TR5051 - Electronic Tools for Translation (2022/23)
| Module specification | Module approved to run in 2022/23 | ||||||||
| Module title | Electronic Tools for Translation | ||||||||
| Module level | Intermediate (05) | ||||||||
| Credit rating for module | 15 | ||||||||
| School | Guildhall School of Business and Law | ||||||||
| Total study hours | 150 | ||||||||
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| Assessment components |
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| Running in 2022/23(Please note that module timeslots are subject to change) | No instances running in the year |
Module summary
This module develops students’ knowledge of the range of electronic tools available for translation. It familiarises them with the principles and methods of Automatic/Computer /Human-assisted translation systems and compares and evaluates these in terms of their relevance for the practice of translating. The focus is on machine translation (MT), post-editing and translation memory (TM) software (also known as Translation Environment Tools); students will work with a variety of packages and systems, both theoretically and practically, developing their skills through "hands-on" sessions, troubleshooting issues which may arise in their workflow, comparing features among tools and reflecting on both the impact these tools have on translators’ workflow and the role they play in current professional settings.
Prior learning requirements
TR4003 or similar
Syllabus
This module will cover the following aspects:
• Introduction to Computer-Assisted Translation (CAT) tools LO1
• Introduction to Translation Environment Tools and their components, including terminology databases LO1
• Introduction to Machine Translation and post-editing LO3
• Comparing translation memory software packages and troubleshooting issues LO2,LO3
Balance of independent study and scheduled teaching activity
This module is structured as a flipped classroom: students perform all or most practical activities in class, under the supervision of the tutor, after engaging with instructional material at home. These activities attract regular formative feedback which allows students to reflect on their progress and improve their performance as they go along.
Learning outcomes
On successful completion of the module, students will be able to:
1. Use a range of electronic tools available for translation, following the relevant workflows, comparing and evaluating principles, methods, pros and cons of Automatic/Computer/Human-assisted translation systems in terms of their relevance for the practice of translation.
2. Troubleshoot issues arising from the use of these tools for professional purposes.
3. Utilise machine translation post-editing skills which are an integral part of Automatic Translation training.
Assessment strategy
1. Formative assessment: weekly in-class practice with feedback
2. Summative assessment: 100% Practical Exam
This will be a timed translation to be performed using Translation Environment Tools (TEnTs) with integrated machine translation (MT), following the relevant workflow to translate and post-edit a text, and dealing with any ensuing troubleshooting, which will be then discussed in a technical report along with an evaluation of the MT post-edited output.
Bibliography
Textbooks:
Core Text
- Sin-wai, C. (ed.) (2015) The Routledge encyclopedia of translation technology. London/New York: Routledge (Chapters 1-3, 5-6).
Websites
- Lionbridge Marketing (2016) ‘Neural Machine Translation: How Artificial Intelligence Works in Multilingual Communication’. Available at: http://content.lionbridge.com/neural-machine-translation-artificial-intelligence-works-multilingual-communication/ (Accessed: 19 June 2018).
- Muegge, U. (2010) ‘Ten good reasons for using a translation memory’, tcworld, January 2010. Available at http://www.tcworld.info/e-magazine/translation-and-localization/article/ten-good-reasons-for-using-a-translation-memory/ (Accessed: 19 June 2018).
- TAUS/CNGL (2011) ‘Machine translation post-editing guidelines’. Available at https://www.taus.net/think-tank/articles/postedit-articles/taus-post-editing-guidelines (Accessed: 19 June 2018).
- Vashee, K. (2016) ‘Comparing Neural MT, SMT and RBMT – The SYSTRAN perspective’. Available at: http://kv-emptypages.blogspot.co.uk/2016/09/comparing-neural-mt-smt-and-rbmt.html (Accessed: 19 June 2018).
- Vashee, K. (2017) ‘An examination of the strengths and weaknesses of Neural Machine Translation’. Available at: http://kv-emptypages.blogspot.co.uk/2017/01/an-examination-of-strenghts-and.html (Accessed: 19 June 2018).
Blogs & Journals
- Slator: https://slator.com/ (language services and technology market news).
- eMpTy Pages: http://kv-emptypages.blogspot.co.uk/ (on translation technology, localization and collaboration).
- Multilingual Computing: http://www.multilingual.com/ (as e-resource via Library Services).
- TC World: http://www.tcworld.info/.
- Translation Journal: http://translationjournal.net/journal/.