Contrastive Analysis of DeepL Translation vs. Google Translate’s Performance in Rendering Academic Texts: Insights from EFL Learners

Numerous online translation tools are available today to assist academic works. This study aimed to explore English students’ insights regarding two most popular translation tools, namely DeepL Translation and Google Translate in terms of accuracy, naturalness, grammar, context understanding, terminology translation, speed, language pairs, and additional features of both tools. This research is descriptive qualitative. Thirty English students of Universitas Serambi Mekkah filled in a Likert-scale questionnaire comprising 10 items. Their responses were analyzed using Thematic Analysis. The findings showed that most participants agreed that DeepL Translation is more accurate and natural than Google Translate in rendering academic texts (58.1% and 68.8%, respectively). DeepL is also believed to have better grammar accuracy (56.3%), excels at deciphering context and language nuances (64.5%), and is faster than Google Translate (71%). Despite numerous advantages of DeepL, Google Translate is deemed superior by 68.8% participants in terms of additional features, such as voice translation, image translation, document translation, and more language pairs (61.3%), offering more advantages than DeepL. A majority of the students (67.7%) also prefer to continue using DeepL to Google Translate. This research can benefit EFL learners and academicians who require using such Neural Machine Translations for the execution of academic tasks.

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Publication Details

Journal
JURNAL SERAMBI ILMU
Published
2025-04-21
DOI
https://doi.org/10.32672/jsi.v26i1.2502
Citations
2
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
4.96
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article

Contrastive Analysis of DeepL Translation vs. Google Translate’s Performance in Rendering Academic Texts: Insights from EFL Learners

Sabrina Sabrina, Mina Fadhillah, Flora Oktaviani Chawdri, Cut Safara et al.
2 citations
JURNAL SERAMBI ILMU
Natural Language Processing Techniques
4.96
article

Contrastive Analysis of DeepL Translation vs. Google Translate’s Performance in Rendering Academic Texts: Insights from EFL Learners

Sabrina Sabrina, Mina Fadhillah, Flora Oktaviani Chawdri, Cut Safara, Lara Juniarti
article en
2 citations

Abstract

Numerous online translation tools are available today to assist academic works. This study aimed to explore English students’ insights regarding two most popular translation tools, namely DeepL Translation and Google Translate in terms of accuracy, naturalness, grammar, context understanding, terminology translation, speed, language pairs, and additional features of both tools. This research is descriptive qualitative. Thirty English students of Universitas Serambi Mekkah filled in a Likert-scale questionnaire comprising 10 items. Their responses were analyzed using Thematic Analysis. The findings showed that most participants agreed that DeepL Translation is more accurate and natural than Google Translate in rendering academic texts (58.1% and 68.8%, respectively). DeepL is also believed to have better grammar accuracy (56.3%), excels at deciphering context and language nuances (64.5%), and is faster than Google Translate (71%). Despite numerous advantages of DeepL, Google Translate is deemed superior by 68.8% participants in terms of additional features, such as voice translation, image translation, document translation, and more language pairs (61.3%), offering more advantages than DeepL. A majority of the students (67.7%) also prefer to continue using DeepL to Google Translate. This research can benefit EFL learners and academicians who require using such Neural Machine Translations for the execution of academic tasks.

JURNAL SERAMBI ILMUVol. 26(1)
Universitas Serambi Mekkah (ID)
Openalex Percentile: Top 5%
Natural Language Processing Techniques
4.96
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