Abstract
A scant number of Google Translate users and researchers continue to be skeptical of the current Google Translate's performance as a machine translation tool. As English passive voice translation often brings problems, especially when translated into Indonesian which rich of affixes, this study works to analyze the way Google Translate (MT) translates English passive voice into Indonesian and to investigate whether Google Translate (MT) can do modulation. The data in this research were in the form of clauses and sentences with passive voice taken from corpus data. It included 497 news articles from the online news platform ‘GlobalVoices,' which were processed with AntConc 3.5.8 software. The data in this research were analyzed quantitatively and qualitatively to achieve broad objectives, depth of understanding, and the corroboration. Meanwhile, the comparative methods were used to analyze both source and target texts. Through the cautious process of collecting and analyzing the data, the results showed that (1) GT (via NMT) was able to translate the English passive voice by distinguishing morphological changes in Indonesian passive voice (2) GT was able to modulate English passive voice into Indonesian base verbs and Indonesian active voice.
Highlights
As translation, both commercial and literary, is one of several activities that have been expanding in today's globalized world (Hatim & Munday, 2004), recently, humans are not the only ones who can be trusted to translate texts
Based on 1,098 sentences containing of 1,550 passive verbs which were detected by AntConc 3.5.8 (since each sentence can consist of more than one passive verb when it possessed as complex clause (e.g., |||It was Gies who saved Anne Frank's diary 1| when their secret hiding place was betrayed 2| and the family was deported to concentration camps 3|||), we have counted (1) 1,297 verbs of English passive voice were translated into Indonesian passive voice marked by prefix di- (2) 211 verbs of English passive voice were translated into Indonesian passive voice marked by prefix ter- (3) 19 verbs of English passive voice were translated into Indonesian root verb, and (4) 23 verbs of English passive voice were translated into Indonesian active voice
We conclude that Google Translate using Neural Machine Translation (NMT) was able to translate English passive voice into Indonesian by distinguishing morphological changes in Indonesian passive voice through the use of affixes
Summary
Both commercial and literary, is one of several activities that have been expanding in today's globalized world (Hatim & Munday, 2004), recently, humans are not the only ones who can be trusted to translate texts. Just as technology is constantly being developed, altered, and improved; machine translation (MT) arose and became one of the options for translating text. Specific errors on translating Source Text (ST) to Target Text (TT) are hard to predict and fix by users. Machine translation was judged to be less acceptable and inaccurate in its early days (Komeili et al, 2011). Compared with SMT, GNMT is capable of fixing translation difficulties and threats by providing a more fluent and legible translation by handling morphology and syntax five times better than SMT systems (Ramesh et al, 2021). GNMT translations were claimed to be more precise and fluent compared to translations of SMT systems.
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