A Mapping on Current Classifying Categories of Emotions Used in Multimodal Models for Emotion Recognition
Ziwei Gong, Muyin Yao, Xinyi Hu, Xiaoning Zhu, Julia Hirschberg
Abstract
In Emotion Detection within Natural Language Processing and related multimodal research, the growth of datasets and models has led to a challenge: disparities in emotion classification methods. The lack of commonly agreed upon conventions on the classification of emotions creates boundaries for model comparisons and dataset adaptation. In this paper, we compare the current classification methods in recent models and datasets and propose a valid method to combine different emotion categories. Our proposal arises from experiments across models, psychological theories, and human evaluations, and we examined the effect of proposed mapping on models.- Anthology ID:
- 2024.law-1.3
- Volume:
- Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Sophie Henning, Manfred Stede
- Venues:
- LAW | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 19–28
- Language:
- URL:
- https://aclanthology.org/2024.law-1.3
- DOI:
- Cite (ACL):
- Ziwei Gong, Muyin Yao, Xinyi Hu, Xiaoning Zhu, and Julia Hirschberg. 2024. A Mapping on Current Classifying Categories of Emotions Used in Multimodal Models for Emotion Recognition. In Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII), pages 19–28, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- A Mapping on Current Classifying Categories of Emotions Used in Multimodal Models for Emotion Recognition (Gong et al., LAW-WS 2024)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2024.law-1.3.pdf