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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.wassa-1.40.pdf