Abstract
Emotion significantly influences human behavior and decision-making processes. We propose a labeling methodology grounded in Plutchik’s Wheel of Emotions theory for emotion classification. Furthermore, we employ a Mixture of Experts (MoE) architecture to evaluate the efficacy of this labeling approach, by identifying the specific emotions that each expert learns to classify. Experimental results reveal that our methodology improves the performance of emotion classification.- Anthology ID:
- 2024.emnlp-main.50
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 857–867
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.50
- DOI:
- 10.18653/v1/2024.emnlp-main.50
- Cite (ACL):
- Dongjun Lim and Yun-Gyung Cheong. 2024. Integrating Plutchik’s Theory with Mixture of Experts for Enhancing Emotion Classification. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 857–867, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Integrating Plutchik’s Theory with Mixture of Experts for Enhancing Emotion Classification (Lim & Cheong, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.50.pdf