Evaluation of Google Translate in Rendering English COVID-19 Texts into Arabic

Autor/inn/en	Almahasees, Zakaryia; Meqdadi, Samah; Albudairi, Yousef
Titel	Evaluation of Google Translate in Rendering English COVID-19 Texts into Arabic
Quelle	In: Journal of Language and Linguistic Studies, 17 (2021) 4, S.2065-2080 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Almahasees, Zakaryia) ORCID (Albudairi, Yousef)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1305-578X
Schlagwörter	Internet; Translation; COVID-19; Pandemics; Error Patterns; Comprehension; English; Semitic Languages; Access to Information; Computational Linguistics; Semantics; Grammar; Punctuation; Language Usage + Suchen Sie Ihr Suchwort? Fehlertyp; Verstehen; Verständnis; English language; Englisch; Arabisch; Hebräisch; Linguistics; Computerlinguistik; Semantik; Grammatik; Interpunktion; Sprachgebrauch
Abstract	Machine Translation (MT) has the potential to provide instant translation in times of crisis. MT provides real solutions that can remove borders between people and COVID-19 information. The widespread of MT system makes it worthy of scrutinizing the capacity of the most prominent MT system, Google Translate, to deal with COVID-19 texts into Arabic. The study adopted (Costa et al., 2015a) framework in analysing the output of Google Translate output service in terms of orography, grammar, lexis, and semantics. The study's corpus was extracted from World Health Organization (WHO), United Nations Children's Emergency Fund (UNICEF), U.S. Food and Drug Administration (FDA), the Foreign, Commonwealth & Development Office (FCDO), and European Centre for Disease Prevention and Control (ECDC). The paper reveals that Google Translate committed a set of errors: semantic, grammatical, lexical, and punctuation. Such errors inhibit the intelligibility of the translated texts. It also indicates that MT might work as an aid to translate general information about COVID-19, but it is still incapable of dealing with critical information about COVID-19. The paper concludes that MT can be an effective tool, but it can never replace human translators. (As Provided).
Anmerkungen	Journal of Language and Linguistic Studies. Hacettepe Universitesi, Egitim Fakultesi B Blok, Yabanci Diller Egitimi Bolumu, Ingiliz Dili Egitimi Anabilim Dali, Ankara 06800, Turkey. e-mail: jllsturkey@gmail.com; Web site: http://www.jlls.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01