The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling
This addresses the need for datasets that measure affective impact rather than just emotion recognition, benefiting researchers in human-agent interaction and affective computing, though it is incremental as it builds on existing emotion datasets.
The paper tackles the problem of evaluating affective impact in storytelling by introducing the OMG-Empathy dataset, which includes dyadic interactions annotated for listener empathy, and proposes evaluation protocols and a baseline to advance artificial empathy research.
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.