YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection

Junpei Chen; You Zhang; Jin Wang; Dan Xu; Xuejie Zhang

YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection

Junpei Chen, You Zhang, Jin Wang, Dan Xu, Xuejie Zhang

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Abstract

In this paper, we present our submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which consists of two tasks: conspiracy marker extraction and conspiracy detection. For conspiracy marker extraction, we formulate the problem as a token classification task and fine-tune pretrained language models, achieving performance above the official baseline and ranking 6th on the final leaderboard. For conspiracy detection, we apply data preprocessing, regularization, and ensemble inference strategies,resulting in improvements over the baseline and a 10th-place ranking. Overall, our results demonstrate the effectiveness of pretrained language models for both tasks.

Anthology ID:: 2026.semeval-1.105
Volume:: Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Ekaterina Kochmar, Debanjan Ghosh, Kai North, Mamoru Komachi
Venues:: SemEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 741–747
Language:
URL:: https://aclanthology.org/2026.semeval-1.105/
DOI:
Bibkey:
Cite (ACL):: Junpei Chen, You Zhang, Jin Wang, Dan Xu, and Xuejie Zhang. 2026. YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 741–747, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection (Chen et al., SemEval 2026)
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PDF:: https://aclanthology.org/2026.semeval-1.105.pdf

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@inproceedings{chen-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 10: Pretrained {D}istil{BERT} Models for Conspiracy Marker Extraction and Detection",
    author = "Chen, Junpei  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  and
      Zhang, Xuejie",
    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://aclanthology.org/2026.semeval-1.105/",
    pages = "741--747",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we present our submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which consists of two tasks: conspiracy marker extraction and conspiracy detection. For conspiracy marker extraction, we formulate the problem as a token classification task and fine-tune pretrained language models, achieving performance above the official baseline and ranking 6th on the final leaderboard. For conspiracy detection, we apply data preprocessing, regularization, and ensemble inference strategies,resulting in improvements over the baseline and a 10th-place ranking. Overall, our results demonstrate the effectiveness of pretrained language models for both tasks."
}

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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection
%A Chen, Junpei
%A Zhang, You
%A Wang, Jin
%A Xu, Dan
%A Zhang, Xuejie
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F chen-etal-2026-ynu
%X In this paper, we present our submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which consists of two tasks: conspiracy marker extraction and conspiracy detection. For conspiracy marker extraction, we formulate the problem as a token classification task and fine-tune pretrained language models, achieving performance above the official baseline and ranking 6th on the final leaderboard. For conspiracy detection, we apply data preprocessing, regularization, and ensemble inference strategies,resulting in improvements over the baseline and a 10th-place ranking. Overall, our results demonstrate the effectiveness of pretrained language models for both tasks.
%U https://aclanthology.org/2026.semeval-1.105/
%P 741-747

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Markdown (Informal)

[YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection](https://aclanthology.org/2026.semeval-1.105/) (Chen et al., SemEval 2026)

YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection (Chen et al., SemEval 2026)

ACL

Junpei Chen, You Zhang, Jin Wang, Dan Xu, and Xuejie Zhang. 2026. YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 741–747, San Diego, California, USA. Association for Computational Linguistics.