@inproceedings{iqbal-etal-2026-clrg,
    title = "{CLRG} at {S}em{E}val-2026 Task 3: One Size Does Not Fit All: A Resource Adaptive Framework for Dimensional Sentiment Regression",
    author = "Iqbal, Wardat  and
      Naswan, Ruwad  and
      Shatabda, Swakkhar",
    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-1.404/",
    pages = "3225--3231",
    ISBN = "979-8-89176-414-9",
    abstract = "Predicting continuous Valence and Arousal scores across diverse languages poses significant challenges due to typological differences and the difficulty of modeling affective intensity. We introduce AdaptStance, a parameter-efficient framework designed for the SemEval-2026 Task 3 benchmark. To address cross-lingual disparities, AdaptStance routes inputs through resource-specific pipelines: direct regression with a hybrid concordance loss for high-resource languages, and an auxiliary multi-task mechanism to stabilize regression in low-resource and non-Western contexts. Architectural analysis reveals that decoupling task heads benefits morphologically related languages, whereas joint representations act as crucial regularizers for distant language families. Ultimately, this lightweight approach achieves competitive performance over generative baselines, demonstrating the efficacy of targeted architectural alignment while identifying Valence as the primary bottleneck in continuous affect prediction. Our code is available on GitHub."
}