@inproceedings{huang-shao-2025-ntulaw,
    title = "{NTULAW} at {ROCLING}-2025 Shared Task: Domain-Adaptive Modeling of Implicit Emotions in Medical Reflections",
    author = "Huang, Sieh-Chuen  and
      Shao, Hsuan-Lei",
    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.43/",
    pages = "390--398",
    ISBN = "979-8-89176-379-1",
    abstract = "This paper describes the NTULAW team{'}s participation in the ROCLING 2025 Dimensional Sentiment Analysis (DSA) shared task, which focuses on predicting valence and arousal ratings for Chinese doctors' self-reflection texts. Unlike previous editions of the DSA task that targeted words, phrases, or educational comments, this year{'}s dataset consists of domain-specific multi-sentence medical narratives, posing challenges such as low-arousal writing styles, implicit emotion expressions, and discourse complexity. To address the domain shift between general affective resources (Chinese EmoBank) and medical reflections, we designed a multi-scale BERT-based architecture and explored different data selection strategies. Our final system adopted a hybrid submission: using a model trained solely on doctors' annotations for arousal prediction, and a combined model with Chinese EmoBank for valence prediction. The system achieved stable performance, ranking third among six participating teams. Error analysis shows systematic overestimation of implicit or negated expressions for valence and regression toward mid-range predictions for arousal. We conclude with limitations of relying only on BERT and outline future work involving domain adaptation, discourse-aware modeling, and large language models (LLMs)."
}