@inproceedings{tong-etal-2025-heimerdinger,
    title = "Heimerdinger at {S}em{E}val-2025 Task 11: A Multi-Agent Framework for Perceived Emotion Detection in Multilingual Text",
    author = "Tong, Zeliang  and
      Ding, Zhuojun  and
      Li, Yingjia",
    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/transition-to-people-yaml/2025.semeval-1.56/",
    pages = "397--406",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system developed for the SemEval-2025 Task 11: Text-Based Emotion Detection (TBED) task, which aims to identify the emotions perceived by the majority of people from a speaker{'}s short text. We introduce a multi-agent framework for emotion recognition, comprising two key agents: the Emotion Perception Profiler, which identifies emotions in text, and the Intensity Perception Profiler, which assesses the intensity of those emotions. We model the task using both generative and discriminative approaches, leveraging BERT series and large-scale generative language models (LLMs). A multi-system collaboration mechanism is employed to further enhance the accuracy, stability, and robustness. Additionally, we incorporate cross-lingual knowledge transfer to improve performance in diverse linguistic scenarios. Our method demonstrates superior results in emotion detection and intensity prediction across multiple subtasks, highlighting its effectiveness, especially in language adaptability."
}