From Discrete to Continuous: Progressive Hybrid-Distributional Learning for Gradual Emotion Transitions

Yunhe Xie; Yang Li

IJCAI 20260 citations

From Discrete to Continuous: Progressive Hybrid-Distributional Learning for Gradual Emotion Transitions

Abstract

Modeling emotional dynamics in multi-turn dialogues remains challenging due to emotion shifts, where emotional states evolve gradually rather than change abruptly. Existing methods often rely on one-hot supervision, which fails to reflect the progressive nature of human emotions, particularly for neutral-valence emotions with subtle trajectories. To address this limitation, we propose a pseudo soft-label guided Progressive Hybrid-Distributional (PHD) learning framework. PHD reconstructs discrete labels into hybrid distributional representations that encode inter-emotion relations and mixed emotional tendencies. Based on these representations, a progressive training strategy is introduced to guide the model from learning blended emotional states toward the standard one-to-one prediction objective. Furthermore, we design a Graded Contrastive Learning mechanism that replaces rigid binary allocation with graded supervision to alleviate label conflicts. Experiments on benchmark datasets demonstrate PHD consistently improves diverse baseline models, yielding average w-F1 gains of 1.29%. Our framework highlights the critical importance of modeling emotional dynamics as a gradual, distributional process.

Humans and AI: Personalization and user modelingNatural Language Processing: Dialogue and interactive systemsMachine Learning: Multi-label learningNatural Language Processing: Sentiment analysis, stylistic analysis, and argument mining

BibTeX

@inproceedings{ijcai2026_fromdiscretetoco,
  title = {From Discrete to Continuous: Progressive Hybrid-Distributional Learning for Gradual Emotion Transitions},
  author = {Yunhe Xie and Yang Li},
  booktitle = {IJCAI 2026},
  year = {2026}
}