@inproceedings{bianchi-etal-2022-xlm,
title = "{XLM}-{EMO}: Multilingual Emotion Prediction in Social Media Text",
author = "Bianchi, Federico and
Nozza, Debora and
Hovy, Dirk",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.18",
doi = "10.18653/v1/2022.wassa-1.18",
pages = "195--203",
abstract = "Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.",
}
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%0 Conference Proceedings
%T XLM-EMO: Multilingual Emotion Prediction in Social Media Text
%A Bianchi, Federico
%A Nozza, Debora
%A Hovy, Dirk
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F bianchi-etal-2022-xlm
%X Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.
%R 10.18653/v1/2022.wassa-1.18
%U https://aclanthology.org/2022.wassa-1.18
%U https://doi.org/10.18653/v1/2022.wassa-1.18
%P 195-203
Markdown (Informal)
[XLM-EMO: Multilingual Emotion Prediction in Social Media Text](https://aclanthology.org/2022.wassa-1.18) (Bianchi et al., WASSA 2022)
ACL
- Federico Bianchi, Debora Nozza, and Dirk Hovy. 2022. XLM-EMO: Multilingual Emotion Prediction in Social Media Text. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 195–203, Dublin, Ireland. Association for Computational Linguistics.