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.
Cite
CITATION STYLE
Alawwad, H. A., Alawwad, L. A., Alharbi, J., & Alharbi, A. I. (2023). AHJL at Qur’an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation. In ArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Proceedings (pp. 702–707). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.arabicnlp-1.77
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