Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
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}.
688c5ffe8c434640078cc4a0
Salah Eddine Bekhouche
claimed_verified
2025-08-06T19:29:34.582Z
63e7493fdb40d9e67feaccdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63e7493fdb40d9e67feaccdd/9d1iTreC4GlxSiWvLT0dP.jpeg
Salah Eddine Bekhouche
user
Bekhouche
688c5ffe8c434640078cc4a1
Azeddine Benlamoudi
688c5ffe8c434640078cc4a2
Yazid Bounab
688c5ffe8c434640078cc4a3
Fadi Dornaika
688c5ffe8c434640078cc4a4
Abdenour Hadid
Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
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}.
An enhanced Dense Passage Retrieval framework for Arabic uses a novel Attentive Relevance Scoring mechanism to improve retrieval performance and ranking accuracy.
Dense Passage Retrieval Attentive Relevance Scoring semantic relevance pre-trained Arabic language models