@inproceedings{de-bruyne-etal-2022-language,
title = "How Language-Dependent is Emotion Detection? Evidence from Multilingual {BERT}",
author = "De Bruyne, Luna and
Singh, Pranaydeep and
De Clercq, Orphee and
Lefever, Els and
Hoste, Veronique",
editor = {Ataman, Duygu and
Gonen, Hila and
Ruder, Sebastian and
Firat, Orhan and
G{\"u}l Sahin, G{\"o}zde and
Mirzakhalov, Jamshidbek},
booktitle = "Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mrl-1.7/",
doi = "10.18653/v1/2022.mrl-1.7",
pages = "76--85",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T How Language-Dependent is Emotion Detection? Evidence from Multilingual BERT
%A De Bruyne, Luna
%A Singh, Pranaydeep
%A De Clercq, Orphee
%A Lefever, Els
%A Hoste, Veronique
%Y Ataman, Duygu
%Y Gonen, Hila
%Y Ruder, Sebastian
%Y Firat, Orhan
%Y Gül Sahin, Gözde
%Y Mirzakhalov, Jamshidbek
%S Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F de-bruyne-etal-2022-language
%X 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.
%R 10.18653/v1/2022.mrl-1.7
%U https://aclanthology.org/2022.mrl-1.7/
%U https://doi.org/10.18653/v1/2022.mrl-1.7
%P 76-85
Markdown (Informal)
[How Language-Dependent is Emotion Detection? Evidence from Multilingual BERT](https://aclanthology.org/2022.mrl-1.7/) (De Bruyne et al., MRL 2022)
ACL