The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling

TL;DR

This paper introduces the OMG-Empathy Dataset, a collection of dyadic storytelling interactions annotated for affective impact, aiming to improve understanding of empathy and emotion contagion in human-agent interactions.

Contribution

The paper presents a novel dataset focused on measuring affective impact and empathy in natural storytelling interactions, addressing limitations of existing emotion recognition datasets.

Findings

Dataset captures affective responses to storytelling

Annotations reflect empathy and emotion contagion

Baseline protocols facilitate future research in artificial empathy

Abstract

Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches that address this problem focus on classifying emotion expressions by grouping them into known categories. Such strategies neglect, among other aspects, the impact of the affective responses from an individual on their interaction partner thus ignoring how people empathize towards each other. This is also reflected in the datasets used to train models for affective processing tasks. Most of the recent datasets, in particular, the ones which capture natural interactions ("in-the-wild" datasets), are designed, collected, and annotated based on the recognition of displayed affective reactions, ignoring how these displayed or expressed emotions are perceived. In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener. Each video of the dataset contains around 5 minutes of interaction where a speaker tells a story to a listener. After each interaction, the listener annotated, using a valence scale, how the story impacted their affective state, reflecting how they empathized with the speaker as well as the story. We also propose different evaluation protocols and a baseline that encourages participation in the advancement of the field of artificial empathy and emotion contagion.