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        <title>GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages</title>
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            <namePart type="given">Debanjan</namePart>
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    <abstract>Emotion recognition is a crucial task in natural language processing, particularly in the domain of multi-label emotion classification, where a single text can express multiple emotions with varying intensities. In this work, we participated in Task 11, Track A and Track B of the SemEval-2025 competition, focusing on emotion detection in low-resource languages. Our approach leverages transformer-based models combined with parameter-efficient fine-tuning (PEFT) techniques to effectively address the challenges posed by data scarcity. We specifically applied our method to multiple languages and achieved 9th place in the Arabic Algerian track among 40 competing teams. Our results demonstrate the effectiveness of PEFT in improving emotion recognition performance for low-resource languages. The code for our implementation is publicly available at: https://github.com/AylinNaebzadeh/Text-Based-Emotion-Detection-SemEval-2025.</abstract>
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%0 Conference Proceedings
%T GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages
%A Naebzadeh, Aylin
%A Askari, Fatemeh
%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 naebzadeh-askari-2025-ginger
%X Emotion recognition is a crucial task in natural language processing, particularly in the domain of multi-label emotion classification, where a single text can express multiple emotions with varying intensities. In this work, we participated in Task 11, Track A and Track B of the SemEval-2025 competition, focusing on emotion detection in low-resource languages. Our approach leverages transformer-based models combined with parameter-efficient fine-tuning (PEFT) techniques to effectively address the challenges posed by data scarcity. We specifically applied our method to multiple languages and achieved 9th place in the Arabic Algerian track among 40 competing teams. Our results demonstrate the effectiveness of PEFT in improving emotion recognition performance for low-resource languages. The code for our implementation is publicly available at: https://github.com/AylinNaebzadeh/Text-Based-Emotion-Detection-SemEval-2025.
%U https://aclanthology.org/2025.semeval-1.263/
%P 2028-2037

Markdown (Informal)

[GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages](https://aclanthology.org/2025.semeval-1.263/) (Naebzadeh & Askari, SemEval 2025)

• GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages (Naebzadeh & Askari, SemEval 2025)

ACL

• Aylin Naebzadeh and Fatemeh Askari. 2025. GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2028–2037, Vienna, Austria. Association for Computational Linguistics.