@inproceedings{munawar-etal-2026-hu,
    title = "{HU} at {S}em{E}val-2026 Task 6: A Hybrid Discriminative Modeling of Political Clarity and Evasion",
    author = "Munawar, Taha  and
      Khan, Basil  and
      Jangda, Arsal  and
      Baig, Sarfaraz  and
      Kumar, Sandesh  and
      Samad, Abdul",
    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-1.34/",
    pages = "235--241",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our submission to SemEval-2026 Task 6: CLARITY, which aims to classify political question{--}answer pairs by response clarity and evasive technique. We investigate several approaches, including long-context transformers, multiple instance learning, hierarchical multi-task models, and a natural language inference (NLI) formulation. On the development set, our best-performing NLI model achieves a macro-F1 of 0.79 for Subtask 1, while our best attention-based MIL model achieves a macro-F1 of 0.43 for Subtask 2. On the hidden evaluation set, our official submission obtains macro-F1 scores of 0.81 for Subtask 1 and 0.45 for Subtask 2. Our findings demonstrate the benefits of entailment-based modeling for clarity prediction and localized reasoning for evasion detection under limited computational resources."
}