Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation

Arabic fixed expressions (AFEs) have a symbolic figurative meaning that cannot be predicted from the individual components or the literal meanings of constituent parts. Many users nowadays rely on NMT systems to translate AFEs since these systems became an essential part of the process of translatio...

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Main Author:	Aldelaa, Abdullah Sanad Mohammad
Format:	Thesis
Language:	English
Published:	2022
Subjects:	P1-1091 Philology Linguistics(General)
Online Access:	http://eprints.usm.my/58218/1/ABDULLAH%20SANAD%20MOHAMMAD%20ALDELAA%20-%20TESIS.pdf
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id	my-usm-ep.58218
record_format	uketd_dc
spelling	my-usm-ep.582182023-04-26T06:58:06Z Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation 2022-02 Aldelaa, Abdullah Sanad Mohammad P1-1091 Philology. Linguistics(General) Arabic fixed expressions (AFEs) have a symbolic figurative meaning that cannot be predicted from the individual components or the literal meanings of constituent parts. Many users nowadays rely on NMT systems to translate AFEs since these systems became an essential part of the process of translation. However, Neural Machine translation (NMT) creates a sort of difficulty and challenge to those who do not have enough experience in the translation of fixed expressions like proverbs and idioms. These translation systems might create a gap between Source Language (SL) and Target Language (TL). The study investigates the syntactic structure of the Arabic Fixed Expressions (AFEs), which allows a high level of accuracy to be achieved in its translation using selected Neural Machine Translation (NMT) systems. Also, this study seeks to identify the most efficient system to render the meaning of the (AFEs) extracted from the the three novels. Moreover, the study examines problems that hinder NMT systems when translating Arabic fixed expressions into English. In order to achieve the aim of this study, the researcher select samples of Arabic proverbs and idioms from three literary texts Banat AlRiyadh (Girls of AlRiyadh) by Rajaa Alsanea, Mawsim al-Hijrah ilâ al-Shamâl (Season of Migration to the North) and by Tayeb Saleh, and Suqut al-lmam (The Fall of the Imam) by Nawal El Saadawi) to be translated automatically by NMT systems in order to measure and evaluate the accuracy level of these software in particular. The researcher inserts texts containing a certain number of these expressions and analyze the obtained results after these systems translate the texts from Arabic into English. 2022-02 Thesis http://eprints.usm.my/58218/ http://eprints.usm.my/58218/1/ABDULLAH%20SANAD%20MOHAMMAD%20ALDELAA%20-%20TESIS.pdf application/pdf en public phd doctoral Perpustakaan Hamzah Sendut Pusat Pengajian Bahasa Literasi & Terjemahan
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	P1-1091 Philology Linguistics(General)
spellingShingle	P1-1091 Philology Linguistics(General) Aldelaa, Abdullah Sanad Mohammad Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
description	Arabic fixed expressions (AFEs) have a symbolic figurative meaning that cannot be predicted from the individual components or the literal meanings of constituent parts. Many users nowadays rely on NMT systems to translate AFEs since these systems became an essential part of the process of translation. However, Neural Machine translation (NMT) creates a sort of difficulty and challenge to those who do not have enough experience in the translation of fixed expressions like proverbs and idioms. These translation systems might create a gap between Source Language (SL) and Target Language (TL). The study investigates the syntactic structure of the Arabic Fixed Expressions (AFEs), which allows a high level of accuracy to be achieved in its translation using selected Neural Machine Translation (NMT) systems. Also, this study seeks to identify the most efficient system to render the meaning of the (AFEs) extracted from the the three novels. Moreover, the study examines problems that hinder NMT systems when translating Arabic fixed expressions into English. In order to achieve the aim of this study, the researcher select samples of Arabic proverbs and idioms from three literary texts Banat AlRiyadh (Girls of AlRiyadh) by Rajaa Alsanea, Mawsim al-Hijrah ilâ al-Shamâl (Season of Migration to the North) and by Tayeb Saleh, and Suqut al-lmam (The Fall of the Imam) by Nawal El Saadawi) to be translated automatically by NMT systems in order to measure and evaluate the accuracy level of these software in particular. The researcher inserts texts containing a certain number of these expressions and analyze the obtained results after these systems translate the texts from Arabic into English.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Aldelaa, Abdullah Sanad Mohammad
author_facet	Aldelaa, Abdullah Sanad Mohammad
author_sort	Aldelaa, Abdullah Sanad Mohammad
title	Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
title_short	Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
title_full	Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
title_fullStr	Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
title_full_unstemmed	Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation
title_sort	translatability of arabic fixed expressions in three novels into english using neural machine translation
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Bahasa Literasi & Terjemahan
publishDate	2022
url	http://eprints.usm.my/58218/1/ABDULLAH%20SANAD%20MOHAMMAD%20ALDELAA%20-%20TESIS.pdf
_version_	1776101213903781888

Translatability Of Arabic Fixed Expressions In Three Novels Into English Using Neural Machine Translation

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