@inproceedings{steigerwald-etal-2026-nurnberg,
    title = {N{\"u}rnberg {NLP} at {P}sy{D}ef{D}etect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification},
    author = "Steigerwald, Philipp  and
      Rudolph, Eric  and
      Albrecht, Jens",
    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-2.9/",
    pages = "59--65",
    ISBN = "979-8-89176-435-4",
    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 an F1 score of .420 on the hidden test set, placing first among 21 registered teams."
}