@inproceedings{thomas-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 6: {CLARITY} {--} Unmasking Political Question Evasions",
    author = "Thomas, Konstantinos  and
      Filandrianos, Giorgos  and
      Lymperaiou, Maria  and
      Zerva, Chrysoula  and
      Stamou, Giorgos",
    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-1.449/",
    pages = "3704--3715",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents CLARITY, the SemEval-2026 shared task on detecting and classifying evasive responses in political discourse. The task is grounded in an expert-designed two-level taxonomy and a benchmark dataset of question-answer pairs from U.S. presidential interviews, requiring systems to distinguish clear from evasive responses at a coarse level and identify one of nine fine-grained evasion strategies at a fine-grained level. With 124 registered teams and over 1,400 combined valid submissions, the task attracted broad participation spanning a wide range of methodological approaches, from fine-tuned encoder models to multi-stage large language model pipelines. Analysis of submitted systems reveals that hierarchical exploitation of the taxonomy and chain-of-thought prompted LLMs were the most effective strategies, while fine-grained evasion classification remained a substantially harder and largely unsolved challenge. CLARITY advances the study of strategic ambiguity in political language as a formal NLP benchmark and highlights key open problems in computational discourse analysis."
}