Google Translate Research Articles

Google translate in academic writing class: How EFL learners use and perceive it

The study of the use of Google Translate (GT) in ELT classroom has been an interesting issue to be discussed. Researchers noted that GT’s translation quality has significantly increased as a result of utilizing an Artificial Intelligence (AI) technique which studies languages through examples. The employment of GT as a tool to help students in ELT classes seems to be advantageous for them. However, many language teachers continue to question the value of this translation machine. This study investigates the learners’ perspectives towards the use of GT in their Academic Writing class; whether they think it is helpful in assisting them in the learning process or not. The researchers attempt to answer the research question of “What are the learners’ perceptions towards the use of GT as a learning support in Academic Writing class?” because there are still very few studies that have examined the use of GT as a learning support tool in Academic Writing class. The study uses a qualitative approach through the use of interviews as a data collection instrument. The interviews were conducted with five participants from the English Language Education program of the Faculty of Language and Arts in one private university in Central Java. The participants are the students who have just taken Academic Writing (AW) class in the previous semester before the research was conducted. The findings of the study show that all participants agree that GT has its own positive and negative impacts in their learning in the classroom.

An expandable voice user interface as lab assistant based on an improved version of Google’s speech recognition

Voice assistants are potentially helpful when working in a scientific laboratory. A big challenge is the extremely specific use of language in every laboratory. As with any voice assistant, another concern is data security. Here, we present Rainbow—an open source voice user interface (VUI) for scientific laboratories, that is adaptable to any Windows PC with Internet access. We used Google Translate Site (GTS) as a voice input and output system to ensure communication to the user. The scripting language AutoIt controls GTS, executes all actions and builds the VUI. Rainbow performs tasks from three different areas—general Microsoft Windows tasks, lab-specific tasks, and device-specific tasks. We achieved significantly higher speech recognition accuracy with our VUI than with GTS alone (91.3% versus 85.1%). Because of Rainbow's architecture, it is possible to improve the voice assistant in terms of functionality and accuracy, allowing each laboratory to optimize its own Rainbow system in a user-friendly way. In a test setup, this led to a speech recognition accuracy of 98.6%. Taken together, Rainbow provides an opportunity for every scientist to implement highly specific scientific terms and tasks to this open source voice assistant system in a very user-friendly way.

Students Attitudes and Perceptions Toward Using Google Translate at Ghor Institute of Higher Education

The easiest and most accessible way for learners to meet their translation needs is by using Google Translate. Machine translation is now an integral part of learning a foreign language, particularly Google Translate (GT), which has influenced EFL learning as an online dictionary. It is used to translate from the source language to the target language. It is widely used by Afghan second-language learners today for translating between English and Farsi languages. The purpose of the present study was to explore students’ attitudes and perceptions toward using Google Translate in EFL classes at the Ghor Institute of Higher Education. The data for this study were collected using a five-point Likert questionnaire and interviews with 145 English-major university students at the English Department and were analyzed both qualitatively and quantitatively. After analyzing the data, the results indicated that almost all of the participants reported using GT. The three most frequent uses of GT were to look up the meanings of unfamiliar words and to translate sentences from English to Farsi and vice versa, while translating paragraphs and articles (discourse level) was the least frequent. The results also showed that a majority of the population frequently used GT in their classes, and less than half said they used it sometimes, but the rest stated they used it always and rarely, with a very small percentage admitting they never utilized it. All in all, most of the students expressed a high level of positive attitudes toward using GT as a language learning tool in EFL classrooms. It is expected that GT will be better utilized in the future after knowing students’ perceptions and attitudes toward using it; that is, it may assist lecturers and educational practitioners in making informed decisions about its utilization in the classroom.

The Influence of the Intensity of Using Google Translate in Learning for Students of the English Education Study Program at Universitas Tidar

This article evaluates the influence of using Google Translate on students' learning within the English Education study program at Universitas Tidar. Language study has been transformed by the popularity of internet translation programs like Google Translate, which give students access to quick translations in various languages. However, concerns have been raised regarding the potential adverse effects of relying heavily on such tools. This article investigates the influence of different intensity levels in using Google Translate on English education students' language acquisition, linguistic proficiency, and overall learning outcomes. To examine this influence, a qualitative research method is employed. The study involves a sample of English Education students from Universitas Tidar, utilizing a questionnaire. The research investigates the relationship between the frequency and depth of Google Translate usage and its influence on students' learning experiences. The research also explores students' perceptions and attitudes towards Google Translate, examining factors such as motivation, confidence, and self-efficacy. It aims to understand how these attitudes impact the intensity of tool usage and, subsequently, affect learning outcomes.

Quality of translation via google translate in comedy texts

A translation process of considerable quality should possess the ability to effectively communicate a clear and definite meaning from the source language to the target language. While Google Translate serves as a useful tool for comprehending translated textual content in a general sense, it is important to note that automated machines still possess inherent weaknesses and limitations. The translation of jokes within comedic texts, particularly in relation to language and cultural differences, proves to be a challenging task. This research was undertaken utilizing a qualitative-descriptive approach, with the bilingual comedy book titled "Jokes in English-Book 1" serving as the subject of study. The primary objective of this research is to assess the level of translation quality achieved by machine translation in terms of accuracy, acceptability, and readability, and to compare these results with the translations provided in the bilingual book. The findings of this research indicate that 62.5% of the translations exhibit a high level of accuracy, while 37.5% are somewhat less accurate. Furthermore, 37.5% of the translations are deemed acceptable, while 62.5% are regarded as less acceptable. Finally, 37.5% of the translations demonstrate a high degree of readability, whereas 62.5% exhibit a moderate level of readability. Upon comparing the translation results obtained from Google Translate with the original translations presented in the bilingual book, it was determined that 100% of the translations in the book were found to be satisfactory in terms of accuracy, acceptability, and readability. Although the translations from English to Indonesian of comedy texts via Google Translate are deemed satisfactory, there is still room for improvement and ongoing development in order to enhance the quality of these translations.

Actual Usage of Machine Translation by Japanese University Students and Verification of Test Results

The objective of this study is to investigate the actual situation of Japanese university students' use of machine translation (MT). The case study focuses on Japanese university students and not only investigates when students use MT, but also examines how their attitudes change before and after they use MT for their assignments. In this study, Google Translate was used as the MT tool, and Microsoft Excel was used for analysis. By analyzing these results, it was found that when students were allowed to use MT, they themselves decided whether or not to use it depending on their task. Of the skills in writing, reading and listening, it is also found that students tend to use MT the most for writing tasks and the least for listening tasks. In addition, no statistical significance of using MT was found for any of these skills, indicating that the use of MT does not necessarily mean that all language-related questions can be solved. These results could provide valuable data for the future introduction of MT into education. The survey included a diverse range of university students selected through an open application process. However, the sample size was limited, so an extensive survey should be undertaken in the future.

AUTHENTICITY AND READABILITY OF GOOGLE TRANSLATE TRANSLATING RELIGIOUS TEXTS RELATED TO CHARACTER EDUCATION WRITTEN IN ENGLISH INTO INDONESIAN

Character education and information and communications technology (ICT) cannot be separated in discussing the Indonesian students in the post-millennial era. In this research, what is meant by character education is the religious teaching in the Holy Quran, whereas what is meant by ICT is the translation machine named Google Translate. The Holy Quran in this research is one which is translated from Arabic into English by Abdullah Yusuf Ali. The character education of the Holy Quran usually refers to the verses of Al Luqman, and the studies investigating the work of Google Translate usually concern the scientific texts and information or news. Different from the previous studies, this research do not investigate the verses of Al Luqman nor the scientific and informative ones. The aims of this research are to observe the religious texts belonging to the Holy Quran translated into English which contain universal character education and to investigate the authenticity and readability of the work of Google Translate translating such texts. The method employed in conducting this research is Mixed Method proposed by Hesse-Biber. Besides, the triangulation approach proposed by Turner is used. Thus, the researcher not only making use of the official Holy Quran of Ministry of Religious Affairs, but also involving both the lecturers of Islam Religion and of Bahasa Indonesia. This research found that the character education which is universal in nature could be read in the Holy Quran not only in Sura Al Luqman but also in Sura Al Furqon verses 63-77. Qualitatively the Google Translate work translating Sura Al Furqon verses 63-77 underwent the meaning distortion due to the Indonesian sentences whose both cohesion and coherence were violated. Meanwhile, quantitatively, it was found that 40% of the verses examined could be categorized unreadable or incomprehensible and that 60% of the verses examined could be categorized as undergoing the meaning distortion.

Žodynai ir automatinis vertimas: ką renkasi besimokantieji lietuvių kalbos kaip svetimosios

This article presents the results of research on using electronic dictionaries and machine translation tools when learning Lithuanian as a foreign language. The aim of this study was to indicate which dictionaries and machine translation tools are used for which purposes and what kinds of difficulties learners experience while using them. The research data were obtained by conducting an anonymous online poll in Lithuanian and English (using Google Forms). The questionnaire contained 15 questions. The participants in the study were chosen by way of availability sampling. The survey was carried out at Vilnius University in March 2023 with 67 respondents: 11 in Lithuanian and 56 in English. The study revealed that when learning Lithuanian as a foreign language, the respondents used both dictionaries and automatic translation tools. Most often, both tools were used for translation from and into Lithuanian. The types of language activities for which these tools were used were reading, writing, and partly listening. Only the respondents to English questionnaires used the mentioned tools for speaking. When learning Lithuanian, both monolingual and bilingual dictionaries were used, but monolingual dictionaries were used more than bilingual ones: 46.27% of respondents used monolingual dictionaries, and 31.34% used bilingual dictionaries.The study confirmed the researchers’ claim that monolingual dictionaries are used more by respondents with a higher level of language proficiency. Automatic translation tools were used by all respondents. The most popular of them is Google Translate, followed by DeepL. Other tools – Reverso, Bing Microsoft Translator, Systran Translate, Yandex Translate, and Lingea – are used by a few and only English questionnaire respondents. Dictionaries were consulted for the meaning of an unknown word, for synonyms, to confirm the meaning and spelling, to find usage examples, the field of the word used, and stress. Automatic translation is used for unknown words, phrases, and sentences to hear the pronunciation and find usage examples. The most common difficulties in using dictionaries were: unclear definitions of words and abbreviations; no text-to-speech function; too few usage examples; and unclear or missing examples. Weaknesses of automatic translation tools indicated by respondents: poor translation quality; lack of a search function; no examples of usage; no text-to-speech.

THE ACCURACY OF STUDENTS’ TRANSLATION OF REPORT TEXT IN THE ENGLISH LANGUAGE STUDY PROGRAM OF KHAIRUN UNIVERSITY

The purpose of this research was to find out students’ translation accuracy of report text in an English education study program. This research used a descriptive qualitative method with the third-semester students as the respondents of this research. The main data source was taken from The Jakarta Post, ‘Bicara Melalui Kain’: Redefining textile narratives in Indonesia. The technique of data collection used in this research was using one instrument: the results of the student’s text translation. In assessing the accuracy of students’ translations, the researcher used Nababan's (2012) accuracy assessment. The data shows that from 14 respondents from third-semester students, the accuracy of students’ translations was still low. The low accuracy of students’ translation was caused by the lack of vocabulary where there were many missing words, mistranslations, and distortion of meaning which affected the meaning of the text based on Nababan’s (2012) Accuracy assessment. Around 7 out of 14 respondents were eliminated by the researcher caused by the used of Google Translate in transferring the English text (SL) into Indonesian (TL) which does not follow the researcher's provisions in her research. Regarding the result, this research could be a reference for lectures in improving students’ translation ability.

Exploring the Utilization of Augmented Reality in Higher Education Perceptions of Media and Communication Students

The present study aims to explore the perceptions and usage of Augmented Reality (AR) technology among media students in Palestinian universities. A quantitative approach was adopted, and data was gathered from a web-based survey of 237 media students. The Technology Acceptance Model (TAM) was utilized to gauge participants' perceptions of AR, and descriptive statistics were used for analysis. The findings reveal a generally positive perception of AR as a beneficial tool for skill enhancement, with mean scores ranging from 3.70 to 4.04 indicating strong agreement. The study also found moderate to high AR usage among participants, particularly for translating texts using Google Translate, but noted that usage patterns were more individual-oriented. Additionally, 91.1% of respondents attributed the COVID-19 pandemic to increased technology usage in higher education. The novelty of this study lies in providing insights into the perception and application of AR in higher education within the Palestinian context, an under-researched area. The study sheds light on the potential for integrating AR more formally into curricula, which could foster a more engaging and immersive educational experience. However, it also highlights the need to address barriers such as lack of technical support and possible discomfort with technology. Doi: 10.28991/ESJ-2023-SIED2-016 Full Text: PDF

Identifying the cognitive and metacognitive approaches using post editing techniques toward English-Indonesian and Indonesian-English Text

The aims to identify toward English-Indonesian and Indonesian-English Text by using cognitive approach and to identify toward English-Indonesian and Indonesian-English Text by using metacognitive approach. Related to cognitive approaches and metacognitive approaches; The person engages in a lot of metacognitive processes. The respondent modified the submit-text using their own methods for the total results. In order to construct a comparative target text, the respondent first retranslated using Google Translator to create the target texts. Second, compared the two translated versions of the text that Google Document (GD) and Google Translate created (GT). The participant appears to be able to rely on her logical reasoning and solely has faith in the resources that have been made available to her.TAP translation techniques work to help students develop a positive attitude about translation as an activity that incorporates a great deal of cognitive and metacognitive activities. The research utilized a qualitative approach with a case study method where the participants is asked to conduct a post editing activity utilizing the Think Aloud Protocol or TAP strategy. The strategies use for collecting the data are: (1) examining, (2) analyzing, (3) categorizing and (4) assessing. Coding analysis is used to analyze the data. Therefore, the goal of the post-editing is basically to identify a gap or a problem in the MT output and find a solution to the problem to achieve the acceptable final output.

The use and abuse of artificial intelligence-enabled machine translation in the EFL classroom: An exploratory study

The integration of machine translation (MT) and artificial intelligence (AI) has significantly enhanced the precision of MT systems producing translations that rival the quality of skilled human translators. This innovation has broadened the applicability of AI-enabled MT systems attracting users for a variety of purposes. This paper explores the following areas: the application of AI-enabled MT, specifically Google Translate (GT) in English as a Foreign Language (EFL) classroom. Empirical findings and recent research indicate that students are increasingly reliant on MT particularly in higher educational settings where English is not their first language. The purpose of this research is to provide insight on the practices, beliefs and goals of EFL learners who use GT in the classroom. Data was gathered from 234 university students through a questionnaire along with pre- and post-tests to compare the quality of writing drafts created with and without the aid of GT. The findings are consistent with those of previous research conducted worldwide highlighting the immense potential of AI-enabled MT in not only boosting students' learning experiences but also cultivating more independent learners. The study also indicates that students have positive attitudes towards GT and employ diverse search strategies to address various language-related challenges.

Predicting attitude, use, and future intentions with translation websites through the TAM framework: a multicultural study among Saudi and South Korean language learners

Machine translation (MT) practice and activity development in education are possible when students with diverse backgrounds contribute to helping define how MT can best be used for language learning. This study employed a questionnaire based on an adapted version of the technology acceptance model (TAM) to gain perspective on the perceptions, attitudes, and use of MT tools like Google Translate among Saudi and Korean university students learning English as a foreign language (EFL). A total of 470 students were recruited from Saudi Arabia and South Korea to complete a questionnaire measuring scales on perceived ease of use, perceived usefulness, attitude, actual use, and intended future use of MT websites. Structural equation modeling analyzed the relationships among the TAM variables. Results found that Saudi and Korean students both reported high levels on the TAM variables, indicating that translation tools were easy to use and useful for studying language. Lower L2 proficiency correlated with greater perceived usefulness and a more positive attitude. Students frequently use MT tools when learning English and plan to continue using them in the future. The model explained 71% of actual use and 42% of behavioral intention to use MT for language learning. Furthermore, Saudi students reported a slightly easier time using MT tools for language learning, while Korean students planned to use MT more often. Other findings, along with practical applications, are presented in this paper.

Translation Performance from the User’s Perspective of Large Language Models and Neural Machine Translation Systems

The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI’s ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes.

Grammatical Errors of Google Translate in Translating Narrative Text in Indonesia to English

Many people have recently turned to machine translation to help them translate their documents. However, it is not widely used in the academic field, owing to worries about its trustworthiness in appropriately rendering the source text in the target language. This tool only translates on a word-by-word basis without considering the actual meaning of the text and grammar rules in English. It often applies to the Indonesian structure. So, when students are using Google Translate to translate a text, a new issue arises because it just transfers language without knowing the correct grammar. This research methodology is qualitative research with a content analysis method as the research design. From 5 narrative texts that have been translated by Google Translate from Indonesia to English found that there are two omission errors, four addition errors, six misinformation, and two misordering error and the most common error is misinformation. This study seeks to educate students about grammatical issues that can arise when using Google Translate, allowing them to avoid making the same mistakes and improve their knowledge.

What Are the English to Spanish Translation Methods Used on Written Health-Related Information?

Objective: This systematic review was conducted to examine methods and tools used to translate written health-related information from English to Spanish. Methods: The design of this study was a systematic review. This review search was of all published articles up to July 2022. Three databases were used: CINAHL Plus, MEDLINE, and PubMed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis flowchart was used in the systematic review. Results: A total of 17 articles were used in the analysis. Six (35.3%) of the articles used a machine and human translation method, and eight (47%) used Google Translate as the machine translation tool. The screening, full-text review, and data extraction were done using the Covidence software. Discussions: As translating health-related information accurately is crucial, using metrics or standardizing one is urgently needed. Nursing implications: This review highlights that in addition to a lack of standardized metrics, there currently is no criterion on the qualifications or credentials required by individuals who translate written information from English to Spanish, allowing anyone to do so. This is concerning because it is critical that Spanish speakers and those with limited English proficiency receive linguistically accurate and appropriate resources that allow them to improve their health literacy and promote health.

Undergraduate English Students' Use of Google Translate in Afghanistan

Google translate (GT) is widely used by students around the world. The current study examined English students' use of GT in Afghanistan. A survey questionnaire was used to collect data from 132 randomly selected students from a public university in Afghanistan. Descriptive and inferential statistics were used to analyze the data. The findings showed that the participants had positive attitudes towards GT, and they were aware of GT drawbacks. The participants utilized GT for numerous reasons including not feeling confident in their own translation. They used GT for a number of purposes to varying extent. They frequently used GT to look up general and technical words as well as idioms and collocations. They also used it for translation purposes. There were differences between female and male students' attitudes towards GT. The study recommends instructors to adopt GT, particularly those teaching in Afghanistan, in their classes and encourage students' critical use of GT.

The Impact of Google Translate Syntactic Strategies on Translation Quality: Translating Journal Articles from Indonesian to English

The prevalent use of Google Translate as a tool for academic translations, despite concerns regarding its quality, frames the core issue of this research. This research aims to classify syntactic strategies used by Google Translate in translating from Indonesia to English. It also aims to analyze the quality of the translation produced and how the syntactic strategies affected the translation quality. The data were collected from a journal article entitled “Pengaruh Bahasa Indonesia dan Bahasa Inggris sebagai Bahasa Pengantar dalam Pendidikan Formal” which was presented using a descriptive qualitative method. The findings of this research reveal there are several types of different syntactic strategies used by Google Translate. The result of this research shows that, out of 273 data collected, there are 123 (45.05%) data of literal translation, 2 (0.73%) data of loan and calque, 53 (19.41%) of transposition, 17 (6.23%) data of unit shift, 45 (16.48%) data of phrase structure change, 1 (0.37%) data of clause structure change, 18 (6.59%) data of sentence structure change, 1 (0.37%) data of cohesion change, 13 (4.76%) data of level shift, and no data found for scheme change. In addition, there are several different classifications found for translation quality. Out of 194 data analyzed, the results show that 163 (84.02%) data are accurate, 30 (15.46%) data are less accurate, and 1 (0.52%) data is not accurate. For acceptability, there are 179 (92.27%) acceptable, 14 (7.22%) data are less accurate, and 2 (1.03%) data are not acceptable. Finally, there are 180 (92.78%) data with high readability, 15 (7.73%) data are medium readability, and no data found for low readability.

The Negative Transfer Effect on the Neural Machine Translation of Egyptian Arabic Adjuncts into English: The Case of Google Translate

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale, and most neural machine translation models, including Google Translate, rely mainly on parallel corpora of standard Arabic and English to train for dialectal Arabic translation. A model well trained to translate to and from standard Arabic is believed to efficiently translate dialectal Arabic, given their similarities. This study demonstrates the impact of not using large-scale, dialect-specific parallel corpora by quantitatively and qualitatively analyzing the performance of Google Translate in translating Egyptian Arabic adjuncts. Compared to human reference translation, Google Translate achieved a low BLEU score of 14.69. Qualitative analysis showed that reliance on standard Arabic parallel corpora caused a negative transfer problem manifested in the literal translation of idiomatic adjuncts, the misinterpretation of dialectal adjuncts as main clause constituents, the translation of dialectal adjuncts after orthographically similar standard Arabic words, and the use of standard Arabic common lexical meanings to translate dialect-specific adjuncts. This study’s findings will be relevant for researchers interested in dialectal Arabic neural machine translation and has implications for investment in the development of large-scale, dialect-specific corpora to better process the peculiarities of Arabic dialects and reduce the effect of negative transfer from standard Arabic.

THE DIFFICULTIES FACED AND THE STRATEGIES USED BY ENGLISH DEPARTMENT STUDENTS IN LEARNING ENGLISH AS A FOREIGN LANGUAGE

The study aims to identify the difficulties faced and the strategies used by English Department students at Universitas Trunojoyo Madura in learning English. The study used a mixed-method approach, which is a combination of quantitative and qualitative approaches. The source of data was one class in the second semester that consisted of thirty-seven students. The data were the difficulties faced by the students in learning English and the strategies they used to deal with them. The research instrument used was questionnaires containing both close-ended and open-ended questions. The data that was obtained from closed-ended questions were analysed quantitatively while those that were derived from open-ended questions were analysed qualitatively. The results showed that the difficulties faced by English Department students in learning English include pronunciation, grammar, and vocabulary and they exercised different strategies to solve them. Specifically, the strategies to solve the difficulty in learning pronunciation were listening to English songs, singing English songs, watching English movies or films, imitating using Google Translate, and practising the pronunciation by themselves. The strategies to tackle the difficulty in learning grammar were watching YouTube, reading English books, learning from the internet, learning through Instagram, and learning by themselves. Furthermore, there were several ways that they used to deal with the difficulty in learning vocabulary, for example, they used English dictionaries, watched English movies, read English books, used English songs, and memorized new vocabulary.