EMNLP 2024main0 citations
Integrating Plutchik’s Theory with Mixture of Experts for Enhancing Emotion Classification
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.
BibTeX
@inproceedings{lim-cheong-2024-integrating,
title = "Integrating {P}lutchik`s Theory with Mixture of Experts for Enhancing Emotion Classification",
author = "Lim, Dongjun and
Cheong, Yun-Gyung",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.50/",
doi = "10.18653/v1/2024.emnlp-main.50",
pages = "857--867"
}