Cover Image

Arabic automatic question generation using transformer model.

Students of all ages benefit greatly from the use of questions in the evaluation process and in the improvement of their overall educational outcomes. The educational process’s adaptation, shift to online education, and the rapid growth of educational content on the internet. Institutions, schools,...

Full description

Saved in:
Bibliographic Details
Main Authors: Alhashedi, Saleh, Mohd. Suaib, Norhaida, Bakri, Aryati
Format: Conference or Workshop Item
Published: 2022
Subjects:
T Technology (General)
T58.6-58.62 Management information systems
Online Access:http://eprints.utm.my/108795/
http://dx.doi.org/10.1063/5.0199032
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.108795
record_format eprints
spelling my.utm.1087952024-12-09T06:23:42Z http://eprints.utm.my/108795/ Arabic automatic question generation using transformer model. Alhashedi, Saleh Mohd. Suaib, Norhaida Bakri, Aryati T Technology (General) T58.6-58.62 Management information systems Students of all ages benefit greatly from the use of questions in the evaluation process and in the improvement of their overall educational outcomes. The educational process’s adaptation, shift to online education, and the rapid growth of educational content on the internet. Institutions, schools, and academic organisations struggle to generate exam questions in a timely manner due to the use of the outdated method. Exam question preparation is a complex and time-consuming activity that calls for an in-depth familiarity with the subject matter and the skill to build the questions, both of which grow more challenging as text size increases. Generating questions that are both natural and relevant from a variety of text data inputs, with the possibility to provide an answer, is the goal of automatic question generation (AQG). The Arabic language has seen a small number of contributions to this problem-solving effort. Many existing works rely on Rule-based methods and input text from children’s books, stories, or textbooks to manually construct question styles. There is a lack of linguistic diversity in these models, and the tasks get increasingly difficult and time-consuming as the quantity of the text increases. When it comes to Natural Language Processing (NLP), Transformer is one of the most flexible deep-learning models. In this research, we propose a fully-automated Arabic AAQG model built on the Transformer architecture, which can take a single document of limitless length in Arabic and create N questions from it. These questions can be used in educational contexts. Our model achieves performance results with (19.12 BLEU, 23.00 METEOR, and 51.99 ROUGE-L) using mMARCO dataset. 2022-06-07 Conference or Workshop Item PeerReviewed Alhashedi, Saleh and Mohd. Suaib, Norhaida and Bakri, Aryati (2022) Arabic automatic question generation using transformer model. In: 4th International Conference on Green Engineering and Technology 2022, IConGETech 2022, 17 November 2022 - 18 November 2022, Seoul, South Korea. http://dx.doi.org/10.1063/5.0199032
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
T58.6-58.62 Management information systems
spellingShingle T Technology (General)
T58.6-58.62 Management information systems
Alhashedi, Saleh
Mohd. Suaib, Norhaida
Bakri, Aryati
Arabic automatic question generation using transformer model.
description Students of all ages benefit greatly from the use of questions in the evaluation process and in the improvement of their overall educational outcomes. The educational process’s adaptation, shift to online education, and the rapid growth of educational content on the internet. Institutions, schools, and academic organisations struggle to generate exam questions in a timely manner due to the use of the outdated method. Exam question preparation is a complex and time-consuming activity that calls for an in-depth familiarity with the subject matter and the skill to build the questions, both of which grow more challenging as text size increases. Generating questions that are both natural and relevant from a variety of text data inputs, with the possibility to provide an answer, is the goal of automatic question generation (AQG). The Arabic language has seen a small number of contributions to this problem-solving effort. Many existing works rely on Rule-based methods and input text from children’s books, stories, or textbooks to manually construct question styles. There is a lack of linguistic diversity in these models, and the tasks get increasingly difficult and time-consuming as the quantity of the text increases. When it comes to Natural Language Processing (NLP), Transformer is one of the most flexible deep-learning models. In this research, we propose a fully-automated Arabic AAQG model built on the Transformer architecture, which can take a single document of limitless length in Arabic and create N questions from it. These questions can be used in educational contexts. Our model achieves performance results with (19.12 BLEU, 23.00 METEOR, and 51.99 ROUGE-L) using mMARCO dataset.
format Conference or Workshop Item
author Alhashedi, Saleh
Mohd. Suaib, Norhaida
Bakri, Aryati
author_facet Alhashedi, Saleh
Mohd. Suaib, Norhaida
Bakri, Aryati
author_sort Alhashedi, Saleh
title Arabic automatic question generation using transformer model.
title_short Arabic automatic question generation using transformer model.
title_full Arabic automatic question generation using transformer model.
title_fullStr Arabic automatic question generation using transformer model.
title_full_unstemmed Arabic automatic question generation using transformer model.
title_sort arabic automatic question generation using transformer model.
publishDate 2022
url http://eprints.utm.my/108795/
http://dx.doi.org/10.1063/5.0199032
_version_ 1818834050081619968
score 13.222552

Similar Items