The Error Analysis of Google Translate and Bing Translator in Translating Indonesian Folklore

Jufriadi,; Asokawati, Amalia; Thayyib, Magfirah

doi:10.24256/foster-jelt.v3i2.89

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…Fourth, Yudiarti (2019) states that the use of Microsoft Translator is still not feasible to be used to translate texts without involving translation experts by using suitable proofreading and editing. Fifth, Thayyib (2022) states that the incorrect term is the most common error identified in translations produced by Google Translate and Bing Translator with 152 errors. The majority of the wrong terms came in the translation of adjectives and adverbs, which Google Translate and Bing Translator rendered as nouns.…”

Section: Several Previous Studies Have Been Conducted Related To Onli...mentioning

confidence: 99%

Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result

Fitria

2023

Al-Lisan

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This research simulates the use of Google Translate and Microsoft Translator in translating Indonesian into English abstract and analyzes errors of translation results in the aspect of grammar and spelling using Grammarly Premium. This research is descriptive qualitative. The result analysis shows several writing issues (errors) of translation results using Microsoft Translator (MT), Google Translate (GT), and DeepL Translator (DT) in the aspect of clarity, correctness (correction), and delivery. First, there are 26 writing issues from MT including 1) 4 clarity (4 unclear sentences), 2) 21 correctness (2 comma misuse within clauses, 4 punctuation in compound/complex sentences, 5 wrong or missing prepositions, 3 incorrect verb forms, 2 determiner use, 2 conjunction use, 2 confusing words, 1 improper formatting), 3) 1 delivery (1 incomplete sentences). Second, there are 25 writing issues from GT including 1) 6 clarity (4 unclear sentences and 2 wordy sentences), 2) 17 correctness (1 misplaced word or phrase, 1 text inconsistencies, 1 comma misuse within clauses, 4 punctuation in compound/complex sentences, 3 wrong or missing prepositions, 3 incorrect verb forms, 2 conjunction use, 1 confusing word, and 1 determiner use.). 3) 2 delivery (2 incomplete sentences). Third, there are 10 writing issues from DT including 3 comma misuse within clauses, 1 incorrect noun number, 1 faulty subject-verb agreement, 1 wrong or missing preposition, 1 improper formatting, 1 misuse of modifiers, 1 conjunction use, and 1 confused word. Online translator including MT, GT, and DT may help in translating foreign language texts, but it does not always provide accurate translations. Thus, whether consciously or unconsciously, each translation machine contains its traits, whether directly or indirectly, human participation cannot be separated from machine translation technology. However, no computer translator can provide translations with the same precision as humans. It means that they are still a machine that is not able to match human capabilities equipped with feelings and thinking processes that describe them.

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Section: Several Previous Studies Have Been Conducted Related To Onli...mentioning

confidence: 99%

Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result

Fitria

2023

Al-Lisan

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“…In the contemporary era of globalization, machine translation has emerged as a crucial facilitator for cross-language communication (Araújo et al, 2020). Prominent machine translation programs, such as Google Translate and Bing Microsoft Translator, have witnessed a notable surge in popularity in recent times (Jufriadi et al, 2022). This translation technology empowers users to promptly and precisely convert text from one language to another (ElShiekh, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…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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“…It was discovered, based on the study's findings and discussion, that Google Translate produced reasonably accurate translations of Indonesian text into English. The second research was conducted by (Jufriadi et al, 2022) entitled The Error Analysis of Google Translate and Bing Translator in Translating Indonesian Folklore. The research found that the most common type of error made by Google Translate and Bing Translator in translating Indonesian folklore "Putri Tandampalik" and "Sigarlaki dan Limbat" into English is the incorrect word.…”

Section: Introductionmentioning

confidence: 99%

Translation of the Word “Bagus” in Folklore Asal Mula Kata Babah Using Google Translate

Zahra

2023

IJEAL

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Translating cultural terms is a study that should be explored further, particularly in translating cultural words using machine translation. Google Translate is a machine translation that was widely used by people to translate one language into another language. This study only focused on the cultural word "bagus" as the source data, and it was translated using machine translation, namely Google Translate from Indonesian to English. This study aimed to examine how Google Translate as a machine translation rendering the cultural word "bagus" in the folklore "Asal Mula Kata Babah" from Indonesian to English and also to find the appropriate translation strategy for translating the word "bagus" as a cultural word from Indonesian to English by using Google Translate. This study used the descriptive-qualitative method. In addition, this study used Newmark (1988) procedures of translation and Suryawinata & Hariyanto (2016) theory of contextual meaning. The result found that there were 8 data consisting of the word "bagus" and later it was translated using Google Translate. The result of this study also discovered that Google Translate accurately translated one data, and likewise with the contextual meaning. Also, the other data were not translated accurately and did not follow the contextual meaning. Moreover, this study obtained only two translation strategies: literal translation with 7 data and cultural equivalents with 1 data.

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The Error Analysis of Google Translate and Bing Translator in Translating Indonesian Folklore

Cited by 3 publications

References 1 publication

Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result

Performance of Google Translate, Microsoft Translator, and DeepL Translator: Error Analysis of Translation Result

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

Translation of the Word “Bagus” in Folklore Asal Mula Kata Babah Using Google Translate

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