@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering
%A Bayrami Asl Tekanlou, Hadi
%A Bakhtiyarzadeh, Mahdi
%A Razmara, Jafar
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F bayrami-asl-tekanlou-etal-2026-simorgh
%X 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.
%U https://aclanthology.org/2026.semeval-1.90/
%P 624-629
Markdown (Informal)
[Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering](https://aclanthology.org/2026.semeval-1.90/) (Bayrami Asl Tekanlou et al., SemEval 2026)
ACL