@inproceedings{agarwal-etal-2026-kcl,
    title = "{KCL}-Cogstack at {P}sy{D}ef{D}etect: A Hierarchical Approach to Detecting Defense Mechanisms in Supportive Dialogue",
    author = "Agarwal, Shubham  and
      Searle, Thomas  and
      Dobson, Richard",
    editor = "Gupta, Deepak  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-2.22/",
    pages = "155--163",
    ISBN = "979-8-89176-435-4",
    abstract = "We present our system for the PsyDefDetect shared task, which focuses on detecting and classifying psychological defense mechanisms in peer emotional support conversations. Our core contribution is a hierarchical classification framework that structures prediction as a coarse-to-fine pipeline over a clinically validated label hierarchy, grounded in the Defense Mechanism Rating Scales (DMRS). Through systematic experimentation with flat fine-tuning, few-shot prompting, and hierarchical classification, we demonstrate that explicitly modelling the structured relationships among defense levels offers a more effective alternative to flat classification, achieving a macro F1 of 0.23 on the official test set."
}