Salah Eddine Bekhouche, Azeddine Benlamoudi, Yazid Bounab, Fadi Dornaika, 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 href{this https URL}{GitHub}.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2507.23404 [cs.CL] |
| (or arXiv:2507.23404v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2507.23404Focus to learn more |
Submission history
From: Salah Eddine Bekhouche SE. Bekhouche [view email]
[v1] Thu, 31 Jul 2025 10:18:28 UTC (166 KB)
The abstract proposes a significant advancement in Arabic information retrieval through the introduction of an Attentive Relevance Scoring (ARS) mechanism within a Dense Passage Retrieval (DPR) framework. The approach specifically addresses unique challenges posed by Arabic, such as morphological complexity, the use of optional diacritics, and variation across dialects and Modern Standard Arabic, which traditionally limit NLP performance in this language.
Strengths
- Clear Problem Motivation:
The abstract effectively identifies why Arabic is particularly challenging for NLP and information retrieval due to linguistic features that are less prevalent or absent in many other languages. - Novel Technical Contribution:
ARS is positioned as a new, adaptive scoring function that replaces standard vector similarity, aiming to improve semantic relevance modeling between Arabic queries and passages. This specific architectural refinement directly targets difficulties conventional systems have with Arabic text. - Integration with State-of-the-Art Models:
The method is built on pre-trained Arabic language models and adds meaningful architectural improvements to improve both retrieval and ranking, reflecting modern best practices in NLP system development. - Open Science and Reproducibility:
Source code availability ensures transparency and supports research and development in the underrepresented area of Arabic IR. - Experimental Validation:
While details are in the paper, the abstract cites “significantly increased ranking accuracy” and outperformance of baseline approaches, supported by comparative evaluation results.
Areas for Improvement
- Quantitative Claims:
The abstract asserts significant performance improvements but does not specify by how much or on which benchmarks (though further details are promised in the main paper). Inclusion of summary figures would strengthen the abstract’s impact. - Clarity on ARS Mechanism:
While ARS is described as adaptive and semantically aware, the abstract could be more explicit about how it differs in practice from traditional similarity measures or interaction models. - Potential Applications Mentioned:
The method’s practical scope could be enhanced by summarizing which IR or QA tasks benefit most, beyond just “answering Arabic questions”.
Overall Assessment
This research addresses a real bottleneck in Arabic NLP by developing a specialized DPR variant that includes an attentive, adaptive relevance scoring strategy. The work demonstrates technical innovation, practical relevance, and a commitment to open science, making it a valuable contribution to the field. Clarifying the quantitative impact and summarizing unique properties of ARS would further strengthen its scientific communication.
