Technologies in the language services industry - LAN00115M
Module summary
This core module will introduce you to the theory and functional principles of the latesttechnologies being used and developed in the language services industry (for example, Artificial Intelligence). Youwill use group projects and problem-based learning to apply acquired knowledge to real-worldscenarios. Throughout the module, you will learn key technological principles which will allowyou to work confidently and competently in the language services industry of today, and to effectively teach yourself about technologies which emerge in the future.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2026-27 |
Module aims
This module is designed to equip you with key knowledge and competence in the principles of technologies applied in the language services industry. Additionally, the module aims to developyour ability to self-learn any relevant technologies emerging in the future, on the basis offunctional principles you have learned as part of the module. You will acquiretheoreticalknowledge during lectures, and gain expertguidance in practical sessions. Group study is anintegral part of the module, as problem-based learning will be used to deal with a series of real-world scenarios.
Module learning outcomes
After completing this module, you should be able to:
- confidently navigate the latest technologies being applied to language services,
- analyse and break down complex technical problems and technological issues,
- competently design technical projects,
- articulate solutions to technological problems,
- conduct self-learning of emerging technologies, on the basis of the principles learnedduring the module.
Module content
Below is an indicative list of topics covered in the module:
- Artificial intelligence
- Machine translation and interpreting
- Network technologies
- Computer hardware
- Human-machine interaction
- Computational linguistics
- Ethics in technology
- Legal aspects of new technologies
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 30.0 |
| Groupwork | 70.0 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100.0 |
Module feedback
- Assessment 1: The components of the portfolio are both formative and summative. As such,you will receive continuous feedback before submission of their portfolio as a summativeassessment.
- Assessment 2: You will receive written and verbal feedback from the course unit convenor.
Indicative reading
Boy, G. A. (Ed.). (2017). The handbook of human-machine interaction: a human-centereddesign approach. CRC Press.
Carl, M., & Braun, S. (2017). Translation, interpreting and new technologies. In The Routledgehandbook of translation studies and linguistics. (pp. 374-390). Routledge.
Domingos, P. (2012). A few useful things to know about machine learning. Communications ofthe ACM, 55(10), 78-87.
Goldstein, I., & Papert, S. (1977). Artificial intelligence, language, and the study of knowledge.Cognitive science, 1(1), 84-123.
Kelleher, J. D., Mac Namee, B.,& Darcy, A. (2020). Fundamentals of machine learning forpredictive data analytics: algorithms, worked examples, and case studies. MIT press.
Kenny, D. (Ed.). (2017). Human issues in translation technology. Taylor & Francis.
Lauriola, I., Lavelli, A., & Aiolli, F. (2022). An introduction to deep learning in natural languageprocessing: Models, techniques, and tools. Neurocomputing, 470, 443-456.
Nandi, G., & Sharma, R. K. (2020). Data Science fundamentals and practical approaches:understand why data science is the next. BPB Publications.
Paris, C. L., Swartout, W. R., & Mann, W. C. (Eds.). (2013). Natural language generation inartificial intelligence and computational linguistics (Vol. 119). Springer Science & BusinessMedia.
Patel, R., & Patel, S. (2021). Deep learning for natural language processing. In Information andCommunication Technology for Competitive Strategies (ICTCS 2020)Intelligent Strategies forICT (pp. 523-533). Springer Singapore.
Rawat, D. B., Awasthi, L. K., Balas, V. E., Kumar, M., & Samriya, J. K. (Eds.). (2023). Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation. John Wiley &Sons.
Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J., &Austermuehl, F. (2023).Translation Tools and Technologies. Taylor & Francis.
Sandrelli, A. (2015). Becoming an interpreter: the role of computer technology. MonTI.Monografías de Traducción e Interpretación, 111-138.
Stahlberg, F. (2020). Neural machine translation: A review. Journal of Artificial IntelligenceResearch, 69, 343-418.