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All Journal Buletin Al-Turas CaLLs : Journal of Culture, Arts, Literature, and Linguistics
Mentari, Diana
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Arts Humanities Education Languange, Linguistic, Communication & Media Social Sciences

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Machine Translation Shifts on The Meaning Equivalence of Culture Sentence and Illocutionary Speech Acts: Back-Translation Mentari, Diana; Al Farisi, Mohamad Zaka; Maulani, Hikmah
CaLLs (Journal of Culture, Arts, Literature, and Linguistics) Vol 10, No 1 (2024): CaLLs, June 2024
Publisher : Fakultas Ilmu Budaya, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/calls.v10i1.15168

Abstract

ABSTRACT Machine translation, as an important tool in the field of artificial intelligence, has made significant progress in recent years. One of the most difficult aspects of machine translation is capturing the cultural nuances and meanings in illocutionary speech acts. Indonesian short story anthologies are an interesting example in this regard, as they contain distinctive and meaning-rich cultural terms. This study aims to explore the dynamic equivalence in machine translation shifts of sentences containing cultural terms and speech acts in Indonesian short story anthologies, as well as to evaluate the translation equivalence and success in transferring the cultural meaning. This study uses a descriptive qualitative research design with a content analysis approach. The analysis shows that the sentences in the short story anthology containing cultural terms and illocutionary speech acts experience translation shifts up to 83 data in the form of level and category shifts, which have an impact on the dynamic equivalence of translation. Google Translate can capture most of the cultural meanings, but there are still translation inequalities. Although machine translation technology such as Google Translate continues to evolve, further efforts are needed to capture cultural nuances and the ability to distinguish Arabic words without harakat (diacritical marks). Human involvement is still needed to ensure the accuracy of the translation results, especially in the cultural context and meaning of illocutionary speech acts. Keywords: Machine Translation, Cultural Terms, Short Story Anthology, Translation Shift, Dynamic Equivalence.
Evaluating Machine Translation of Cultural Terms: Readability Comparison Between Google and Yandex Mentari, Diana
Buletin Al-Turas Vol 31, No 1 (2025): Buletin Al-Turas
Publisher : Fakultas Adab dan Humaniora, UIN Syarif Hidayatullah Jakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/bat.v31i1.44469

Abstract

Purpose This study aimed to analyze the readability of Google Translate (GT) and Yandex Translate (YT) translation results on dialogue texts containing cultural terms from the book Antologi Cerita Anak Indonesia (ACAI). This study evaluated the effectiveness of the Neural Machine Translation (NMT) approach in GT and the Hybrid Machine Translation (HMT) approach in YT in conveying text meanings clearly and comprehensibly to readers.Method This research employed a cloze test involving 28 participants aged 18-24 years, along with a questionnaire to assess user preferences regarding GT and YT translation results. Text readability was analyzed using the Flesch-Kincaid Grade Level and Gunning Fog Index to measure the linguistic complexity of the translations.Results/Findings The study results show that GT's readability reaches 81.1%, while YT's readability is 74.5%, both categorized as the independent level according to Rankin & Culhane's (1969) criteria. Additionally, 80% of the 20 questionnaire respondents stated that GT's translations were clearer than those of YT. Analysis using the Flesch-Kincaid Grade Level and Gunning Fog Index shows that the readability level of GT and YT translations is classified as advanced suitable for readers with a minimum education level equivalent to a bachelor's degree.Conclusion This study showed that GT has a higher readability level than YT, which might be because of its use of NMT, producing more natural sentence structures. Meanwhile, YT, which also relied on SMT, translates based on statistical patterns, making its translations more rigid. Although both systems could produce comprehensible translations, they still struggled with accurately translating cultural terms without additional context. Therefore, human involvement remained essential to improving accuracy and contextual appropriateness in machine translation.
Evaluating Machine Translation of Cultural Terms: Readability Comparison Between Google and Yandex Mentari, Diana
Buletin Al-Turas Vol. 31 No. 1 (2025): Buletin Al-Turas
Publisher : Fakultas Adab and Humaniora, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/bat.v31i1.44469

Abstract

Purpose This study aimed to analyze the readability of Google Translate (GT) and Yandex Translate (YT) translation results on dialogue texts containing cultural terms from the book Antologi Cerita Anak Indonesia (ACAI). This study evaluated the effectiveness of the Neural Machine Translation (NMT) approach in GT and the Hybrid Machine Translation (HMT) approach in YT in conveying text meanings clearly and comprehensibly to readers.Method This research employed a cloze test involving 28 participants aged 18-24 years, along with a questionnaire to assess user preferences regarding GT and YT translation results. Text readability was analyzed using the Flesch-Kincaid Grade Level and Gunning Fog Index to measure the linguistic complexity of the translations.Results/Findings The study results show that GT's readability reaches 81.1%, while YT's readability is 74.5%, both categorized as the independent level according to Rankin & Culhane's (1969) criteria. Additionally, 80% of the 20 questionnaire respondents stated that GT's translations were clearer than those of YT. Analysis using the Flesch-Kincaid Grade Level and Gunning Fog Index shows that the readability level of GT and YT translations is classified as advanced suitable for readers with a minimum education level equivalent to a bachelor's degree.Conclusion This study showed that GT has a higher readability level than YT, which might be because of its use of NMT, producing more natural sentence structures. Meanwhile, YT, which also relied on SMT, translates based on statistical patterns, making its translations more rigid. Although both systems could produce comprehensible translations, they still struggled with accurately translating cultural terms without additional context. Therefore, human involvement remained essential to improving accuracy and contextual appropriateness in machine translation.
Co-Authors Hikmah Maulani