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Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations

Jun Yang

The Language Scholar, Issue: 1

Swansea University Author: Jun Yang

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Abstract

Translation quality assessment (TQA) is essential in translator training. For formative andmeaningful feedback, quantitative methods of error-type categories are frequently used toevaluate students’ translations. However, because the annotation of the errors made bystudents still tends to be done in...

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Published in: The Language Scholar
ISSN: 2398-8509
Published: Leeds Centre for Excellence in Language Teaching 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa58589
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spelling 2021-11-26T18:30:00.6265164 v2 58589 2021-11-09 Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations 97bd77e98c19f4447cbacbebe2b8f108 Jun Yang Jun Yang true false 2021-11-09 AMOD Translation quality assessment (TQA) is essential in translator training. For formative andmeaningful feedback, quantitative methods of error-type categories are frequently used toevaluate students’ translations. However, because the annotation of the errors made bystudents still tends to be done in a Word document, which requires redundant manual workand the result often lacks consistency and clarity. We propose using CAT environments formore efficient TQA. One the one hand, it will bridge the gap between the training and industryby familiarising students with current industry translation practices; on the other hand, it willassist with the design and analysis for formative assessment that could guide the learning ofnot only one student, but of whole cohort of students. The current paper uses SDL TradosStudio as an example to demonstrate how translation evaluation works in a CAT environment.Through the discussion of practical challenges and advantages of implementing a TQA in thetranslation classroom, we highlight the clarity both of expression and of presentation offeedback and evaluation and the long-term benefits in recording the students’ individual andgroup progress of all times to make data-driven choices regarding the training curriculum. Journal Article The Language Scholar 1 Leeds Centre for Excellence in Language Teaching 2398-8509 translation quality assessment, computer-assisted translation tools, translator training 15 5 2017 2017-05-15 https://languagescholar.leeds.ac.uk/using-computer-assisted-translation-tools-translation-quality-assessment-functionalities-to-assess-students-translations/ COLLEGE NANME Modern Languages COLLEGE CODE AMOD Swansea University 2021-11-26T18:30:00.6265164 2021-11-09T14:29:34.7239771 Faculty of Humanities and Social Sciences School of Culture and Communication - Modern Languages, Translation, and Interpreting Jun Yang 1 58589__21703__1d0b3bfe2d1a4c83836df024a001308b.pdf 58589.pdf 2021-11-26T18:27:18.3186403 Output 830194 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License. true eng https://creativecommons.org/licenses/by-sa/4.0/
title Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
spellingShingle Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
Jun Yang
title_short Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
title_full Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
title_fullStr Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
title_full_unstemmed Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
title_sort Using Computer Assisted Translation tools’ Translation Quality Assessment functionalities to assess students’ translations
author_id_str_mv 97bd77e98c19f4447cbacbebe2b8f108
author_id_fullname_str_mv 97bd77e98c19f4447cbacbebe2b8f108_***_Jun Yang
author Jun Yang
author2 Jun Yang
format Journal article
container_title The Language Scholar
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publishDate 2017
institution Swansea University
issn 2398-8509
publisher Leeds Centre for Excellence in Language Teaching
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
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hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Culture and Communication - Modern Languages, Translation, and Interpreting{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Culture and Communication - Modern Languages, Translation, and Interpreting
url https://languagescholar.leeds.ac.uk/using-computer-assisted-translation-tools-translation-quality-assessment-functionalities-to-assess-students-translations/
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description Translation quality assessment (TQA) is essential in translator training. For formative andmeaningful feedback, quantitative methods of error-type categories are frequently used toevaluate students’ translations. However, because the annotation of the errors made bystudents still tends to be done in a Word document, which requires redundant manual workand the result often lacks consistency and clarity. We propose using CAT environments formore efficient TQA. One the one hand, it will bridge the gap between the training and industryby familiarising students with current industry translation practices; on the other hand, it willassist with the design and analysis for formative assessment that could guide the learning ofnot only one student, but of whole cohort of students. The current paper uses SDL TradosStudio as an example to demonstrate how translation evaluation works in a CAT environment.Through the discussion of practical challenges and advantages of implementing a TQA in thetranslation classroom, we highlight the clarity both of expression and of presentation offeedback and evaluation and the long-term benefits in recording the students’ individual andgroup progress of all times to make data-driven choices regarding the training curriculum.
published_date 2017-05-15T04:15:14Z
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score 11.013619