@inproceedings{bernal-beltran-etal-2026-umuteam,
    title = "{UMUT}eam at {S}em{E}val-2026 Task 6: Soft-Voting Transformer Ensembles for Detecting and Classifying Response Ambiguity in Political Discourse",
    author = "Bernal-Beltr{\'a}n, Tom{\'a}s  and
      Pan, Ronghao  and
      G{\'o}mez-Naval{\'o}n, Jorge  and
      Garc{\'i}a-D{\'i}az, Jos{\'e} Antonio  and
      Valencia-Garcia, Rafael",
    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-1.68/",
    pages = "475--482",
    ISBN = "979-8-89176-414-9",
    abstract = "Political discourse frequently involves strategically ambiguous responses, particularly in high-stakes settings such as presidential debates and interviews. Detecting whether a politician has directly answered a question, provided an ambiguous reply or issued a clear non-reply remains a challenging task due to the pragmatic and rhetorical nature of political language. This paper describes our participation in the SemEval 2026 CLARITY shared task on response ambiguity detection and classification in English. We focused exclusively on Task 1 (Clarity-level Classification) and proposed a weighted soft-voting ensemble that combines four fine-tuned encoder-only transformer models: RoBERTa-large, BERT-large-cased, DistilBERT-cased and ModernBERT-large. Each model was optimized through grid search and their predicted class probability distributions were aggregated using a weighted linear combination. On the official test set, our system achieved a macro-F1 score of 0.71, ranking 26th out of 41 participating teams. Even with the performance gap compared to top-ranked systems, our results demonstrate that a lightweight set of moderately sized encoder models can provide stable and competitive performance without relying on external data or large-scale architectures."
}