@inproceedings{eyasu-etal-2025-tewodros,
title = "Tewodros at {S}em{E}val-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models",
author = "Eyasu, Mikiyas and
Abebaw, Wendmnew Sitot and
Hafeez, Nida and
Uroosa, Fatima and
Bizuneh, Tewodros Achamaleh and
Sidorov, Grigori and
Gelbukh, Alexander",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.196/",
pages = "1485--1494",
ISBN = "979-8-89176-273-2",
abstract = "Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team {\{}textbf{\{}Tewodros{\}}{\}} participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages."
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<abstract>Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team {textbf{Tewodros}} participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages.</abstract>
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%0 Conference Proceedings
%T Tewodros at SemEval-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models
%A Eyasu, Mikiyas
%A Abebaw, Wendmnew Sitot
%A Hafeez, Nida
%A Uroosa, Fatima
%A Bizuneh, Tewodros Achamaleh
%A Sidorov, Grigori
%A Gelbukh, Alexander
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F eyasu-etal-2025-tewodros
%X Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team {textbf{Tewodros}} participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages.
%U https://aclanthology.org/2025.semeval-1.196/
%P 1485-1494Markdown (Informal)
[Tewodros at SemEval-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models](https://aclanthology.org/2025.semeval-1.196/) (Eyasu et al., SemEval 2025)
ACL