Admission requirements
None.
Description
Have you ever relied on Google Translate to read a menu in a language you don’t speak? Or to conduct a conversation when traveling abroad? Do you often turn on the automatic subtitles when watching YouTube? Machine Translation (MT) has become a powerful tool in the hands of many, helping break down language barriers—but how does it actually work, and what are its limits?
This course prepares you to think critically and work practically in MT through a mix of theory, hands-on exercises, and discussions around current research. We begin with the fundamentals: how MT systems are built and function, how neural networks and large language models are used for translation, and what data they are trained on. You will learn how to work with multilingual datasets, fine-tune models for specific domains or language pairs, and evaluate output using both automatic metrics and human judgments. Beyond the basics, the course explores advanced challenges MT faces today. How do we assess translation quality in an explainable way? What does it mean to build fair and inclusive MT systems for low-resource languages? We will look at human-in-the-loop approaches, where translators are supported by AI, and critically examine the social and ethical dimensions of automated translation—including issues of bias, representation, and trust.
Course objectives
- Understand the basics of MT systems, the linguistic challenges of translation and how MT addresses them, by relying on scientific literature and specialised articles.
- Gain practical experience with MT tools and workflows, including training, fine-tuning, and evaluating machine translation models using real-world data.
- Process and manage different types of data used in machine translation, including parallel corpora, monolingual data, and databases.
- Critically assess the quality of machine-translated output, using both automatic evaluation metrics (e.g., BLEU, COMET) and human-centered approaches such as ratings and error analysis.
- Reflect on the social, cultural, and ethical implications of machine translation, including issues of bias, inclusivity, human-AI collaboration, and the evolving role of professional translators.
- Demonstrate ability to conduct and write up MT-related research in groups or independently.
Timetable
The timetables are available through My Timetable.
Mode of instruction
Lecture
Tutoring
Assessment method
Assessment
Written examination with closed questions (eg. multiple choice) and short open questions
Essay, paper or project report
Weighing
The final mark for the course is established by determining the weighted average grade between the exam and the research paper (50%, 50%). To pass the course, the weighted average of the partial grades must be 5.5 or higher.
Resit
There will be a resit opportunity for each of the course components in the same format as the initial assignments.
Inspection and feedback
How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.
Reading list
Kenny, Dorothy. 2022. Machine translation for everyone: Empowering users in the age of artificial intelligence. (Translation and Multilingual Natural Language Processing 18). Berlin: Language Science Press. DOI: 10.5281/zenodo.6653406.
Koehn, P. (2020). Neural Machine Translation. Cambridge: Cambridge University Press. https://www.cambridge.org/core/books/neural-machine-translation/7AAA628F88ADD64124EA008C425C0197
The course books are available open access online or at the university library. Print copies can be purchased at bookstores or online.
Additional reading materials will be made available on Brightspace.
Registration
Enrolment through MyStudyMap is mandatory.
General information about course and exam enrolment is available on the website.
Registration À la carte education, Contract teaching and Exchange
For the registration of exchange students contact Humanities International Office.
Contact
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Education Administration Office: Reuvensplaats
Remarks
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