end-to-end models.

Findings

Role-labeling improves emotion inference accuracy.

Semantic understanding enhances emotion tracking coherence.

Method outperforms traditional end-to-end approaches.

Abstract

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.