@inproceedings{mahmoud-etal-2025-bertastic,
    title = "{BERT}astic at {S}em{E}val-2025 Task 10: State-of-the-Art Accuracy in Coarse-Grained Entity Framing for {H}indi News",
    author = "Mahmoud, Tarek  and
      Xie, Zhuohan  and
      Nakov, Preslav",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.55/",
    pages = "386--396",
    ISBN = "979-8-89176-273-2",
    abstract = "We describe our system for SemEval-2025 Task 10 Subtask 1 on coarse-grained entity framing in Hindi news, exploring two complementary strategies. First, we experiment with LLM prompting using GPT-4o, comparing hierarchical multi-step prompting with native single-step prompting for both main and fine-grained role prediction. Second, we conduct an extensive study on fine-tuning XLM-R, analyzing different context granularities (full article, paragraph, or sentence-level entity mentions), monolingual vs. multilingual settings, and main vs. fine-grained role labels. Our best system, trained on fine-grained role annotations across languages using sentence-level context, achieved 43.99{\%} exact match, 56.56 {\%} precision, 47.38{\%} recall, and 51.57{\%} F1-score. Notably, our system set a new state-of-the-art for main role prediction on Hindi news, achieving 78.48 {\%} accuracy - outperforming the next best model at 76.90{\%}, as per the official leaderboard. Our findings highlight effective strategies for entity framing in multilingual and low-resource settings."
}