Alsaleh, A, Althabiti, S, Alshammari, I et al. (6 more authors) (2022) LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an. In: Proceedings of the OSACT 2022 Workshop. OSACT'2022 Open-Source Arabic Corpora and Processing Tools, 20 Jun 2022, Marseille, France. ELRA European Language Resources Association , pp. 120-125. ISBN 979-10-95546-75-7
Abstract
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-trainer 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © European Language Resources Association (ELRA). This is an open access article under the terms of the Creative Commons Attribution License CC-BY-NC-4.0 |
Keywords: | NLP, Simple-Transformers, AraBERT, Question-Answering, Quran |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jul 2022 12:41 |
Last Modified: | 25 Jul 2022 12:41 |
Published Version: | http://www.lrec-conf.org/proceedings/lrec2022/work... |
Status: | Published |
Publisher: | ELRA European Language Resources Association |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189377 |