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.- Anthology ID:
- 2023.arabicnlp-1.81
- Volume:
- Proceedings of ArabicNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore (Hybrid)
- Editors:
- Hassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, Rawan Almatham
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 728–742
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.81
- DOI:
- 10.18653/v1/2023.arabicnlp-1.81
- Cite (ACL):
- Mohammed Elkomy and Amany Sarhan. 2023. TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA. In Proceedings of ArabicNLP 2023, pages 728–742, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA (Elkomy & Sarhan, ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.arabicnlp-1.81.pdf