Alaa Alsaqer


2022

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LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an
Abdullah Alsaleh | Saud Althabiti | Ibtisam Alshammari | Sarah Alnefaie | Sanaa Alowaidi | Alaa Alsaqer | Eric Atwell | Abdulrahman Altahhan | Mohammad Alsalka
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection

Question answering is a specialized area in the field of NLP that aims to extract the answer to a user question from a given text. Most studies in this area focus on the English language, while other languages, such as Arabic, are still in their early stage. Recently, research tend to develop question answering systems for Arabic Islamic texts, which may impose challenges due to Classical Arabic. In this paper, we use Simple Transformers Question Answering model with three Arabic pre-trained language models (AraBERT, CAMeL-BERT, ArabicBERT) for Qur’an Question Answering task using Qur’anic Reading Comprehension Dataset. The model is set to return five answers ranking from the best to worst based on their probability scores according to the task details. Our experiments with development set shows that AraBERT V0.2 model outperformed the other Arabic pre-trained models. Therefore, AraBERT V0.2 was chosen for the the test set and it performed fair results with 0.45 pRR score, 0.16 EM score and 0.42 F1 score.