Translation Software Research Articles

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Energy doing work equation and energy intensity(There may be many translation errors when the English version is converted by translation software)

Translation Technology in English Studies Within the System of Higher Education in Poland

The study is focused on translation technology within the system of higher education in Poland, specifically English Studies offering translation specialization at BA and MA level, as well as postgraduate studies aimed at translators of English. The conducted analysis of translation curricula of Polish universities investigates the presence of courses devoted to the use of translation technology and seeks to determine whether such courses are offered at a given level of higher education, where in the system most of the courses are placed, and when they are mostly organized. First, however, a brief overview of different aspects determining the inclusion of translation technology in curricula are discussed. Here, the main stress is placed on its importance for the translation markets, the skills and knowledge obtained by students entering the market which are desired by translation agencies, elements affecting the selection of given translation software, the necessary infrastructure to run such courses, the costs of the programmes, ‘human resources’, the policies of universities, etc. The short discussion is followed by an analysis of the available courses, with each section devoted to one of the levels of the Polish higher education system, namely BA, MA and postgraduate studies. The courses within each level are briefly compared to provide some general tendencies for each type of studies. The final, concluding part of the study summarizes the results and stresses the need for further introduction of translation technology into translation curricula.

Retraction: Machine Learning Based Classification Models for Heart Disease Prediction (J. Phys.: Conf. Ser 1916 012092)

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt.The authors have not responded to confirm whether they agree or disagree to this retraction.IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention.1. "Deep learning" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Deep_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Siamala Devi, S., Harini Karthika, G. & Deepika, M., 2021. Machine learning based classification for heart disease identification. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1Retraction published: 17 December 2021

Retraction: Machine Learning based Classification for Heart Disease Identification (J. Phys.: Conf. Ser 1916 012174)

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt.The authors have not responded to confirm whether they agree or disagree to this retraction.IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention. 1. "Machine learning" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Machine_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Karthikeyan, N. et al., 2021. Machine learning based classification models for heart disease prediction. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1Retraction published: 17 December 2021

Machine Translation and Computer Aided English Translation

With the development of globalization, the contacts and exchanges between countries in the world are getting closer and more frequent. As the most widely used language in the world, English is favored and valued by many non-English speaking countries. In order to facilitate communication, people use computers technology has developed a computer translation tool. The purpose of this article is to explore the difference between machine translation and computer-assisted translation in computer translation, compare the advantages and characteristics of the two in English translation, and point out the direction for future English translation trends. This article uses Google Translator, Youdao Translator and Baidu Translator in machine translation software and Wordfast, WordFisher and iCAT Huoyun Translator in computer-aided translation software as the experimental research objects to compare and analyze the different features and advantages between machine translation and computer-aided translation, and compare the error rate and match rate of the two in English translation. Finally, the experimental results show that the total number of errors in computer-assisted translation software translation is less than that of machine translation software. Among them, WordFisher has the least number of errors, 31 errors in vocabulary, 25 errors in sentences, and 3 errors in punctuation, a total of 59 errors; with the improvement of translation matching rate, the number of translation errors of English professional terminology of machine translation software is increasing, and the error rate is getting higher and higher, when the matching rate is 90-100, Google Translate becomes the software with the most translation errors, and the number of errors reached 78. Studies have shown that computer-assisted English is better and more accurate than machine translation.

German Translation Teaching Reform Based on Computer Aided Translation Software

With the rapid development of the times, the role of computer in all aspects of society is becoming more and more obvious, and the translation industry has also been greatly affected. Now the traditional translation no longer meets the requirements of the society, the emergence of computer-supported translation software. Relying on the powerful computing and translation storage functions of computers, translation can be managed more effectively. As a new research field, computer-aided foreign language research has been widely concerned by scholars from all walks of life. Among them, due to its application value, computer-aided translation research has been paid more attention by scholars. Computer assisted translation is computer-assisted translation. The basic operation principle is to search the existing translation language reference room, compare the original text with the translation language group, and select the language materials most similar to the original text. In this paper, through the analysis and understanding of computer-aided translation and its software requirements, as well as the investigation of the current application status and future development forecast, and the understanding of the application defects of computer-aided translation, we can know the operability of computer-aided translation software in practical translation work.

How Parallel Should Parallel Passages Be?

The Parallel Passages tool in the Bible translation software Paratext is a powerful tool. As with all powerful tools, it can have both positive and negative impacts on a project. Consultants need to be aware of some of the known “issues” that will sometimes result in false positives or false negatives in the highlighting of which parts of the vernacular verse are parallel. Correct usage of the tool will help a team improve the quality of the translation, but incorrectly used it can lead to a lack of naturalness and accuracy because the grammatical features of the vernacular will not allow perfect parallels in certain contexts. In this article, the positive and negative features of the tool will be illustrated with examples from the Apal language in Papua New Guinea. The article will conclude with a list of suggestions for consultants with respect to the Parallel Passages tool.

러-한 기계 번역의 현황 및 지향점: 형식적 등가(formal equivalence)와 역동적 등가(dynamic equivalence)에 따른 번역 소프트웨어 결과물의 품질 연구를 중심으로

러-한 기계 번역의 현황 및 지향점: 형식적 등가(formal equivalence)와 역동적 등가(dynamic equivalence)에 따른 번역 소프트웨어 결과물의 품질 연구를 중심으로

Construction of Parallel Corpus for English Translation Teaching Based on Computer Aided Translation Software

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

A Pragmatic Assessment of Google Translate for Emergency Department Instructions

ABSTRACTBackgroundBecause many hospitals have no mechanism for written translation, ED providers resort to the use of automated translation software, such as Google Translate (GT) for patient instructions. A recent study of discharge instructions in Spanish and Chinese suggested that accuracy rates of Google Translate (GT) were high.Study ObjectiveTo perform a pragmatic assessment of GT for the written translation of commonly used ED discharge instructions in seven commonly spoken languages.MethodsA prospective assessment of the accuracy of GT for 20 commonly used ED discharge instruction phrases, as evaluated by a convenience sample of native speakers of seven commonly spoken languages (Spanish, Chinese, Vietnamese, Tagalog, Korean, Armenian, and Farsi). Translations were evaluated using a previously validated matrix for scoring machine translation, containing 5-point Likert scales for fluency, adequacy, meaning, and severity, in addition to a dichotomous assessment of retention of the overall meaning.ResultsTwenty volunteers evaluated 400 google translated discharge statements. Volunteers were 50% female and spoke Spanish (5), Armenian (2), Chinese (3), Tagalog (4), Korean (2), and Farsi (2). The overall meaning was retained for 82.5% (330/400) of the translations. Spanish had the highest accuracy rate (94%), followed by Tagalog (90%), Korean (82.5%), Chinese (81.7%), Farsi (67.5%), and Armenian (55%). Mean Likert scores (on a 5-point scale) were high for fluency (4.2), adequacy (4.4), meaning (4.3), and severity (4.3) but also varied.ConclusionGT for discharge instructions in the ED is inconsistent between languages and should not be relied on for patient instructions.

RETRACTED ARTICLE: Coastal atmospheric climate and artificial intelligence English translation based on remote sensing images

In the twenty-first century, in the form of intelligent digitization, the rise of artificial intelligence, as well as the birth of black technology, have made good products convenient for our lives. For example, Sogou is a translation software used in translation treasure or travel portable pen. The emergence of artificial intelligence translation software gradually into the public vision, to more and more people to bring convenience in life, we think that artificial intelligence translation software can replace the traditional meaning of simultaneous translation, for people is more liberation. But the author thinks that the birth of artificial intelligence translation software should be more about the topic of learning methods. The aerosol optical thickness index of Kuihua-8 satellite is used to reflect the air freshness of satellite remote sensing, and a province is taken as an example to judge the seasonal environmental changes. The results show that the AOD values of coastal cities in the four seasons are higher than those in the western regions. The AOD numbers in autumn and winter of the whole province are low, and the AOD values in four quarters are different from those of neighboring provinces. The tree coverage rate of the land in the province is high, and the temperature changes steadily in four seasons. After the research of AOD, NDVI, and RSAI, the air freshness of the province is good, the pollution is small, the vegetation coverage is high, and the air freshness of satellite remote sensing is better, which has great improvement on the development of tourism industry and people’s own health.

Design of English Translation System Based on Deep Learning

Intelligent translation software is one of the important tools for people to learn, but the existing intelligent translation system usually needs to be further improved in terms of the flexible application of phrases and the accurate expression of professional vocabulary. The translation system based on deep learning designed in this paper mainly includes professional vocabulary database module, memory analysis module, English translation module and correction module. The professional vocabulary module accurately classifies words related to various disciplines. The memory analysis module compares the professional vocabulary usage in published documents on the Internet to analyze and memorize, so as to recommend relatively accurate professional vocabulary to enter the translation module. Need to enter the correction module to complete the correction of grammar and phrases, and finally complete the translation task with higher accuracy.

Machine learning maps reaction space

A machine-learning technique first developed for understanding language can accurately classify reactions according to type ( Nat. Mach. Intell. 2021, DOI: 10.1038/s42256-020-00284-w ). The model also tagged reactions with computer-readable codes that allow chemists to search for similar reactions. Transformers are a type of machine-learning algorithm useful for interpreting sequences of information. They’re widely used in translation software and voice assistants like Amazon’s Alexa, but chemists recently have shown their utility in chemistry (see page 19) . Philippe Schwaller of IBM Research–Zurich and the University of Bern and colleagues show the transformer approach can classify reactions by type, identifying broad categories like carbon-carbon bond formation or deprotection and finer-scale groups like chloro or bromo Suzuki coupling . When categorizing reactions, the machine-learning model matched the classifications assigned by software that used human-coded rules 98% of the time. The researchers tested their model on data sets containing tens of thousands of

Research on English Translation based on Computer Technology

With the rapid development of science and technology, computer technology has been applied to all walks of life, including English translation. Through computer technology translation, we can greatly improve the efficiency of translation production, which has an unprecedented impact on the language service industry. At the same time, with the rapid development of global economy, the economy and society put forward higher requirements for the translation industry, which requires us to constantly improve the quality and speed of translation. Therefore, in order to better English translation, computer science and technology has brought a significant basis for it. However, due to the limitations of computer language translation technology, we must adopt post editing method to adjust the machine translation, which will improve the accuracy of translation, such as computer aided technology (hereinafter referred to as CAT) software. First, this paper introduces the main role of computer-aided translation technology. Then, this paper analyzes the important principles of computer-aided translation software. Finally, some suggestions are put forward.

SDL Trados and Tmxmall: A Comparative Study of Computer-Aided Translation Tools

<p>Technological development has brought about a series of changes, including influences on the traditional translation industry which is unable to meet the future needs due to its low efficiency and limited forms. Computer-aided translation software, shortened as CAT software, is therefore developed. Featuring powerful memory and fast computing speed of computers, CAT software allows translators to give full scope to creativity, which enhances traditional translation efficiency. In recent years, both CAT software and cloud translation software have been rapidly developed. The latter is built based on cloud computing technology, and is emerging one after another. This article, taking such CAT software as SDL Trados and such cloud translation platform as Tmxmall as examples, studies similarities and differences between the two different kinds of tools in terms of operation interface, translation project management and corpus management. By analyzing features of the two tools, this article seeks to provide references for translators and software engineers.</p>

Building a Parallel Corpus for English Translation Teaching Based on Computer-aided Translation Software

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Automatiс Translation Process and Translation Technologies Advancement on International Translation Market: the Current Status and Development Trends

The given paper views automatic translation process and the current state of translation market in different countries of the world. The author has analysed digitalization of translators and interpreters’ activity on international translation market, creation of translator’s workstation with computer making sophisticated multi-component working environment. The author underlines new IT translation technologies presuppose computer use as a tool in complex specialized translation software implementation. The author, as a practical translator, analyses the computer use in translation process, and translation and interpretation issues which is equal in its complexity for all countries in the world. The paper considers topical issues of machine translation, new economy, digital culture. It suggests some findings in streamlining translation process both in theRepublicofBelarusand in the world markets.

Latest advancements in audiovisual translation education

ABSTRACT The consumption of audiovisual content, from the more traditional animations, documentaries, movies, and TV shows to the more recent online user-generated content found on social media platforms, including video games, has grown exponentially over the last few decades. The omnipresence of screens in society has led to transformations in audiences’ watching habits, now impatient to enjoy their programmes as soon as possible and inclined to binge watch. Recent technological advances in the production of specialist audiovisual translation (AVT) software and web-based applications have paved the way for further changes and enhancements in the ways professionals localise audiovisual content and in the nature of the services provided. This special issue sheds light on the current teaching and learning practices, methodologies and issues encountered by translator trainers specialised in AVT, with particular emphasis on pedagogical innovation, media accessibility, and translation technology.