mlsanityData to 14 Oct 2026

Efficient and Versatile Model for Multilingual Information Retrieval of Islamic Text: Development and Deployment in Real-World Scenarios

arXiv 2509.15380 ↗First posted 18 Sep 2025cs.IR cs.AI cs.CLcited by 1

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Abstract (arXiv)

Despite recent advancements in Multilingual Information Retrieval (MLIR), a significant gap remains between research and practical deployment. Many studies assess MLIR performance in isolated settings, limiting their applicability to real-world scenarios. In this work, we leverage the unique characteristics of the Quranic multilingual corpus to examine the optimal strategies to develop an ad-hoc IR system for the Islamic domain that is designed to satisfy users' information needs in multiple languages. We prepared eleven retrieval models employing four training approaches: monolingual, cross-lingual, translate-train-all, and a novel mixed method combining cross-lingual and monolingual techniques. Evaluation on an in-domain dataset demonstrates that the mixed approach achieves promising results across diverse retrieval scenarios. Furthermore, we provide a detailed analysis of how different training configurations affect the embedding space and their implications for multilingual retrieval effectiveness. Finally, we discuss deployment considerations, emphasizing the cost-efficiency of deploying a single versatile, lightweight model for real-world MLIR applications.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

28 of 37 references.

References · 28

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16 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learningcited by 0