Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding
Summary: arXiv:2604.00819v1 Announce Type: cross
Abstract
Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact through context, interpersonal relations, and situational cues. However, most existing emotion understanding benchmarks rely on short texts and predefined emotion labels, reducing this process to independent label prediction and ignoring the structured dependencies among emotions.
Introduction
To address this limitation, researchers have introduced Emotional Scenarios (EmoScene), a theory-grounded benchmark comprising 4,731 context-rich scenarios annotated with an 8-dimensional emotion vector derived from Plutchik’s basic emotions. This innovative approach aims to enhance the understanding of emotion dynamics in natural language processing.
Benchmark Overview
EmoScene serves as a challenging benchmark for studying multi-dimensional emotion understanding. The scenarios are designed to capture the complexities of human emotions by providing a rich contextual background where emotions can co-occur and interact. This structure reflects the reality that emotions are rarely experienced in isolation.
Methodology
In the evaluation of EmoScene, six instruction-tuned large language models were assessed in a zero-shot setting. Despite the advanced capabilities of these models, the results showed modest performance overall. The best-performing model achieved a Macro F1 score of 0.501, emphasizing the difficulties faced in context-aware multi-label emotion prediction.
Proposed Framework
Motivated by the observation that emotions typically do not occur independently, the authors propose an entanglement-aware Bayesian inference framework. This framework incorporates emotion co-occurrence statistics to perform joint posterior inference over the emotion vector. The implementation of this lightweight post-processing step significantly improves the structural consistency of predictions.
Results
Notably, the proposed framework yields considerable gains for weaker models. For instance, the Qwen2.5-7B model experienced an improvement of +0.051 in Macro F1 score. This enhancement illustrates the potential of integrating Bayesian inference with emotion understanding tasks, paving the way for more robust emotion recognition systems.
Conclusion
EmoScene provides a vital benchmark for advancing the study of multi-dimensional emotion understanding and highlights the limitations of current language models. By acknowledging the complex interplay of emotions and introducing innovative methodologies, this research contributes significantly to the field of natural language processing and emotion analysis.
Future Directions
As the field continues to evolve, further exploration of multi-dimensional emotion understanding is essential. Future research may focus on enhancing model architectures, improving co-occurrence statistics, and refining the emotional scenario framework to foster a deeper understanding of emotions in textual contexts.