@inproceedings{tamsal-2026-pfw,
    title = "{PFW} at {S}em{E}val-2026 Task 6: Multi-Seed {D}e{BERT}a Ensembles for Political Response Clarity and Evasion Classification",
    author = "Tamsal, Taleef",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.197/",
    pages = "1518--1525",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the PFW system for SemEval-2026 Task 6 (CLARITY), which addresses the classification of response clarity and evasion techniques in political interview question-answer pairs. Rather than relying on large language model prompting, we pursue a competitive non-LLM approach based on fine-tuning DeBERTa-xlarge and DeBERTa-v3-large with a multi-seed ensemble strategy: 5-fold cross-validation with 10 random seeds yields 50 models per architecture, combined through simple logit averaging. Our system achieves a macro F1 of 0.76 on Subtask 1 (clarity-level classification) and 0.50 on Subtask 2 (evasion-type classification). We also find that three post-hoc optimization techniques{---}learned ensemble weights, thresh old calibration, and hierarchical masking{---} each improve out-of-fold performance yet degrade evaluation scores by 0.02{--}0.10 F1. This pattern should be interpreted cautiously: the 237-sample evaluation set likely contributes substantial variance, and two of the three degradations fall within the {\ensuremath{\pm}}0.06 95{\%} CI expected from sampling noise. Still, the consistent directional pattern across all three prediction-level interventions provides suggestive evidence for an optimization paradox, highlighting the risk of overfitting to cross-validation predictions when evaluation data is limited. Our code is publicly available at https://github.com/ Taleef7/semeval-2026-task6."
}