@inproceedings{wang-etal-2025-teleai,
    title = "{T}ele{AI} at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation",
    author = "Wang, Shiquan  and
      Li, Mengxiang  and
      Peng, Shengxiong  and
      Yu, Fang  and
      He, Zhongjiang  and
      Song, Shuangyong  and
      Li, Yongxiang",
    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-1.15/",
    pages = "102--108",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents the approach we employed in SemEval-2025 Task 11: ``Bridging the Gap in Text-Based Emotion Detection.'' The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model{'}s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method."
}