Emotion Train at SemEval-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition
Demidova, Anastasiia Hamed, Injy Lynn, Teresa Solorio, Thamar
Demidova, Anastasiia
Hamed, Injy
Lynn, Teresa
Solorio, Thamar
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Department
Natural Language Processing
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
The emotion recognition task has become increasingly popular as it has a wide range of applications in many fields, such as mental health, product management, and population mood state monitoring. SemEval 2025 Task 11 Track A framed the emotion recognition problem as a multi-label classification task. This paper presents our proposed system submissions in the following languages: English, Algerian and Moroccan Arabic, Brazilian and Mozambican Portuguese, German, Spanish, Nigerian-Pidgin, Russian, and Swedish. Here, we compare the emotion-detecting abilities of generative and discriminative pre-trained language models, exploring multiple approaches, including curriculum learning, in-context learning, and instruction and few-shot fine-tuning. We also propose an extended architecture method with a feature fusion technique enriched with emotion scores and a self-attention mechanism. We find that BERT-based models fine-tuned on data of a corresponding language achieve the best results across multiple languages for multi-label text-based emotion classification, outperforming both baseline and generative models.
Citation
A. Demidova λ et al., “Emotion Train at SemEval-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition,” 2025. [Online]. Available: https://aclanthology.org/2025.semeval-1.133/
Source
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
Conference
19th International Workshop on Semantic Evaluation (SemEval-2025)
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Source
19th International Workshop on Semantic Evaluation (SemEval-2025)
Publisher
Association for Computational Linguistics