Abstract
In this work, we approach the problem of Qur’anic information retrieval (IR) in Arabic and English. Using the latest state-of-the-art methods in neural IR, we research what helps to tackle this task more efficiently. Training retrieval models requires a lot of data, which is difficult to obtain for training in-domain. Therefore, we commence with training on a large amount of general domain data and then continue training on in-domain data. To handle the lack of in-domain data, we employed a data augmentation technique, which considerably improved results in MRR@10 and NDCG@5 metrics, setting the state-of-the-art in Qur’anic IR for both English and Arabic. The absence of an Islamic corpus and domain-specific model for IR task in English motivated us to address this lack of resources and take preliminary steps of the Islamic corpus compilation and domain-specific language model (LM) pre-training, which helped to improve the performance of the retrieval models that use the domain-specific LM as the shared backbone. We examined several language models (LMs) in Arabic to select one that efficiently deals with the Qur’anic IR task. Besides transferring successful experiments from English to Arabic, we conducted additional experiments with retrieval task in Arabic to amortize the scarcity of general domain datasets used to train the retrieval models. Handling Qur’anic IR task combining English and Arabic allowed us to enhance the comparison and share valuable insights across models and languages.- Anthology ID:
- 2023.arabicnlp-1.7
- 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:
- 76–88
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.7
- DOI:
- 10.18653/v1/2023.arabicnlp-1.7
- Cite (ACL):
- Vera Pavlova. 2023. Leveraging Domain Adaptation and Data Augmentation to Improve Qur’anic IR in English and Arabic. In Proceedings of ArabicNLP 2023, pages 76–88, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Domain Adaptation and Data Augmentation to Improve Qur’anic IR in English and Arabic (Pavlova, ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.arabicnlp-1.7.pdf