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2023/24 Taught Postgraduate Module Catalogue

MODL5029M Principles and Applications of Machine Translation

15 creditsClass Size: 40

Module manager: Faruk Mardan

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2023/24

This module is not approved as an Elective

Module summary

This module aims to equip students with the knowledge and the ability to use machine translation (MT) to support multilingual information needs. It complements the core modules in (human) Translation and in Translation Memories.


The module aims to:
1. equip students with an advanced understanding of the current development and advantages and limitations of machine translation.
2. analyse the differences between various types of machine translation solutions and their application.
3. analyse the role of MT output in different translation and localisation projects and workflows and how it increases quality and productivity.
4. learn the principles of pre-editing source text and evaluating and post-editing MT output.

Learning outcomes
On completion of this module, students should be able to:
1. Explain the principal architectures and different types of machine translation (MT) and their rationales.
2. Conduct critical evaluations of MT systems and MT output from a user's perspective.
3. Carry out MT pre-editing and post-editing, including light and full post-editing.


This module aims to equip students with the ability to advise organisations whether it makes sense to use machine translation (MT) to support their multilingual information needs. To do this, they need a combination of conceptual knowledge and practical experience.
The module explains the general problems with MT and what text types work best with it. It introduces the different ways users can improve MT output before and after translation and how MT can be integrated in the translation workflow. The module covers topics such as MT structure, translating different text types, pre-editing and post-editing, as well as how MT is integrated into the translation workflow.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours130.00
Total Contact hours20.00
Total hours (100hr per 10 credits)150.00

Private study

Private study and independent learning include: Reading literature from the reading list on the latest development of MT technology (60 hours). Students will also be asked to complete various assignments including testing the quality of different MT engines’ translation output (10 hours), human and automatic MT evaluation (10 hours), case studies (30 hours), pre-editing (10 hours) and post-editing (10 hours).

Opportunities for Formative Feedback

1. One verbal feedback on performance on case study (3 hours of work)
2. One verbal feedback on case study design
3. One written feedback on post-editing performance

Methods of assessment

Assessment typeNotes% of formal assessment
Case StudyCase Study of 1,500 Words100.00
Total percentage (Assessment Coursework)100.00

The course work requires submission of a case study/report on critical application and evaluation on MT engines or on the pre-editing and post-editing processes of MT output.

Reading list

The reading list is available from the Library website

Last updated: 28/04/2023 14:42:36


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