Evaluation of Google Image Translate in Rendering Arabic Signage into English

Almahasees, Zakaryia; Mahmoud, Sameh

doi:10.5430/wjel.v12n1p185

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…2023). Al Mahasees (2020) contends that Machine Translation (hereafter, MT) and the internet allow people to understand other languages instantaneously from the translated version of a source text to the target text. In particular, MT adopts multilingual dictionaries, corpus-based, neural networks and diverse algorithm-based processes to translate a language into another (Al Mahasees, 2020;Tsai, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Al Mahasees (2020) contends that Machine Translation (hereafter, MT) and the internet allow people to understand other languages instantaneously from the translated version of a source text to the target text. In particular, MT adopts multilingual dictionaries, corpus-based, neural networks and diverse algorithm-based processes to translate a language into another (Al Mahasees, 2020;Tsai, 2019). MT emerges as a result of revolutionized global communication leading to the employment of digitally-driven communication in which the development of natural language processing (NLP) studies and media transformation remains burgeoning.…”

Section: Introductionmentioning

confidence: 99%

“…(2023) argue that MT has enhanced the translation quality results viewed from coherence, cohesion, nativelike translation output, and loyalty to the source texts. This makes MT more necessary in a range of disciplines, such as science, legal, commercial, business, administration, engineering, education, health, journalism and so forth (Al Mahasees, 2020).…”

Section: Introductionmentioning

confidence: 99%

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English-Indonesian Translation in a Selected Chapter of Ferreira’s Critical Theory: Evaluating the Google Translate Output

Abdullah,

Siregar,

Nurlia

et al. 2024

jels Untirta

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In this digital era, translation has undergone a radical paradigmatic shift from traditional to automated practices in terms of technological, pedagogical, empirical and economic perspectives, such as the emergence of Machine Translation (MT). Unfortunately, scrutiny accentuating the evaluation of GT output in the English-Indonesian translation setting remains under-researched. Hence, this study aimed at poring over how the English-Indonesian translation in a selected chapter of Ferreira's critical theory was represented from the GT output. The corpus of this study was a selected chapter of a book entitled International Relations Theory edited by Stephen McGlinchey, Rosie Walters and Christian Scheinpflug (McGlinchey, et. al., 2017), namely chapter 6 in part 1 Critical Theory (Marcos Farias Ferreira) (Ferreira, 2017). The corpus was collected through document analysis and analyzed with Baker's translation equivalence framework (Baker, 2018) and thematic analysis (TA) (Braun & Clarke, 2006). The findings unveiled that GT output represented English-Indonesian translation in five prominent themes, viz. inappropriate word level equivalence, grammatical equivalence and lexical cohesion in the English-Indonesian translated text, decontextualized pragmatic equivalence in Indonesian as the target language, syntactically disordered English-Indonesian translated words, literally translated Indonesian as the target language, and accepted equivalence of English-Indonesian translation. Pedagogically, this study suggests that a combination strategy of GT-based translation and human translation can be a breakthrough to reach the translation quality, namely accuracy, naturalness and readability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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English-Indonesian Translation in a Selected Chapter of Ferreira’s Critical Theory: Evaluating the Google Translate Output

Abdullah,

Siregar,

Nurlia

et al. 2024

jels Untirta

View full text Add to dashboard Cite

show abstract

“…Previously, several studies have been conducted to examine translation errors in machine translation. Among them have analyzed common types of translation errors (Abu-Ayyash, 2017; Almahasees & Mahmoud, 2022;Jufriadi et al, 2022), In the study, a qualitative approach was employed to identify common errors in machine translation. The qualitative approach utilized in the research aimed to delve into and comprehend the detailed aspects of these errors, thereby providing a more profound insight into the performance of machine translation.…”

Section: Introductionmentioning

confidence: 99%

Mapping The Landscape of Machine Translation Error Research: A Bibliometric Review (1980-2023)

Siregar,

Al Farisi,

Naser

2024

Buletin Al-Turas

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PurposeDespite machine translation enhances communication process, it faces problems to guarantee accurate translation. Efforts to address these problems have been done by many researchers. The study aimed to reveal a comprehensive bibliometric review considering research trends, publishing contributions, and development in 1980-2023.MethodThis qualitative study made uses of a descriptive research design engaging some research on machine translation errors. This study employed bibliometric analysis to comprehensively examine the landscape of previous research on machine translation errors from 1980 to 2023. Based on a Scopus database, 138 publications were initially identified and subsequently refined to 98 articles that were directly relevant to the research topic. The bibliometric analysis highlighted the intriguing patterns and trends in this field.Results/FindingsThe findings revealed that the year 2021 witnessed the highest number of article publications, totaling 13 articles, indicating a growing interest in the topic. Moreover, a notable citation trend emerged in 2011, with 103 citations, signifying the significance and influence of research related to translation errors in machine translation. China notably emerged as the leading country to publish articles on this subject, with 23 publications and 28 collaborative links established with other countries. Among the 98 journals that published research in this domain, 45 of them were classified as Q1 journals, signifying their high impact and scholarly reputation.ConclusionThe three main aspects comprising errors and human factors, exploration of machine translation, machine learning, and translation languages, and investigations within the field of computational linguistics collectively contributed to a deeper understanding of the complexities and challenges associated with translation errors in machine translation.

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Artificial Intelligence for Medical Image Analysis: An Opportunity for Automation

Nwaiwu,

Park

2024

Artificial Intelligence and the Perspective of Autonomous Surgery

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Evaluation of Google Image Translate in Rendering Arabic Signage into English

Cited by 5 publications

References 12 publications

English-Indonesian Translation in a Selected Chapter of Ferreira’s Critical Theory: Evaluating the Google Translate Output

English-Indonesian Translation in a Selected Chapter of Ferreira’s Critical Theory: Evaluating the Google Translate Output

Mapping The Landscape of Machine Translation Error Research: A Bibliometric Review (1980-2023)

Artificial Intelligence for Medical Image Analysis: An Opportunity for Automation

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