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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.mrl-1.7.pdf