@inproceedings{wen-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 6: Hierarchical Taxonomy Prompting and {C}o{T} Distillation for Political Clarity Classification",
    author = "Wen, Canning  and
      Wang, Jin  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://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.45/",
    pages = "308--313",
    ISBN = "979-8-89176-414-9",
    abstract = "In political interviews, politicians frequently employ evasion strategies to avoid direct answers, making it challenging to evaluate response clarity in Natural Language Processing. This paper presents the YNU-HPCC system for SemEval-2026 task 6: clarity classification in political interviews. To address the limitation where traditional models capture only surface-level semantics, this paper proposes two reasoning-enhanced frameworks. First, we introduce Hierarchical Taxonomy Prompting. This method guides LLMs to follow a strict top-down classification logic. Specifically, the model determines the clarity level before identifying specific evasion techniques. Furthermore, it explicitly articulates the reasoning process. Second, to balance reasoning capability with resource constraints, we employ Chain-of-Thought Distillation. We utilize DeepSeek V3.1 as a teacher model to generate comprehensive reasoning chains, which are then used to SFT the smaller student models. Experimental results demonstrate the effectiveness of our approach: The system achieved 6th place in Task 1 and 5th place in Task 2 among all participating teams, highlighting the importance of reasoning processes in detecting complex linguistic evasion."
}