@inproceedings{elkomy-sarhan-2023-tce,
title = "{TCE} at Qur{'}an {QA} 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur{'}anic {QA}",
author = "Elkomy, Mohammed and
Sarhan, Amany",
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-1.81",
doi = "10.18653/v1/2023.arabicnlp-1.81",
pages = "728--742",
abstract = "In this paper, we present our approach to tackle Qur{'}an QA 2023 shared tasks A and B. To address the challenge of low-resourced training data, we rely on transfer learning together with a voting ensemble to improve prediction stability across multiple runs. Additionally, we employ different architectures and learning mechanisms for a range of Arabic pre-trained transformer-based models for both tasks. To identify unanswerable questions, we propose using a thresholding mechanism. Our top-performing systems greatly surpass the baseline performance on the hidden split, achieving a MAP score of 25.05{\%} for task A and a partial Average Precision (pAP) of 57.11{\%} for task B.",
}
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%0 Conference Proceedings
%T TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA
%A Elkomy, Mohammed
%A Sarhan, Amany
%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 elkomy-sarhan-2023-tce
%X In this paper, we present our approach to tackle Qur’an QA 2023 shared tasks A and B. To address the challenge of low-resourced training data, we rely on transfer learning together with a voting ensemble to improve prediction stability across multiple runs. Additionally, we employ different architectures and learning mechanisms for a range of Arabic pre-trained transformer-based models for both tasks. To identify unanswerable questions, we propose using a thresholding mechanism. Our top-performing systems greatly surpass the baseline performance on the hidden split, achieving a MAP score of 25.05% for task A and a partial Average Precision (pAP) of 57.11% for task B.
%R 10.18653/v1/2023.arabicnlp-1.81
%U https://aclanthology.org/2023.arabicnlp-1.81
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.81
%P 728-742
Markdown (Informal)
[TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA](https://aclanthology.org/2023.arabicnlp-1.81) (Elkomy & Sarhan, ArabicNLP-WS 2023)
ACL