@inproceedings{garcia-sanabria-etal-2026-uned,
    title = "{UNED} at {S}em{E}val-2026 Task 9: Sentiment-Aware Transformer Models with Back-Translation Augmentation for Online polarisation Detection",
    author = "Garcia Sanabria, Victor  and
      Rodrigo, Alvaro  and
      Centeno, Roberto",
    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-1.176/",
    pages = "1366--1371",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission to SemEval-2026 Task 9 (Subtask 1) on Spanish online polarisation detection. We investigate whether sentiment-adapted pretrained language models provide an advantage over general-purpose multilingual models for binary polarisation classification. Under a controlled training setup, we compare a base XLM-RoBERTa model, an emotion-adapted model, and a sentiment-adapted XLM-R model trained on Twitter data. To mitigate overfitting in the relatively small training dataset, we additionally apply back-translation as a data augmentation strategy. Experimental results show that the sentiment-adapted checkpoint consistently outperforms the alternative pretrained models under identical conditions. When combined with back-translation augmentation, the final system achieves a macro-averaged F1 score of 0.743 on the preliminary competition leaderboard. These findings suggest that prior adaptation to affective signals in social media can provide beneficial inductive bias for polarisation detection."
}