@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://preview.aclanthology.org/corrections-2025-08/2025.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."
}