@inproceedings{wang-chen-2026-ccnu,
title = "{CCNU} at {S}em{E}val-2026 Task 10: Conspiracy Marker Extraction and Detection via Multi-task Learning and {LLM}-based Data Augmentation",
author = "Wang, Zijun and
Chen, Guanyi",
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-1.58/",
pages = "402--408",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents the system of CCNU forSemEval-2026 Task 10: Psycholinguistic Con-spiracy Marker Extraction and Detection. Thetask requires identifying fine-grained conspir-acy markers that characterize conspiracy think-ing, as well as determining whether a Redditcomment constitutes conspiratorial discourse.For Conspiracy Marker Extraction (Subtask 1),we adopt a Unified Multi-Task Sequence La-beling Framework that jointly models multi-ple conspiracy markers within a single labelingspace. This formulation enables collaborativelearning across marker types while maintaininga compact architecture. For Conspiracy Detec-tion (Subtask 2), we formulate the problem assentence-level classification. Across both sub-tasks, we apply data augmentation powered bylarge language models and ensemble inferenceto improve robustness and generalization. Oursystem achieves strong performance on Sub-task 1, ranking 3rd on the official test set, anddelivers competitive results on Subtask 2."
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<abstract>This paper presents the system of CCNU forSemEval-2026 Task 10: Psycholinguistic Con-spiracy Marker Extraction and Detection. Thetask requires identifying fine-grained conspir-acy markers that characterize conspiracy think-ing, as well as determining whether a Redditcomment constitutes conspiratorial discourse.For Conspiracy Marker Extraction (Subtask 1),we adopt a Unified Multi-Task Sequence La-beling Framework that jointly models multi-ple conspiracy markers within a single labelingspace. This formulation enables collaborativelearning across marker types while maintaininga compact architecture. For Conspiracy Detec-tion (Subtask 2), we formulate the problem assentence-level classification. Across both sub-tasks, we apply data augmentation powered bylarge language models and ensemble inferenceto improve robustness and generalization. Oursystem achieves strong performance on Sub-task 1, ranking 3rd on the official test set, anddelivers competitive results on Subtask 2.</abstract>
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%0 Conference Proceedings
%T CCNU at SemEval-2026 Task 10: Conspiracy Marker Extraction and Detection via Multi-task Learning and LLM-based Data Augmentation
%A Wang, Zijun
%A Chen, Guanyi
%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 wang-chen-2026-ccnu
%X This paper presents the system of CCNU forSemEval-2026 Task 10: Psycholinguistic Con-spiracy Marker Extraction and Detection. Thetask requires identifying fine-grained conspir-acy markers that characterize conspiracy think-ing, as well as determining whether a Redditcomment constitutes conspiratorial discourse.For Conspiracy Marker Extraction (Subtask 1),we adopt a Unified Multi-Task Sequence La-beling Framework that jointly models multi-ple conspiracy markers within a single labelingspace. This formulation enables collaborativelearning across marker types while maintaininga compact architecture. For Conspiracy Detec-tion (Subtask 2), we formulate the problem assentence-level classification. Across both sub-tasks, we apply data augmentation powered bylarge language models and ensemble inferenceto improve robustness and generalization. Oursystem achieves strong performance on Sub-task 1, ranking 3rd on the official test set, anddelivers competitive results on Subtask 2.
%U https://aclanthology.org/2026.semeval-1.58/
%P 402-408
Markdown (Informal)
[CCNU at SemEval-2026 Task 10: Conspiracy Marker Extraction and Detection via Multi-task Learning and LLM-based Data Augmentation](https://aclanthology.org/2026.semeval-1.58/) (Wang & Chen, SemEval 2026)
ACL