Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Authors: name: Salah Eddine Bekhouche, name: Azeddine Benlamoudi, name: Yazid Bounab, name: Fadi Dornaika, name: Abdenour Hadid

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 https://github.com/Bekhouche/APR{GitHub}.