CuriosAI at SemEval-2026 Task 10:Hybrid approaches to conspiracy span extraction and conspiracy detection
Year: 2026
Type: conference-paper
Abstract: We present CuriosAI's system for SemEval-2026 Task 10, addressing Conspiracy Marker Extraction and Conspiracy Detection.For marker extraction, we employ multi-label token classification with a bidirectional transformer (DeBERTa-v3-large) to predict overlapping spans.Alternative feature-based and LLM-based approaches do not surpass the encoder baseline.For Conspiracy Detection, we compare heterogeneous models, including transformer fine-tuning, lexical classifiers, embedding-based models, and LLM-based refinement.Development-optimal models do not always generalize best; logit-level ensembling achieves the strongest test performance (F 1 = 0.7620).These results highlight the importance of bidirectional token modeling for span extraction and calibration-aware ensembling for robust detection.
Institution: SoftBank Corp. (Japan)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166831649