# Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations Source: [Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:13.467Z Authors: Hongbin Na, Zimu Wang, Zhaoming Chen, Yining Hua, Rena Gao, Kailai Yang, Ling Chen, Wei Wang, Shaoxiong Ji, John Torous, Sophia Ananiadou Published: 2026-05-24T07:17:54.000Z ## Abstract We present an overview of PsyDefDetect, the shared task on detecting levels of psychological defense mechanisms in emotional support dialogues, co-located with BioNLP@ACL 2026. Grounded in the clinically validated Defense Mechanism Rating Scales (DMRS) framework, the task asks systems to classify a target seeker utterance, given its preceding dialogue context, into one of nine categories: seven hierarchical DMRS levels plus two auxiliary labels. Participants worked on PsyDefConv, a newly released corpus of 200 dialogues and 2336 help-seeker utterances annotated under DMRS with substantial inter-annotator agreement. The task attracted 172 participants on CodaBench who produced 563 submissions, with 21 teams officially registering their results for the final ranking. The best system achieved a macro F1-score of 0.420, surpassing the strongest fine-tuned baseline reported in the dataset paper by a notable margin, yet leaving clear headroom. Our analysis highlights (i) a persistent tendency to over-predict the majority High-Adaptive class, (ii) a widening gap between accuracy and macro-F1 that reveals class-imbalance sensitivity, and (iii) the value of theory-aware and LLM-based approaches for fine-grained defensive-function classification. We release all task materials and invite the community to continue work on this novel intersection of clinical psychology and NLP. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Psychological Defense Classification on PsyDefConv 2026 (official leaderboard)](): Psychological Defense Classification; PsyDefConv 2026 (official leaderboard); Accuracy: 64.83. - [Psychological Defense Detection on PsyDefDetect (test)](): Psychological Defense Detection; PsyDefDetect (test); Accuracy: 30.36. ## Original sources and implementation [Original paper]() [PDF]()