<abstract xmlns="http://www.w3.org/1999/xhtml"> Automated translation (machine translation, MT) is systematically gaining popularity among professional translators, who claim that editing MT output requires less time and effort than translating from scratch. MT technology is also offered in leading translator’s workstations, e.g., SDL Trados Studio, memoQ, Déjà Vu and Wordfast. Therefore, the dilemma arises: should MT be introduced into formal translation training? In order to answer this question, first, it is necessary to understand how trainee translators actually use MT.This study is an attempt to obtain this knowledge. The methodology applied in this investigation is text analysis. During the experiment sessions the students were asked to translate a legal text using MT tools, which in practice meant the post-editing of the MT raw output. The post-edited versions of the text underwent analysis in order to answer the following research questions:- What are the most typical errors contained in both French and English MT output?- How critical are the students towards the text generated by MT?- How perceptive are the students during the post-editing task?- Are they able to detect and correct errors using their knowledge and skills?The results of this study suggest that the post-editing of the MT raw output is as demanding for translation students as traditional translation, however, it requires a different set of skills, such as critical thinking and perceptiveness. Therefore, a special kind of training related to the effective use of MT technology should be implemented during translation classes.</abstract>

Automated translation (machine translation, MT) is systematically gaining popularity among professional translators, who claim that editing MT output requires less time and effort than translating from scratch. MT technology is also offered in leading translator’s workstations, e.g., SDL Trados Studio, memoQ, Déjà Vu and Wordfast. Therefore, the dilemma arises: should MT be introduced into formal translation training? In order to answer this question, first, it is necessary to understand how trainee translators actually use MT.This study is an attempt to obtain this knowledge. The methodology applied in this investigation is text analysis. During the experiment sessions the students were asked to translate a legal text using MT tools, which in practice meant the post-editing of the MT raw output. The post-edited versions of the text underwent analysis in order to answer the following research questions:- What are the most typical errors contained in both French and English MT output?- How critical are the students towards the text generated by MT?- How perceptive are the students during the post-editing task?- Are they able to detect and correct errors using their knowledge and skills?The results of this study suggest that the post-editing of the MT raw output is as demanding for translation students as traditional translation, however, it requires a different set of skills, such as critical thinking and perceptiveness. Therefore, a special kind of training related to the effective use of MT technology should be implemented during translation classes.

Machine Translation in the Hands of Trainee Translators – an Empirical Study

Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Automated translation (machine translation, MT) is systematically gaining popularity among professional translators, who claim that editing MT output requires...

Machine Translation in the Hands of Trainee Translators – an Empirical Study

Published Online: Apr 26, 2017

Page range: 195 - 212

DOI: https://doi.org/10.1515/slgr-2017-0012

Keywords
post-editing, translation training, machine translation

© Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Machine Translation in the Hands of Trainee Translators – an Empirical Study

Published Online: Apr 26, 2017

Page range: 195 - 212

DOI: https://doi.org/10.1515/slgr-2017-0012

Keywordspost-editing, translation training, machine translation

© Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
post-editing, translation training, machine translation