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The impact of contextual understanding on neural machine translation accuracy: A case study of Indonesian cultural idioms in English translation

Englisia : Journal of Language, Education, and Humanities · Vol. 12 No. 1 (2024) · hlm. 223–236

DOI
10.22373/ej.v12i1.24433
ISSN
2527-6484, 2339-2576
Terbit
2024-10-31
Penerbit
Universitas Islam Negeri Ar-Raniry Banda Aceh
Bahasa
eng

Abstrak

The translation of Indonesian idiomatic expressions into English using Neural Machine Translation (NMT) systems presents significant challenges due to the intricate nature of idiomatic language. Idioms are culturally embedded constructs conveying meanings different from their literal interpretations. This study explores the effectiveness of NMT systems in capturing and translating Indonesian idiomatic expressions into English. Using a qualitative approach, 150 diverse Indonesian idioms were evaluated through Google Translate and DeepL. Each idiom was assessed for semantic accuracy, syntactic coherence, and contextual fidelity. Qualitative analysis provided a comprehensive evaluation of translation quality. Findings reveal significant challenges for NMT systems in translating idiomatic expressions. Both Google Translate and DeepL show strengths in some areas, but also have limitations. While they generally capture literal meanings, they often miss metaphorical nuances and cultural connotations. Syntactic errors such as incorrect word order and tense inconsistencies are common, especially in complex idioms. In addition, contextual fidelity analysis shows that NMT systems struggle with contextual appropriateness and pragmatic usage, resulting in translations lacking cultural sensitivity and relevance. These challenges highlight the need for improved algorithms to better interpret and translate idiomatic language across cultural contexts. Accurate idiom translation is crucial for language learners to understand cultural nuances and idiomatic usage. Enhancing the capabilities of NMT systems requires refining algorithms, developing context-aware models, and expanding training datasets with diverse idiomatic expressions. Overcoming these challenges will advance machine translation capabilities and improve cross-cultural communication and language learning experiences.

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  • Politeknik Negeri Semarang
  • Sebelas Maret University
  • Universitas Muhammadiyah Riau
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Muji SetiyoAfiliasi tidak diketahui2
Yusuf Dewantoro HerlambangAfiliasi tidak diketahui2
Wahyu SulistiyoAfiliasi tidak diketahui2
Nanang ApriandiAfiliasi tidak diketahui2
Marliyati MarliyatiAfiliasi tidak diketahui2
Jin Cherng ShyuAfiliasi tidak diketahui2
Siti MukminatunAfiliasi tidak diketahui2
Tjetjep Rohendi RohidiAfiliasi tidak diketahui1
Iswidayati IswidayatiAfiliasi tidak diketahui1
Dharsono Sony KartikaAfiliasi tidak diketahui1
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Henita HenitaAfiliasi tidak diketahui1
Sibakhul Milad Malik HidayatullohAfiliasi tidak diketahui1
Septiantar Tebe NursaputroPoliteknik Negeri Semarang1
Wawan PurwantoAfiliasi tidak diketahui1
Muhammad Latifur RochmanAfiliasi tidak diketahui1
Nur Hidayanto Pancoro Setyo PutroAfiliasi tidak diketahui1
Yasir A. AlsamiriAfiliasi tidak diketahui1
G.A.P. SupriantiAfiliasi tidak diketahui1
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Pratomo WidodoAfiliasi tidak diketahui1
Nur Hidayanto Pancoro Setyo PutroAfiliasi tidak diketahui1
Ashadi AshadiSebelas Maret University1
Amrih Bekti UtamiAfiliasi tidak diketahui1
Mulyanto MulyantoUniversitas Muhammadiyah Riau1
Riski Utma DonaAfiliasi tidak diketahui1
Binar WinantakaAfiliasi tidak diketahui1
Rozanah Katrina HerdaAfiliasi tidak diketahui1
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Sumber metadata: OAI-PMH (metadata jurnal) https://jurnal.ar-raniry.ac.id/index.php/englisia/oai, pertama di-harvest 7 Nov 2026, terakhir terlihat 7 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">The impact of contextual understanding on neural machine translation accuracy: A case study of Indonesian cultural idioms in English translation</dc:title>
	<dc:creator xml:lang="en">Dewayanti, Desakh Putu Setyalika Putri</dc:creator>
	<dc:creator xml:lang="en">Margana, Margana</dc:creator>
	<dc:subject xml:lang="en">Translation accuracy</dc:subject>
	<dc:subject xml:lang="en">Neural machine translation</dc:subject>
	<dc:subject xml:lang="en">Cultural idioms</dc:subject>
	<dc:description xml:lang="en">The translation of Indonesian idiomatic expressions into English using Neural Machine Translation (NMT) systems presents significant challenges due to the intricate nature of idiomatic language. Idioms are culturally embedded constructs conveying meanings different from their literal interpretations. This study explores the effectiveness of NMT systems in capturing and translating Indonesian idiomatic expressions into English. Using a qualitative approach, 150 diverse Indonesian idioms were evaluated through Google Translate and DeepL. Each idiom was assessed for semantic accuracy, syntactic coherence, and contextual fidelity. Qualitative analysis provided a comprehensive evaluation of translation quality. Findings reveal significant challenges for NMT systems in translating idiomatic expressions. Both Google Translate and DeepL show strengths in some areas, but also have limitations. While they generally capture literal meanings, they often miss metaphorical nuances and cultural connotations. Syntactic errors such as incorrect word order and tense inconsistencies are common, especially in complex idioms. In addition, contextual fidelity analysis shows that NMT systems struggle with contextual appropriateness and pragmatic usage, resulting in translations lacking cultural sensitivity and relevance. These challenges highlight the need for improved algorithms to better interpret and translate idiomatic language across cultural contexts. Accurate idiom translation is crucial for language learners to understand cultural nuances and idiomatic usage. Enhancing the capabilities of NMT systems requires refining algorithms, developing context-aware models, and expanding training datasets with diverse idiomatic expressions. Overcoming these challenges will advance machine translation capabilities and improve cross-cultural communication and language learning experiences.</dc:description>
	<dc:publisher xml:lang="en">Universitas Islam Negeri Ar-Raniry Banda Aceh</dc:publisher>
	<dc:date>2024-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jurnal.ar-raniry.ac.id/index.php/englisia/article/view/24433</dc:identifier>
	<dc:identifier>10.22373/ej.v12i1.24433</dc:identifier>
	<dc:source xml:lang="en">Englisia : Journal of Language, Education, and Humanities; Vol. 12 No. 1 (2024); 223-236</dc:source>
	<dc:source xml:lang="id">Englisia Journal; Vol 12 No 1 (2024); 223-236</dc:source>
	<dc:source>2527-6484</dc:source>
	<dc:source>2339-2576</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jurnal.ar-raniry.ac.id/index.php/englisia/article/view/24433/10106</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Desakh Putu Setyalika Putri Dewayanti</dc:rights>
</oai_dc:dc>