@inproceedings{demidova-etal-2025-emotion,
title = "Emotion Train at {S}em{E}val-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition",
author = "Demidova, Anastasiia and
Hamed, Injy and
Lynn, Teresa and
Solorio, Thamar",
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-1.133/",
pages = "1004--1014",
ISBN = "979-8-89176-273-2",
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."
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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.</abstract>
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%0 Conference Proceedings
%T Emotion Train at SemEval-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition
%A Demidova, Anastasiia
%A Hamed, Injy
%A Lynn, Teresa
%A Solorio, Thamar
%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 demidova-etal-2025-emotion
%X 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.
%U https://aclanthology.org/2025.semeval-1.133/
%P 1004-1014
Markdown (Informal)
[Emotion Train at SemEval-2025 Task 11: Comparing Generative and Discriminative Models in Emotion Recognition](https://aclanthology.org/2025.semeval-1.133/) (Demidova et al., SemEval 2025)
ACL