@inproceedings{alsaleh-etal-2022-lk2022,
title = "{LK}2022 at Qur{'}an {QA} 2022: Simple Transformers Model for Finding Answers to Questions from Qur{'}an",
author = "Alsaleh, Abdullah and
Althabiti, Saud and
Alshammari, Ibtisam and
Alnefaie, Sarah and
Alowaidi, Sanaa and
Alsaqer, Alaa and
Atwell, Eric and
Altahhan, Abdulrahman and
Alsalka, Mohammad",
editor = "Al-Khalifa, Hend and
Elsayed, Tamer and
Mubarak, Hamdy and
Al-Thubaity, Abdulmohsen and
Magdy, Walid and
Darwish, Kareem",
booktitle = "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",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.osact-1.14",
pages = "120--125",
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-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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="alsaleh-etal-2022-lk2022">
<titleInfo>
<title>LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an</title>
</titleInfo>
<name type="personal">
<namePart type="given">Abdullah</namePart>
<namePart type="family">Alsaleh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saud</namePart>
<namePart type="family">Althabiti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ibtisam</namePart>
<namePart type="family">Alshammari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sarah</namePart>
<namePart type="family">Alnefaie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sanaa</namePart>
<namePart type="family">Alowaidi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alaa</namePart>
<namePart type="family">Alsaqer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eric</namePart>
<namePart type="family">Atwell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdulrahman</namePart>
<namePart type="family">Altahhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="family">Alsalka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>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</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tamer</namePart>
<namePart type="family">Elsayed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamdy</namePart>
<namePart type="family">Mubarak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdulmohsen</namePart>
<namePart type="family">Al-Thubaity</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Walid</namePart>
<namePart type="family">Magdy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kareem</namePart>
<namePart type="family">Darwish</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association</publisher>
<place>
<placeTerm type="text">Marseille, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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-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.</abstract>
<identifier type="citekey">alsaleh-etal-2022-lk2022</identifier>
<location>
<url>https://aclanthology.org/2022.osact-1.14</url>
</location>
<part>
<date>2022-06</date>
<extent unit="page">
<start>120</start>
<end>125</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an
%A Alsaleh, Abdullah
%A Althabiti, Saud
%A Alshammari, Ibtisam
%A Alnefaie, Sarah
%A Alowaidi, Sanaa
%A Alsaqer, Alaa
%A Atwell, Eric
%A Altahhan, Abdulrahman
%A Alsalka, Mohammad
%Y Al-Khalifa, Hend
%Y Elsayed, Tamer
%Y Mubarak, Hamdy
%Y Al-Thubaity, Abdulmohsen
%Y Magdy, Walid
%Y Darwish, Kareem
%S 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
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F alsaleh-etal-2022-lk2022
%X 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.
%U https://aclanthology.org/2022.osact-1.14
%P 120-125
Markdown (Informal)
[LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an](https://aclanthology.org/2022.osact-1.14) (Alsaleh et al., OSACT 2022)
ACL
- Abdullah Alsaleh, Saud Althabiti, Ibtisam Alshammari, Sarah Alnefaie, Sanaa Alowaidi, Alaa Alsaqer, Eric Atwell, Abdulrahman Altahhan, and Mohammad Alsalka. 2022. LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an. In 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, pages 120–125, Marseille, France. European Language Resources Association.