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.

翻译：本文介绍了KCLarity团队在CLARITY任务中的参与情况，该任务是SemEval 2026中关于政治话语中模糊性与规避技术分类的共享任务。我们研究了两种建模方案：（i）直接预测清晰度标签，以及（ii）预测规避标签并通过任务分类层次结构推导清晰度。我们进一步探索了多种辅助训练变体，并在规避优先方案下评估了纯解码器模型在零样本设置中的表现。总体而言，两种方案取得了相当的性能。在基于编码器的模型中，RoBERTa-large在公开测试集上获得了最佳结果，而零样本GPT-5.2在隐藏评估集上表现出更好的泛化能力。