@inproceedings{zekiye-amroush-2023-al,
title = "Al-Jawaab at Qur{'}an {QA} 2023 Shared Task: Exploring Embeddings and {GPT} Models for Passage Retrieval and Reading Comprehension",
author = "Zekiye, Abdulrezzak and
Amroush, Fadi",
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-1.82",
doi = "10.18653/v1/2023.arabicnlp-1.82",
pages = "743--747",
abstract = "This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur{'}an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI{'}s {``}text-embedding-ada-002{''} embedding model to transform textual content into numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur{'}anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.",
}
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<abstract>This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s “text-embedding-ada-002” embedding model to transform textual content into numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.</abstract>
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%0 Conference Proceedings
%T Al-Jawaab at Qur’an QA 2023 Shared Task: Exploring Embeddings and GPT Models for Passage Retrieval and Reading Comprehension
%A Zekiye, Abdulrezzak
%A Amroush, Fadi
%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 zekiye-amroush-2023-al
%X This paper introduces a comprehensive system designed to address two natural language processing tasks: Passage Retrieval (Task A) and Reading Comprehension (Task B), applied to datasets related to the Holy Qur’an. Task A was treated as a measurement of a textual similarity problem where the system leverages OpenAI’s “text-embedding-ada-002” embedding model to transform textual content into numerical representations, with cosine similarity serving as the proximity metric. Task B focuses on the extraction of answers from Qur’anic passages, employing the Generative Pre-trained Transformer-4 (GPT-4) language model. In Task A, the system is evaluated using the Mean Average Precision (MAP) metric, achieving MAP scores of 0.109438 and 0.06426543057 on the development and test datasets with an optimal similarity threshold set at 0.85. Task B evaluation employs partial Average Precision (pAP), where our system surpasses a baseline whole-passage retriever with pAP scores of 0.470 and 0.5393130538 on the development and test datasets, respectively.
%R 10.18653/v1/2023.arabicnlp-1.82
%U https://aclanthology.org/2023.arabicnlp-1.82
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.82
%P 743-747
Markdown (Informal)
[Al-Jawaab at Qur’an QA 2023 Shared Task: Exploring Embeddings and GPT Models for Passage Retrieval and Reading Comprehension](https://aclanthology.org/2023.arabicnlp-1.82) (Zekiye & Amroush, ArabicNLP-WS 2023)
ACL