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YNU-HPCC at SemEval-2026 Task 6: Hierarchical Taxonomy Prompting and CoT Distillation for Political Clarity Classification

Year: 2026
Type: conference-paper
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.
DOI: 10.18653/v1/2026.semeval-1.45
Institution: Yunnan University
Language: English
Open Access status: hybrid
OpenAlex ID: W7166814888
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