The paradox of multilingual emotion detection
- Publication type
- C1
- Publication status
- Published
- Author
- De Bruyne, L.
- Editor
- Jeremy Barnes, Orphée De Clercq and Roman Klinger
- Series
- Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis
- Pagination
- 458-466
- Publisher
- Association for Computational Linguistics (ACL) (Toronto, Canada)
- Conference
- 13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, collocated with ACL 2023 (WASSA 2023) (Toronto, Canada)
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- (.pdf)
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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.