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Journal articles on the topic 'DeepL'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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3

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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Maryam, Syfa Wahida, Made Hery Santosa, and Kadek Sintya Dewi. "THE IMPACT OF USING ARTIFICIAL INTELLIGENCE (DEEPL) ON ELEVENTH GRADE STUDENTS’ WRITING SKILLS AT MAN KARANGASEM." Jurnal JOEPALLT (Journal of English Pedagogy, Linguistics, Literature, and Teaching) 13, no. 1 (2025): 200. https://doi.org/10.35194/jj.v13i1.5072.

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The purpose of this study is to determine whether the use of DeepL in learning can have an impact on students' ability to write English and to find out how the implementation of DeepL impacts students' writing ability. This study uses a mixed method in helping researchers to obtain data, namely Quasi Experimental Non-Equivalent (Pretest and Posttest) Control Group Design on quantitative pre-test and post-test student provision as a form of quantitative data and interviews for qualitative data. The sample involved in this study was 56 students who were divided into two groups: the experimental

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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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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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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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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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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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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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Wiarishintia, Ni Komang Jenisa, Made Hery Santosa, and Kadek Sintya Dewi. "The Impact of Using DeepL Artificial Intelligence on Students’ Writing Skills in an Indonesian Senior Highschool Context." ELT Worldwide: Journal of English Language Teaching 11, no. 2 (2024): 475. http://dx.doi.org/10.26858/eltww.v11i2.67029.

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This study looks at how eleventh-grade students at SMA Negeri 2 Singaraja's writing abilities are affected by the use of DeepL artificial intelligence. The study uses mixed methods with an explanatory sequential design; for quantitative analysis, a quasi-experimental non-equivalent control group design is used, and for qualitative insights, in-depth interviews utilizing the RASE model are conducted. Purposive random sampling was used to choose the samples, and two classes participated in the investigation. While the control group received traditional instruction, the experimental group receive

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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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Purnamawati, Luh Desi, Made Hery Santosa, and Kadek Sintya Dewi. "THE EFFECT OF USING DEEPL ON THE WRITING SKILLS OF 11TH GRADE STUDENTS IN HOSPITALITY MAJOR AT SMK NEGERI 1 SINGARAJA." Jurnal JOEPALLT (Journal of English Pedagogy, Linguistics, Literature, and Teaching) 13, no. 1 (2025): 125. https://doi.org/10.35194/jj.v13i1.5047.

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This research aims to determine the effect of using DeepL on EFL students' writing skills. An explanatory sequential mixed-methods design with quasi experimental and open-ended interviews was used in this research. Through purposive sampling, seventy-one EFL students from North Bali, Indonesia participated in the quasi-experimental phase, with ten additional students volunteering for interview sessions. The writing test data were analyzed using descriptive and inferential analysis, while the interview transcripts were subjected to Interactive Model analysis. The research results show that ther

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Dolmacı, Mustafa. "Yapay Zekâ Destekli Çeviride Dilsel ve Kültürel Sadakat: İngilizce-Türkçe Edebi Kısa Öykülerde DeepL ve İnsan Çevirilerinin Karşılaştırmalı Analizi." International Journal of Language and Translation Studies 5, no. 1 (2025): 92–109. https://doi.org/10.63673/lotus.1662868.

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Bu çalışmada, İngilizceden Türkçeye çevrilen kurgusal kısa öykülerde DeepL’ın makine çevirileri ile insan çevirileri arasındaki farkları, Katharina Reiss’in çeviri eleştirisi modeli kullanılarak dilbilimsel bileşenler açısından incelenmektedir. Beş edebi eserden alınan ve DeepL ile profesyonel insan çevirmenler tarafından çevrilen 381 cümle karşılaştırılmış ve farklılıklar belirlenmiştir. En yaygın farklılık türü sözcüksel farklılık olup (%33,86), ardından anlamsal farklılık (%22,05), yakın anlamlılık (%20,73), dilbilgisel farklılık (%9,16), mantıksız (yanlış çeviri) olma durumu (%8,40) ve üsl

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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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Handzel, Zbigniew. "TRANSLATOR DeepL – PRZEŁOM W TŁUMACZENIU KOMPUTEROWYM." ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA 1, no. 6 (2021): 22–24. http://dx.doi.org/10.15199/13.2021.6.3.

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17

Sparenberg, J. M. "Deepl-Independent Potentials from Supersymmetric Inversion." Physical Review Letters 85, no. 13 (2000): 2661–64. http://dx.doi.org/10.1103/physrevlett.85.2661.

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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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Imran, Muhammad Chairil, Mutmainnah Marzuki, Luana Sasabobe, Yuriatson Jubhari, and Sujarwo Sujarwo. "DeepL Translate can be a scaffold to speaking fluency: Technology Acceptance Model (TAM)." EDULEC : Education, Language, and Culture Journal 5, no. 1 (2025): 56–66. https://doi.org/10.56314/edulec.v5i1.302.

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The numerous applications bring benefits to students; the fact that students spend more time using existing technology and demonstrate mastery of information technology allows instructors to motivate students who are unmotivated to speak. Lecturers have introduced DeepL Translate to improve students' speaking, while academics investigate the Technology Acceptance Model (TAM), which is designed to assess students' adoption of information systems. This study utilised a descriptive quantitative approach to examine students' reactions to the application of DeepL Translate in the educational proces

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Isnaeni, Resti, Pari Purnaningsih, and Nur Najibah Sukmawati. "EFEKTIVITAS PENGGUNAAN DEEPL TRANSLATE DALAM PEMBELAJARAN BAHASA INGGRIS UNTUK MENINGKATKAN KOSAKATA SISWA." EDUCATIONAL : Jurnal Inovasi Pendidikan & Pengajaran 5, no. 2 (2025): 422–30. https://doi.org/10.51878/educational.v5i2.6211.

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Along with the rapid development of artificial intelligence technology, this study aims to analyze the effectiveness of using DeepL Translate as a tool to improve students' English vocabulary mastery. The main focus of the study is to determine whether there is a significant difference between learning integrated with translation technology and conventional methods. This study uses a quantitative approach with a quasi-experimental design, involving 60 XII di SMK YPUI Parung who are divided into experimental and control groups. The important stages of the study include giving a pre-test, implem

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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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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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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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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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Septi Putri, Yohanes Agung Apriyanto, and Andri Wijaya. "ANALISIS SENTIMEN ULASAN APLIKASI DEEPL PADA GOOGLE PLAY DENGAN METODE SUPPORT VECTOR MACHINE (SVM)." Jurnal Sistem Informasi (JUSIN) 4, no. 2 (2023): 59–66. http://dx.doi.org/10.32546/jusin.v4i2.2368.

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Di era globalisasi dan kemajuan teknologi, pertumbuhan penggunaan aplikasi mobile mengalami perkembangan yang pesat. Memahami tingkat kepuasan dan ketidakpuasan pengguna terhadap aplikasi seluler sangatlah penting. Salah satu aplikasi penerjemah online yang sangat populer di Google Play Store adalah DeepL yang menggunakan teknologi kecerdasan buatan untuk menerjemahkan bahasa. DeepL telah menjadi alat yang sangat berguna bagi individu dan organisasi dalam mengatasi hambatan bahasa. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap review pengguna aplikasi DeepL pada platform

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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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Nellyca, Nellyca, Lily Thamrin, and Bun Yan Khiong. "Analysis of DeepL Translation Results in the Movie “You Are The Apple Of My Eye”." Edumaspul: Jurnal Pendidikan 8, no. 2 (2024): 3724–278. https://doi.org/10.33487/edumaspul.v8i2.8435.

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Tujuan dari penelitian ini adalah untuk mengetahui keakuratan terjemahan DeepL dan untuk mengkaji perubahan bentuk dan makna yang terjadi ketika menggunakan terjemahan DeepL. Metode yang digunakan oleh penulis adalah penelitian kualitatif dengan pendekatan penulisan deskriptif. Pengumpulan data dilakukan dengan menggunakan teknik Simak Bebas Libat Cakap. Penjelasan lebih lanjut mengenai proses pengumpulan data meliputi pencatatan dan pengkategorian data berdasarkan karakteristiknya. Setelah data terkumpul, penulis mengelompokkannya ke dalam sebuah tabel.Film You Are The Apple Of My Eye menunju

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Silvan De la O, Ana Isabel, and Nadya Vanesa Ortiz Moya. "Uso del Traductor Automático DEEPL en los Estudiantes del Área Traducción de la Licenciatura en Idiomas de la DAEA." Ciencia Latina Revista Científica Multidisciplinar 8, no. 5 (2024): 13000–13020. https://doi.org/10.37811/cl_rcm.v8i6.14760.

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Los traductores automáticos son todos aquellos que se utilizan mediante dispositivos electrónicos y nos dan una traducción instantánea. El objetivo principal de este artículo de investigación sobre el uso del traductor automático DEEPL en estudiantes del área de traducción de la Licenciatura en Idiomas de la DAEA, es conocer el uso y eficiencia del traductor automático DEEPL en estudiantes del área de traducción de la Licenciatura en Idiomas; los estudiantes deberán identificar una traducción manual de una traducción automática realizada por el traductor. Es una investigación cualitativa, desc

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Wittenhagen, Julia. "„Größtes Risiko ist Diskriminierung“." Lebensmittel Zeitung 76, no. 31 (2024): 38. http://dx.doi.org/10.51202/0947-7527-2024-31-038.

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Minervini, Rosaria. "La traducción automática español-italiano del turismo enogastronómico: un estudio de caso." Cuadernos de Lingüística Hispánica, no. 42 (July 18, 2023): 1–20. http://dx.doi.org/10.19053/0121053x.n42.2023.16000.

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En este artículo se presentan los resultados de la aplicación del traductor automático DeepL al ámbito del turismo gastronómico y enológico. En el mundo actual, que exige traducir una cantidad copiosa de textos en un tiempo cada vez más reducido, los motores de traducción representan una herramienta muy útil empleada cada vez más por los profesionales del sector y por las personas en su día a día. Sin embargo, uno de los límites que suele atribuírseles a estos motores es que no siempre consiguen una traducción válida de los elementos culturales ni de lasdiferentes connotaciones lingüísticas, e

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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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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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Dalmış, Emrullah, and Musa Yıldız. "MAKİNE ÇEVİRİSİNİN SINIRLARI: EDEBİ METİN TÜRÜNDE MAKİNE ÇEVİRİSİ KALİTE ANALİZİ." Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi 8, no. 1 (2025): 314–26. https://doi.org/10.37999/udekad.1627217.

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Bu çalışmada Arapçadan Türkçe yönüne makine çevirisi araçlarının (Google, Yandex, DeepL) edebi metin türünde çeviri kaliteleri ve post-editing işlemi için yeterlikleri incelenmiştir. Bunun için Ala el-Esvânî’nin Yakupyan Apartmanı adlı romanından sekiz segmentlik bir pasaj seçilmiştir. Çalışmada ayrıca Türkçe-Arapça dil çiftinde çeviri kalitesini ölçmek için hata taksonomisi ve puanlaması yapılmıştır. Hatalar kritik, majör ve minör olmak üzere üç kategoriye ayrılmıştır. Belirlenen ölçme aracının uygulanması neticesinde edebi metin türünün Google, Yandex ve DeepL çevirileri incelendiğinde; Goog

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Wein, Thomas. "Data Protection, Cookie Consent, and Prices." Economies 10, no. 12 (2022): 307. http://dx.doi.org/10.3390/economies10120307.

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A legislative process is currently ongoing in the European Union to supplement the 2018 General Data Protection Regulation regarding ePrivacy regulation. The supplement is intended to complete the European data protection policy in significant areas. One addition would be for service providers on the Internet, who currently obtain the consent of their users via an opt-out provision, to always provide a paid alternative without disclosing data. This procedure is essentially aimed at overcoming “cookie consent fatigue”, which can be observed in many cases. A simple economic exchange model shows

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Sebo, Paul, and Sylvain de Lucia. "Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT." PLOS ONE 19, no. 2 (2024): e0297183. http://dx.doi.org/10.1371/journal.pone.0297183.

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Background Non-English speaking researchers may find it difficult to write articles in English and may be tempted to use machine translators (MTs) to facilitate their task. We compared the performance of DeepL, Google Translate, and CUBBITT for the translation of abstracts from French to English. Methods We selected ten abstracts published in 2021 in two high-impact bilingual medical journals (CMAJ and Canadian Family Physician) and used nine metrics of Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1 recall/precision/F1-score, ROUGE-2 recall/precision/F1-score, and ROUGE-L recall/pr

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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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Florio, Nicola. "Un Un estudio comparado de la traducción automática de eventos de movimiento de cruce de límites en inglés, español e italiano con Google Translate y DeepL." Linguamática 14, no. 2 (2023): 37–57. http://dx.doi.org/10.21814/lm.14.2.368.

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En este artículo se presenta un estudio comparado de las traducciones generadas por dos herramientas de traducción automática en línea (Google Translate y DeepL) para eventos de movimiento que implican un cruce de límites. Partiendo del inglés, una lengua tipológicamente opuesta al español y al italiano, se combinan verbos de movimiento que especifican la Manera en la que se produce el desplazamiento con complementos postverbales que expresan una Trayectoria de cruce de límites. El objetivo es analizar comparativamente las traducciones automáticas obtenidas en español e italiano con Google Tra

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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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Rodríguez-Rubio Mediavilla, Santiago. "An analysis of Google Translate and DeepL translation of source text typographical errors in the economic and legal fields." Revista de Llengua i Dret, no. 81 (June 13, 2024): 88–105. http://dx.doi.org/10.58992/rld.i81.2024.4188.

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Training neural machine translation systems with noisy data has been shown to improve robustness (Heigold et al., 2018). The objective of the present study is to test Google Translate and DeepL performance in the detection and correction of typographical errors, by introducing 1,820 source text typos found in a previous work on specialised Spanish-English dictionaries (Rodríguez-Rubio & Fernández-Quesada, 2020a, 2020b; Rodríguez-Rubio Mediavilla, 2021). Typos were introduced in isolation and also in co-text. Results showed that Google Translate clearly outperformed DeepL. Moreover, the rep

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Nur Meiza, Afia, Natasya Miranda Gihar, Khairun Nisa, Najla Regar, Khairunnisa Khairunnisa, and Siti Ismahani. "English Syntactic Analysis in Automatic Translation Text: A Study on Sentence Structure Accuracy." Journal on Teacher Education 6, no. 2 (2024): 20–30. https://doi.org/10.31004/jote.v6i2.37394.

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Penelitian ini menyelidiki peran analisis sintaksis dalam meningkatkan akurasi penerjemahan mesin (MT), dengan fokus pada struktur kalimat dalam bahasa Inggris. Penelitian ini menggunakan pendekatan kualitatif, menganalisis 500 kalimat bahasa Inggris yang diterjemahkan ke dalam bahasa Indonesia dan Spanyol menggunakan tiga sistem MT: Google Translate, DeepL, dan Microsoft Translator. Penelitian ini mengidentifikasi masalah sintaksis utama seperti urutan kata, kesepakatan, dan struktur klausa, serta mengevaluasi dampaknya terhadap kualitas terjemahan. Hasil penelitian menunjukkan bahwa meskipun

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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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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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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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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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Noriega-Santiáñez, Laura, and Gloria Corpas Pastor. "Machine vs Human Translation of Formal Neologisms in Literature:." Tradumàtica tecnologies de la traducció, no. 21 (December 22, 2023): 233–64. http://dx.doi.org/10.5565/rev/tradumatica.338.

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This article compares the output of three neural machine translation systems (Google Translate, DeepL, and Phrase TMS) and human translation (undergraduate level students, English into Spanish). It focuses on five formal neologisms extracted from literary texts, thus considering creativity, and technology adoption and training.

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Roiss, Silvia, and Petra Zimmermann González. "DeepL y su potencial para el desarrollo de la capacidad de análisis crítico en la clase de Traducción inversa." Hermēneus. Revista de traducción e interpretación, no. 22 (February 1, 2021): 363–82. http://dx.doi.org/10.24197/her.22.2020.363-382.

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En nuestras clases de Traducción Inversa hemos observado que los alumnos poco experimentados confían de manera casi ciega en el traductor automático DeepL, alentados por el lema de la propia empresa, que lo define como «el mejor traductor del mundo». Nos hemos propuesto corregir el uso indiscriminado del programa y promover un aprendizaje que se centraba en dos objetivos: (1) Que el alumno valore la competencia traductora humana por encima del potencial de DeepL. (2) Que el alumno tome consciencia de la necesidad de una serie de actuaciones para asegurar el uso seguro del traductor automático.

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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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Birdsell, Brian. "Student Writings with DeepL: Teacher Evaluations and Implications for Teaching." JALT Postconference Publication - Issue 2021.1; August 2022 2021, no. 1 (2022): 117. http://dx.doi.org/10.37546/jaltpcp2021-14.

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Technological changes have the power to disrupt standard educational practices. One recent advancement is neural machine translation (NMT) systems such as Google Translate and DeepL which due to their widespread use have already impacted foreign language education. To explore the effect of NMTs on student essay writing and teachers’ evaluation of it, a small-scale study was conducted in which students were divided into two groups, one group used the NMT DeepL and the other did not. English teachers assessed these essays by evaluating them using a standard rubric and then judging whether they b

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Borbenchuk, Iryna, and Alina Shtanko. "FEATURES OF ENGLISH TOURISM TERMS MACHINE TRANSLATION INTO UKRAINIAN." Advanced Linguistics, no. 14 (December 25, 2024): 42–47. https://doi.org/10.20535/.2024.14.315750.

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The article is a study of the English tourism terminology and the features of its translation into Ukrainian through the use of the translation systems DeepL Translate and Google Translate. Taking into account the constant changes in the development of the tourism sector, modern online platforms provide active communication and exchange of experience, promote various types of recreation as well as encourage the study of tourist texts, being an object of interest for linguists. On the basis of the English language tourist texts, the article explores lexical units denoting tourist concepts and r

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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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