@inproceedings{khan-etal-2025-nust-titans,
title = "{NUST} Titans at {S}em{E}val-2025 Task 11: {A}fro{E}mo: Multilingual Emotion Detection with Adaptive {A}fro-{XLM}-{R}",
author = "Fatima, Mehwish and
Khan, Maham and
Naeem, Hajra Binte and
Khan, Faiza and
Rana, Laiba and
Latif, Seemab and
Shahzad, Raja Khurram",
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-1.289/",
pages = "2225--2232",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents AfroEmo, a multilingual, multi label emotion classification system designed for SemEval 2025 Task 11, leveraging the Afro XLMR model. Our approach integrates adaptive pretraining on domain specific corpora followed by fine tuning on low resource languages. Through comprehensive exploratory data analysis, we assess label distribution and model performance across diverse linguistic settings. By incorporating perceived emotions, how emotions are interpreted rather than explicitly stated, we enhance emotion recognition capabilities in underrepresented languages. Experimental results demonstrate that our method achieves competitive performance particularly in Amharic, while addressing key challenges in low resource emotion detection."
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<abstract>This paper presents AfroEmo, a multilingual, multi label emotion classification system designed for SemEval 2025 Task 11, leveraging the Afro XLMR model. Our approach integrates adaptive pretraining on domain specific corpora followed by fine tuning on low resource languages. Through comprehensive exploratory data analysis, we assess label distribution and model performance across diverse linguistic settings. By incorporating perceived emotions, how emotions are interpreted rather than explicitly stated, we enhance emotion recognition capabilities in underrepresented languages. Experimental results demonstrate that our method achieves competitive performance particularly in Amharic, while addressing key challenges in low resource emotion detection.</abstract>
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%0 Conference Proceedings
%T NUST Titans at SemEval-2025 Task 11: AfroEmo: Multilingual Emotion Detection with Adaptive Afro-XLM-R
%A Fatima, Mehwish
%A Khan, Maham
%A Naeem, Hajra Binte
%A Khan, Faiza
%A Rana, Laiba
%A Latif, Seemab
%A Shahzad, Raja Khurram
%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 khan-etal-2025-nust-titans
%X This paper presents AfroEmo, a multilingual, multi label emotion classification system designed for SemEval 2025 Task 11, leveraging the Afro XLMR model. Our approach integrates adaptive pretraining on domain specific corpora followed by fine tuning on low resource languages. Through comprehensive exploratory data analysis, we assess label distribution and model performance across diverse linguistic settings. By incorporating perceived emotions, how emotions are interpreted rather than explicitly stated, we enhance emotion recognition capabilities in underrepresented languages. Experimental results demonstrate that our method achieves competitive performance particularly in Amharic, while addressing key challenges in low resource emotion detection.
%U https://aclanthology.org/2025.semeval-1.289/
%P 2225-2232
Markdown (Informal)
[NUST Titans at SemEval-2025 Task 11: AfroEmo: Multilingual Emotion Detection with Adaptive Afro-XLM-R](https://aclanthology.org/2025.semeval-1.289/) (Fatima et al., SemEval 2025)
ACL