Google Translate Research Articles

Recursive editing with Google Translate: the impact on writing and error correction

Machine Translation (MT) tools have advanced to a level of reliability such that it is now opportune to consider their place in language teaching and learning. Given their potential, the current study sought to engage EFL university sophomores in recursive editing afforded by Google Translate (GT) for one semester, and investigated (1) whether the students were able to correct errors assisted by GT, (2) whether GT facilitated better writing, (3) which aspects of writing, fluency, complexity or accuracy GT better assisted the learners with, and (4) the student’s attitudes toward GT. A quasi-experimental approach was adopted where the experimental group, comprising 34 students, received training in recursive editing while the control group, comprising 33 students, did not. Both groups completed an error-correction test and an essay pre- and post-intervention. The experimental group completed an additional Affective Filter and Task Difficulty questionnaire post-intervention. The results showed that the experimental group significantly outperformed the control group on the error correction test; however, no significant difference in the two groups’ writing scores was found. Further, the evaluation of writing fluency, accuracy, and complexity yielded a mixed picture. The control group demonstrated significantly better fluency as shown in sentence, clause, and T-unit counts, but not in total word count. The experimental group, conversely, showed significantly higher syntactic complexity in mean length of clause, and significantly better accuracy in writing post-intervention. Finally, the students were generally positive about using GT to learn in terms of anxiety, motivational belief, and task complexity.

Emerging Political Expressions in Arab Spring for Translation Media with Implications Pedagogy

A sample of political expressions that have been common in Arab media since the Arab Spring in 2011 was collected from TV newscasts, online news websites and social media pages. Analysis of their structure, denotative and connotative meanings revealed the following features: (i) Use of lexical hybrids (اخوانجي- الدم قراطية – سني ستان - الحقيقة ليكس- عسكريتاريا; (ii) revival of ancient Islamic expressionsأمير المؤمنين - غزوة الموصل) ); (iii) use of blends (صهيوأمريكية); (iv) few borrowings (BRICS); (v) use of new acronyms (ISIS) with verbs, nouns and agents derived from them (دواعش استدعاش); and (vi) phonological and lexical substitutions in names of political and religious personalities that the users oppose (Kerdogan for Erdogan;حزب اللات for Hezbollah). Semantically, Arab Spring political terms refer to types of governments, places (towns), minority groups, religious sects, names of militia groups, weapons and military tactics, revolution squares that were not widely used before. They are also characterized by their non-literal use, i.e., use of loaded expressions, dysphemisms, and slurs that express disparagement, derogation, criticism, and disrespect towards those they oppose and towards the social and political situation as inالخرفان المتأسلمين - الفئة الضالة – شبيحة – الفلول - المخلوع. Translation and interpreting instructors need to integrate emerging political terms commonly used in the media in political and media translation courses. students majoring in translation and interpreting need to be familiar with new political terms and should be able to translate them from Arabic to English and vice versa. Students need to keep their own lists of new terms used in the media together with their equivalents in English or Arabic. They need to use Google Translate with caution as Google Translate usually gives incorrect word order in compounds. It also gives equivalents that do not match source terms in part of speech and in the type of derivative.

The Correlation Between Students’ Perception of Google Translate and Their English Writing Skill

The aim of this research was to find out the correlation of students’ perception of Google Translate and their writing skill. This research was a correlational research. The population of this research was the Fourth Semester English Education Study Program Students of Universitas Bengkulu. The samples were taken by using total sampling. There were 74 students as the samples from two classes of Paragraph Writing subject. There were two research instruments used in this research, Questionnaire and Documentation of student writing score. In analysis data, the researcher used Correlation Product Moment formula developed by Carl Pearson and the researcher using statistical analysis program SPSS. Based on the data analysis, it was found that the r-count was 0.469. It means that the correlation between the students’ perception of Google Translate and their writing skill was moderate. As a result of this, the alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) was rejected. Since r-count 0.469 was higher than the r-table 0.296, the correlation was significant. Based on the research finding, it can be concluded that there was a moderate correlation between students’ perception of Google Translate and their writing skill. It means that Google Translate was not a really dominant factor that affects writing skill of the fourth semester students of the English Education Study Program of Universitas Bengkulu.

A Comparative Study of AI translation - Google Translate and Papago

Since the mid 2010s, there have been a number of studies on the reliability of the translation capability of AI translation programs. Shin (2017) notes how machine translation systems have developed from a translation researcher’s perspective. Johnson et al. (2016) proposes a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Kim (2017) discusses how human spiritual aspect can be kept intact in the AI technology-dominated society. Kwak et al. (2020) conducted a study on AI translation focusing on proverb translation. Besides, Bahdanau et al. (2015) and Chung (2020) conducted research on machine translation. Following Park (2017, 2018)’s seminal research on the accuracy of the translation capability of Google Translate, this study investigates whether Google Translate and Papago, a translation program developed in Korea, have acquired native-like ability in translating sentences with semantically ambiguous Korean expressions such as –ya hayssta (had to vs was supposed to), -su issessta (could vs was able to), construction involving negation and sayngkakhata (think), mitta (believe), nukkita (feel) + embedded clause, and –cul alassta (knew or thought).

Learning Tools for EFL Writing: What and How based on Upper Secondary School Students’ Perspectives

Writing in English is bit challenging since students should deal with not only organization and idea development but also the language. This study aims to explore the preferred learning tools used of upper secondary school students while writing and how the learning tools accommodate them in increasing the quality of their writing based upon their perspectives. This study belongs to descriptive qualitative research and took place at one of private upper secondary schools in Yogyakarta, Indonesia, with six students serving as research participants. A standardized open-ended interview and students’ writing works were used to acquire the data. The first finding revealed the learning tools opted by the students to support their writing are Youtube as a video sharing platform, Padlet as an online notice board, Grammarly, Google Translate, Grammar Checker as language support tools, and Google Docs and Microsoft Words for word processors. Meanwhile, from the two types of data, it is noticeable that students are helped by the tools. All of the aforementioned tools are able to linguistically and substantially improve their writing works by being language accommodation, idea development support, and writing organization assistance. Conclusion and recommendation are also discussed in this article after the findings and discussion are presented.

Learning Model for Local Wisdom-based Prophet's Hadith Translation

Google Translate is a famous translation machine among students. It is just that in the Arabic-Indonesian translation, they still need to re-edit the translation. This means that there is a problem with the quality of the machine translation results, so the accuracy still needs to be improved. Therefore, it is essential to research Arabic-Indonesian translation learning. The purpose of this study is to describe the forms of Arabic-Indonesian machine translation errors. In addition, this study also offers the Ngalodern method, Indonesian local wisdom, so that students can improve the quality of Arabic-Indonesian translation. The method used in this research is descriptive analysis and uses an eclectic combination of theories to achieve the objectives of this study. There are three stages of this research. First, providing data through selected questionnaires and interviews. Second, the data analysis uses the translational equivalent method to determine the exact equivalent of meaning, while the distributional method is used to describe the level of grammatical errors. Third, is the presentation of data in the form of a table of the percentage of errors in translation cases in the field of Islam. Ngalodern, Indonesian local wisdom as an effort to improve the quality of Arabic-Indonesian translation is described descriptively. There are two findings from this research namely, The first is a description of translation errors at the level of morphemes, words, phrases, and clauses. Second, Ngalodern learning on Arabic-Indonesian translation with critical literacy based on the Sundanese culture in the field of education. In conclusion, Ngalodern learning can improve the learner's ability to edit Arabic-Indonesian machine translation results.

Identifying cross-sectoral cooperation for urban health

Abstract Empirical evidence for collaborative health promotion practices is still limited. The Cross-Sectoral Cooperation Self-Assessment Tool (CroCo SA) presented here provides a tool for cities to assess their current commitment to promote urban health and well-being across sectors. The SA tool was validated by co-operating with 9 pilot cities and seven countries in the framework of the Healthy Boost project funded by Interreg Baltic Sea Region (2019-2022). Addressing the current complexity, interdependence and emerging challenges of health promotion requires cross-sectoral cooperation and tools to assess cross-sectoral processes and make their impact visible. A digitized CroCo SA tool was developed and validated. An SA evaluation matrix was created, including 162 cells of options for reflective and guiding responses according to 6 scale x 27 items used in the SA tool. After submission of the electronic form, respondents can view anonymized results in a digital form on the Healthy Boost platform. The feedback report summarizes the results for respondents and provides, both numerical and descriptive, verbal feedback on the state of the assessed city's capacity for cross-sectoral cooperation, based on each assessment. The steps the respondents could take in their respective cities to improve cross-sectoral cooperation are outlined. Recommendations are based on the evidence found to be the prerequisites for effective cross-sectoral work for health promotion. SA can be conducted in many languages offered by the Google translation programme. Guiding feedback of CroCo SA defines areas for improvement in strategies and actions, allowing respondents also to do benchmarking and learn from other cities and countries. Self-assessment itself is a learning process. Key messages • The CroCo SA tool offers a method for gathering information about the state of cross-sectoral cooperation for urban health promotion. • By identifying strengths and weaknesses in cooperation, the city can build capacity for cross-sectoral cooperation.

THE USE OF GOOGLE TRANSLATE BY STUDENTS IN WRITING A NARRATIVE ESSAY

Learning English means learning the four skills, namely listening, speaking, reading, and writing. Of the four skills, writing is the most difficult skill to acquire. Fortunately, in this 21st century, technological developments are very helpful for us, especially in language. Many translation tools are very easy to use. One of the translation tools that is available to use by people nowadays is Google Translate. This research focused on the use of Google Translate in the writing narrative essay of 3rd semester English education students at Jambi University and used qualitative approach. The results of this research showed that students use Google Translate to help them solve their problems with vocabulary and grammar. Even though Google Translate could not fully help students to improve their writing skills, it is known to have a contribution to making students more independent, they will work alone, not depending on lecturers and friends. Students used Google Translate to ease their work in writing.

Investigating Academic Writing in EFL Contexts: Students’ Voices on Complexities and Coping Strategies

This research aimed to investigate students’ voices regarding the complexities that contribute to challenges and the coping strategies that address academic writing challenges they use in academic writing. Through a qualitative method, four female students majoring in English Education in the eighth semester at a private university in Indonesia were chosen as participants in this study's interview. Data analysis techniques employed include analyzing the data, coding the data, and interpreting the data. The findings indicated that twelve complexities contributed, namely learners’ low motivation, lack of lecturers’ feedback, lack of translation, lack of references, lack of time, lack of research background, low students' self-confidence, lack of experience in writing, lack of proficiency in reading, lack of critical thinking skill, lack of proper preparation in writing, and lack of a supportive writing learning environment. Students used twelve coping strategies for organization, ideas development, vocabulary, grammar, and mechanics. These strategies included exercises, feedback, reading academic papers, extensive reading, Google Translate, QuillBot, Grammarly, and Mendeley for arranging academic style. The implication of this research has been discussed here.

THE PERFORMANCE OF GOOGLE TRANSLATE IN TRANSLATING THREE CATHOLIC FUNDAMENTAL PRAYERS

Literary translation is one of the most difficult areas of machine translation (MT) development. Accuracy in MT is susceptible to issues that are frequently encountered in literary works, such as lexical ambiguity, syntax complexity, and structural grammatical constructs. This study examines the literary translation of three Catholic prayers: the "Sign of the Cross," "The Lord's Prayer," and "Hail Mary." These objects were chosen for their language's unique characteristics, which include antiquated vocabulary, strange structures, and unusual line breaks. The research is conducted to determine the MT's ability, that is represented by Google Translate (GT), to overcome hurdles in literary translation as measured by the number of errors made, their discussion, and their relative difficulty rectification based on Hutchins and Somers' assertions. The quality of a translation, whether human or computer, has long been a subject of discussion, with no universally accepted metric. There are a variety of factors to consider based on the circumstances around the exercise, including accuracy, naturalness, fluency, and function. For machine translation, which is frequently used to obtain quick information about a document, semantic accuracy should definitely take precedence over fluency as Koponen states.The research attemps to measure GT’s performance in translating three Catholic prayers using Koponen’s error category. Koponen's notion of semantic accuracy categorizes the errors produced by the MT into two broad categories: individual concept errors and relational concept errors. The two groups are further subdivided. The results indicate that GT is underperformed when confronted with the individual concept errors, but performs admirably when the ST line is straightforward and well-structured. GT made a total of 12 errors of individual concepts, but generated no "destructive error” in relational concepts. In conclusion, GT shows a sufficiently reliable translation, and performs admirably and consistently in the three Catholic prayers.

“You don't actually experience it until you're actually in it”: A translanguaging simulation for preservice teachers

“You don't actually experience it until you're actually in it”: A translanguaging simulation for preservice teachers

Google Translate: Perception among PhD Students

Google Translate: Perception among PhD Students

Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations

The rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-translation (MT) with human translation (HT), considering the metadiscoursal interactional features. The study uses a parallel corpus consisting of 79 texts translated from Chinese to English by professional human translators and machine translations (Baidu translate & Google translate) and a comparable reference corpus of non-translated English text. The statistical analysis revealed no statistically significant difference between Baidu and Google translate regarding all types of metadiscoursal indicators. However, the findings of this study demonstrate significant disparities in the interactional characteristics of various HT and MT groups. Compared to the metadiscourse features in non-translated English political texts, human translators were found to outperform machine translations in the use of attitude markers. In contrast, the distribution of directives in machine-translated texts is more native-like. In addition, MT and HT have utilized a significantly smaller number of hedges, self-mention, and readers than non-translated texts. Our results indicate that the MT systems, though still calling for further improvement, have shown tremendous growth potential and may complement human translators.

Google Translate Application for Simple Writing

<div class="WordSection1"><p>The common problem in English teaching to a group of Indonesian speakers is that the group has difficulty understanding to an English topic. Such as in tenses, traditionally most teachers have done their best to explain in simple English and provide numerous examples. There's nothing wrong with this approach. However, as many Indonesian-speaking English teachers probably know, it can be helpful to quickly explain the concept in Indonesian. Then the lesson can turn back to English. Consequently, it wastes time and not effective, a solution should be found. One of the solutions is the Google Translate where Google Translate offers the most powerful, free online translation tools available. This English teaching article focuses on using Google Translate to help out in difficult situations, as well as provide ideas on how to use Google Translate in class in lesson plans. This research tries to provide and share the strength and weaknesses of Google translate tool for writing learning. This research took some students from seventh semester of UNINDRA students as sample. The methodology used was descriptive. This research took time for about four (4) months. The findings of this research point out that many people are helped by the benefit of the Google Translate application. Despite the weaknesses of it, they believe that the results of the translation are accurate as long as they revise the translations manually.</p></div>

"한국어-스페인어 기계 번역 성능 분석 연구 - 구글 번역기와 네이버 파파고를 중심으로"

This article performs performance evaluation and error analysis of the Korean-Spanish translation service engine. Unlike previous studies, the research is conducted by diversifying the types of texts to be studied and dividing them into texts with and without official translations. The translation services for study are Google Translate and Naver Papago. The translation engine performance evaluation uses BLEU score, which is one of the types of automatic evaluation, and as a result, both translation services showed relatively low evaluation scores. Also, in the case of error analysis, it is divided into four types: words, grammar, sentences, and typos. As a result of the error evaluation, word-related errors were seen the most, and the word substitution phenomenon was particularly prominent. In the case of word errors, it is characteristic that grammatical errors have a great influence on the meaning transfer of the translation, whereas there are often cases where it does not have a great influence on the meaning transfer of the translation. This study is significant in that it suggested the future direction of the development of Korean-Spanish machine translation through the evaluation of the Korean-Spanish translation service.

한국어 원인ㆍ이유 연결형 문법ㆍ표현의 기계번역 양상 —카카오아이, 구글, 파파고 번역기를 중심으로—

Jiyong Lee. 2022. A Study on Machine Translation Aspects of Korean Cause and Reason Connection Grammar and Expression Items: Focusing on Kakaoi, Google, and Papago Translator. Studies in Modern Grammar 115, 207-226.
 Along with the 4th Industrial Revolution, the era of artificial intelligence has arrived. Artificial intelligence technology is already actively being used in many areas that were considered to be areas that only humans could do. A representative example of this situation is an artificial intelligence-based translator. As time goes by, it is obvious that a high-performance translator will be developed. However, it is time to discuss how translators can be used for learning foreign languages. Therefore, this study aims to examine how the items presented as similar grammar items in Korean language education are calculated in the artificial intelligence translator. Korean similar grammar items are one of the most difficult for teachers and learners in the field of Korean grammar education. This is because grammar items with similar functions exist in various forms. This study aims to analyze how equivalent grammar items that perform these similar functions are realized through machine translation.
 The results presented in three types of translators are compared, and furthermore, the accuracy is determined by using the reverse translation method. Based on these research contents, it is expected that it is possible to think about the parts to be supplemented in machine translation and the parts to be considered when using them in Korean language education.

Machine English Translation Evaluation System Based on BP Neural Network Algorithm.

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

Home language literacy practices: learning opportunities for primary EAL students

It is well-established in both scholarship and policy documents that students’ home language practices can be leveraged as a key resource for their learning. However, it can be challenging for teachers to embed students’ language resources in class activities in ways that link to wider learning objectives. We report how the inclusion of home language literacy practices can both enhance English language learning and promote multiliteracies. Data are drawn from a design-based study on the incorporation of home languages in the teaching of English. In the article, we focus on the teaching and learning that occurred in the classroom of two English as an Additional Language (EAL) teachers. Data collected comprised lesson sequences, teacher reflection, student work samples and student reflection. It was found that teacher-initiated support for home language literacy practices led to positive student engagement. The support included giving an active role to bi/multilingual teaching assistants and Google Translate.

THE STRATEGY OF DEVELOPING STUDENTS’ TRANSLATION SKILL THROUGH ANALYSIS TECHNIQUE OF MACHINE ASSISTED TRANSLATION (MAT) AND MANUAL TRANSLATION (MT) IN TRANSLATION CLASS

ABSTRACTMachine-Assisted Translation (MAT) is a sophisticated intellectual technology made by man as a means of instant translation. One of them is Google Translate. This research is a case study with a qualitative approach. This research was conducted to answer the following questions; 1) about implementation of teaching techniques by applying MAT and MT analysis in the Translation course; 2) about students' perceptions of learning techniques using MAT and MT analysis in the Translation course; and 3) the students' strategies in doing MT. The research data collection techniques were obtained from classroom observations, interviews with lecturers in Translation subject and documents from the results of student translations. All of these were conducted online because Covid had not passed. The object of his research was a lecturer in the Translation subject and 10 students from the Translation class. The data analysis technique used the data credibility test through the triangulation of techniques and sources. The result and the finding of this study are that the lecturer implements the MAT and MT analysis in the translation course with various stages. Meanwhile, students have the perception that the translation technique with the MAT and MT analysis strategy is very beneficial for their translation result. The strategy carried out by students in translating is using MAT and MT analysis in addition to using special translation techniques.Keywords: Translation, Machine-Assisted Translation (MAT), Manual Translation (MT)

A human evaluation of English-Slovak machine translation

ABSTRACT The paper aims to obtain an error profile for machine translation (MT) from English into Slovak. We present an adjusted framework for MT evaluation, which is based on Vanko's categorical framework, but reflects machine translation peculiarities of synthetic and/or inflectional languages. Based on the framework, we analyse the errors generated by Google Translate and identify the most frequent categories of errors occurring in machine translation when translating newspaper articles from English into Slovak. While we have seen research on widely-spoken languages, such as English or other major official EU languages, little is known about Slovak, which is also an official EU language. This paper provides the first human MT evaluation study of English-Slovak machine translation using professional translators for a more detailed depiction of translation quality. Our research has revealed that the highest numbers of errors occurred in the sphere of lexical semantics, as well as in syntactic-semantic correlativeness, both being closely related. Additionally, based on the results of the Cochran Q test, we show how individual MT errors located in the examined categories differ in co-incidence and in how they impact translation quality.