{"type":"ScholarlyArticle","title":"DELTA: Disentangled Hierarchical Interaction and Adaptive Adjustment for Emotion and Intent Understanding in Multimodal Conversations","authors":[{"name":"Shenjie Jiang","slug":"shenjie-jiang","given":"Shenjie","family":"Jiang","affiliation":""},{"name":"Xiangfeng Liu","slug":"xiangfeng-liu","given":"Xiangfeng","family":"Liu","affiliation":""},{"name":"Xianghua Li","slug":"xianghua-li","given":"Xianghua","family":"Li","affiliation":""},{"name":"Xuecheng Wu","slug":"xuecheng-wu","given":"Xuecheng","family":"Wu","affiliation":""}],"abstract":"Emotion and intent joint understanding in multimodal conversations (MC-EIU) aims to infer emotion and intent information by leveraging the semantic dependencies among multimodal data. However, prior works largely ignore redundancy interference and suffer from insufficient interaction and inter-task noise propagation due to their reliance on shallow mechanisms. To overcome these limitations, we propose a novel framework named Disentangled Hierarchical Interaction and Adaptive Adjustment (DELTA) for MC-EIU. We first design a Disentangled Feature Denoising module based on orthogonal decomposition to effectively filter out noise and redundancy from heterogeneous data. Second, we propose a Dual-Level Adaptive Adjustment mechanism to dynamically optimize learning dynamics from both modality and instance perspectives. Furthermore, we introduce a Hierarchical Task-Bottleneck Interaction(HTBI) module, which employs a set of progressively compressed bottleneck tokens as a communication hub. This design can effectively simulating the multi-round iterative interaction between emotion and intent tasks. Experiments on the benchmark MC-EIU bilingual dataset demonstrate that our framework significantly outperforms state-of-the-art baselines in both emotion and intent tasks.","doi":"10.24963/ijcai.2026/301","url":"https://ijcai.org/proceedings/2026/301","pdf":"https://ijcai.org/proceedings/2026/0301.pdf","markdown":"https://ijcai.org/proceedings/2026/301.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/301","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":"Data Mining","track":"Main Track","number":301,"pages":{"first":2708,"last":2716},"keywords":["Search: Applications","Data Mining: Applications","Machine Learning: Applications","Knowledge Representation and Reasoning: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}