@inproceedings{alawwad-etal-2023-ahjl,
title = "{AHJL} at Qur{'}an {QA} 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation",
author = "Alawwad, Hessa and
Alawwad, Lujain and
Alharbi, Jamilah and
Alharbi, Abdullah",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.77",
doi = "10.18653/v1/2023.arabicnlp-1.77",
pages = "702--707",
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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<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.</abstract>
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%0 Conference Proceedings
%T AHJL at Qur’an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation
%A Alawwad, Hessa
%A Alawwad, Lujain
%A Alharbi, Jamilah
%A Alharbi, Abdullah
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F alawwad-etal-2023-ahjl
%X 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.
%R 10.18653/v1/2023.arabicnlp-1.77
%U https://aclanthology.org/2023.arabicnlp-1.77
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.77
%P 702-707
Markdown (Informal)
[AHJL at Qur’an QA 2023 Shared Task: Enhancing Passage Retrieval using Sentence Transformer and Translation](https://aclanthology.org/2023.arabicnlp-1.77) (Alawwad et al., ArabicNLP-WS 2023)
ACL