IT skills are central to the translator's work, and the aim of this module is to demonstrate how the efficiency of the translation process and the translator's marketability can be improved by an enhanced knowledge of the relevant technological tools. Theoretical knowledge is coupled with hand-on-training and students develop transferable skills through "hands-on" sessions that emphasise the "real world" translation environment. The main focus of the module will be on Computer-assisted tools (CAT-tools, RWS Trados, Phrase, BWX, XTM κ.ά.), including cloud-based tools and open-source tools. An introduction to Machine Translation (MT) (with an emphasis on Neural Machine Translation and Generative AI) and MT evaluation https://mateo.ivdnt.org/Evaluate, including human and automatic evaluation metrics is included in the module. Extensive post-editing practice is offered.
Upon completion of the module, students will be able to:
Week 1: Preview of the course. Translation and its compatibility with technology. Machine translation: introduction to machine translation, historical background, ethical and moral issues.
Week 2: Types and approaches to machine translation (statistical, neural, etc.) and how to train them.
Week 3: Evaluation of machine translation systems. Automatic metrics and quality estimation. Practical application of metrics, https://mateo.ivdnt.org/Evaluate.
Week 4: Human evaluation. Adequacy/fluency, ranking and error analysis (MQM/DQF typology). Pre-editing and post-editing of machine translated text: introduction.
Week 5: Techniques and types of post-editing. Types and techniques of editing and proofreading of machine-translated text: practical exercises. Editing of machine-translated text: practice and error categorisation.
Week 6: Introduction to CAT tools. BWX I: Introduction to the tool. Managing translation memories and terminology bases, importing and exporting files, text alignment, using machine translation and the built-in version of ChatGPT.
Week 7: BWX II: Translating a Word file. Practical exercise and discussion on the use of AI in the process.
Week 8: Phrase I: Presentation. Managing translation memories and terminology bases, importing and exporting tmx and tbx files, text alignment.
Week 9: Phrase II: Translation of a Word file and translation of different file formats (PDF, Excel, SRT, XML, etc.).
Week 10: Multiterm: searching a terminology base, creating a terminology base by converting a ready-made file/entirely new base, adding entries to a terminology base, exporting terminology. Trados Studio I: initial start-up, overview of the user interface, personal settings.
Week 11: Trados Studio I: Translation of a Word file (auto-propagation, fuzzy match, exact match, context match, tags, concordance search, spell check, quality assurance check).
Week 12: Trados Studio II: Translation of different file formats (PDF, Excel, SRT, XML, etc.). Basic keyboard shortcuts. Management of translation memories, text alignment, creation of autosuggest dictionaries.
Week 13: Trados Studio III: Using the built-in version of ChatGPT.
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Castilho S, Moorkens J, Gaspari F, Calixto I, Tinsley J, Way A (2017a) Is Neural Machine Translation the New State of the Art? The Prague Bulletin of Mathematical Linguistics, 108(1): 109–120
Castilho S, Moorkens J, Gaspari F, Sennrich R, Sosoni V, Georgakopoulou Y, Lohar P, Way A, Miceli Barone A, Gialama M (2017b) A Comparative quality evaluation of PBSMT and NMT using professional translators. Proceedings of Machine Translation Summit XVI. Nagoya, Japan.
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Koponen M (2016a) Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. Jostrans: The Journal of Specialised Translation, 25: 131-148
Koponen M (2016b) Machine translation post-editing and effort: Empirical Studies on the post-editing effort. PhD Thesis, University of Helsinki
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Stasimioti M, Sosoni V (2020) Translation vs Post-editing of NMT Output: Measuring effort in the English-Greek language pair. Proceeding of 1st Workshop on Post-Editing in Modern-Day Translation, AMTA 2020, 6 October 2020, Online Conference
Stasimioti M, Sosoni V, Kermanidis KL, Mouratidis, D (2020) Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs. Proceedings of the 22nd Annual Conference of the European Association for Machine Translation. Lisbon November 2020, 441-450.https://www.aclweb.org/anthology/2020.eamt-1.47.pdf
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Toral A, Wieling M, and Way A (2018) “Post-editing Effort of a Novel with Statistical and Neural Machine Translation”. Frontiers in Digital Humanities 5:9.
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Yamada M (2015) Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings. Machine Translation, 29: 49-67.
Yamada M (2019) The impact of Google Neural Machine Translation on Post-editing by student translators, Jostrans: The Journal of Specialised Translation, 31: 87-106.
Zhechev V (2014) Analysing the post-editing of machine translation at autodesk. In: O’Brien S, Balling LW, Carl M, Simard M, Specia L (eds) (2014) Postediting of machine translation: Processes and application. Cambridge Scholars, pp 2–13.
The lesson has a hybrid lecture-workshop format. It is largely based on the interaction with students and classroom discussion. Hands-on-sessions focus on practical aspects of the issues covered and described during the lectures.
Students have hands-on sessions on CAT-tools (RWS Trados Studio, Multiterm, Phrase, BWX) and MT systems, and automatic metrics https://mateo.ivdnt.org/Evaluate. The material is made available through the Open e-class platform.
A final written assignment is used to assess the students. It consists of using a CAT tool, post-editing of MToutput, as well as error analysis/annotation, accuracy and fluency assessment and commentary of the procedure.