The Paradox of Multilingual Emotion Detection

Luna De Bruyne


Abstract
The dominance of English is a well-known issue in NLP research. In this position paper, I turn to state-of-the-art psychological insights to explain why this problem is especially persistent in research on automatic emotion detection, and why the seemingly promising approach of using multilingual models to include lower-resourced languages might not be the desired solution. Instead, I campaign for the use of models that acknowledge linguistic and cultural differences in emotion conceptualization and verbalization. Moreover, I see much potential in NLP to better understand emotions and emotional language use across different languages.
Anthology ID:
2023.wassa-1.40
Volume:
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Jeremy Barnes, Orphée De Clercq, Roman Klinger
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
458–466
Language:
URL:
https://aclanthology.org/2023.wassa-1.40
DOI:
10.18653/v1/2023.wassa-1.40
Bibkey:
Cite (ACL):
Luna De Bruyne. 2023. The Paradox of Multilingual Emotion Detection. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 458–466, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
The Paradox of Multilingual Emotion Detection (De Bruyne, WASSA 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.wassa-1.40.pdf
Video:
 https://aclanthology.org/2023.wassa-1.40.mp4