# N\\"urnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification Source: [N\\"urnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification]() Collection index: [llms.txt]() Data updated: 2026-05-12T05:00:12.036Z Authors: Philipp Steigerwald, Eric Rudolph, Jens Albrecht Published: 2026-05-08T11:32:31.000Z ## Abstract Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement. On such a task the decisive lever is not a stronger single model but error independence, since any single representation will waver on the overlapping defence boundaries. We translate this insight into a 9-voter ensemble spanning three orthogonal axes: class granularity (all nine classes for the gatekeeper, only the eight defence classes for the specialists), training method (generative and discriminative) and base model. The system reaches $F1\_{test}{=}.420$ on the hidden test set, placing first among 21 registered teams. ## 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 mechanism classification on PsyDefDetect classes 1-8 (hidden test)](): Psychological defense mechanism classification; PsyDefDetect classes 1-8 (hidden test); Macro F1 Score: 42. ## Original sources and implementation [Original paper]() [PDF]()