Investigating Machine Translation Errors in Rendering English Literary Texts into Arabic

Author:

Tahseen Wesam Mohsen,Hussein Shifa'a Hadi

Abstract

Machine translation is a machine that employs artificial intelligence (AI) to translate texts between languages without human intervention. Machine translation approaches translate text or speech from one language to another, including the contextual, idiomatic and pragmatic issues of both different languages. The present study aims to analyze the translation of literary texts selected from different novels, plays, and poems and clarify the method for translating them from English into Arabic. This study also aims to discover machine translation errors in rendering English literary texts and clarify the translator's role in transferring the rhetorical impact on the reader who reads the (TT). This study hypothesizes that translators(students) face difficulties regarding words and structures when translating literary texts from English into Arabic because they misunderstand rhetorical devices. So they tend to use machine translations that translate literally, such as (Google Translate, Reverso translation and Bing Microsoft translation). This study adopted two models: First, Newmark's translation model (1988b), which includes two basic types of translation: semantic and communicative. This model is used widely in the analysis of literary texts. Second, Harris (2018) linguistic model theory of rhetorical question and the general purpose of the rhetorical devices to analyze the data. Finally, the study ends with the conclusions that all machine translation programs (Google Translate (GT), Reverso Translation (Reverso. T), Bing Microsoft Translation (Bing. M.T) in rendering English literary texts from English into Arabic are unacceptable and have more problems because these programs are just machines and cannot think or feel as well as all these machines renderings are meaningless and ambiguous. So Human translation is better than Machine Translation because the first uses communicative translation while the other uses semantic translation.

Publisher

Stallion Publication

Reference25 articles.

1. Abdelaal, N. M., & Alazzawie, A. (2020). Machine Translation: The Case of Arabic-English Translation of News Texts. In Theory & Practice in Language Studies ,Vol.10,(No.4),pp. pp. 408-418.

2. Abdulaal, M. A. A. D. (2022). Tracing machine and human translation errors in some literary texts with some implications for EFL translators. In Journal of Language and Linguistic Studies, Vol.18,(No. S1),pp. 176-191.‏

3. Abdullah, A. H. (2020). The Linguistic Problem-based Effect of Using Machine Translation in Mobile & Computer Apps: English and Arabic as a case study. In Al-Qalam journal,Vol.4,(No.8),pp.325-349.‏

4. Abdullah, T. A., & Thanoon, S. I. (2019). The Treatment of Lexical Ambiguity in Machine Translation. In Adab AL Rafidayn, Vol. 49,(No.79),pp. 1-12.‏

5. Abdullah, Y. N., & Nasser, L. A. (2021). Text Typology and Lexical Problems in Arabic-English Machine Translation. In Adab AL Rafidayn, Vol.51,(No.87),pp. 29-54.‏

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3