@inproceedings{solanki-etal-2026-varh,
    title = "{VARH}-{AI} at {S}em{E}val-2026 Task 10: Exploiting Architectural Diversity with Transformer-{SSM} Ensembles and Confidence-Based Iterative Refinement for Conspiracy Detection",
    author = "Solanki, Hritav  and
      Sharma, Shubham  and
      Prasad, Manish  and
      Agrawal, Rakhi  and
      Sharma, Yashvardhan",
    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-1.138/",
    pages = "1000--1005",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for SemEval 2026 Task 10 (PsyCoMark), focusing on Subtask 2: binary conspiracy classification in Reddit submission statements. We present a heterogeneous ensemble approach that combines Transformer-based models (DeBERTa, RoBERTa) with State-Space Models (Mamba) to leverage architectural diversity for improved generalization. Our key contributions include: (1) Bidirectional Mamba (BiMamba), adapting state-space sequence models for bidirectional document classification; (2) (2) a safety-switched multi-task training setup that uses marker supervision only for gold-annotated samples, preventing noisy pseudo-labeled rows from affecting the span extraction objective; and (3) Confidence-Based Iterative Refinement, using committee voting for high-quality pseudo-label generation. Our best official submission achieved a weighted F1 score of 0.78 on the Subtask 2 test set, ranking 4th on the public CodaBench leaderboard. We provide detailed ablation studies demonstrating the complementary contributions of each architectural component to inform future research directions."
}