@inproceedings{jian-etal-2025-cyut,
    title = "{CYUT}-{NLP} at {ROCLING}-2025 Shared Task: Valence{--}Arousal Prediction in Physicians' Texts Using {BERT}, {RAG}, and Multi-Teacher Pseudo-Labeling",
    author = "Jian, Yi-Min  and
      Hsiao, An Yu  and
      Wu, Shih-Hung",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.rocling-main.42/",
    pages = "381--389",
    ISBN = "979-8-89176-379-1",
    abstract = "Accurately modeling physicians' emotional states from self-reflection texts remains challenging due to the lowresource, domain-specific nature of medical corpora. The proposed workflow performs Retrieval-Augmented Generation (RAG) and multi-teacher pseudo-labeling to generate high-quality augmented data. This workflow enables effective crossdomain adaptation from general text corpora to professional medical texts. Evaluations on the ROCLING 2025 test set demonstrate substantial improvements over the best-performing baseline in Valence{--}Arousal prediction accuracy and model stability. Importantly, the workflow is domain-agnostic and provides a generalizable methodology for systematically transferring models to new, low-resource domains, making it applicable beyond medical text analysis."
}