@inproceedings{liu-etal-2025-dut,
title = "{DUT}{\_}{IR} at {S}em{E}val-2025 Task 11: Enhancing Multi-Label Emotion Classification with an Ensemble of Pre-trained Language Models and Large Language Models",
author = "Liu, Chao and
Liu, Junliang and
Lv, Tengxiao and
Li, Huayang and
Zeng, Tao and
Luo, Ling and
Sun, Yuanyuan and
Lin, Hongfei",
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-1.17/",
pages = "116--121",
ISBN = "979-8-89176-273-2",
abstract = "In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A."
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<abstract>In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.</abstract>
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%0 Conference Proceedings
%T DUT_IR at SemEval-2025 Task 11: Enhancing Multi-Label Emotion Classification with an Ensemble of Pre-trained Language Models and Large Language Models
%A Liu, Chao
%A Liu, Junliang
%A Lv, Tengxiao
%A Li, Huayang
%A Zeng, Tao
%A Luo, Ling
%A Sun, Yuanyuan
%A Lin, Hongfei
%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 liu-etal-2025-dut
%X In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.
%U https://aclanthology.org/2025.semeval-1.17/
%P 116-121
Markdown (Informal)
[DUT_IR at SemEval-2025 Task 11: Enhancing Multi-Label Emotion Classification with an Ensemble of Pre-trained Language Models and Large Language Models](https://aclanthology.org/2025.semeval-1.17/) (Liu et al., SemEval 2025)
ACL
- Chao Liu, Junliang Liu, Tengxiao Lv, Huayang Li, Tao Zeng, Ling Luo, Yuanyuan Sun, and Hongfei Lin. 2025. DUT_IR at SemEval-2025 Task 11: Enhancing Multi-Label Emotion Classification with an Ensemble of Pre-trained Language Models and Large Language Models. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 116–121, Vienna, Austria. Association for Computational Linguistics.