@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://aclanthology.org/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."
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%0 Conference Proceedings
%T KCL-Cogstack at PsyDefDetect: A Hierarchical Approach to Detecting Defense Mechanisms in Supportive Dialogue
%A Agarwal, Shubham
%A Searle, Thomas
%A Dobson, Richard
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F agarwal-etal-2026-kcl
%X 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.
%U https://aclanthology.org/2026.bionlp-2.22/
%P 155-163
Markdown (Informal)
[KCL-Cogstack at PsyDefDetect: A Hierarchical Approach to Detecting Defense Mechanisms in Supportive Dialogue](https://aclanthology.org/2026.bionlp-2.22/) (Agarwal et al., BioNLP 2026)
ACL