用编码器与零样本模型检测政治话语中的模糊与规避行为
KCLarity at SemEval-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection
- 分两步预测：直接判清晰度，或先判规避再推导清晰度
- RoBERTa-large在公开测试集表现最佳，零样本GPT-5.2在隐藏集泛化更强
- 首次尝试零样本框架，适合关注可解释性与跨场景应用的研究者
本文介绍KCLarity团队在SemEval 2026 CLARITY共享任务中的参与情况，该任务旨在识别政治话语中的模糊与规避策略。我们探索两种建模方式：(i) 直接预测清晰度标签；(ii) 先预测规避标签，再通过任务分类层级推导清晰度。此外，我们测试多种辅助训练方案，并在规避优先框架下评估解码器仅模型的零样本性能。总体而言，两种方法表现相当。在编码器模型中，RoBERTa-large在公开测试集上取得最佳效果；而零样本的GPT-5.2在隐藏评估集上展现出更强的泛化能力。
原文摘要 · Abstract (English)
This paper describes the KCLarity team's participation in CLARITY, a shared task at SemEval 2026 on classifying ambiguity and evasion techniques in political discourse. We investigate two modelling formulations: (i) directly predicting the clarity label, and (ii) predicting the evasion label and deriving clarity through the task taxonomy hierarchy. We further explore several auxiliary training variants and evaluate decoder-only models in a zero-shot setting under the evasion-first formulation. Overall, the two formulations yield comparable performance. Among encoder-based models, RoBERTa-large achieves the strongest results on the public test set, while zero-shot GPT-5.2 generalises better on the hidden evaluation set.
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