@inproceedings{bayrami-asl-tekanlou-etal-2026-simorgh,
    title = "Simorgh at {S}em{E}val-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering",
    author = "Bayrami Asl Tekanlou, Hadi  and
      Bakhtiyarzadeh, Mahdi  and
      Razmara, Jafar",
    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-1.90/",
    pages = "624--629",
    ISBN = "979-8-89176-414-9",
    abstract = "We propose a region-aware hybrid retrieval framework for culturally grounded multilingual question answering. Our system combines BM25-based lexical matching with dense semantic similarity using sentence embeddings, integrating both signals into a unified ranking function. To further prioritize culturally relevant evidence, we introduce a regional weighting heuristic that boosts documents containing explicit region-specific references. The top-ranked evidence passages are incorporated into a structured prompt and processed by a 4-bit quantized Qwen3-14B model. Instead of generating free-form text, the model selects answers deterministically using a logit-based scoring mechanism over the four multiple-choice options. This design enables efficient inference while improving cross-lingual stability, particularly in culturally explicit contexts."
}