How Language-Dependent is Emotion Detection? Evidence from Multilingual BERT
Luna De Bruyne, Pranaydeep Singh, Orphee De Clercq, Els Lefever, Veronique Hoste
Abstract
As emotion analysis in text has gained a lot of attention in the field of natural language processing, differences in emotion expression across languages could have consequences for how emotion detection models work. We evaluate the language-dependence of an mBERT-based emotion detection model by comparing language identification performance before and after fine-tuning on emotion detection, and performing (adjusted) zero-shot experiments to assess whether emotion detection models rely on language-specific information. When dealing with typologically dissimilar languages, we found evidence for the language-dependence of emotion detection.- Anthology ID:
- 2022.mrl-1.7
- Volume:
- Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Duygu Ataman, Hila Gonen, Sebastian Ruder, Orhan Firat, Gözde Gül Sahin, Jamshidbek Mirzakhalov
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–85
- Language:
- URL:
- https://aclanthology.org/2022.mrl-1.7
- DOI:
- 10.18653/v1/2022.mrl-1.7
- Cite (ACL):
- Luna De Bruyne, Pranaydeep Singh, Orphee De Clercq, Els Lefever, and Veronique Hoste. 2022. How Language-Dependent is Emotion Detection? Evidence from Multilingual BERT. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 76–85, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- How Language-Dependent is Emotion Detection? Evidence from Multilingual BERT (De Bruyne et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.mrl-1.7.pdf