Hi-EF: Benchmarking Emotion Forecasting in Human-interaction
This work addresses emotion prediction for psychology and AI applications, but it appears incremental as it refines an existing task rather than introducing a new paradigm.
The paper tackles the problem of predicting future emotional responses by narrowing affective forecasting to human-interaction-based emotion forecasting, focusing on two-party interactions to enhance feasibility.
Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccuracies, and typically remains at a qualitative analysis stage. To address these challenges, we narrows the scope of Affective Forecasting by introducing the concept of Human-interaction-based Emotion Forecasting (EF). This task is set within the context of a two-party interaction, positing that an individual's emotions are significantly influenced by their interaction partner's emotional expressions and informational cues. This dynamic provides a structured perspective for exploring the patterns of emotional change, thereby enhancing the feasibility of emotion forecasting.