Year: 2026
Type: conference-paper
Abstract: Political speakers often avoid answering questions directly while maintaining the appearance of responsiveness.Despite its importance for public discourse, such strategic evasion remains underexplored in Natural Language Processing.We introduce SemEval-2026 Task 6, CLARITY, a shared task on political question evasion consisting of two subtasks: (i) claritylevel classification into Clear Reply, Ambivalent, and Clear Non-Reply, and (ii) evasionlevel classification into nine fine-grained evasion strategies.The benchmark is constructed from U.S. presidential interviews and follows an expert-grounded taxonomy of response clarity and evasion.The task attracted 124 registered teams, who submitted 946 valid runs for clarity-level classification and 539 for evasionlevel classification.Results show a substantial gap in difficulty between the two subtasks: the best system achieved 0.89 Macro-F1 on clarity classification, surpassing the strongest baseline by a large margin, while the top evasionlevel system reached 0.68 Macro-F1, matching the best baseline.Overall, large language model prompting and hierarchical exploitation of the taxonomy emerged as the most effective strategies, with top systems consistently outperforming those that treated the two subtasks independently.CLARITY establishes political response evasion as a challenging benchmark for computational discourse analysis and highlights the difficulty of modeling strategic ambiguity in political language.
Institution: National Technical University of Athens
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166883432