Abstract
Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4%-16% for most of the metrics on three benchmark datasets.- Anthology ID:
- 2021.findings-emnlp.229
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2694–2704
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.229
- DOI:
- 10.18653/v1/2021.findings-emnlp.229
- Cite (ACL):
- Yuzhao Mao, Guang Liu, Xiaojie Wang, Weiguo Gao, and Xuan Li. 2021. DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2694–2704, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (Mao et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-emnlp.229.pdf
- Data
- IEMOCAP, MELD