@inproceedings{marchitan-dinu-2026-unibuc,
    title = "{U}nibuc-{NLP} at {S}em{E}val-2026 Task 10: Unmasking Conspiracies with Pre-Trained Language Models",
    author = "Marchitan, Teodor-George  and
      Dinu, Liviu",
    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-1.178/",
    pages = "1379--1384",
    ISBN = "979-8-89176-414-9",
    abstract = "The paper describes the system submitted to SemEval-2026 Task 10 (PsyCoMark) Subtask 2: detecting whether a Reddit comment expresses a conspiracy belief. We investigate three modeling paradigms: (A) an embedding-and-classify pipeline using Jina-embeddings-v3, HateBERT and BERT-Sentiment with Optuna-tuned classical ML models, optionally enriched by 19 readability features from textstat; (B) end-to-end fine-tuning of encoder transformers (DeBERTa-v3-base, DistilBERT) with a compact 128-unit classifier head and multiple pooling strategies; and (C) parameter-efficient QLoRA fine-tuning of large decoder-only models (Mistral-7B-v0.3, Qwen3-0.6B). Our best system, DeBERTa-v3-base with a 128-dimensional classifier, achieves a weighted F1 of 0.74, ranking 29/52 on the official leaderboard. Post-submission analysis further reveals that a weighted pooling strategy outperforms [CLS] on the official validation split by +0.04, achieving a weighted F1 of 0.78 (rank 8/52), suggesting that conspiracy-relevant features are distributed across transformer layers rather than concentrated at the final output."
}