# SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions

arXiv: https://arxiv.org/abs/2603.14027 · HTML: https://mlsanity.com/paper/2603.14027/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Stop Measuring Calibration When Humans Disagree](https://mlsanity.com/paper/2210.16133/) (2022)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Did they answer? Subjective acts and intents in conversational discourse](https://mlsanity.com/paper/2104.04470/) (2021)
- [Stance Detection: A Practical Guide to Classifying Political Beliefs in Text](https://mlsanity.com/paper/2305.01723/) (2023)
- ["I Never Said That": A dataset, taxonomy and baselines on response clarity classification](https://mlsanity.com/paper/2409.13879/) (2024)
- [C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset](https://mlsanity.com/paper/2509.21548/) (2025)
- [Prompt-Based Clarity Evaluation and Topic Detection in Political Question Answering](https://mlsanity.com/paper/2601.08176/) (2026)
- [CSE-UOI at SemEval-2026 Task 6: A Two-Stage Heterogeneous Ensemble with Deliberative Complexity Gating for Political Evasion Detection](https://mlsanity.com/paper/2603.12453/) (2026)

## Built on by (most cited first, 5 of 5)

- [SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection](https://mlsanity.com/paper/2604.26375/) (2026)
- [CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse](https://mlsanity.com/paper/2605.02170/) (2026)
- [ttda704 at SemEval-2026 Task 6: Structured Chain-of-Thought Prompting for Political Evasion Detection](https://mlsanity.com/paper/2606.15770/) (2026)
- [AsymVerify at SemEval-2026 Task 6: Asymmetric Confidence-Gated Verification for Political Evasion Detection](https://mlsanity.com/paper/2607.20439/) (2026)
- [PolERo: Studying Political Evasion in Romanian](https://mlsanity.com/paper/2609.02391/) (2026)

## Abstract (arXiv)

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) clarity-level classification into Clear Reply, Ambivalent, and Clear Non-Reply, and (ii) evasion-level 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 evasion-level 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 evasion-level 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.