LKAU23 at Qur’an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur’an
Sarah Alnefaie, Abdullah Alsaleh, Eric Atwell, Mohammad Alsalka, Abdulrahman Altahhan
Abstract
The Qur’an QA 2023 shared task has two sub tasks: Passage Retrieval (PR) task and Machine Reading Comprehension (MRC) task. Our participation in the PR task was to further train several Arabic pre-trained models using a Sentence-Transformers architecture and to ensemble the best performing models. The results of the test set did not reflect the results of the development set. CL-AraBERT achieved the best results, with a 0.124 MAP. We also participate in the MRC task by further fine-tuning the base and large variants of AraBERT using Classical Arabic and Modern Standard Arabic datasets. Base AraBERT achieved the best result with the development set with a partial average precision (pAP) of 0.49, while it achieved 0.5 with the test set. In addition, we applied the ensemble approach of best performing models and post-processing steps to the final results. Our experiments with the development set showed that our proposed model achieved a 0.537 pAP. On the test set, our system obtained a pAP score of 0.49.- Anthology ID:
- 2023.arabicnlp-1.80
- 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:
- 720–727
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.80
- DOI:
- 10.18653/v1/2023.arabicnlp-1.80
- Cite (ACL):
- Sarah Alnefaie, Abdullah Alsaleh, Eric Atwell, Mohammad Alsalka, and Abdulrahman Altahhan. 2023. LKAU23 at Qur’an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur’an. In Proceedings of ArabicNLP 2023, pages 720–727, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- LKAU23 at Qur’an QA 2023: Using Transformer Models for Retrieving Passages and Finding Answers to Questions from the Qur’an (Alnefaie et al., ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.arabicnlp-1.80.pdf