Beta
400392

Google Translate for Medical Texts: A Quantitative-Qualitative Analysis of English into Arabic Package Inserts Translation

Article

Last updated: 30 Dec 2024

Subjects

-

Tags

-

Abstract

Although machine translation systems like Google Translate have made great strides, there are still concerns about their use for medical translation. Medical experts, researchers, and end-users doubt that Google Translate could pose serious risks, as it may distort the original meaning or omit vital information. This study argues that Google Translate should not be perceived as risky, mainly when translating package inserts from English into Arabic, as one example of medical texts. This argument stems from a quantitative-qualitative analysis of Google Translate's translation performance, utilizing a corpus of 50 package inserts obtained from the Saudi Food and Drug Authority with their official Arabic translations. The quantitative analysis employed statistical measures to compare Google Translate's output to the official translations, assess post-editing effort, validate whether end-users can distinguish between Google Translate's output and official translations, and describe the accuracy and fluency error distribution. Simultaneously, the qualitative analysis involved a manual inspection of a random sample of 760 sentence pairs, employing Tezcan et al.'s (2018) taxonomy of translation errors to identify and categorize errors as accuracy-related or fluency-related. The results revealed significant differences between Google Translate's output and the official translations, although these disparities were predominantly attributed to stylistic variations rather than errors. The results also showed that end-users were mostly unable to discern between Google Translate's output and the official translations. Moreover, only 165 out of the 760 sentences contained errors, with the majority being fluency-related rather than accuracy-related. Google Translate's output, evaluated in this study, was generated in November 2023.

DOI

10.21608/ttaip.2024.400392

Keywords

English-Arabic Translation, Google Translate, Machine Translation, medical translation, package inserts

Authors

First Name

Rania

Last Name

Al-Sabbagh

MiddleName

-

Affiliation

Department of English, Faculty of Al-Alsun (Languages), Ain Shams University, Egypt.

Email

rsabbagh@alsun.asu.edu.eg

City

-

Orcid

0000-0002-1208-5115

Volume

6

Article Issue

1

Related Issue

52493

Issue Date

2024-12-01

Receive Date

2024-05-16

Publish Date

2024-12-15

Page Start

141

Page End

158

Print ISSN

2636-4069

Online ISSN

2735-3451

Link

https://ttaip.journals.ekb.eg/article_400392.html

Detail API

https://ttaip.journals.ekb.eg/service?article_code=400392

Order

400,392

Type

Original Article

Type Code

1,357

Publication Type

Journal

Publication Title

Textual Turnings: An International Peer-Reviewed Journal in English Studies

Publication Link

https://ttaip.journals.ekb.eg/

MainTitle

Google Translate for Medical Texts: A Quantitative-Qualitative Analysis of English into Arabic Package Inserts Translation

Details

Type

Article

Created At

30 Dec 2024