{"type":"ScholarlyArticle","title":"From Discrete to Continuous: Progressive Hybrid-Distributional Learning for Gradual Emotion Transitions","authors":[{"name":"Yunhe Xie","slug":"yunhe-xie","given":"Yunhe","family":"Xie","affiliation":""},{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/411","url":"https://ijcai.org/proceedings/2026/411","pdf":"https://ijcai.org/proceedings/2026/0411.pdf","markdown":"https://ijcai.org/proceedings/2026/411.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/411","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Humans and AI","track":"Main Track","number":411,"pages":{"first":3691,"last":3699},"keywords":["Natural Language Processing: Sentiment analysis, stylistic analysis, and argument mining","Natural Language Processing: Dialogue and interactive systems","Humans and AI: Personalization and user modeling","Machine Learning: Multi-label learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}