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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...
Published in: | The Language Scholar |
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ISSN: | 2398-8509 |
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Leeds Centre for Excellence in Language Teaching
2017
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URI: | https://cronfa.swan.ac.uk/Record/cronfa58589 |
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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 |
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97bd77e98c19f4447cbacbebe2b8f108 |
author_id_fullname_str_mv |
97bd77e98c19f4447cbacbebe2b8f108_***_Jun Yang |
author |
Jun Yang |
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Jun Yang |
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Journal article |
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The Language Scholar |
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2017 |
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Swansea University |
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2398-8509 |
publisher |
Leeds Centre for Excellence in Language Teaching |
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Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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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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11.013619 |