MACHINE TRANSLATION AS COMPONENT OF PREPARATION OF FUTURE DOCUMENT MANAGERS

Authors

  • Iryna Borodkina Kiev National University of Culture and Arts, Ukraine
  • Heorhii Borodkin National University of Life and Environmental Sciences of Ukraine, Ukraine

DOI:

https://doi.org/10.32461/2409-9805.4.2018.170610

Keywords:

machine translation, online translation, electronic translation services, document management, office manager.

Abstract

The purpose of the work is a comprehensive study of machine translation systems in the structure of the professional activity of documentation specialists. The methodology of the study.The selection of materials was based on the general scientific methods of analysis and synthesis, comparison and generalization, comprehensiveness and objectivity. The scientific novelty of the research is determined by the fact that today there are practically no works devoted to justifying the need for training in working with machine translation systems of future specialists in the field of documentation. Conclusions. A comprehensive study of machine translation systems showed that the driving force behind the development of the electronic translation services market is the professional needs of specialists. Today, a large number of software products of this type are available for users, which makes it necessary to develop skills in working with machine translation systems already at the stage of professional training of future document managers and office managers.

Author Biographies

Iryna Borodkina, Kiev National University of Culture and Arts

PhD, Associate Professor, Department of Computer Science

Heorhii Borodkin, National University of Life and Environmental Sciences of Ukraine

The purpose of the work is a comprehensive study of machine translation systems in the structure of the professional activity of documentation specialists. The methodology of the study.The selection of materials was based on the general scientific methods of analysis and synthesis, comparison and generalization, comprehensiveness and objectivity. The scientific novelty of the research is determined by the fact that today there are practically no works devoted to justifying the need for training in working with machine translation systems of future specialists in the field of documentation. Conclusions. A comprehensive study of machine translation systems showed that the driving force behind the development of the electronic translation services market is the professional needs of specialists. Today, a large number of software products of this type are available for users, which makes it necessary to develop skills in working with machine translation systems already at the stage of professional training of future document managers and office managers.

References

Abraham-Barna C.G., Abraham-Barna T. Сurrent trends in the translation market Studii de ştiinţă şi cultură, 2016. Vol. XII. № 3. P. 33-43.

Burchardt A. Comparing Errors: Neural MT vs. Traditional Phrase-based and Rule-based MT. URL: https:// www.gala-global.org/publications/ comparing-errors-neural-mt-vs-traditional-phrase-based-and-rule-based-mt

Craciunescu O., Gerding-Salas С., Stringer-O’Keeffe S. Machine Translation and Computer-Assisted Translation: a New Way of Translating? Translation Journal. 2004. Vol. 8. № 3. URL: http://translationjournal. net/journal/29computers.htm

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. URL: https://arxiv.org/abs/1609.08144.

Machine Translation Market P&S Market Research. URL: https://www.psmarketresearch.com/market-analysis/machine-translation-market

Novak B. Machine Translation: Advantages and Disadvantages. URL:http://dlsdc.com/blog/machine-translation-advantages-and-disadvantages.

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Abraham-Barna C. G., Abraham-Barna T. (2016). Сurrent trends in the translation market. Studii de ştiinţă şi cultură. XII, 3, 33-43 [in English].

2. Burchardt,A. (2017). Comparing Errors: Neural MT vs. Traditional Phrase-based and Rule-based MT. Retrieved from https://www.gala-global.org/publications/comparing-errors-neural-mt-vs-traditional-phrase-based-and-rule-based-mt [in English].

Craciunescu, O. (2004). Machine Translation and Computer-Assisted Translation: a New Way of Translating? Translation Journal. 8(3). Retrieved from http://translationjournal.net/journal/29computers.htm [in English].

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016). Retrieved from https://arxiv.org/abs/1609.08144. [in English].

Machine Translation Market P & S Market Research. (2017). Retrieved from https://www. psmarketresearch.com/market-analysis/machine-translation-market [in English].

Novak B. Machine Translation: Advantages and Disadvantages (2014). Retrieved from http://dlsdc. com/blog/machine-translation-advantages-and-disadvantages. [in English].

Qun L. (2015). Machine translation: general. The Routledge Encyclopedia of Translation Technology, 105–119 [in English].

Kolesnyk, A. S. (2013). Machine translation systems review. Proceedings from II Ukrainian Scientific and Practical Conference “Intelligent Systems and Applied Linguistics”. (pp. 41–44). Kharkiv. [in Russian].

Published

2018-11-27

Issue

Section

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