Abstract
Emotion recognition in conversation (ERC) aims to analyze the speaker’s state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.- Anthology ID:
- 2022.findings-acl.126
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1610–1618
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.126
- DOI:
- 10.18653/v1/2022.findings-acl.126
- Cite (ACL):
- Zaijing Li, Fengxiao Tang, Ming Zhao, and Yusen Zhu. 2022. EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1610–1618, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition (Li et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.126.pdf
- Data
- IEMOCAP, MELD