@inproceedings{riad-2026-syntaxmind,
    title = "{S}yntax{M}ind at {S}em{E}val-2026 Task 6: Exploring Transformers and {LLM}s for Unmasking Political Question Evasions",
    author = "Riad, Md. Shihab Uddin",
    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-1.419/",
    pages = "3377--3381",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our approach to Subtask 1: Clarity-level Classification in SemEval-2026 Task 6. The task focuses on determining the clarity of political responses with respect to their corresponding questions. To enhance model performance, we introduced a direct answer generation strategy as an additional input feature and applied Task-Adaptive Pre-Training (TAPT) to enhance encoder-only Transformer models with the task domain. We further explored both cross-entropy and focal loss to address potential class imbalance. Experimental results show that TAPT enhanced encoder models, particularly DeBERTa-V3-base, achieved the strongest performance, while generative small language models fine-tuned via parameter-efficient methods exhibited comparatively lower results. Our system obtained a macro-F1 score of 0.72 on the official evaluation set, ranking 24th out of 40 teams."
}