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Webinar: “Arabic Reading Comprehension on the Holy Qur’an using CL-AraBERT” with Dr. Rana Malhas, University of Leeds, February 24, 2023 @ 7:00 AM ET
February 24, 2023 @ 7:00 am - 8:00 am
School of Computing Colloquium, University of Leeds, Friday 24.2.23 12:00 (UK)
Guest speaker: Dr Rana Malhas, Qatar University
Title: Arabic Reading Comprehension on the Holy Qur’an using CL-AraBERT
Venue: Teams Click here to join the meeting
Abstract: In this talk, we tackle the problem of machine reading comprehension (MRC) on the Holy Qur’an to address the lack of Arabic datasets and systems for this important task. We construct QRCD as the first Qur’anic Reading Comprehension Dataset, composed of 1,337 question passage-answer triplets for 1,093 question-passage pairs, of which 14% are multi-answer questions. We then introduce CLassical-AraBERT (CL-AraBERT for short), a new AraBERTbased pre-trained model, which is further pre-trained on about 1.0B-word Classical Arabic (CA) dataset, to complement the Modern Standard Arabic (MSA) resources used in pre-training the initial model, and make it a better fit for the task. Finally, we leverage cross-lingual transfer learning from MSA to CA, and fine-tune CL-AraBERT as a reader using two MSA-based MRC datasets followed by our QRCD dataset to constitute the first (to the best of our knowledge) MRC system on the Holy Qur’an. To evaluate our system, we introduce Partial Average Precision (𝑝𝐴𝑃 ) as an adapted version of the traditional rank-based Average Precision measure, which integrates partial matching in the evaluation over multi-answer and single-answer MSA questions. Adopting two experimental evaluation setups (hold-out and cross validation (CV)), we empirically show that the fine-tuned CL-AraBERT reader model significantly outperforms the baseline fine-tuned AraBERT reader model by 6.12 and 3.75 points in 𝑝𝐴𝑃 scores, in the hold-out and CV setups, respectively. To promote further research on this task and other related tasks on Qur’an and Classical Arabic text, we make both the QRCD dataset and the pre-trained CL-AraBERT model publicly available.