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

The journal of language and linguistic studies, 2021, Vol.17 (4), p.2065 [Peer Reviewed Journal]

ISSN: 1305-578X ;EISSN: 1305-578X ;DOI: 10.52462/jlls.149

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  • Title:
    Evaluation of Google Translate in Rendering English COVID-19 Texts into Arabic
  • Author: Almahasees, Zakaryia ; Meqdadi, Samah ; Albudairi, Yousef
  • Subjects: Access to Information ; Comprehension ; Computational Linguistics ; COVID-19 ; English ; Error Patterns ; Grammar ; Internet ; Language Usage ; Pandemics ; Punctuation ; Semantics ; Semitic Languages ; Translation
  • Is Part Of: The journal of language and linguistic studies, 2021, Vol.17 (4), p.2065
  • Description: 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.
  • Publisher: Journal of Language and Linguistic Studies
  • Language: English
  • Identifier: ISSN: 1305-578X
    EISSN: 1305-578X
    DOI: 10.52462/jlls.149
  • Source: ERIC Full Text Only (Discovery)
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