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Fitria, Tira Nur. "ANALYSIS ON CLARITY AND CORRECTNESS OF GOOGLE TRANSLATE IN TRANSLATING AN INDONESIAN ARTICLE INTO ENGLISH." International Journal of Humanity Studies (IJHS) 4, no. 2 (March 31, 2021): 256–66. http://dx.doi.org/10.24071/ijhs.v4i2.3227.
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The objective of this study is to analyze the aspects of clarity and correctness in Google Translate’s ability in translating an Indonesian article from English into Indonesian. This research refers to qualitative research. Data used in this research is a published Indonesian article which is translated into English by using Google Translate. Based on the analysis, the researcher concludes that Google Translate is a machine translator, but there is always going to be potentially less clarity and correctness at the end of the translation product such as in Indonesian articles into English. Because English grammar is a complicated thing to be learned, people perhaps cannot expect more that machine translator understands every aspect of the way human beings communicate with each other. That is why the answer about the clarity and the correctness of Google Translate is that it still has a way to go before it can consistently, clearly, and correctly translate the language without errors. In the clarity aspect, there is still no clarity in English translation by Google Translate, even it translated the language word-for-word. In the correctness aspect, it refers to the mechanical rule in writing which is related to grammar, punctuation, and spelling. Some examples of non-correctness are related to grammar and punctuation errors. Machine translators have come a long way in a short amount of time, but some features still lack good translation such as in aspects of grammar and punctuation.
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Ma'shumah, Nadia Khumairo, Isra F. Sianipar, and Cynthia Yanda Salsabila. "Google Translate Performance in Translating English Passive Voice into Indonesian." PIONEER: Journal of Language and Literature 13, no. 2 (December 31, 2021): 271. http://dx.doi.org/10.36841/pioneer.v13i2.1292.
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A scant number of Google Translate users and researchers continue to be skeptical of the current Google Translate's performance as a machine translation tool. As English passive voice translation often brings problems, especially when translated into Indonesian which rich of affixes, this study works to analyze the way Google Translate (MT) translates English passive voice into Indonesian and to investigate whether Google Translate (MT) can do modulation. The data in this research were in the form of clauses and sentences with passive voice taken from corpus data. It included 497 news articles from the online news platform ‘GlobalVoices,' which were processed with AntConc 3.5.8 software. The data in this research were analyzed quantitatively and qualitatively to achieve broad objectives, depth of understanding, and the corroboration. Meanwhile, the comparative methods were used to analyze both source and target texts. Through the cautious process of collecting and analyzing the data, the results showed that (1) GT (via NMT) was able to translate the English passive voice by distinguishing morphological changes in Indonesian passive voice (2) GT was able to modulate English passive voice into Indonesian base verbs and Indonesian active voice.
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Amilia, Ika Kartika, and Darmawan Eko Yuwono. "A STUDY OF THE TRANSLATION OF GOOGLE TRANSLATE." LINGUA : JURNAL ILMIAH 16, no. 2 (October 31, 2020): 1–21. http://dx.doi.org/10.35962/lingua.v16i2.50.
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This study is intended to analyze errors made by Google Translate in translating Eliza Riley’s Return to Paradise short story. The method is qualitative descriptive. The data are collected by comparing the translation of Google Translate with that of a professional translator. The errors are analyzed based on Mossop’s revision parameters. The findings show that Google Translate failed to recognize idiomatic expressions which caused fatal errors in the target text; errors in word choice that caused word translated out of context; and illogical sentence at the target text caused by cultural difference. As a conclusion, Google Translate may be useful to help translate few words, phrase, and particular sentence in general, and also gives a general comprehension in translating text. However, it may not give an adequate result as a fine translation product.
Keyword: google translate, error analysis, Mossop’s revision parameters
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Winiharti, Menik, Syihabuddin Syihabuddin, and Dadang Sudana. "On Google Translate: Students’ and Lecturers’ Perception of the English Translation of Indonesian Scholarly Articles." Lingua Cultura 15, no. 2 (November 30, 2021): 207–14. http://dx.doi.org/10.21512/lc.v15i2.7335.
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The research investigated the translation results of Google Translate based on the users’ perceptions. It was aimed at describing the users’ frequency in using Google Translate web, finding the users’ perceptions on the acceptability and the readability of Google Translation in translating scholarly articles from Indonesian into English, as well as finding whether students and lecturers have the same perceptions regarding these two criteria. The data were collected from scholarly articles written in Indonesian then translated into English using Google Translate web. Then a survey regarding this translation was distributed to users; they were students and lecturers of Computer Science/Information Technology/Information System. The analysis was conducted with regard to the acceptability and the readability of the translations using a rating scale of translation assessment. The findings suggest that more than half of the participants often use Google Translate Web, which means that the academics are part of the users of Google Translate. However, students and lecturers have a rather different perception of the results of Google Translate. Students consider Google Translate quite acceptable and readable, while lecturers view Google Translate as rather acceptable and moderately readable. In addition, the findings indicate that, to some extent, Google Translate still translates the Indonesian text into English literally.
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Putra, I. Putu Ambara. "The Translation Process of Machine Translation for Cultural Terms on Balinese Folktales." Linguistika: Buletin Ilmiah Program Magister Linguistik Universitas Udayana 29, no. 1 (March 8, 2022): 27. http://dx.doi.org/10.24843/ling.2022.v29.i01.p04.
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The goal of this study is to determine the capability of Google Translate in term of translating cultural terms. This study is conducted to analyze the translation performed by one of the well-known machine translation, Google Translate. The data is collected from the translation of seven traditional Balinese folktales chosen in random via online, namely Manik Angkeran, Kebo Iwa, Lubdaka, Tampaksiring, Origin of Bali, Origin of Singaraja, and Pan Balang Tamak. The data of the study is the translation on cultural terms translated by Google Translate. The cultural terms are classified with Cultural Term Classification Theory by Newmark. The analysis is conducted by comparing the translatied text and the original text to identify the translation method utilized by Google Translate in translating cultural term into English. The Translation Method Theory and Classification by Newmark is used to identify the method of translation utilized by Google Translate. The methods then is used in order to determine the tendency of Google Translate in the translation toward cultural terms.
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Wulansari, Dwi Windah. "Bias Gender Dalam Perbandingan Hasil Terjemahan Buku Cerita Anak Dongeng Bawang Merah Dan Bawang Putih Melalui Penerjemah Dan Google Translate." Wanastra: Jurnal Bahasa dan Sastra 12, no. 2 (September 26, 2020): 229–35. http://dx.doi.org/10.31294/w.v12i2.8516.
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Abstrak - Penelitian ini bertujuan untuk mencari bias gender yang terdapat pada hasil terjemahan buku cerita anak di bandingankan dengan hasil terjemahan google translate. Dalam penelitian ini menggunakan metode deskriptif kualitatif. Sumber data dalam penelitian ini adalah dongeng Bawang Merah dan Bawang Putih yang diterjemahkan dan diceritakan kembali oleh Gibran Maulana dan diterjemahkan melalui aplikasi Google Translate Hasil penerjemahan antara Google Translate dan penerjemah hampir sama yaitu mengenai nama tokoh, nama ganti orang dan nama ganti kepemilikan. Pada aplikasi Google Translate dapat melakukan kesalahan karena konteks, budaya, nama orang, dan kata ganti orang tidak dapat terbaca dalam aplikasi tersebut. sedangakan hasil terjemahan dari penerjemah mengalami human error. Penerjemah dalam buku cerita anak masih belum bisa lepas dari pengaruh ideologi patriarki yang dapat ditujukkan dalam peran gender tradisional yang digambarkan yang membuat peran laki-laki lebih unggul daripada perempuan. Kata Kunci: bias gender, cerita dongeng, google translate Abstract - This study aims to look for gender biases found in the results of the translation of children's storybooks in light with the results of the google translate translation. In this study using a qualitative descriptive method. The data source in this study is the fairy tale of Bawang Merah and Bawang Putih which were translated and retold by Gibran Maulana and translated through the Google Translate application. The results of the translation between Google Translate and the translator are almost the same, namely regarding the names of characters, people's names and ownership names. The Google Translate application can make mistakes because the context, culture, people's names, and pronouns cannot be read in the application. while the translation results from translators experienced human error. Translators in children's story books still cannot be separated from the influence of patriarchal ideology which can be shown in traditional gender roles which are described which make the role of men superior to women. Keyoword : gender bias, fairy tales, google translate
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van Arnhem, Jolanda-Pieta. "Google Translate App." Charleston Advisor 17, no. 3 (January 1, 2016): 24–27. http://dx.doi.org/10.5260/chara.17.3.24.
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Daly, Shelagh. "Google Translate app." Nursing Standard 28, no. 29 (March 19, 2014): 33. http://dx.doi.org/10.7748/ns2014.03.28.29.33.s38.
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Johnson, Gregory. "Google Translate http://translate.google.com/." Technical Services Quarterly 29, no. 2 (March 5, 2012): 165. http://dx.doi.org/10.1080/07317131.2012.650971.
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Fitria, Tira Nur. "Gender Bias in Translation Using Google Translate: Problems and Solution." Language Circle: Journal of Language and Literature 15, no. 2 (April 26, 2021): 285–92. http://dx.doi.org/10.15294/lc.v15i2.28641.
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This study discusses gender bias in terms of language especially from Indonesian into English translation by using Google Translate. This research is descriptive qualitative research. The result shows that most likely every language has gender-biased sides, including English because the type of society in the reality of life is more represented by men and women. In Google translate, the unequal differences between men and women translated into google translate causes the system to be considered biased and sexist towards gender. Whereas in fact, nowadays all genders can have various activities and jobs. Indonesian is also a gender-neutral language. When google translates to change into English, the sentence becomes gendered. The Indonesian language in this case seems to have been saved from being sexist because it does not associate a particular profession or activity with any gender. Unlike English, which adjusts personal pronouns based on gender. Google Translate is not always accurate, especially when translating from English to other languages. That is where Google Translate tends to go astray. The problem is that many languages have gender-based words, whereas English does not. But some words, like profession or occupation, can be masculine or feminine depending on the subject of the sentence, by assigning gender to certain adjectives and words that describe them. Equality in gender and race has been very difficult to achieve in machine technology situations because these systems are trained on existing content, and are not demographically representative. Google decided to make changes. It is important to adapt and build technology that can better serve humans. What may seem like small changes to everyday life are big steps towards gender equality. The way people speak their respective languages is one of the strongest ways of gender discrimination.
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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 (February 10, 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 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results.
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Hidayati, Niswatin Nurul. "Google Translate on Grab Application: Translation Study." PANYONARA: Journal of English Education 3, no. 1 (March 30, 2021): 49–61. http://dx.doi.org/10.19105/panyonara.v3i1.4204.
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The Google Translate service is widely used in Indonesia because it is considered very helpful, including being used by the Grab application platform, which the public has increasingly used in recent years. This research focused on the study of translation used in conversations between drivers and consumers. There were indeed many kinds of research on translation, but not many have discussed the translation used on Grab platforms. This research aimed to describe some of the translation styles used and deemed necessary for Grab and Google Translate improvement. Researchers used a qualitative approach by presenting data in the form of descriptions and analysis. Several points were found to be improved in the process of translating sentences in the grab application, including omitting the translation into the target language, word by word translation, inconsistency in translating a term, the application was unable to detect abbreviations, so it was not translated, the interrogative sentence in the source language was translated into a statement sentence in the target language. This research had many limitations so that other researchers can develop it more widely in the future.
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Ghasemi, Hadis, and Mahmood Hashemian. "A Comparative Study of Google Translate Translations: An Error Analysis of English-to-Persian and Persian-to-English Translations." English Language Teaching 9, no. 3 (January 31, 2016): 13. http://dx.doi.org/10.5539/elt.v9n3p13.
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<p>Both lack of time and the need to translate texts for numerous reasons brought about an increase in studying machine translation with a history spanning over 65 years. During the last decades, Google Translate, as a statistical machine translation (SMT), was in the center of attention for supporting 90 languages. Although there are many studies on Google Translate, few researchers have considered Persian-English translation pairs. This study used Keshavarzʼs (1999) model of error analysis to carry out a comparison study between the raw English-Persian translations and Persian-English translations from Google Translate. Based on the criteria presented in the model, 100 systematically selected sentences from an interpreter app called Motarjem Hamrah were translated by Google Translate and then evaluated and brought in different tables. Results of analyzing and tabulating the frequencies of the errors together with conducting a chi-square test showed no significant differences between the qualities of Google Translate from English to Persian and Persian to English. In addition, lexicosemantic and active/passive voice errors were the most and least frequent errors, respectively. Directions for future research are recognized in the paper for the improvements of the system.</p>
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Karjo, Clara Herlina, and Ecclesia Metta. "The Translation of Lexical Collocations in Undergraduate Students’ Theses’ Abstract: Students Versus Google Translate." Lingua Cultura 13, no. 4 (December 10, 2019): 289. http://dx.doi.org/10.21512/lc.v13i4.6067.
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This research intended to compare the translations of lexical collocations found in the abstract section of students’ theses. The purposes were to find out the errors in translating lexical collocation either by Google Translate or student translator. The data were taken from twenty working papers of English Literature students at Binus University. The abstracts of these theses (in English and Indonesian) were then processed with Google Translate. Thus, there were four sets of data to analyze: (1) Students’ Text in Indonesian (STI), (2) Google Translate of STI in English (GTE), (3) Students’ Text in English (STE), and (4) Google Translate of STE in Indonesian (GTI). From the data, samples of collocations were taken and categorized based on Hill’s classification of lexical collocations. The lexical collocations found in the four sets of data were scrutinized, compared, and analyzed to find the errors in forms and meaning as well as in the translation. The results reveal that errors in translating collocations are mostly made by Google Translate rather than the students. This research implies that Google Translate still needs improvement in translating collocations, but it is also possible that translation errors occur because of students’ misuse of collocation.
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Ramati, Ido, and Amit Pinchevski. "Uniform multilingualism: A media genealogy of Google Translate." New Media & Society 20, no. 7 (August 22, 2017): 2550–65. http://dx.doi.org/10.1177/1461444817726951.
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This article applies a media geneaology perspective to examine the operative logic of Google Translate. Tracing machine translation from post–World War II (WWII) rule-based methods to contemporary algorithmic statistical methods, we analyze the underlying power structure of algorithmic and human collaboration that Translate encompasses. Focusing on the relationship between technology, language, and speakers, we argue that the operative logic of Translate represents a new model of translation, which we call uniform multilingualism. In this model, the manifest lingual plurality on the user side is mediated by lingual uniformity on the system side in the form of an English language algorithm, which has recently given way to an artificial neural network interlingual algorithm. We conclude by considering the significance of this recent shift in Translate’s algorithm.
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Anggaira, Aria Septi. "LINGUISTIC ERRORS ON NARRATIVE TEXT TRANSLATION USING GOOGLE TRANSLATE." Pedagogy : Journal of English Language Teaching 5, no. 1 (July 30, 2017): 1. http://dx.doi.org/10.32332/pedagogy.v5i1.717.
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This study aims to identify and analyze errors of language aspects that appear on the machine translator from Google-Translate on narrative texts in English into Indonesian. Based on the results of the analysis, it is showed that the morphological aspects occupy the highest positions in the data summary types of errors, as many as 13 errors. Next is the syntactic aspect for 9 errors, and morphology of for 12 errors. It can be concluded that the translation using Google Translate is not the right solution for someone who wants to translate foreign language text, especially if it is used in the learning process at schools.
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Wiwanitkit, Viroj. "How to Verify and Manage the Translational Plagiarism?" Open Access Macedonian Journal of Medical Sciences 4, no. 3 (June 24, 2016): 533. http://dx.doi.org/10.3889/oamjms.2016.070.
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The use of Google translator as a tool for determining translational plagiarism is a big challenge. As noted, plagiarism of the original papers written in Macedonian and translated into other languages can be verified after computerised translation in other languages. Attempts to screen the translational plagiarism should be supported. The use of Google Translate tool might be helpful. Special focus should be on any non-English reference that might be the source of plagiarised material and non-English article that might translate from an original English article, which cannot be detected by simple plagiarism screening tool. It is a hard job for any journal to detect the complex translational plagiarism but the harder job might be how to effectively manage the case.
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Harahap, Khoirul Amru. "Analisis Kesalahan Linguistik Hasil Terjemahan Mesin Terjemah Google Translate dari Teks Bahasa Arab ke dalam Bahasa Indonesia." Jurnal Penelitian Agama 15, no. 1 (June 20, 2014): 26–43. http://dx.doi.org/10.24090/jpa.v15i1.2014.pp26-43.
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Abstract: This study is aimed at analyzing linguistic errors of Arabic-to-Indonesian translation resulted from Google Translate. This is a literary research inwhich data were collected through taking sample errors in the provided texts. Thefindings of this research are as follows. Arabic-to-Indonesian translation resultedfrom Google Translate contains some linguistic errors, comprising morphological,syntactic, and semantic levels. Morphological errors include errors related to bothism and fi’il. Related to ism, for example, Google translate misread the harakat ismmusytaq; kasrah is read as fathah; which causes changes in meaning. GoogleTranslate also mistranslate ism mudzakkar to ism muannats. In addition, Googletranslates ism jamak into mufrad and not appropriately gives the equivalence toism maqshur. Meanwhile, in relation to fi’il, Google translates fi’il ma’lum into majhul,and fi’il majhul into ma’lum.Keywords: Linguistic, Translation, Google Tanslate, Indonesian Language.
Abstrak: Hasil penelitian ini menunjukkan bahwa terdapat beberapakesalahan linguistik dalam hasil terjemahan Google Translate dari teks bahasaArab ke dalam bahasa Indonesia, baik kesalahan pada tataran morfologis dankesalahan pada tataran sintaksis, maupun kesalahan pada tataran semantik.Kesalahan morfologis dari hasil terjemahan Google Translate, ada yang berkaitandengan ism dan ada yang berkaitan dengan fi’il. Yang berkaitan dengan ism,misalnya, Google keliru membaca harakat ism musytaq, yang seharusnya kasrahdibaca fathah. Hal ini membawa dampak pada perubahan makna. Google jugasalah menerjemahkan ism mudzakkar dan menerjemahkannya dengan bentukism muannats. Di samping itu, Google juga menerjemahkan ism jamak denganmufrad dan keliru mencari padanan arti yang tepat untuk ism maqshur. Sedangyang berkaitan dengan fi’il, Google menerjemahkan fi’il ma’lum menjadi majhuldan fi’il majhul menjadi ma’lum.Kata Kunci: Linguistik, Terjemahan, Google Tanslate, dan Bahasa Indonesia.
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Koletnik Korošec, Melita. "Applicability and Challenges of Using Machine Translation in Translator Training." ELOPE: English Language Overseas Perspectives and Enquiries 8, no. 2 (October 10, 2011): 7–18. http://dx.doi.org/10.4312/elope.8.2.7-18.
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During the last decade, translation as well as translator training have experienced a significant change. This change has been significantly influenced by the development of the Internet and the successive availability of web-based translation resources, such as Google Translate. Their introduction into the translation didactic process and training is no longer a matter of a teacher’s personal preference and IT skills, but a necessity imposed by the ever-swifter advancement of technology. This article presents the experimental results of an ongoing broader research study focusing on the modes and frequency of use of the Internet, Google Translate and Google Translator Toolkit among translation students at the undergraduate level. The preliminary results, presented in this article, are based on a questionnaire which was prepared in relation to the use of Google Translate while considering the latest professional findings. The article concludes with the author’s observations as to the applicability of these resources in translator training and the challenges thereof.
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Kim, Kyu-Seok. "A Method to Improve the Accuracy of Voice Translation by Adding Intentional Spaces." Korean Society of Technical Education and Training 25, no. 3 (September 30, 2020): 93–98. http://dx.doi.org/10.29279/kostet.2020.25.3.93.
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Real-time voice translation systems receive a speaker s voice and translate their speech into another language. However, the meaning of a whole Korean sentence can be unintentionally changed because Korean words and syllables can be merged or divided by spaces. Therefore, the spaces between the speaker s sentences are occasionally not identified by the speech recognition system, so the translated sentences are sometimes incorrect. This paper presents a methodology to enhance the accuracy of voice translation by adding intentional spaces. An Android application was implemented using Google speech recognizer for Android and Google translator for the Web. The Google speech recognizer app for Android receives the speaker s voice sentences in Korean and shows the text results. Next, the proposed Android application adds spaces when the speaker speaks the dedicated word for the space. Finally, the modified Korean sentences are translated into English by Google translator for the Web. Using this method can enhance interpretation accuracy for translation systems.
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AbuSa'aleek, Atef Odeh. "The Adequacy and Acceptability of Machine Translation in Translating the Islamic Texts." International Journal of English Linguistics 6, no. 3 (May 26, 2016): 185. http://dx.doi.org/10.5539/ijel.v6n3p185.
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<p>Islamic translation is considered as a special distinguished sub-discipline of applied linguistics. It is one of the most important areas of translation because it carries the values and eternal message. Through the history, the first translation work was of religious books. This study attempts to evaluate the adequacy and acceptability of four machine translation (MT) systems (World lingo, Babylon translation, Google translate, Bing translator) in translating the Islamic texts. In addition, it aims to evaluate the Islamic translation outputs based on functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability, and fidelity). The findings indicted that Google Translate System is the most adequate and acceptable among the other three systems (World lingo, Babylon translation, Bing translator) in translating the Islamic texts. The findings also revealed that Google Translate is acceptable in producing Islamic translation outputs in regard to the following functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability and fidelity) due to Google Translate advancement.</p><strong></strong>
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Almahasees, Zakaryia, and Sameh Mahmoud. "Evaluation of Google Image Translate in Rendering Arabic Signage into English." World Journal of English Language 12, no. 1 (February 22, 2022): 185. http://dx.doi.org/10.5430/wjel.v12n1p185.
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When people travel to another country for work or leisure, they regularly need a medium to help them understand the written messages in other languages. Google Translate offers a new service: translating the content of images (texts) instantly and freely into 100 languages powered by the Neural Machine Translation approach (NMT). In this vein, the current research paper attempts to evaluate the accuracy of Google Image Translate service in rendering the texts printed on Arabic signage: banners and road and shop signs from Arabic into English. Besides, it aims to identify the capacity of Google Translate in rendering Arabic signage into English effectively without the help of human translators. The paper adopts the Linguistic Error Analysis Framework of Costa et al. (2015) in analyzing the output of Google image service in terms of orthography, grammar, lexis, and semantics. The paper shows that Google Translate made the following errors while rendering the content of images into English: mistranslation, omission, additions, wrong choice, misordering, subject-verb disagreement, and semantic errors. In conclusion, the Google Image Translate service helps the users configure the gist of the image. However, a human translator is still needed since MT may not provide an adequate and effective translation as humans do.
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Khoiriyah, Hidayatul. "KUALITAS HASIL TERJEMAHAN GOOGLE TRANSLATE DARI BAHASA ARAB KE BAHASA INDONESIA." Al Mi'yar: Jurnal Ilmiah Pembelajaran Bahasa Arab dan Kebahasaaraban 3, no. 1 (April 10, 2020): 127. http://dx.doi.org/10.35931/am.v3i1.205.
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<p style="text-align: justify;"><em>The development of technology has a big impact on human life. The existence of a machine translation is the result of technological advancements that aim to facilitate humans in translating one language into another. The focus of this research is to examine the quality of the google translate machine in terms of vocabulary accuracy, clarity, and reasonableness of meaning. Data of mufradāt taken from several Arabic translation dictionaries, while the text is taken from the phenomenal work of Dr. Aidh Qorni in the book Lā Tahzan. The method used in this research is the translation critic method. </em></p><p style="text-align: justify;"><em>The results showed that in terms of the accuracy of vocabulary and terms, Google Translate has a good translation quality. In terms of clarity and reasonableness of meaning, google translate has not been able to transmit ideas from the source language well into the target language. Furthermore, in grammatical, the results of the google translate translation do not have a grammatical arrangement, the results of the google translate translation do not have a good grammatical structure and are by following the rules that applied in the target Indonesian language.</em></p><p style="text-align: justify;"><em>From the data, it shows that google translate should not be used as a basis for translating an Arabic text into Indonesian, especially in translating verses of the Qur'</em><em>ā</em><em>n and Hadīts. A beginner translator should prefer a dictionary rather than using google translate to effort and improve the ability to translate.</em></p><p style="text-align: justify;"><strong><em>Key Words: Translation, Google Translate, Arabic</em></strong></p>
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Resende, Natália, and Andy Way. "Can Google Translate Rewire Your L2 English Processing?" Digital 1, no. 1 (March 4, 2021): 66–85. http://dx.doi.org/10.3390/digital1010006.
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In this article, we address the question of whether exposure to the translated output of MT systems could result in changes in the cognitive processing of English as a second language (L2 English). To answer this question, we first conducted a survey with 90 Brazilian Portuguese L2 English speakers with the aim of understanding how and for what purposes they use web-based MT systems. To investigate whether MT systems are capable of influencing L2 English cognitive processing, we carried out a syntactic priming experiment with 32 Brazilian Portuguese speakers. We wanted to test whether speakers re-use in their subsequent speech in English the same syntactic alternative previously seen in the MT output, when using the popular Google Translate system to translate sentences from Portuguese into English. The results of the survey show that Brazilian Portuguese L2 English speakers use Google Translate as a tool supporting their speech in English as well as a source of English vocabulary learning. The results of the syntactic priming experiment show that exposure to an English syntactic alternative through GT can lead to the re-use of the same syntactic alternative in subsequent speech even if it is not the speaker’s preferred syntactic alternative in English. These findings suggest that GT is being used as a tool for language learning purposes and so is indeed capable of rewiring the processing of L2 English syntax.
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ODACIOĞLU-, Cem. "ÇEVİRİ SÜRECİNDE “GOOGLE TRANSLATE” KULLANIMININ DEĞERLENDİRİLMESİ." Turkish Studies - Language and Literature Volume 14 Issue 3, Volume 14 Issue 3 (2019): 1375–93. http://dx.doi.org/10.29228/turkishstudies.25324.
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Kol, Sara, Miriam Schcolnik, and Elana Spector-Cohen. "Google Translate in Academic Writing Courses?" EuroCALL Review 26, no. 2 (September 30, 2018): 50. http://dx.doi.org/10.4995/eurocall.2018.10140.
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<p>The aim of this study was to explore the possible benefits of using Google Translate (GT) at various tertiary English for Academic Purposes (EAP) course levels, i.e., to see if the use of GT affects the quantity and quality of student writing. The study comprised preliminary work and a case study. The former included an awareness task to assess student awareness of GT mistakes, and a correction task to assess their ability to correct the mistakes identified. The awareness and correction tasks showed that intermediate students identified 54% of the mistakes, while advanced students identified 73% and corrected 87% of the mistakes identified. The case study included two writing tasks, one with GT and one without. Results showed that when using GT students wrote significantly more words. They wrote longer sentences with longer words and the vocabulary profile of their writing improved. We believe that GT can be a useful tool for tertiary EAP students provided they are able to critically assess and correct the output.</p>
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Martina Mulyani and Fakhrunisa Afina. "THE STUDENTS’ ATTITUDE TOWARDS GOOGLE TRANSLATE." JELA (Journal of English Language Teaching, Literature and Applied Linguistics) 3, no. 1 (April 1, 2021): 1–13. http://dx.doi.org/10.37742/jela.v3i1.36.
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Google Translate (GT) is a tool commonly used by those who learn translation in higher education. While the research on GT mostly evaluated GT through experimental design, very few studies have focused on student perceptions of GT. Hence, this research intends to investigate their students’ attitudes of GT performance. 24 students of higher education in Cimahi participated in the study. The data about students’ attitude include behavior, cognitive, and affective attitude. They were gathered through questionnaire and interview. The result revealed that in behavioral attitude, the students’ often use GT to check the meaning of unknown word and translating a sentence. Meanwhile, the cognitive attitude indicated that few students assume that GT is ethically acceptable regardless of how it is used because it is helpful in the language learning process. In the affective attitude, GT is positive because they felt like using GT in translation. Even, some of them felt helped by GT’s assistance and the other reason was GT was easy to use. In short, student’s regard GT as a useful tool in translation depending on the way how one uses the tool.
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Maulida, Hidya. "Persepsi Mahasiswa Terhadap Penggunaan Google Translate Sebagai Media Menerjemahkan Materi Berbahasa Inggris." Jurnal SAINTEKOM 7, no. 1 (April 13, 2017): 56. http://dx.doi.org/10.33020/saintekom.v7i1.21.
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The use of smartphone for helping students in studying is so familiar. It is used almost in every students’ studying activity. For example, it is often used for browsing material needed or for translating english word to indonesian or the opposite. In efforting to comprehend english material, students always try to translate it first. Google translate is a service often used by students to translate.
This study describes of students’ perception towards the use of google translate to translate english material. Interview is used in collecting data. The subject is the seventh grade students by considering that based on prelimanary study, they use google translate and they get many assigments to translate English material.
Data shows that students’ perception toward the use online dictionary in translating english material is positive. It is stated that google translate giving help a lot. Students can translate faster and complete their assignments. Although there is still weakness of translation result using google translate, google translate saves time in translating english material. The weakness of it overcome by rereading and fixing the translation with context. It is suggested to the students take other benefits of google translate.
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Ordanovska, Oleksandra, and Alexander Iliadi. "FEATURES OF COMPUTER TRANSLATION OF ENGLISH SCIENTIFIC AND TECHNICAL LITERATURE INTO UKRAINIAN (on the example of texts on physics and engineering mechanics)." Naukovy Visnyk of South Ukrainian National Pedagogical University named after K. D. Ushynsky: Linguistic Sciences 2019, no. 29 (November 2019): 200–215. http://dx.doi.org/10.24195/2616-5317-2019-29-15.
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The article is devoted to the problem of the quality of computer translation of scientific texts that today is very relevant because of intensive progress and mass using of the Systems of Computer Aid Translation. The research aim was the analysis and comparison of computer translations of English texts on physics and engineering sciences into Ukrainian with using Pereklad.online.ua, Google Translate, PROMT, Pragma. The quality comparison of the texts' computer translations took place according to the parameters taking into account syntactic features, technical adaptation of the text, and correct use of terminological vocabulary. As a result of the research it was found that Google Translate translations which are based on the statistical (phrase-based) method turned out to be better. Google Translate translations took into account the syntactic features of the text and made a little of errors in grammatical forms; the technical adaptation of the text was carried out (the use of correct mathematical records of decimal fractions, signs of mathematical actions, transliteration of units of measurement; equivalent terminological vocabulary was used etc.) unlike another online translators' translations. The following Google Translate translations were improved due to the built-in translation memory system. At the same time the analysis of the Google Translate translation of the text on physics that used terms without unambiguous equivalents in Ukrainian has showed the inability of the online translator to perform the contextual translation. So computer translators can only play a supporting role and be used as the primary translator of standard scientific and technical texts.
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Umam, Mustolikh Khabibul. "Google Translate in Tarjamah Learning at Arabic Language Education UIN Walisongo Semarang." Mantiqu Tayr: Journal of Arabic Language 1, no. 1 (January 10, 2021): 61–70. http://dx.doi.org/10.25217/mantiqutayr.v1i1.1279.
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The development of translator applications in the last decade, has had a significant impact on the results of learning tarjamah to students majoring in Arabic language education (PBA), the ability of translator applications is increasing all the time with improved data retrieval processes and systematic search procedures. A number of Students of PBA UIN Walisongo Semarang consider the application of translation known as Google Translate to be an indispensable tool to save time and effort. However, how this application has a role to play in the process of learning tarjamah. This research is intended to answer the problem: 1) What is the role of Google Translate in the process of translating Arabic text?. 2) What impact does it have on the use of Google Translate apps?. This research is a qualitative research category that is analyzed using statistical techniques with data collection techniques in the form of interviews, questionnaires and also documentation. The data is analyzed using qualitative descriptive analysis strategies and qualitative verification. The results showed that Google Translate has a considerable contribution to the translation process of PBA students. The average test results of questionnaires get more than 56% in several categories, namely in terms of intensity of use, function, effectiveness, how to use, impact of use, efficiency, quality, facilities, benefits, disadvantages and advantages. The use of this machine translator also has an impact on students. The results showed 58% for positive impact and 42% for negative impact on the use of internet-based translation application.
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Angi, Brevian Rival R. "Kualitas terjemahan itranslate dan Google Translate dari Bahasa Inggris ke dalam Bahasa Indonesia." Deskripsi Bahasa 2, no. 1 (March 11, 2019): 6–11. http://dx.doi.org/10.22146/db.v2i1.337.
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Penelitian ini berfokus pada hasil terjemahan kesepadanan gramatikal oleh mesin penerjemah Google translate dan Itranslate. Dewasa ini telah banyak perkembangan minat pada terjemahan dan orang pada umumnya melakukan penerjemahan menggunakan mesin penerjemah. Dengan perkembangan mesin penerjemah saat ini telah membawa pada harapan untuk mendapatkan hasil terjemahan yang baik seperti hasil terjemahan seorang pakar penerjamah. Salah satu mesin yang digunakan untuk melakukan penerjemahan adalah Google translate. Hal ini didasari oleh layanan Google translate yang tidak berbayar sehingga mesin penerjemah ini dipilih oleh banyak orang untuk melakukan penerjemahan. Itranslate pun hadir sebagai rival dan mengklaim sebagai pemimpin untuk jasa penerjemahan mesin (www.itranslate.com). Tujuan penelitian ini adalah untuk melihat kualitas terjemahan antara Itranslate dan Google translate dan penelitian ini hanya berfokus pada terjemahan kesepadanan gramatikal pada mesin penerjemah iTranslate dan Google translate dari bahasa Inggris ke dalam bahasa Indonesia. Hasil penelitian menunjukkan bahwa hasil terjemahan Google translate lebih unggul dari Itranslate. Google translate memiliki kesalahan lebih sedikit dibandingkan dengan Itranslate.
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Aliurridha, Aliurridha, and Sufriati Tanjung. "POST-EDITING PROPORTION OF GOOGLE TRANSLATE IN INFORMATIVE AND EXPRESSIVE TEXTS." LEKSEMA: Jurnal Bahasa dan Sastra 4, no. 1 (June 20, 2019): 41. http://dx.doi.org/10.22515/ljbs.v4i1.1558.
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The massive development of Google Translate (GT) is remarkable and there are many people all over the world use it. Yet, the texts that have been translated by GT still need post-editing by the human translator. This research aims to find the proportion that translator needed in post-editing when using GT in translating of informative and expressive text from English to Indonesia and what they need to pay attention in translating informative and expressive text using GT. The data in this research were words, phrases, and sentences that were analyzed using descriptive-comparative with error analysis at three different levels: accuracy and acceptability. The result shows that the proportion of post-editing for informative text is 5% for accuracy and 22% for acceptability, while in the expressive text, 33% for accuracy and 47% for acceptability. Humans need more effort in post-editing the expressive text because the structure of the sentences in the expressive text is more complex and longer. Furthermore, there are many CSI (Cultural Specific Items) found in the expressive text that makes the result unacceptable for TT readers. Another problem in using GT that the result of GT translation is literal while the expressive text has many of figurative meaning. It means that GT is acceptable for semantic translation. Thus, this research suggests GT can be used directly in translating informative text because informative, as long as the users conduct post-editing and the users should not use GT in translating expressive text directly except for decoding the semantic, pragmatic, and contextual meaning to find the suitable translation.
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Jumatulaini, Jumatulaini. "ANALISIS KEAKURATAN HASIL PENERJEMAHAN GOOGLE TRANSLATE DENGAN MENGGUNAKAN METODE BACK TRANSLATION." ALSUNIYAT: Jurnal Penelitian Bahasa, Sastra, dan Budaya Arab 3, no. 1 (April 30, 2020): 77–87. http://dx.doi.org/10.17509/alsuniyat.v3i1.23616.
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This research analyses comparison between the original Arabic text and the translation back to Arabic by Google Translate on Arabic newspapers. To achieve test the accuracy of text according to translated theory. Then researchers use qualitative descriptive method of research with translation method back translations that is, a validation tool that is widely used in international research settings and original documents compared to translation results Back to see the inconsistencies, and if nothing of the translation is considered equivalent. Based on the results of the study the accuracy results of Google Translate is inaccurate, but understanding of the meaning text still understandable. As for inaccuracies of translations found such as linguistic studies such as syntactic, semantics, mistakes in writing numbers, lack of words, reduction.
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Aiken, Milam. "An Updated Evaluation of Google Translate Accuracy." Studies in Linguistics and Literature 3, no. 3 (July 17, 2019): p253. http://dx.doi.org/10.22158/sll.v3n3p253.
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In 2011, a comprehensive evaluation of accuracy using 51 languages with Google Translate showed that many European languages had good results, but several Asian languages performed poorly. The online service has improved its accuracy over the intervening eight years, and a reevaluation using the same text as the original study shows a 34% improvement based upon BLEU scores. This new study shows that translations between English and German, Afrikaans, Portuguese, Spanish, Danish, Greek, Polish, Hungarian, Finnish, and Chinese tend to be the most accurate.
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Wade, R. G. "Try Google Translate to overcome language barriers." BMJ 343, no. 15 2 (November 15, 2011): d7217. http://dx.doi.org/10.1136/bmj.d7217.
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Noviarini, Tiara. "THE TRANSLATION RESULTS OF GOOGLE TRANSLATE FROM INDONESIAN TO ENGLISH." Jurnal Smart 7, no. 1 (January 31, 2021): 21–26. http://dx.doi.org/10.52657/js.v7i1.1335.
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Google Translate is a free multilingual translation machine developed by Google that can assist translators to make their translation functions easier and faster. The aim of the research is to analyze whether it can be relied on as a substitute for translators. This research used a literature analysis method by analyzing the results of the translated book and machine translation. The result found that it cannot replace translators. It has its limitations, including understanding the context and cultural situation of a nation. Therefore, this machine is useful only in assisting the translation process.
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Khasanah, Uswatun, A. Hilal Madjdi, and Nuraeningsih Nuraeningsih. "Students' perception on the use of Google Translate in learning Pronunciation." Borneo Educational Journal (Borju) 4, no. 1 (February 28, 2022): 50–60. http://dx.doi.org/10.24903/bej.v4i1.912.
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Since the Covid-19 pandemic that occurred in Indonesia, teaching and learning activities must be carried out online. Therefore, to improve students’ pronunciation skills, the teacher suggests to students to use Google Translate Application as media to learn pronunciation independently. Google Translate is an application that has various features, one of them is the pronunciation feature that can convert sound into text. This feature can be used by students’ to practice pronunciation so that it can train students’ ability to pronounce difficult words. The aim of this research is to describe the students’ perceptions of using Google Translate Application and to describe the obstacles do students find using Google Translate Application as media in learning pronunciation.
This research used descriptive qualitative research design. The participants of this research is 15 students of the tenth grade an Islamic public senior high school in Kudus, Indonesia. The data was collected by distributing a questionnaire to the respondents.
The result of this research shows the students’ perception on using Google Translate Application as media in learning pronunciation was positive, because students can learn practically and independently. While the obstacles that students found when learning pronunciation using Google Translate were that Google Translate often had errors, could not be used offline, took a long time to load, and had low accuracy.
Therefore, to help students improve their pronunciation ability, students’ can use Google Translate Application as learning media. To minimize obstacles when using Google Translate Application, it is recommended to use Wi-Fi, so that the connection is good so that loading can be faster and reduce errors.
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Ibrahim Fattah, Ammar. "Los servicios de la traducción automática de Google y sus problemas The problems of Google Translate." Journal of the College of languages, no. 43 (January 2, 2021): 303–18. http://dx.doi.org/10.36586/jcl.2.2021.0.43.0303.
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Existen varios servicios de la traducción automática que los usuarios del internet pueden elegir entre varios con el fin depoder traducir automáticamente un determinado texto, uno de estos servicios de la traducción automática es elGoogle que es uno de los servicios más popularesque permite traducir los textos a 51 idiomas. El presente trabajo de investigación tiene como objetivo explorar la naturaleza del proceso de la traducción dada por el servicio de traducción de Google, analizando los problemas más destacados que surjan a la hora de traducir, ya que el traductorque usa el Google es, de ninguna manera, un traductor profesional, y por lo tanto, puede llegar a una traducción totalmente catastrófica. Abstract There are numbers of automatic translation services that internet users can choose to automatically translate a certain text, and Google translate is one of these automatic services that proposes over 51 Languages. The present paper sheds light on the nature of the translation process offered by Google, and analyze the most prominent problems faced when Google translate is used. Direct translation is common with Google Translate and often results in nonsensical literal translations, particularly with long compound sentences. This is due to the fact that Google translation system uses a method based on language pair frequency that does not take into account grammatical rules which, in turn, affects the quality of the translation. The complexity of the text, as well as any context which cannot be interpreted without a true knowledge of the language, makes the likelihood of errors greater. Professional translators take great pains to ensure that this does not happen by using well-established online glossaries, back translation methods, proof readers and reviewers. The present paper discusses the Google translate of advertisements, Al-Qur’an holy texts as well as literary texts out of Arabic into Spanish stressing the pros and cons of Google translate that not only impact professional translators in the language service industry but also take the interest of the language experts.
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Nugraha, Gilang, Ratnawati Ratnawati, and AM Surachmat. "EXPLORING LOW AND HIGH STUDENTS� PERCEPTION ON ENGAGING E-DICTIONARY IN MASTERING VOCABULARY: CROSS-SECTIONAL SURVEY." Indonesian EFL Journal 5, no. 1 (January 16, 2019): 37. http://dx.doi.org/10.25134/ieflj.v5i1.1609.
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This paper is a survey study that aims at investigating low and high achievement students� perception on engaging electronic dictionary in enhancing their vocabulary mastery. There were 30 students involved in this study consisting of 16 students as the high achievement students and 14 students as the low achievement students. They were considered as high and low students based on the result of their last examination score. Questionnaire and interview were used to collect the data. The results of the study showed that low achievement students have positive perception on the use of electronic dictionary especially Google Translate. Most of them agreed that the use of Google Translate helped them in improving their English vocabulary mastery because Google Translate is easy to use and fast in translating meaning of words. Moreover, most �of the high achievement students also have positive perception on the use of Google Translate because it is easy to use and free, it can be accessed by using their smartphones. Nevertheless, the problems also found in the use of electronic dictionary especially Google Translate, including bad internet connection which affected the performance of Google Translate. It can be conluded that there are almost similar perception between low and high achievement students on the use of electronic dictionary especially Google Translate.Keywords: electronic dictionary; google translate; low and high achievement students; perception; vocabulary mastery.
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Abdi, Hamidreza. "Considering Machine Translation (MT) as an Aid or a Threat to the Human Translator:." Journal of Translation and Language Studies 2, no. 1 (March 31, 2021): 19–32. http://dx.doi.org/10.48185/jtls.v2i1.122.
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The present study aims to evaluate the output quality of an online MT; namely, Google Translate, from English into Persian and compare its output with the translations made by the human translators to find out that whether MT applications are considered as an aid or a threat to human translators. For the application of the study, the researcher designed a translation test consisting of 60 statements from different types of texts proposed by Reiss (1989). The translation test was translated via Google Translate and administrated to three human translators to be rendered. The translations made by Google Translate and by the three human translators alongside the 60 statements were given to 40 judges to be evaluated based on Dorr et al. s' (2010) criterion of MT quality assessment, including semantic adequacy, fluency, and understandability. As results indicated, Google Translate gave a good overall performance in the translation of the 60 statements into Persian in terms of semantic adequacy and understandability, but not in terms of fluency. Thus, there should be no fear of human translators being replaced by the MT. As conclusion, MT applications cannot be considered a threat to human translators, but as an aid for them. The present study also offers some recommendations that can be beneficial to translation students, trainee translators, translation teachers, and professional translators.
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Lazebna, Natalia. "Semantic ambiguity of urbanistic terminology (Ukrainian-English Google translate vs human translation)." Nova fìlologìâ, no. 76 (2019): 61–65. http://dx.doi.org/10.26661/2414-1135/2019-76-11.
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Stapleton, Paul. "Using Google Translate as a Tool to Improve L2 Writing." International Journal of Computer-Assisted Language Learning and Teaching 11, no. 3 (July 2021): 92–98. http://dx.doi.org/10.4018/ijcallt.2021070106.
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In the present study, two sets of scripts from primary school students were collected, one written in English and the other in their native Chinese on the same topic. The Chinese scripts were translated into English by Google Translate (GT) and compared with the scripts written in English. Sentences in the two sets of passages that were clearly parallel in meaning were then extracted and compared for accuracy, vocabulary, substance, and length. Findings revealed that in some cases the GT versions (i.e., those originally written in the native tongue) displayed language that was significantly better that what the students produced when writing in English. Patterns of improvement are analyzed and discussed.
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Aflah, Laila Nur. "KOMPARASI HASIL TERJEMAHAN GOOGLE TRANSLATE DAN BING TRANSLATOR DALAM MENERJEMAHKAN HEDGING WORDS." PRASASTI: Journal of Linguistics 5, no. 1 (June 8, 2020): 68. http://dx.doi.org/10.20961/prasasti.v5i1.38168.
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<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>This study aims to explain the comparison of the translation results of two translation machines, Google Translate and Bing Translator. The object of this research is the hedging words contained in one of the opinions in The Jakarta Post's online newspaper entitled Why Indonesians should write about Indonesia in English more often. The types of hedges that appear in the article and become the object of research are in four types, namely: lexical verb, modal auxiliary verb, compound hedges, and adverb of frequency. From the analysis conducted, the writer found that the translation of hedging words with two translation machines assisted different results of the translation are identical. The method used by the two translation machines is literal translation so that the form and meaning of the source text is equivalent to the form and meaning of the target text. The author believes that the artificial intelligence developed in those two translation machines has different abilities and can be proven by research with wider and more varied sentence objects.</p></td></tr></tbody></table>
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Chompurach, Wichuta. "“Please Let me Use Google Translate”: Thai EFL Students’ Behavior and Attitudes toward Google Translate Use in English Writing." English Language Teaching 14, no. 12 (November 16, 2021): 23. http://dx.doi.org/10.5539/elt.v14n12p23.
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The present study aims to investigate how Thai EFL university students use Google Translate (GT) in English writing, how they post-edit (PE) its outputs, and how they view GT use in English writing. The participants were 15 third-year non-English major students from three universities in Thailand. The data collection tools were an interview and two writing assignments. After the data analysis, the findings revealed the students’ behavior of GT use and their output PE as well as their attitudes toward GT use in English writing. The results reported the students always used GT in completing writing tasks at both sentence and paragraph levels, and most students did PE the outputs before applying them. However, a few students used the outputs with no PE because they trusted in GT more than they did in themselves. Regarding the PE level, the students intended to address lexical and syntax errors, so their correcting covered the light level. The results also revealed mixed messages in their attitudes toward GT use in English writing. Most students viewed GT as a helpful, reliable assistant enhancing their writing quality, but some raw GT outputs of phrases, idioms, long sentences, and paragraphs were found incomprehensible. Also, the students acquired some bad habits from using GT. However, most students disagreed with not being allowed to use GT in English writing. The study recommended language teachers to provide Thai EFL students adequate instructions for the effective use of GT and its output PE.
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Herlina, Ninin, Ratna Dewanti, and Ninuk Lustiyantie. "Google Translate as an Alternative Tool for Assisting Students in Doing Translation : A Case Study at Universitas Negeri Jakarta, Indonesia." BAHTERA : Jurnal Pendidikan Bahasa dan Sastra 18, no. 1 (January 1, 2019): 70–78. http://dx.doi.org/10.21009/bahtera.181.06.
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Abstract
The paper examines the use of Google Translate as an Alternative tool for assisting students at Universitas Negeri Jakarta, Indonesia to translate and develop their knowledge and skills in doing Translation. The participants of the study were 36 students at the Applied Linguistic of Doctoral Program at Universitas Negeri Jakarta who had registered of the year 2017. Based on literature review, analysis of the collecting data, and an assessment of the course content and activities inside and outside the learning process, the findings suggest that most Applied Linguistic of Doctoral Program students at Universitas Negeri Jakarta recognize Google Translate as an Alternative tool for doing references book translation. In fact, some students reported that they could optimally ,benefit from self-learning if they were assisted to use Google Translate effectively. Moreover, using Google Translate for doing classroom tasks and reference books translation encourage students to study independently, and to shape their own strategies for solving language problem.
Keywords : Google Translate, Alternative Tool, Translation, Assisting Student
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Samokhin, I. S., N. L. Sokolova, and M. G. Sergeyeva. "Some Weaknesses of Modern Machine Translation (by Example of Google Translate Web Service)." Nauchnyy dialog, no. 10 (2018): 148–57. http://dx.doi.org/10.24224/2227-1295-2018-10-148-157.
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Bowker, Lynne. "Machine translation and author keywords: A viable search strategy for scholars with limited English proficiency?" Advances in Classification Research Online 29, no. 1 (June 28, 2019): 13. http://dx.doi.org/10.7152/acro.v29i1.15455.
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Author keywords are valuable for indexing articles and for information retrieval (IR). Most scientific literature is published in English. Can machine translation (MT) help researchers with limited English proficiency to search for information? We used two MT systems (Google Translate, DeepL Translator) to translate into English 71 Spanish keywords and 43 French keywords from articles in the domain of Library and Information Science. We then used the English translations to search the Library, Information Science and Technology Abstracts (LISTA) database. Half of the translated keywords returned relevant results. Of the half that did not, 34% were well translated but did not align with LISTA descriptors. Translation-related problems stemming from orthographic variation, synonymy, differing syntactic preferences, and semantic field coverage interfered with IR in just 16% of cases. Some of the MT errors are relatively “predictable” and if knowledge organization systems could be augmented to deal with them, then MT may prove even more useful for searching.
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Abd Rahman, Lubna, and Arnida A.Bakar. "Penterjemahan Makna Unsur Budaya dalam Novel Arab “Saacah Baghdad: Riwayah” ke dalam Bahasa Inggeris melalui Aplikasi e-Translasi Google." Malaysian Journal of Social Sciences and Humanities (MJSSH) 6, no. 3 (March 8, 2021): 69–79. http://dx.doi.org/10.47405/mjssh.v6i3.685.
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Kajian ini memfokuskan penterjemahan makna unsur budaya dalam novel Arab yang bertajuk “Saacah Baghdad: Riwayah” ke bahasa Inggeris melalui aplikasi e-Translasi Google. Budaya dimiliki oleh masyarakat tertentu dan dipraktikkan daripada satu generasi kepada generasi yang lain dalam satu kelompok manusia berdasarkan tempat dan situasi. Budaya mempunyai kaitan dengan norma serta tatacara hidup dan bahasa yang dipertuturkan dalam masyarakat tersebut. Unsur budaya yang terbahagi kepada budaya kebendaan (material) dan budaya bukan kebendaan (bukan material). Bagi penterjemahan makna unsur budaya tersebut, selalunya berlaku kesulitan dan cabaran yang dihadapi bagi mendapatkan kesepadanan makna kata budaya tidak kira sama ada oleh penterjemah itu sendiri mahupun mesin yang mengendalikan terjemahan atau aplikasi e-Translasi seperti Google Translate (GT). Kesukaran ini disebabkan oleh jurang serta kelainan budaya dan bahasa yang wujud antara teks sumber dan teks sasaran. Faktor inilah yang mendorong pengkaji untuk melakukan kajian berkaitan unsur budaya antara dua bahasa. Oleh itu, objektif kajian ini adalah menelusuri penterjemahan makna unsur budaya yang dilakukan oleh Google Translate dalam novel Arab "Saacah Baghdad: Riwayah" ke bahasa Inggeris. Kajian turut meninjau pendekatan terjemahan yang digunakan oleh aplikasi tersebut bagi membekalkan makna yang dikehendaki dan mencadangkan penambahbaikan yang boleh menjelaskan lagi mesej yang hendak disampaikan ke dalam bahasa sasaran. Kajian menganalisis sampel unsur budaya terpilih berdasarkan kategori yang diketengahkan oleh Newmark (1988) serta berpandukan kamus atas talian (almaany dan Merriam-Webster) bagi mendapatkan ketepatan maksud yang dikehendaki. Kajian mendapati aplikasi e-Translasi Google menghadapi kesukaran dalam menterjemah makna unsur budaya dengan baik, di samping berlaku penyelewengan makna dalam membekal dan menyalurkan maklumat ke dalam bahasa sasaran. Kajian ini menyumbang kepada perkembangan disiplin terjemahan dengan mengenal pasti permasalahan yang dihadapi yang dikendalikan oleh Google Translate, terutamanya dalam terjemahan unsur budaya. Kajian ini diharap dapat membantu pengkaji dalam bidang penterjemahan khususnya berkaitan elemen budaya dan terjemahannya antara dua bahasa.
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Siemens, Elena. "JE SUIS EN TRANSIT (Adventures in Google Translate)." TranscUlturAl: A Journal of Translation and Cultural Studies 12, no. 2 (September 30, 2020): 61–64. http://dx.doi.org/10.21992/tc29510.
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Börner, N., S. Sponholz, K. König, S. Brodkorb, C. Bührer, and C. Roehr. "Erste Erfahrungen mit Google Translate in der Neonatologie." Klinische Pädiatrie 225, no. 07 (August 14, 2013): 413–17. http://dx.doi.org/10.1055/s-0033-1349062.
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