@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://aclanthology.org/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."
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%0 Conference Proceedings
%T SemEval-2026 Task 6: CLARITY – Unmasking Political Question Evasions
%A Thomas, Konstantinos
%A Filandrianos, Giorgos
%A Lymperaiou, Maria
%A Zerva, Chrysoula
%A Stamou, Giorgos
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F thomas-etal-2026-semeval
%X 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.
%U https://aclanthology.org/2026.semeval-1.449/
%P 3704-3715
Markdown (Informal)
[SemEval-2026 Task 6: CLARITY – Unmasking Political Question Evasions](https://aclanthology.org/2026.semeval-1.449/) (Thomas et al., SemEval 2026)
ACL
- Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou, Chrysoula Zerva, and Giorgos Stamou. 2026. SemEval-2026 Task 6: CLARITY – Unmasking Political Question Evasions. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3704–3715, San Diego, California, USA. Association for Computational Linguistics.