Module manager: Faruk Mardan
Email: F.Mardan@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
MODL5089M Machine Translation and Natural Language Processing
This module is not approved as an Elective
This module aims to equip students with the knowledge and the ability to use machine translation (MT) to support multilingual information needs. It introduces the theoretical and practical principles of natural language processing and the use of conventional methods as well as Large Language Models. It complements the core modules in (human) Translation and in Computer-Assisted Translation. Please note this is an optional module and runs subject to enrolments. If a low number of students choose this module, then the module may not run and you may be asked to choose another module.
This module aims to build:
1. an advanced understanding of the principles of different types of machine translation models;
2. ability to evaluate machine translation quality with human as well as automatic techniques;
3. a good understanding of the theory and application of Natural Language Processing
On completion of this module, students should be able to:
LO1. Explain the principal architectures and different types of machine translation (MT) techniques/technologies, their functions and objectives rationales
LO2. Critically evaluate MT systems and MT output taking into account the user's perspective
LO3. Carry out simple Natural Language Processing tasks with small sample-size data
LO4. Critically analyse and appraise data, tools, and resources with the aim to execute a task.
LO5. Use effectively digital tools and software
This module offers a blend of conceptual knowledge and practical experience in machine translation and quality assessment thereof, including human and automatic approaches. It also builds a theoretical foundation of Natural Language Processing to prepare students for a fast-evolving language service industry thanks to advancements in Large Language Models and Artificial Intelligence. To do this, they need a combination of conceptual knowledge and practical experience.
Classes take the form of a blend of lectures and discussion of Machine Translation architecture and Natural Language Processing, accompanied by practical tasks to practice the use of Machine Translation in different scenarios and processing small amounts of language data. Students will also engage with practical scenarios whereby they conduct human and automatic assessment of Machine Translation quality.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 5 | 1 | 5 |
| Practical | 10 | 1 | 10 |
| Seminar | 5 | 1 | 5 |
| Private study hours | 130 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
- In-class weekly exercises focusing on Machine Translation architecture, quality assessment or Natural Language Processing
- Continuous interaction through Minerva
- Feedback on the mini in-class projects on the application of machine translation in real-life scenarios.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Case Study | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team