@inproceedings{karan-2026-credenceai,
    title = "{C}redence{AI} at {S}em{E}val-2026 Task 10: A Span-Consistency Network with Cross-Marker Attention for Conspiracy Marker Extraction",
    author = "Karan, Ishaan",
    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-1.406/",
    pages = "3240--3249",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a Span-Consistency Network (SCN) for conspiracy marker extraction in English social media text. The task requires identifying character-level spans for five marker types (Actor, Action, Effect, Evidence, and Victim) under overlap-based Macro F1 evaluation. Standard token-level classifiers often produce fragmented spans, ignore inter-marker dependencies, and struggle with severe class imbalance.Our approach addresses these challenges through three components. First, a Span Consistency Layer (SCL) propagates span-level confidence signals to encourage coherent boundary formation. Second, Cross-Marker Attention (CMA) models co-occurrence patterns between marker types via a learned correlation matrix. Third, we introduce Span Count Regularization (SCR), a total-variation-based constraint that aligns soft token probabilities with the expected number of discrete spans, mitigating prediction collapse under threshold decoding.Built on DeBERTa-v3-large and trained with a recall-biased Tversky loss, our system is ensembled across five stratified folds. It achieved a Macro F1 of 0.24 on the official test set, placing second among participating teams. Ablation studies show that SCR plays a critical role in maintaining span structure, particularly for low-frequency and long-span markers."
}