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Journal articles on the topic 'Translation by DEEPL'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Dora Nurcahyani, Feby, Dimas Adika, and Widyasari. "Translating the Untranslatable: DeepL and ChatGPT on Academic Idioms." Linguistik Terjemahan Sastra (LINGTERSA) 5, no. 2 (2024): 85–93. http://dx.doi.org/10.32734/lingtersa.v5i2.15086.

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This research explores the efficacy of two prominent machine translation platforms, DeepL and ChatGPT, in translating academic idioms from English to Indonesian. Academic idioms, situated between discipline-specific jargon and universally understood expressions, pose a challenge for existing translation systems, particularly those rooted in Neural Machine Translation (NMT). The study employs a qualitative descriptive methodology, focusing on translation precision and naturalness, with bilingual experts evaluating translations through a questionnaire, focusing on translation precision and natur

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Telaumbanua, Yasminar Amaerita, Angelin Marpaung, Ceria Putri Damai Gulo, Dodi Kardo Wijaya Waruwu, Erika Zalukhu, and Novita Purnawirati Zai. "Analysis of Two Translation Applications : Why is DeepL Translate more accurate than Google Translate?" Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 1 (2024): 82–86. http://dx.doi.org/10.59934/jaiea.v4i1.560.

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DeepL Translate and Google Translate are two leading machine translation tools. The focus of this research is to analyze the accuracy of translation results provided by DeepL Translation and Google Translation specifically in translating English to Indonesian. This research used a qualitative approach of document analysis and interviews. The advanced neural machine translation technology of DeepL, by utilizing extensive data, enables it to recognize language nuances and provide contextually accurate translations. In contrast, Google Translate, despite having grown to be supported by hundreds o

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Karabayeva, Irina, and Anna Kalizhanova. "Evaluating machine translation of literature through rhetorical analysis." Journal of Translation and Language Studies 5, no. 1 (2024): 1–9. http://dx.doi.org/10.48185/jtls.v5i1.962.

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This paper looks at how well ChatGPT and DeepL, two AI tools, translate literary works. Not only can ChatGPT translate text, but it can also carry out other jobs. DeepL is a service that performs computer translation and uses neural networks. The paper looks at how ChatGPT and DeepL translate books, poems, and dialogues compared to translations done by humans. The paper also talks about the pros and cons of using machine translation for literary reasons, including issues of creativity, style, and adapting to different cultures. The paper uses both new and old studies on machine translation tec

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Uryadi, Uryadi, Rina Agustin, and Azka Adilya Rachma. "Translation of Kompositum of German Legal Texts Into Indonesian: A Comparative Analysis Between Google Translate And DeepL." Interference: Journal of Language, Literature, and Linguistics 6, no. 1 (2025): 11. https://doi.org/10.26858/interference.v6i1.70473.

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Abstract. This study examines the translation of kompositum (compound words) in German legal texts into Indonesian through a qualitative comparative analysis of two machine translation tools: Google Translate and DeepL. The analysis is conducted based on three key criteria: accuracy, acceptability, and readability. Selected legal texts containing complex kompositum, such as "ärztliche Bescheinigung für Jugendliche” and “Berufsausbildungsvertrag”, are analyzed to evaluate the performance of each tool in conveying meaning, contextual appropriateness, and linguistic fluency. The findings reveal t

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Sabrina, Sabrina, Mina Fadhillah, Flora Oktaviani Chawdri, Cut Safara, and Lara Juniarti. "Contrastive Analysis of DeepL Translation vs. Google Translate’s Performance in Rendering Academic Texts: Insights from EFL Learners." Jurnal Serambi Ilmu 26, no. 1 (2025): 73–82. https://doi.org/10.32672/jsi.v26i1.2502.

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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 find

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Stepanov, Ievgenii M., та Zhongzheng Wang. "STRATEGIES FOR TRANSLATING CULTURALLY LOADED INFORMATION FROM CHINESE IN THE CONTEXT OF THE USE OF ARTIFICIAL INTELLIGENCE (BASED ON THE TRANSLATION OF YU HUA’S NOVEL “TO LIVE” / “活着»)". Мова, № 42 (19 листопада 2024): 21–28. https://doi.org/10.18524/2307-4558.2024.42.325845.

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The purpose of this study is a comparative analysis of the use of machine and human translation of the text of fiction. The object of the analysis is the translation of culturally loaded information from the novel “活着» / «To Live» by the modern Chinese writer Yu Hua (余华) into Russian. The human translation of Roman Shapiro and online machine translations of the ChatGPT chatbot based on a neural network developed by OpenAI, and of the DeepL (DeepL GmbH) were compared. The subject of the study is the optimal possibilities of combining modern translation strategies with the involvement of human a

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Hasanah, Ummi, Lulu Eka Aprilia, Muhammad Fahmi, Rifki Maulana Hidayat, and Aulia Fadhila Wirmansyah. "Akurasi Hasil Terjemahan Puisi Nizar Qabbani “Asyhadu An La Imraatan Illa Anti” Menggunakan Deepl Translator." Deskripsi Bahasa 8, no. 1 (2025): 39–56. https://doi.org/10.22146/db.13977.

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The rapid advancement of machine translation today is influenced by the progress in human intelligence. For instance, DeepL, as a translation machine, is considered highly accurate. The aim of this research was to analyze the accuracy of the translation of Nizar Qabbani's poem “Asyhadu An La Imra’atan Illa Anti” using Nababan's and Paul Ricoeur's theories. This study is qualitative in nature and employs a reading and note-taking method. Data analysis techniques followed Miles and Huberman's methods. Based on the research results, conclusions were drawn according to the research questions. Out

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Olkhovska, Alla, and Liliya Levchenko. "Experimental study of the impact of using hybride machine translation engines on the quality of translation of texts in the field of ornamental horticulture." 98, no. 98 (December 25, 2023): 48–52. http://dx.doi.org/10.26565/2786-5312-2023-98-06.

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The article is devoted to the study of the impact of using hybrid machine translation engines (Onlinedoctranslator and DeepL Translate) on the quality of translation of texts in the field of ornamental horticulture. Within our experimental study the quality of translation was determined by the number of errors and quality of rendering horticultural terminology. Machine translation is gradually evolving, the market is extremely active and proposes many new engines uniting most promising algorithms, but it is important to determine the effectiveness of hybrid machine translation engines in trans

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Asmara, Devi Sunita Mutia, and Farida Repelitawati Br Kembaren. "Student’s Perception Towards the Use of DeepL Translator in Writing Thesis or Journal for English Education Students." IJLECR - INTERNATIONAL JOURNAL OF LANGUAGE EDUCATION AND CULTURE REVIEW 10, no. 1 (2024): 117–26. http://dx.doi.org/10.21009/ijlecr.v10i1.47937.

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Translation is a crucial aspect for non-native English students, especially when writing academic theses or journals. This study aims to discuss the use of the DeepL Translator application in writing a thesis or journal of English students. This research employs qualitative methods with a case analysis design by analyzing data derived from questionnaire data and deeper interviews related to the use of DeepL Translator. The use of questionnaire data and further interviews were conducted with 24 final semester English students. The data were analyzed using Miles and Huberman's techniques, which

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Sun, Rui. "Evaluating the Translation Accuracy of ChatGPT and DeepL Through the Lens of Implied Subjects." Arab World English Journal For Translation and Literary Studies 8, no. 4 (2024): 41–53. http://dx.doi.org/10.24093/awejtls/vol8no4.5.

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This research aims to offer deeper insights into the translation quality of large language models and neural machine translation systems. It compares the performance of two state-of-the-art models, ChatGPT and DeepL, to explore which system achieves higher translation accuracy. This research employs a highly focused and specialized method to evaluate translation performance, enabling the identification of nuanced differences between advanced AI/machine models. It focuses on implied subjects in contemporary Chinese writer Qiuyu Yu’s Iraq travelogue and examines how the two models handle these i

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Payung, Naftalia Fany, and Sukarno Sukarno. "Enhancing EFL Students’ Writing Skills: The Impact of Deepl Translation." Journal of Languages and Language Teaching 13, no. 1 (2025): 483. https://doi.org/10.33394/jollt.v13i1.12301.

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The integration of the DeepL translation tool as a method to enhance students' writing skills in EFL classrooms holds great potential. Its capability to provide accurate, context-sensitive translations can aid students in expanding their vocabulary and understanding sentence structures. This study investigated the impact of using DeepL translation on EFL learners’ writing proficiency through an experimental approach. Two groups of senior high school students participated: an experimental group utilizing DeepL as a writing aid and a control group that did not. Both groups underwent pre-tests an

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Moisieieva, Nataliia, Olga Dzykovych, and Alina Shtanko. "MACHINE TRANSLATION: COMPARISON OF WORKS AND ANALYSIS OF ERRORS MADE BY DEEPL AND GOOGLE TRANSLATE." Advanced Linguistics, no. 11 (August 11, 2023): 78–82. http://dx.doi.org/10.20535/2617-5339.2023.11.277593.

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The article is focused on the study of machine translation errors on the example of the results of DeepL and Google Translate. The aim of the study is to compare the results of these services on the basis of literary and journalistic texts. The following methods were used to achieve this goal: theoretical analysis, descriptive, comparative, contextual, deductive, and quantitative methods. The results of this study make an important contribution to further detailed research on machine translation services and provide a basis for improving the algorithms of these services. The article will also

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Karim, Haji Abdul. "ChatGPT vs DeepL: Comparing the English Translation Quality of Digital Business and Information Technology Texts Using BLEU Metric." Journal of Digital Business and Information Technology 1, no. 2 (2025): 50–60. https://doi.org/10.23971/jobit.v1i2.297.

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In the era of digital globalization, the demand for accurate and efficient translation tools has grown significantly. Advances in artificial intelligence (AI) have given rise to various automated translation platforms that facilitate cross-lingual communication. Among the most notable are ChatGPT and DeepL. This study focusses on translating digital business and information technology texts in English using both AI. All text is analyzed by determining their quality grammar and context-preserved, in addition, the accuracy assessment is being analyzed by using BLEU metric to ensure the translati

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Peña Aguilar, Argelia. "Challenging machine translation engines: some Spanish-English linguistic problems put to the test." Cadernos de Tradução 43, no. 1 (2023): 1–26. http://dx.doi.org/10.5007/2175-7968.2023.e85397.

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This work is an evaluation of machine translation engines completed in 2018 and 2021, inspired by Isabelle, Cherry & Foster (2017), and Isabelle & Kuhn (2018). The challenge consisted of testing MTs Google Translate and Bing and DeepL in the translation of certain linguistic problems normally found when translating from Spanish into English. The divergences representing a “challenge” to the engines were of morphological and lexical-syntactical types. The absolute winner of the challenge was DeepL, in second place was Bing from Microsoft, and Google was the engine that was the poorest i

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Smirnova, Vesta N. "DEEPL TRANSLATE TECHNOLOGY IN THE TRANSLATION OF PROVERBS AND SAYINGS (ON THE MATERIAL OF THE ENGLISH LANGUAGE)." Bulletin of Chelyabinsk State University 479, no. 9 (2023): 126–31. http://dx.doi.org/10.47475/1994-2796-2023-479-9-126-131.

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The article presents the results of the study of the linguistic capabilities of machine translation of English proverbs and sayings based on DeepL Translate technology. Methodological recommendations for style adaptation of digital translation have been developed. Correction of automated translation of proverbs and sayings is recommended to be carried out with the use of such translation methods as communicative way of translation, interpretation, holistic transformation technique, equivalence, and others. It was concluded that the DeepL Translate program needs to be improved in terms of pragm

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Dewayanti, Desakh Putu Setyalika Putri, and Margana Margana. "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 12, no. 1 (2024): 223. http://dx.doi.org/10.22373/ej.v12i1.24433.

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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 c

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Sarhsian, Evelina Levonivna, and Olha Andriivna Zinchenko. "UTILIZING MACHINE TRANSLATION TECHNOLOGY FOR REPRODUCING SHORT PROSE LITERARY TEXTS." Studia Linguistica, no. 24 (2024): 101–10. http://dx.doi.org/10.17721/studling2024.24.101-110.

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Machine translation systems permeate all spheres of human activity, including the field of literary translation. Scholars note that machine translation holds significant potential for development and is less labor-intensive compared to the work of human translators. Consequently, there is a need to analyze common errors in machine translation to help avoid them in the future as neural machine translation systems continue to evolve. This article examines and analyzes the quality of a Ukrainian translation of the Spanish short story “Amigos” by Argentine writer Julio Cortázar, performed by the m

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Poddubskaya, Olga, and Victoria Daryina. "Machine translation difficulties of culture-specific elements in English news articles." Bulletin of the Donetsk National University. Series D: Philology and Psychology 5 (December 5, 2024): 94–103. https://doi.org/10.5281/zenodo.14284008.

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The article examines the quality of machine translation in comparison to human translation on the material of the news article “Revealed: Tories Failed to Do Impact Check before Approving Banned Pesticide” from Britain's most widely read newspaper ‘The Guardian”, dated July 24, 2024. In the absence of an official Russian translation of this article, the focus shifts to advanced online translators such as DeepL, Google Translate, Reverso, Microsoft Bing, and Yandex Translate. The analysis highlights culture-specific elements, idioms, and specialized terminology that may

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Bunga, Elsaty Lam Marya, and Caroline V. Katemba. "COMPARING TRANSLATION QUALITY: GOOGLE TRANSLATE VS DEEPL FOR FOREIGN LANGUAGE TO ENGLISH." EDUSAINTEK: Jurnal Pendidikan, Sains dan Teknologi 11, no. 3 (2024): 1147–71. http://dx.doi.org/10.47668/edusaintek.v11i3.1264.

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Google Translate dan Deeple saat ini sudah sangat dikenal dan telah digunakan oleh mahasiswa di Indonesia. Penelitian ini meneliti bagaimana mahasiswa Jurusan Pendidikan Bahasa Inggris dan Jurusan Filsafat di Universitas Advent Indonesia mengevaluasi penggunaan Deepl dan Google Translate serta terjemahan yang dihasilkan. Penelitian ini menggunakan pendekatan kuantitatif dengan pengumpulan data melalui kuesioner tertutup tipe Likert. Sebanyak 85 mahasiswa dari dua program studi yang ada, yaitu mahasiswa jurusan Pendidikan Bahasa Inggris dan juga mahasiswa Filsafat Universitas Advent Indonesia t

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Yulianto, Ahmad, and Rina Supriatnaningsih. "Google Translate vs. DeepL: A quantitative evaluation of close-language pair translation (French to English)." AJELP: The Asian Journal of English Language and Pedagogy 9, no. 2 (2021): 109–27. http://dx.doi.org/10.37134/ajelp.vol9.2.9.2021.

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Machine translation has improved in quality and worked best when applied to language pair of the same language family. This research was aimed to assess the quality of Google Translate and DeepL in terms of accuracy and readability. French to English translation data of En attendant Godot playscript by GT and DeepL were evaluated. The English Original version (EO) of the text served as reference. Two quantitative methods were employed i.e., manual with SAE J2450 translation metric and automatic assessment with Coh Metrix tool. The result of manual assessment shows that GT and DeepL outputs pas

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Krüger, Ralph. "Explicitation in Neural Machine Translation." Across Languages and Cultures 21, no. 2 (2020): 195–216. http://dx.doi.org/10.1556/084.2020.00012.

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This paper is concerned with the following question: to what extent does neural machine translation (NMT) – a relatively new approach to machine translation (MT), which can draw on richer contextual information than previous MT architectures – perform explicitation shifts in translation and how are these shifts realised in linguistic terms? In order to answer this question, the paper attempts to identify instances of explicitation in the machine-translated version of a research report on carbon dioxide capture and storage. The machine-translated text was created using the publicly available ge

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Başer, Zeynep. "Exploring Turkish equivalents of terms for musculoskeletal radiology: insights for a standardized terminology." Journal of Health Sciences and Medicine 8, no. 2 (2025): 275–85. https://doi.org/10.32322/jhsm.1612372.

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Aims: This study aimed to provide an analysis of Turkish equivalents of English terms for musculoskeletal radiology. Methods: The present study focuses on a global endorsement of English terms in musculoskeletal radiology, and explores how their Turkish equivalents are used in reference books (Turkish translation of the books, Diagnostic Imaging: Musculoskeletal: Trauma and Diagnostic Imaging: Musculoskeletal: Non-Traumatic Disease). Furthermore, the study attempts to provide a picture of how AI-based tools (i.e. neural machine translation tools such as DeepL, Google Translate and an AI Chatbo

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Henkel, Daniel. "Passive constructions in En/Fr human and machine translation." Across Languages and Cultures 25, no. 2 (2024): 240–67. http://dx.doi.org/10.1556/084.2024.00921.

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AbstractPassive constructions in English (be+past participle) and French (être+past participle) were inventoried on a text-per-text basis in a bi-directional French/English corpus. The corpus consisted of 70 (35 × 2) original source texts in each language, 70 (35 × 2) target texts produced by human translators, and the same number of target texts obtained by machine translation with DeepL Pro. Passive constructions were found to be about 70% more frequent in original English than in original French. In the corpora of translated texts, no large disparities were observed, with the exception of F

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Kirana, Amanda, Farida Repelita Waty Kembaren, and Benni Ichsanda Rahman Hz. "The Influence of DeepL Translator On EFL Students’ Writing." Jurnal Ilmu Sosial, Humaniora dan Seni 3, no. 1 (2024): 746–53. https://doi.org/10.62379/jishs.v3i1.2059.

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DeepL Translators are one form of technology that is developing today. The use of technology has a positive impact on the language learning process. Most students make mistakes in writing. DeepL Translator is a translation machine that can be utilised to help with writing. Therefore, a researcher examined the role of DeepL Translator in the language learning process, especially in writing revision. While there have been several studies on machine translation in writing, few have examined the impact of DeepL Translator on the writing of EFL students who use DeepL Translator. A study conducted w

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Li, Zilong, Lei Xiao, Ruijin Lin, and Zhengxinchao Xiao. "Metamorphic Robustness Testing for DeepL Translation." Journal of Physics: Conference Series 2456, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1742-6596/2456/1/012018.

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Abstract Due to the complexity and flexibility of natural languages, such robustness testing involving the ability of machine translation software to handle incorrect or unintended input are extremely challenging, especially if there is no human oracle or reference translation. In this paper, using metamorphic robustness testing methods, we compare the translation of the primitive input with the translation of a subsequent input that has different types of very small changes and may cause large changes in the translation results. The lack of robustness of the DeepL translation is shown by our

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Xiang, Cailing. "Study on the Effectiveness of ChatGPT in Translating Forestry Sci-tech Texts." International Journal of Linguistics, Literature and Translation 7, no. 9 (2024): 88–94. http://dx.doi.org/10.32996/ijllt.2024.7.9.11.

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ChatGPT, an advanced language model by OpenAI, enhances translation with its powerful language generation and understanding. In comparison to traditional human translation, ChatGPT is less costly, time-consuming, and knowledge-constraint, showcasing the substantial value of its application in translation practice. In the context of globalization, forestry translation plays an increasing role in facilitating global forestry development. To meet the growing need for efficient and high-quality translation in the forestry sector, this paper did research on the effectiveness of ChatGPT in the trans

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Ibáñez Moreno, Ana, and María Esther Domínguez Mora. "Google Translate versus DeepL in Spanish to English translation of Don Quixote." Translation and Translanguaging in Multilingual Contexts 11, no. 1 (2025): 65–87. https://doi.org/10.1075/ttmc.00154.iba.

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Abstract This paper analyses the effectiveness of neural machine translation when applied to literary translation and, more specifically, to the translation of collocations, one of the most difficult aspects in machine translation (Corpas-Pastor 2015; Shraiden and Mahadin 2015). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012). A comparative analysis of the translation of the first chapter of the world literature masterpiece El ingenioso hidalgo

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RICARDEZ MARCHENA, AHTZIRIZ DAYANA, and NELLY DEL CARMEN CORDOVA PALOMEQUE. "La inteligencia artificial en la traducción automática: precisión creciente, pero sin reemplazar al humano." International Journal of Humanities and Social Science Invention 14, no. 3 (2025): 01–07. https://doi.org/10.35629/7722-14030107.

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The impact of artificial intelligence (AI) on machine translation has led to significant improvements in accuracy and fluency, particularly through neural network models such as Transformer, BERT, and GPT. This study evaluates the quality of translations generated by Google Translate, DeepL, and ChatGPT, comparing them with human translations in terms of lexical accuracy, fluency, and cultural adequacy. The results show that DeepL and ChatGPT outperform Google Translate, yet human translators consistently achieve the highest scores across all metrics. Surveys indicate that while general users

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Munawwarah, Munirah, and Martriwati. "STUDENTS’ PERCEPTION OF USING DEEPL AS MACHINE TRANSLATION IN ENGLISH LEARNING." ELLTER Journal 5, no. 2 (2024): 284–95. http://dx.doi.org/10.22236/ellter.v5i2.15813.

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Technological advances influence the development of educational support with various facilities offered to students such as DeepL as a translation machine that can be used as an alternative by students to understand English texts. This study aims to describe students' perceptions of the use of DeepL in English learning in Jabodetabek. The population of this study is 210 students who have had the experience of learning English by utilizing DeepL. This study is descriptive quantitative research using a questionnaire. In collecting data, this study used a 5-point Likert scale questionnaire to det

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Irawan, Reynald Satria Dwiky, and Ressi Maulidina Delijar. "Akurasi hasil terjemahan manual book bahasa Mandarin ke dalam bahasa Indonesia melalui mesin terjemahan otomatis." Diglosia: Jurnal Kajian Bahasa, Sastra, dan Pengajarannya 7, no. 4 (2024): 735–48. https://doi.org/10.30872/diglosia.v7i4.1107.

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There are so many conveniences that can be accessed in this sophisticated era, such as the convenience of automatically translating Chinese text into Indonesian. However, the accuracy and precision of the translation results obtained from the automatic translation machine still need to be reviewed further. This research aims to identify language errors in the translation of Chinese documents into Indonesian through Youdao Fanyi and DeepL Translator. This research is descriptive qualitative research based on language error analysis. The source used is taken from the manual book of an e-cigarett

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Zupan, Simon, Zmago Pavličič, and Melanija Larisa Fabčič. "Machine Translation of Independent Nominal Phrases in Technical Texts." ELOPE: English Language Overseas Perspectives and Enquiries 22, no. 1 (2025): 171–84. https://doi.org/10.4312/elope.22.1.171-184.

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This paper deals with machine translations of independent noun phrases in technical texts, which are not part of any sentence structure but function on their own, typically in tables and illustrations. Such nominal structures are common in technical texts because they allow technical writers to increase lexical density and precision in expression. On the other hand, these phrases pose a challenge for machine translation engines, as their meaning depends on the context. Independent noun phrases from a service manual, which were translated from English into Slovene by two different machine trans

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Ziganshina, Liliya Eugenevna, Ekaterina V. Yudina, Azat I. Gabdrakhmanov, and Juliane Ried. "Assessing Human Post-Editing Efforts to Compare the Performance of Three Machine Translation Engines for English to Russian Translation of Cochrane Plain Language Health Information: Results of a Randomised Comparison." Informatics 8, no. 1 (2021): 9. http://dx.doi.org/10.3390/informatics8010009.

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Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30

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Kamaluddin, Mohamad Ihsan, Moch Wildan Khoerul Rasyid, Fourus Huznatul Abqoriyyah, and Andang Saehu. "Accuracy Analysis of DeepL: Breakthroughs in Machine Translation Technology." Journal of English Education Forum (JEEF) 4, no. 2 (2024): 122–26. http://dx.doi.org/10.29303/jeef.v4i2.681.

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This study examines the accuracy and technological innovations of DeepL, a prominent machine translation tool. Through a comprehensive literature review, we analyze DeepL's performance compared to other translation systems, exploring its advanced neural network architecture and training methods. Key findings indicate that DeepL consistently outperforms other tools in BLEU scores and human evaluations, particularly excelling in handling context, idiomatic expressions, and specialized terminology. The research highlights DeepL's use of the Transformer model, diverse training data, and techniques

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VARELA SALINAS, MARÍA-JOSÉ, and Ruth Burbat. "Google Translate and DeepL: Breaking taboos in translator training." Ibérica, no. 45 (June 7, 2023): 243–66. http://dx.doi.org/10.17398/2340-2784.45.243.

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In recent decades, when we read about the use of machine translation, we were told that it was not suitable for professional translation. The reason was that machine translation used to produce poor results as long as it was not applied to controlled language of pre-edited texts within a specific field of specialization, and with post-editing of the target text, all of it using the appropriate specialized tools. However, three considerations led us to want to assess to what extent machine translation could be used in a German-Spanish/Spanish-German Specialized Translation course, since w

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Canga Alonso, Andrés, and Maria Cira Napoletano. "The role of metrics to assess the quality of British teenage language translation into Spanish and Italian using machine translation tools." Cadernos de Tradução 45 (May 8, 2025): 1–20. https://doi.org/10.5007/2175-7968.2025.e101329.

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The rapid evolution of adolescence language, characterized by slang and idiomatic expressions, presents a significant challenge for machine translation systems. Existing research has extensively covered the translation of languages in general; however, there remains a gap in understanding these systems’ ability when faced with adolescent language. This study aims at (i) the evaluation and the comparison of the accuracy of the translations of colloquial language by Bing Translator, DeepL and HelsinkiNLP from English into Spanish and Italian, (ii) the validity and reliability of two different me

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Xue, Jiaxi. "Combating Gender Biases from Source Language in Machine Translation." Advances in Education, Humanities and Social Science Research 13, no. 1 (2025): 303. https://doi.org/10.56028/aehssr.13.1.303.2025.

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Effective Chinese language translations that communicate clear and unbiased messages form the core fabric of peaceful relationships among people in society. Existing machine translations models like Google Translate, DeepL, Youdao Translate or even LLM sometimes generate Chinese-to-English translations filled with prejudice against females. The prejudice-prone Chinese-to-English translations are inherited from some terms in the source texts which are merely norms inherited from Chinese history and may not indicate female discrimination in terms of context. Such a gap must be eradicated to mini

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Oshanova, E. S., and V. N. Dekterev. "TO THE QUESTION OF ANALYSIS OF MACHINE TRANSLATION TECHNOLOGIES." Social’no-ekonomiceskoe upravlenie: teoria i praktika 18, no. 4 (2022): 81–91. http://dx.doi.org/10.22213/2618-9763-2022-4-81-91.

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The article deals with the issue of the efficiency of translation by means of machine translation systems. The relevance of the work is related to the importance of a comprehensive analysis of machine translation systems and a detailed description of popular and well-known online translators. Scientific work in the field of machine translation is an important and urgent task, aimed at achieving equivalent translations and contributing to the solution of many applied problems associated with the development and formation of a new communication environment. In this paper, we will consider such c

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Dongge, LIU, and ZHAO Meijuan. "Human vs. Machine: Assessing Translation Quality of Four-character Terms in the Classical Chinese Medical Text Huangdi Neijing." Advances in Research 26, no. 4 (2025): 220–29. https://doi.org/10.9734/air/2025/v26i41405.

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With the rapid advancement of technology, machine translation has been increasingly applied in the field of medical translation. As the foundational text of Traditional Chinese Medicine (TCM), the Huangdi Neijing plays a critical role in shaping TCM theory and clinical practice. This study focuses on the translation of four-character terms in the Huangdi Neijing and compares the translation quality between human and machine translation. A mixed-methods approach, incorporating both qualitative and quantitative analysis, was employed. A total of 463 samples were selected, and four mainstream mac

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Linlin, Li. "Artificial Intelligence Translator DeepL Translation Quality Control." Procedia Computer Science 247 (2024): 710–17. http://dx.doi.org/10.1016/j.procs.2024.10.086.

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Merzlaya, Anna V. "Neural Machine Translation and Conveying Cold Perception (Based on Finnish-Russian Translations of Meteorological Texts)." Vestnik of Northern (Arctic) Federal University. Series Humanitarian and Social Sciences 25, no. 2 (2025): 73–82. https://doi.org/10.37482/2687-1505-v421.

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This article presents a systematic description of translation failures resulting from neural machine translation of Finnish-language meteorological media texts (forecasting a cold front and negative temperatures) into Russian. The importance of the topic is determined by the increasing attention of meteorologists to phenomena related to climate change and weather anomalies as well as by the interest of IT specialists in optimizing neural machine translation systems that can significantly accelerate the process of interlingual communication. The key aim of the study is to identify and systemati

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Ismailia, Titik. "Analysis of Machine Translation Performance on Translating Informative Text from English into Indonesian." EBONY: Journal of English Language Teaching, Linguistics, and Literature 3, no. 2 (2023): 129–38. http://dx.doi.org/10.37304/ebony.v3i2.9809.

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This study aims to investigate the performance of 6 machine translation. The text translated was informative text from English into Indonesian. The document taken from 48 students paper in semester final test. The research design is descriptive qualitative and content analysis approach. The data obtained from the students translation result in final test, observation, and interview. In analyzing the result of translating, there were three categories: grammatical structure, cultural words, and mechanic writing (composition writing). The result shows the performance of 6 machine translations: Go

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Kusuma Nata Laksana and Cahya Komara. "Indonesian EFL Students’ Perceptions of DeepL Machine Translation Tool: Utilization, Advantages, and Disadvantages." Journal of Language and Literature Studies 4, no. 2 (2024): 256–76. http://dx.doi.org/10.36312/jolls.v4i2.1931.

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The integration of artificial intelligence (AI) technologies, such as machine translation tools, has gained traction in language education, offering potential benefits while raising concerns. This mixed-methods study investigates the perceptions and experiences of Indonesian students learning English as a Foreign Language (EFL) regarding the use of DeepL Machine Translation. With advancements in AI and increasing reliance on translation tools, understanding users' perspectives is crucial for effective integration. The research aims to explore EFL students' perceptions of DeepL's utilization, a

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Deng, Mingjun, and Xianming Fan. "Post-Editing Efficiency And Quality Assessment: A Comparative Analysis Of Google Translate And DeepL." IOSR Journal of Humanities and Social Science 30, no. 1 (2025): 10–24. https://doi.org/10.9790/0837-3001021024.

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Neural Machine Translation (NMT) has revolutionized the translation industry by improving fluency, grammatical accuracy, and contextual understanding. However, its impact on post-editing efficiency and translation quality for domain-specific texts remains underexplored. This study compares the translation performance of Google Translate and DeepL, focusing on medical and legal texts. Using a simulated experimental setup, the study evaluates translation outputs based on editing time, automated metrics (BLEU, TER), and error analysis. The findings reveal that DeepL consistently outperforms Googl

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Idami, Zahratul, Meta Keumala, Meutia Saifani, Evi Nadila, and Dakiani. "Translating Indonesian to English: Problems and Solutions Experienced by English Education Students." JUMPER: Journal of Educational Multidisciplinary Research 4, no. 1 (2025): 64–74. https://doi.org/10.56921/jumper.v4i1.277.

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This study investigates the challenges faced by students in translating Indonesian to English, focusing on students from the English Education Department at IAIN Langsa who had enrolled in the Scientific Translation course. A descriptive qualitative methodology was employed, with five students as the participants. Data were collected through documentation analysis and interviews. The research reveals four key problems in translation, including achieving equivalent meaning, determining particular terminology, dealing with idiomatic expression, and addressing mechanical errors especially punctua

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Yu, Ashley. "Quality Evaluation of C-E Translation of Legal Texts by Mainstream Machine Translation Systems—An Example of DeepL and Metasota." English Language Teaching and Linguistics Studies 5, no. 2 (2023): p180. http://dx.doi.org/10.22158/eltls.v5n2p180.

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Despite significant progress made in machine translation technology and the ongoing efforts in practical and commercial application of neural machine translation systems, their performance in vertical fields remains unsatisfactory. To avoid misunderstandings and excessive expectations of a specific machine translation system, this research selected legal texts as its real data research object. The text translation tasks were accomplished using two popular neural machine translation systems, DeepL and Metasota, both domestically and internationally, and evaluated using internationally recognize

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Jiang, Jingjing. "The Effect of Using DeepL on Improving Students' Writing Vocabulary." Lecture Notes in Education Psychology and Public Media 64, no. 1 (2024): 98–106. http://dx.doi.org/10.54254/2753-7048/64/20241004.

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Abstract: As artificial intelligence technologies increasingly integrate into education, neural translation machines, such as Google Translate and DeepL, are gradually becoming an important tool for enhancing the effectiveness of foreign language learning. These technologies not only improve the efficiency of the learning experience but also address the limitations of traditional vocabulary acquisition methods. While research has examined DeepL's translation quality, there is limited focus on Spanish learners' attitudes and experiences with DeepL during the writing process, particularly regard

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Mitura, Magdalena. "« Vous pouvez entendre les mâchoires de l’arme de l’ange ». Le traducteur humain face à la traduction automatique dans le contexte littéraire : Les Livres de Jakób d’Olga Tokarczuk." Studia Romanica Posnaniensia 51, no. 3 (2024): 61–72. http://dx.doi.org/10.14746/strop.2024.51.3.5.

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The dynamic development of automatic translators raises questions about the future of human translators. The aim of this article is to evaluate the French versions of an excerpt from The Books of Jacob by Olga Tokarczuk, made by Systran, Google Translate and DeepL, and to compare them with the reference translation made by a human translator. The translations were carried out in 2019 as part of the ATLAS association project ‘L’Observatoire de la traduction automatique’. The analyses show that the inability to interpret the original in a complex way leads to weaknesses in machine translators. T

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Jiang, Yue, and Jiang Niu. "A corpus-based search for machine translationese in terms of discourse coherence." Across Languages and Cultures 23, no. 2 (2022): 148–66. http://dx.doi.org/10.1556/084.2022.00182.

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AbstractEarlier studies have corroborated that human translation exhibits unique linguistic features, usually referred to as translationese. However, research on machine translationese, in spite of some sparse efforts, is still in its infancy. By comparing machine translation with human translation and original target language texts, this study aims to investigate if machine translation has unique linguistic features of its own too, to what extent machine translations are different from human translations and target-language originals, and what characteristics are typical of machine translatio

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Schmitt, Peter A. "Translation 4.0 – Evolution, Revolution, Innovation or Disruption?" Lebende Sprachen 64, no. 2 (2019): 193–229. http://dx.doi.org/10.1515/les-2019-0013.

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Abstract The international translation industry is undergoing fundamental changes with the potential of disrupting the market and the business basis of many freelance translators. This paper outlines the present scenario in the field of non-literary translations, its development, reasons, symptoms and current trends. Analogous to the established concept of “Industry 4.0”, the philosophy of an emerging new translation industry can be called “Translation 4.0”. Challenged by a continuously growing demand for translations, increasingly volatile markets, fierce global competition and aggressive pri

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Baxadirovna, Utedjanova Dilbar. "How Businesses Use Machine Translation for Global Expansion." International Journal of Pedagogics 5, no. 5 (2025): 352–53. https://doi.org/10.37547/ijp/volume05issue05-91.

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This article examines the strategic role of machine translation (MT) in facilitating global business expansion. With the increasing accessibility of neural machine translation tools like Google Translate and DeepL, companies are better equipped to engage international customers, localize content, and streamline multilingual communication. The paper discusses practical applications, benefits, and challenges of using MT in global business contexts, offering insights into how companies integrate this technology for cross-cultural success.

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