@inproceedings{s-etal-2025-pixel,
title = "Pixel Phantoms at {S}em{E}val-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and m{T}5-Based Approach",
author = "S, Jithu Morrison and
Hariharakrishnan, Janani and
Singh, Harsh Pratap",
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-1.86/",
pages = "617--622",
ISBN = "979-8-89176-273-2",
abstract = "Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages."
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<abstract>Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages.</abstract>
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%0 Conference Proceedings
%T Pixel Phantoms at SemEval-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and mT5-Based Approach
%A S, Jithu Morrison
%A Hariharakrishnan, Janani
%A Singh, Harsh Pratap
%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 s-etal-2025-pixel
%X Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages.
%U https://aclanthology.org/2025.semeval-1.86/
%P 617-622
Markdown (Informal)
[Pixel Phantoms at SemEval-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and mT5-Based Approach](https://aclanthology.org/2025.semeval-1.86/) (S et al., SemEval 2025)
ACL