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Cited by 7Topic ModelingInformation Retrieval and Search BehaviorNatural Language Processing Techniques

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AHJL at Qur’an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation

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Alawwad, Hessa, Alawwad, Lujain, Alharbi, Jamilah, Alharbi, Abdullah·2023·Proceedings of ArabicNLP 2023
DOI 10.18653/v1/2023.arabicnlp-1.77
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Abstract

The Holy Qur'an is central to Islam, influencing around two billion Muslims globally, and is known for its linguistic richness and complexity.This article discusses our involvement in the PR task (Task A) of the Qur'an QA 2023 Shared Task.We used two models: one employing the Sentence Transformer and the other using OpenAI's embeddings for document retrieval.Both models, equipped with a translation feature, help interpret and understand Arabic language queries by translating them, executing the search, and then reverting the results to Arabic.Our results show that incorporating translation functionalities improves the performance in Arabic Question-Answering systems.The model with translation enhancement performed notably better in all metrics compared to the non-translation model.

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Metadata

Authors
Alawwad, H., Alawwad, L., Alharbi, J., & Alharbi, A.
Affiliations
  • Imam Mohammad ibn Saud Islamic University (SA)
  • Saudi Electronic University (SA)
  • King Abdulaziz University (SA)
Venue
Proceedings of ArabicNLP 2023
Publisher
Association for Computational Linguistics
Published
2023
Pages
702-707
DOI
10.18653/v1/2023.arabicnlp-1.77
OpenAlex
W4389518368
Cited by
7
References
1
Citations by year
2026: 2 · 2025: 3 · 2024: 1 · 2023: 1
FWCI
1.24
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Arabic Natural Language Processing Conference (ArabicNLP) (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
passage retrieval, Arabic question answering, Sentence Transformers, document retrieval, query translation

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