The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA
Open accessAbstract
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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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · arXiv · doi:10.48550/arxiv.2104.08051 · Cited by 251
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462880 · Cited by 248
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Burhan at IslamicEval: Fact-Augmented and LLM-Driven Retrieval for Islamic QA
Basheer, M., Mansour, W., Touma, A., & Qadeib Alban, A. (2025) · Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks · doi:10.18653/v1/2025.arabicnlp-sharedtasks.73
“Several teams participated in the task and employed various technique such as augmentation (Elkomy and Sarhan, 2024; Basem et al., 2024), translation (Alawwad et al., 2023).”
Metadata
- Authors
- Elkomy, M., & Sarhan, A.
- Affiliations
- Tanta University (EG)
- ORCID
- Sarhan, Amany
- Venue
- Proceedings of ArabicNLP 2023
- Published
- 2023
- Pages
- 728-742
- OpenAlex
- W4389518327
- Cited by
- 5
- References
- 3
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 0.89
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- cc-by
- Locations
- Arabic Natural Language Processing Conference (ArabicNLP) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- transfer learning, ensemble voting, Quran
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