Pré-Publication, Document De Travail Année : 2025

Enhanced Arabic Text Retrieval with Attentive Relevance Scoring

Salah Eddine Bekhouche
Azeddine Benlamoudi
  • Fonction : Auteur
Yazid Bounab
  • Fonction : Auteur
Fadi Dornaika
  • Fonction : Auteur

Résumé

Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a novel Attentive Relevance Scoring (ARS) that replaces standard interaction mechanisms with an adaptive scoring function that more effectively models the semantic relevance between questions and passages. Our method integrates pre-trained Arabic language models and architectural refinements to improve retrieval performance and significantly increase ranking accuracy when answering Arabic questions. The code is made publicly available at \href{https://github.com/Bekhouche/APR}{GitHub}.

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Dates et versions

hal-05253890 , version 1 (15-09-2025)

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Citer

Salah Eddine Bekhouche, Azeddine Benlamoudi, Yazid Bounab, Fadi Dornaika, Abdenour Hadid. Enhanced Arabic Text Retrieval with Attentive Relevance Scoring. 2025. ⟨hal-05253890⟩

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